Clustering_Time_Series-TSClust-Study-SatO2_scaled
Gonzalo Aris 16021
2023-03-05
Libraries
library(TSA) # time series
library(TSclust)
library(tidyr)
library(readr)
library(ggplot2) # ggplot graphs
library(knitr)
library(readxl)
library(xlsx)
library(openxlsx)
library(reactable) # reactable(df)
library(naniar) # miss_case_summary
library(dplyr)
## KNN imputation
library(caret)
library(RANN)
# CLustering
library(factoextra) # Clustering visualization
library(cluster) # Clustering algorithms
library(dendextend) # For comparing two dendrograms
library(corrplot) # Corelation between dendrograms
library(tidyverse) # Data manupulation
library(NbClust) # Determine optimal no. of clusters [not working...]
library(TSclust)
library(mclust) # Adjusted Rand index
#RandomForest
library(randomForest) # RandomForest Discrete Classification
library(imbalance) # To create a more balanced datasetFunctions
source("../../scripts/useful-functions/get_column_position.R")
# In a normal script it will be: source("./scripts/useful-functions/get_column_position.R")Reading Data
Time Series Data: SatO2 Heart Rate
SatO2_TS_HR_P2 = data.frame(read_xlsx("../../data/clean-data/BoxBasedImputation/SatO2_valid_patients_input_P2.xlsx", sheet = "SatO2_valid_patients_input_P2" ))
SatO2_scaled_TS_HR_P2 <- as.data.frame(lapply(SatO2_TS_HR_P2, scale))
# First patients with OAF
name_patients_DETERIORO_OAF_0 <- data.frame(read_csv("../../data/clean-data/name_patients_DETERIORO_OAF_0.csv"))
name_patients_DETERIORO_OAF_0 <- name_patients_DETERIORO_OAF_0$x
name_patients_DETERIORO_OAF_0_8 <- data.frame(read_csv("../../data/clean-data/name_patients_DETERIORO_OAF_0_8.csv"))
name_patients_DETERIORO_OAF_0_8 <- name_patients_DETERIORO_OAF_0_8$x
## Deterioro and Not deterioro
file_patient_name_NO_DETERIORO <- data.frame(read_csv("../../data/info-patients/file_patient_name_NO_DETERIORO.csv"))
file_patient_name_NO_DETERIORO <- file_patient_name_NO_DETERIORO$x
file_patient_name_DETERIORO <- data.frame(read_csv("../../data/info-patients/file_patient_name_DETERIORO.csv"))
file_patient_name_DETERIORO <- file_patient_name_DETERIORO$xDescriptive Data for Discriminant analysis
valid_patients_P2 <- data.frame(read_xlsx("../../data/clean-data/valid_patients_P2.xlsx"))
valid_patients_P2 <- valid_patients_P2$x
valid_patients_P2 <- valid_patients_P2[! valid_patients_P2 %in% union(name_patients_DETERIORO_OAF_0,name_patients_DETERIORO_OAF_0_8)]
file_patient_name <- data.frame(read_csv("../../data/clean-data/file_patient_name.csv", show_col_types = FALSE))
file_patient_name <- file_patient_name$x
df1 <- data.frame(read_xlsx("../../data/clean-data/descriptive-data/descriptive_data.xlsx"))
rownames(df1) <- file_patient_name
df1 <- df1[valid_patients_P2,]df_descriptive <- data.frame(read_xlsx("../../data/clean-data/descriptive-data/descriptive_data_imputed.xlsx"), row.names = TRUE)
rownames(df_descriptive) <- file_patient_namedf_descriptive <- df_descriptive %>% select(-c(FR_8_16h, FR_16_24h, FLUJO2_8_16h,FLUJO2_16_24h,SCORE_WOOD_DOWNES_24H,SAPI_16_24h, SAPI_8_16h))# Class
pos_1 = get_column_position(df_descriptive,"SAPI_0_8h")
pos_2 = get_column_position(df_descriptive,"PAUSAS_APNEA")
df_descriptive[,c(pos_1:pos_2)] <- lapply(df_descriptive[,c(pos_1:pos_2)], as.factor)
#lapply(df_descriptive,class)
df_descriptive <- df_descriptive[valid_patients_P2,]Create a dataframe with ACF [Heart Rate ]
SatO2_scaled_TS_HR_P2 <- SatO2_scaled_TS_HR_P2[,valid_patients_P2]Restando Media
#SatO2_scaled_TS_HR_P2 = data.frame(scale(SatO2_scaled_TS_HR_P2))dimension_col <- dim(SatO2_scaled_TS_HR_P2)[2]
dimension_row <- 480 #lag.max -1
# Heart Rate
SatO2_scaled_TS_HR_P2_ACF <- data.frame(matrix(nrow = dimension_row, ncol = dimension_col))
colnames(SatO2_scaled_TS_HR_P2_ACF) <- names(SatO2_scaled_TS_HR_P2)[1:dimension_col]for (i in names(SatO2_scaled_TS_HR_P2_ACF)) {
acf_result_SatO2_scaled <- forecast::Acf(SatO2_scaled_TS_HR_P2[[i]], lag.max = (dimension_row - 1), plot = FALSE, drop.lag.0 = FALSE)
SatO2_scaled_TS_HR_P2_ACF[, i] <- acf_result_SatO2_scaled$acf
}Create a dataframe with peridiogram
# Generar un dataset con varias series temporales
df <- SatO2_scaled_TS_HR_P2
# Crear una matriz para almacenar los periodogramas
pg_mat <- data.frame(matrix(nrow = nrow(df), ncol = ncol(df)))
colnames(pg_mat) = colnames(SatO2_scaled_TS_HR_P2)
# Calcular el periodograma de cada serie temporal y almacenarlo en la matriz
library(stats)
# Calcular el periodograma de cada serie temporal y almacenarlo en la matriz
for (i in colnames(SatO2_scaled_TS_HR_P2)) {
pg_mat[,i] <- stats::spec.pgram(SatO2_scaled_TS_HR_P2[,i], plot = FALSE)$spec
}TsClust Comprobation
datos <- SatO2_scaled_TS_HR_P2diss.ACF Computes the dissimilarity between two time
series as the distance between their estimated simple (ACF) or partial
(PACF) autocorrelation coefficients
DD_ACF <- diss(datos, "ACF", lag.max = 50)
DD_ACF_matrix <- as.matrix(DD_ACF)diss.EUCL
DD_EUCL <- diss(datos, "EUCL")
DD_EUCL_matrix <- as.matrix(DD_EUCL)diss.PER
DD_PER <- diss(datos, "PER")
DD_PER_matrix <- as.matrix(DD_PER)Euclidean Distance first 50 ACF
datos_ACF = t(SatO2_scaled_TS_HR_P2_ACF[c(1:51),])
distance <- dist(t(SatO2_scaled_TS_HR_P2_ACF[c(1:51),]), method = "euclidean")
distance_matrix_ACF <- as.matrix(distance)Euclidean Distance
datos_EUCL <- t(datos)
distance <- dist(datos_EUCL, method = "euclidean")
distance_matrix_EUCL <- as.matrix(distance)Eculidean PER Distance
datos_PER <- t(pg_mat)
distance_PER <- dist(t(pg_mat), method = "euclidean")
distance_matrix_PER <- as.matrix(distance_PER)
distance_matrix_PER_NORM = distance_matrix_PER / 480TSCLust in Action
ACF TSclust
# DD_ACF <- diss(datos, "ACF", lag.max = 50)Agnes study
To find which hierarchical clustering methods that can identify stronger clustering structures. Here we see that Ward’s method identifies the strongest clustering structure of the four methods assessed.
#method to assess
m <- c("average", "single","complete","ward")
names(m) <- c("average", "single","complete","ward.D2")
#function to compute coefficient
ac <- function(x){agnes(datos_ACF, method = x)$ac}
map_dbl(m,ac)## average single complete ward.D2
## 0.8135257 0.5037581 0.8916885 0.9454334NbClust study
This package will help us identify the optimum number of clusters
based our criteria in the silhouette index
diss_matrix<- DD_ACF
res<-NbClust(datos_ACF, diss=diss_matrix, distance = NULL, min.nc=2, max.nc=5, method = "ward.D2", index = "silhouette")
res$All.index## 2 3 4 5
## 0.6476 0.3127 0.2860 0.2742res$Best.nc## Number_clusters Value_Index
## 2.0000 0.6476#res$Best.partitionhcintper_ACF <- hclust(DD_ACF, "ward.D2")
fviz_dend(hcintper_ACF, palette = "jco",
rect = TRUE, show_labels = FALSE, k = 2)## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
## of ggplot2 3.3.4.
## ℹ The deprecated feature was likely used in the factoextra package.
## Please report the issue at <https://github.com/kassambara/factoextra/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
DDclust_ACF_SatO2_scaled <- cutree( hclust(DD_ACF, "ward.D2"), k = 2)fviz_cluster(list(data = t(datos), cluster = DDclust_ACF_SatO2_scaled))
fviz_silhouette(silhouette(DDclust_ACF_SatO2_scaled, DD_ACF))## cluster size ave.sil.width
## 1 1 51 0.66
## 2 2 7 0.54
Contingency ACF lag.max = 50
DETERIORO_CLUST <- union(intersect(file_patient_name_DETERIORO,names_1),intersect(file_patient_name_DETERIORO,names_2))
NO_DETERIORO_CLUST <- union(intersect(file_patient_name_NO_DETERIORO,names_1),intersect(file_patient_name_NO_DETERIORO,names_2))#DETERIORO
DETERIORO_patients = data.frame(t(rep("#4A235A", length(DETERIORO_CLUST))))
colnames(DETERIORO_patients)<- DETERIORO_CLUST
#NO DETERIORO
NO_DETERIORO_patients = data.frame(t(rep("#117864", length(NO_DETERIORO_CLUST))))
colnames(NO_DETERIORO_patients)<- NO_DETERIORO_CLUST
COLOR_ACF <- cbind(DETERIORO_patients,NO_DETERIORO_patients)order_ACF <- union(names(DDclust_ACF_SatO2_scaled[DDclust_ACF_SatO2_scaled == 2]),names(DDclust_ACF_SatO2_scaled[DDclust_ACF_SatO2_scaled == 1]))
fviz_dend(hcintper_ACF, k = 2,
k_colors = c("blue", "green3"),
label_cols = as.vector(COLOR_ACF[,order_ACF]), cex = 0.6) 
n1 = length(intersect(file_patient_name_DETERIORO,names_1))
n2 = length(intersect(file_patient_name_DETERIORO,names_2))
n3 = length(intersect(file_patient_name_NO_DETERIORO,names_1))
n4 = length(intersect(file_patient_name_NO_DETERIORO,names_2))
conttingency_table <- data.frame("CLust1" = c(n1,n3), "Clust2" = c(n2,n4))
rownames(conttingency_table) <- c("DETERIORO","NO DETERIORO")
knitr::kable(conttingency_table, align = "lccrr")| CLust1 | Clust2 | |
|---|---|---|
| DETERIORO | 6 | 0 |
| NO DETERIORO | 45 | 7 |
conttingency_table_prop <- data.frame(c(n1,n3)/(n1+n3),c(n2,n4)/(n2+n4))
rownames(conttingency_table_prop) <- c("DETERIORO","NO DETERIORO")
colnames(conttingency_table_prop) <- c("Clust1","Clust2")
knitr::kable(conttingency_table_prop, align = "lccrr")| Clust1 | Clust2 | |
|---|---|---|
| DETERIORO | 0.1176471 | 0 |
| NO DETERIORO | 0.8823529 | 1 |
Random Forest: Discriminant TSCLust ACF
data_frame1_ACF = data.frame("CLUSTER" = DDclust_ACF_SatO2_scaled)
data_frame2 = df_descriptive
data_frame_merge_ACF <-
merge(data_frame1_ACF, data_frame2, by = 'row.names', all = TRUE)
data_frame_merge_ACF <- data_frame_merge_ACF[, 2:dim(data_frame_merge_ACF)[2]]
data_frame_merge_ACF$CLUSTER = factor(data_frame_merge_ACF$CLUSTER)table(data_frame_merge_ACF$CLUSTER)##
## 1 2
## 51 7data_frame_merge_ACF[,c(1:dim(data_frame_merge_ACF)[2])]<- lapply(data_frame_merge_ACF[,c(1:dim(data_frame_merge_ACF)[2])], as.numeric)
head(data_frame_merge_ACF)## CLUSTER EDAD PESO EG FR_0_8h FLUJO2_0_8H DIAS_GN DIAS_O2_TOTAL DIAS_OAF
## 1 1 10.0 8.20 41 48 2.00 3 3 0
## 2 2 13.0 7.78 40 56 2.00 2 2 0
## 3 1 3.1 5.66 37 44 1.00 4 4 0
## 4 1 5.3 8.44 38 65 0.40 3 3 0
## 5 1 15.0 7.00 34 37 2.00 4 4 0
## 6 1 1.6 3.80 37 42 0.94 4 4 0
## SAPI_0_8h SCORE_CRUCES_INGRESO SCORE_WOOD_DOWNES_INGRESO SEXO PALIVIZUMAB LM
## 1 3 3 6 1 1 2
## 2 4 4 8 1 1 1
## 3 3 3 7 1 1 2
## 4 4 3 6 1 1 2
## 5 1 3 6 1 2 1
## 6 2 4 7 1 1 2
## DERMATITIS ALERGIAS TABACO ENFERMEDAD_BASE RADIOGRAFIA ANALITICA SUERO
## 1 1 2 1 1 1 1 1
## 2 1 2 2 2 1 1 2
## 3 1 1 1 1 1 1 1
## 4 1 1 1 1 1 1 1
## 5 1 1 2 2 1 1 2
## 6 1 1 2 2 1 1 1
## ETIOLOGIA PREMATURIDAD ALIMENTACION SNG GN_INGRESO OAF OAF_AL_INGRESO
## 1 2 1 2 1 2 1 1
## 2 1 1 1 1 2 1 1
## 3 2 1 2 1 2 1 1
## 4 2 1 2 1 1 1 1
## 5 2 2 2 1 2 1 1
## 6 1 1 2 1 1 1 1
## OAF_TRAS_INGRESO UCIP DETERIORO PAUSAS_APNEA
## 1 1 1 1 1
## 2 1 1 1 1
## 3 1 1 1 1
## 4 1 1 1 1
## 5 1 1 1 1
## 6 1 1 1 1data_frame_merge_ACF$CLUSTER <- factor(data_frame_merge_ACF$CLUSTER)
newSMOTE_ACF <- oversample(data_frame_merge_ACF, ratio = 0.85, method = "SMOTE", classAttr = "CLUSTER")
newSMOTE_ACF <- data.frame(newSMOTE_ACF)
pos_1 <- get_column_position(newSMOTE_ACF, "SAPI_0_8h")
pos_2 <- get_column_position(newSMOTE_ACF, "PAUSAS_APNEA")
columns_to_round <- c(pos_1:pos_2)
newSMOTE_ACF[, columns_to_round] <- lapply(newSMOTE_ACF[, columns_to_round], function(x) round(x, 1))
table(newSMOTE_ACF$CLUSTER)##
## 1 2
## 51 44set.seed(123)
pos_1 = get_column_position(newSMOTE_ACF, "SAPI_0_8h")
pos_2 = get_column_position(newSMOTE_ACF, "PAUSAS_APNEA")
col_names_factor <- names(newSMOTE_ACF[pos_1:pos_2])
newSMOTE_ACF[col_names_factor] <- lapply(newSMOTE_ACF[col_names_factor] , factor)
RF_ACF <- randomForest(CLUSTER ~ ., data = newSMOTE_ACF)
print(RF_ACF)##
## Call:
## randomForest(formula = CLUSTER ~ ., data = newSMOTE_ACF)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 5
##
## OOB estimate of error rate: 5.26%
## Confusion matrix:
## 1 2 class.error
## 1 50 1 0.01960784
## 2 4 40 0.09090909Importance
kable(RF_ACF$importance[order(RF_ACF$importance, decreasing = TRUE),])| x | |
|---|---|
| SCORE_WOOD_DOWNES_INGRESO | 6.9904913 |
| SCORE_CRUCES_INGRESO | 6.3766205 |
| SAPI_0_8h | 5.8686141 |
| EDAD | 3.9669244 |
| ENFERMEDAD_BASE | 2.9533939 |
| DIAS_GN | 2.8443034 |
| SEXO | 1.7167167 |
| PESO | 1.7071637 |
| FLUJO2_0_8H | 1.6412857 |
| ALIMENTACION | 1.5490696 |
| ETIOLOGIA | 1.5267039 |
| DIAS_O2_TOTAL | 1.4017639 |
| EG | 1.0241249 |
| FR_0_8h | 0.9445228 |
| SUERO | 0.9350600 |
| PALIVIZUMAB | 0.8761918 |
| PREMATURIDAD | 0.7690630 |
| LM | 0.7548396 |
| TABACO | 0.7198383 |
| ANALITICA | 0.7185272 |
| RADIOGRAFIA | 0.5327186 |
| ALERGIAS | 0.2996115 |
| OAF | 0.1028297 |
| DETERIORO | 0.0900122 |
| SNG | 0.0808216 |
| OAF_TRAS_INGRESO | 0.0697980 |
| DIAS_OAF | 0.0676047 |
| GN_INGRESO | 0.0345121 |
| UCIP | 0.0283953 |
| DERMATITIS | 0.0248943 |
| PAUSAS_APNEA | 0.0197693 |
| OAF_AL_INGRESO | 0.0000000 |
Importance of first 50 ACF
data_frame1_ACF = data.frame("CLUSTER" = DDclust_ACF_SatO2_scaled)
data_frame2_ACF = data.frame(t(SatO2_scaled_TS_HR_P2_ACF[c(1:51),]))
data_frame_merge_ACF <-
merge(data_frame1_ACF, data_frame2_ACF, by = 'row.names', all = TRUE)
data_frame_merge_ACF <- data_frame_merge_ACF[, 2:dim(data_frame_merge_ACF)[2]]set.seed(123)
data_frame_merge_ACF$CLUSTER <- as.factor(data_frame_merge_ACF$CLUSTER)
RF_0_ACF <- randomForest(CLUSTER ~ ., data = data_frame_merge_ACF)
print(RF_0_ACF)##
## Call:
## randomForest(formula = CLUSTER ~ ., data = data_frame_merge_ACF)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 7
##
## OOB estimate of error rate: 1.72%
## Confusion matrix:
## 1 2 class.error
## 1 51 0 0.0000000
## 2 1 6 0.1428571plot(RF_0_ACF$importance, type = "h")
### ACF by clusters
plot_data_ACF <- data.frame(datos_ACF)
cluster_data_ACF <- data.frame(DDclust_ACF_SatO2_scaled)
plotting_ACF <- cbind(plot_data_ACF, cluster_data_ACF)
head(plotting_ACF)## X1 X2 X3 X4 X5 X6 X7
## ACR_11231843 1 0.5082890 0.3997243 0.3055021 0.3009323 0.2725452 0.2828684
## ADAO_11159808 1 0.7960148 0.7358783 0.7023573 0.7034097 0.6571457 0.6266538
## AGG_11236448 1 0.4506480 0.4176547 0.3266226 0.3352883 0.2867152 0.3288047
## AHL_11239959 1 0.6522007 0.4130156 0.3200723 0.3440361 0.3568053 0.3408829
## AJGD_11119689 1 0.6469179 0.5880904 0.5481336 0.5084136 0.4652971 0.4792604
## AMP_11228639 1 0.3765707 0.3564648 0.2828661 0.2304710 0.2216612 0.1820556
## X8 X9 X10 X11 X12 X13
## ACR_11231843 0.2414714 0.2329953 0.18395173 0.1467580 0.12033122 0.08302370
## ADAO_11159808 0.6305916 0.6168577 0.59417999 0.6000893 0.60607048 0.60015069
## AGG_11236448 0.2209685 0.2946372 0.24923513 0.2791107 0.25321237 0.23780416
## AHL_11239959 0.3217067 0.3072727 0.25540711 0.2257664 0.22431145 0.24252531
## AJGD_11119689 0.4275522 0.3935165 0.35060010 0.3330913 0.30196103 0.25973001
## AMP_11228639 0.1759353 0.1038674 0.07915669 0.1212448 0.08580592 0.08996543
## X14 X15 X16 X17 X18 X19
## ACR_11231843 0.08933466 0.05687640 0.05274341 0.06126060 0.08978425 0.02651201
## ADAO_11159808 0.58930211 0.59429754 0.56563344 0.55562686 0.55569350 0.55287474
## AGG_11236448 0.18830545 0.29476738 0.24013660 0.29281568 0.24484356 0.27312417
## AHL_11239959 0.26020561 0.23401364 0.19351172 0.17366679 0.14454853 0.11669907
## AJGD_11119689 0.21434187 0.22249353 0.21308087 0.18224214 0.17989537 0.16992608
## AMP_11228639 0.07081004 0.05479702 0.00521051 0.02134299 0.04160247 0.07214742
## X20 X21 X22 X23 X24
## ACR_11231843 0.078210876 0.05633258 0.007953371 0.02307094 0.05578342
## ADAO_11159808 0.553927137 0.53920741 0.525401555 0.53638341 0.53743580
## AGG_11236448 0.178482125 0.22141972 0.170925445 0.15660776 0.11918869
## AHL_11239959 0.149636558 0.16472810 0.173167686 0.16825227 0.13524343
## AJGD_11119689 0.118883946 0.14000960 0.165611015 0.12406405 0.14798910
## AMP_11228639 -0.004522566 0.02031293 0.111043640 0.09620470 0.07466595
## X25 X26 X27 X28 X29
## ACR_11231843 0.05754983 0.01633989 0.03651988 0.03014404 0.03653216
## ADAO_11159808 0.53454517 0.54348362 0.52573474 0.51981495 0.50410946
## AGG_11236448 0.15172497 0.14043635 0.13107597 0.09380520 0.10983555
## AHL_11239959 0.08820634 0.09185565 0.09736712 0.07803585 0.06991932
## AJGD_11119689 0.20780663 0.16061276 0.21066161 0.18263507 0.18302649
## AMP_11228639 0.05033299 0.01874388 0.06265166 0.01221945 0.06245386
## X30 X31 X32 X33 X34
## ACR_11231843 0.026885553 0.018244632 0.03425803 0.03731653 0.08545823
## ADAO_11159808 0.503118465 0.488398734 0.47360713 0.48528722 0.49035453
## AGG_11236448 0.073366759 0.122373113 0.07317205 0.09085445 0.11009158
## AHL_11239959 0.089334524 0.053043299 0.04793588 0.03307846 0.04347559
## AJGD_11119689 0.167654826 0.196464213 0.20440416 0.24276020 0.21465966
## AMP_11228639 0.008097181 -0.006432008 -0.04581565 -0.01922558 0.02923064
## X35 X36 X37 X38 X39
## ACR_11231843 0.08212560 0.04122430 0.02554620 0.04391622 0.02081907
## ADAO_11159808 0.48337710 0.47154277 0.47153753 0.46864689 0.43414012
## AGG_11236448 0.11357759 0.14531284 0.11294811 0.06122085 0.12123779
## AHL_11239959 0.01617575 -0.02722358 -0.04350629 -0.03287029 0.02951063
## AJGD_11119689 0.17716046 0.15298218 0.13635243 0.12934334 0.10067326
## AMP_11228639 -0.03056929 0.01440266 0.01397162 -0.01940303 0.03909295
## X40 X41 X42 X43 X44
## ACR_11231843 0.058707749 0.03614286 0.01274100 0.04554895 0.078224804
## ADAO_11159808 0.423147789 0.40624091 0.40820718 0.40425891 0.383480876
## AGG_11236448 0.127562364 0.06081561 0.11997640 0.09993272 0.085353394
## AHL_11239959 0.069235684 0.06395972 0.07775235 0.05163507 0.029005208
## AJGD_11119689 0.079159171 0.05660577 0.03686780 0.00625108 0.004644364
## AMP_11228639 0.001048235 0.08743216 0.01282564 0.06121771 0.114447275
## X45 X46 X47 X48 X49
## ACR_11231843 0.03627727 0.03475069 -0.01733608 -0.007981913 -0.05040679
## ADAO_11159808 0.36664587 0.35974033 0.36459200 0.337971294 0.32803660
## AGG_11236448 0.10265570 0.03053047 0.04854476 0.065612107 0.04693021
## AHL_11239959 0.05880395 0.05077536 0.04689787 0.040638027 0.04475613
## AJGD_11119689 0.03616619 0.02081380 -0.01999312 -0.019823713 -0.02207396
## AMP_11228639 0.06079782 0.07559045 0.07100495 0.064710841 0.03629374
## X50 X51 DDclust_ACF_SatO2_scaled
## ACR_11231843 -0.034433928 -0.05749838 1
## ADAO_11159808 0.342673967 0.34456836 2
## AGG_11236448 0.093592552 0.09988380 1
## AHL_11239959 0.039269127 0.04749549 1
## AJGD_11119689 -0.063125407 -0.07161460 1
## AMP_11228639 -0.005938136 0.02909861 1## Mean by groups
rp_tbl_ACF <- aggregate(plotting_ACF, by = list(plotting_ACF$DDclust_ACF_SatO2_scaled), mean)
row.names(rp_tbl_ACF) <- paste0("Group",rp_tbl_ACF$DDclust_ACF_SatO2_scaled)
rp_tbl_ACF <- rp_tbl_ACF %>%
select(starts_with('X'))
rp_tbl_ACF <- data.frame(t(rp_tbl_ACF))
head(rp_tbl_ACF)## Group1 Group2
## X1 1.0000000 1.0000000
## X2 0.4160338 0.7830190
## X3 0.3241161 0.7392115
## X4 0.2856382 0.7003584
## X5 0.2544281 0.6769579
## X6 0.2261663 0.6447044# Create plotting data-frame
ACF_values_by_group <- data.frame("value_ACF" = c(rp_tbl_ACF$Group1,rp_tbl_ACF$Group2),
"cluster" = c(rep("Group1", times = length(rp_tbl_ACF$Group1)),
rep("Group2", times = length(rp_tbl_ACF$Group2))),
"index" = c(c(1:length(rp_tbl_ACF$Group1)),c(1:length(rp_tbl_ACF$Group2))))
p <- ggplot(ACF_values_by_group, aes(x = index, y = value_ACF, group = cluster)) +
geom_line(aes(color=cluster)) +
scale_color_brewer(palette="Paired") + theme_minimal()
p
EUCL TSclust
# DD_EUCL <- diss(datos, "EUCL")Agnes study
To find which hierarchical clustering methods that can identify stronger clustering structures. Here we see that Ward’s method identifies the strongest clustering structure of the four methods assessed.
#method to assess
m <- c("average", "single","complete","ward")
names(m) <- c("average", "single","complete","ward.D2")
#function to compute coefficient
ac <- function(x){agnes(datos, method = x)$ac}
map_dbl(m,ac)## average single complete ward.D2
## 0.6116783 0.5442184 0.7148574 0.8649377NbClust study
This package will help us identify the optimum number of clusters
based our criteria in the silhouette index
diss_matrix<- DD_EUCL
res<-NbClust(datos, diss=diss_matrix, distance = NULL, min.nc=2, max.nc=5, method = "ward.D2", index = "silhouette")
res$All.index## 2 3 4 5
## 0.0340 0.0324 0.0501 0.0485res$Best.nc## Number_clusters Value_Index
## 4.0000 0.0501#res$Best.partitionhcintper_EUCL <- hclust(DD_EUCL, "ward.D2")
fviz_dend(hcintper_EUCL, palette = "jco",
rect = TRUE, show_labels = FALSE, k = 4)
DDclust_EUCL_SatO2_scaled <- cutree( hclust(DD_EUCL, "ward.D2"), k = 4)fviz_cluster(list(data = t(datos), cluster = DDclust_EUCL_SatO2_scaled))
fviz_silhouette(silhouette(DDclust_EUCL_SatO2_scaled, DD_EUCL))## cluster size ave.sil.width
## 1 1 24 0.01
## 2 2 20 0.01
## 3 3 12 0.03
## 4 4 2 1.00
Contingency EUCL
DETERIORO_CLUST <- union(intersect(file_patient_name_DETERIORO,names_1),intersect(file_patient_name_DETERIORO,names_2))
NO_DETERIORO_CLUST <- union(intersect(file_patient_name_NO_DETERIORO,names_1),intersect(file_patient_name_NO_DETERIORO,names_2))#DETERIORO
DETERIORO_patients = data.frame(t(rep("#4A235A", length(DETERIORO_CLUST))))
colnames(DETERIORO_patients)<- DETERIORO_CLUST
#NO DETERIORO
NO_DETERIORO_patients = data.frame(t(rep("#117864", length(NO_DETERIORO_CLUST))))
colnames(NO_DETERIORO_patients)<- NO_DETERIORO_CLUST
COLOR_EUCL <- cbind(DETERIORO_patients,NO_DETERIORO_patients)order_EUCL <- union(names(DDclust_EUCL_SatO2_scaled[DDclust_EUCL_SatO2_scaled == 2]),names(DDclust_EUCL_SatO2_scaled[DDclust_EUCL_SatO2_scaled == 1]))
fviz_dend(hcintper_EUCL, k = 2,
k_colors = c("blue", "green3"),
label_cols = as.vector(COLOR_EUCL[,order_EUCL]), cex = 0.6) 
n1 = length(intersect(file_patient_name_DETERIORO,names_1))
n2 = length(intersect(file_patient_name_DETERIORO,names_2))
n3 = length(intersect(file_patient_name_NO_DETERIORO,names_1))
n4 = length(intersect(file_patient_name_NO_DETERIORO,names_2))
conttingency_table <- data.frame("CLust1" = c(n1,n3), "Clust2" = c(n2,n4))
rownames(conttingency_table) <- c("DETERIORO","NO DETERIORO")
knitr::kable(conttingency_table, align = "lccrr")| CLust1 | Clust2 | |
|---|---|---|
| DETERIORO | 2 | 4 |
| NO DETERIORO | 22 | 30 |
conttingency_table_prop <- data.frame(c(n1,n3)/(n1+n3),c(n2,n4)/(n2+n4))
rownames(conttingency_table_prop) <- c("DETERIORO","NO DETERIORO")
colnames(conttingency_table_prop) <- c("Clust1","Clust2")
knitr::kable(conttingency_table_prop, align = "lccrr")| Clust1 | Clust2 | |
|---|---|---|
| DETERIORO | 0.0833333 | 0.1176471 |
| NO DETERIORO | 0.9166667 | 0.8823529 |
Random Forest: Discriminant TSCLust EUCL
data_frame1_EUCL = data.frame("CLUSTER" = DDclust_EUCL_SatO2_scaled)
data_frame2 = df_descriptive
data_frame_merge_EUCL <-
merge(data_frame1_EUCL, data_frame2, by = 'row.names', all = TRUE)
data_frame_merge_EUCL <- data_frame_merge_EUCL[, 2:dim(data_frame_merge_EUCL)[2]]
data_frame_merge_EUCL$CLUSTER = factor(data_frame_merge_EUCL$CLUSTER)table(data_frame_merge_EUCL$CLUSTER)##
## 1 2
## 24 34data_frame_merge_EUCL[,c(1:dim(data_frame_merge_EUCL)[2])]<- lapply(data_frame_merge_EUCL[,c(1:dim(data_frame_merge_EUCL)[2])], as.numeric)
head(data_frame_merge_EUCL)## CLUSTER EDAD PESO EG FR_0_8h FLUJO2_0_8H DIAS_GN DIAS_O2_TOTAL DIAS_OAF
## 1 1 10.0 8.20 41 48 2.00 3 3 0
## 2 2 13.0 7.78 40 56 2.00 2 2 0
## 3 1 3.1 5.66 37 44 1.00 4 4 0
## 4 2 5.3 8.44 38 65 0.40 3 3 0
## 5 2 15.0 7.00 34 37 2.00 4 4 0
## 6 1 1.6 3.80 37 42 0.94 4 4 0
## SAPI_0_8h SCORE_CRUCES_INGRESO SCORE_WOOD_DOWNES_INGRESO SEXO PALIVIZUMAB LM
## 1 3 3 6 1 1 2
## 2 4 4 8 1 1 1
## 3 3 3 7 1 1 2
## 4 4 3 6 1 1 2
## 5 1 3 6 1 2 1
## 6 2 4 7 1 1 2
## DERMATITIS ALERGIAS TABACO ENFERMEDAD_BASE RADIOGRAFIA ANALITICA SUERO
## 1 1 2 1 1 1 1 1
## 2 1 2 2 2 1 1 2
## 3 1 1 1 1 1 1 1
## 4 1 1 1 1 1 1 1
## 5 1 1 2 2 1 1 2
## 6 1 1 2 2 1 1 1
## ETIOLOGIA PREMATURIDAD ALIMENTACION SNG GN_INGRESO OAF OAF_AL_INGRESO
## 1 2 1 2 1 2 1 1
## 2 1 1 1 1 2 1 1
## 3 2 1 2 1 2 1 1
## 4 2 1 2 1 1 1 1
## 5 2 2 2 1 2 1 1
## 6 1 1 2 1 1 1 1
## OAF_TRAS_INGRESO UCIP DETERIORO PAUSAS_APNEA
## 1 1 1 1 1
## 2 1 1 1 1
## 3 1 1 1 1
## 4 1 1 1 1
## 5 1 1 1 1
## 6 1 1 1 1data_frame_merge_EUCL$CLUSTER <- factor(data_frame_merge_EUCL$CLUSTER)
newSMOTE_EUCL <-data_frame_merge_EUCL
table(newSMOTE_EUCL$CLUSTER)##
## 1 2
## 24 34set.seed(123)
pos_1 = get_column_position(newSMOTE_EUCL, "SAPI_0_8h")
pos_2 = get_column_position(newSMOTE_EUCL, "PAUSAS_APNEA")
col_names_factor <- names(newSMOTE_EUCL[pos_1:pos_2])
newSMOTE_EUCL[col_names_factor] <- lapply(newSMOTE_EUCL[col_names_factor] , factor)
RF_EUCL <- randomForest(CLUSTER ~ ., data = newSMOTE_EUCL)
print(RF_EUCL)##
## Call:
## randomForest(formula = CLUSTER ~ ., data = newSMOTE_EUCL)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 5
##
## OOB estimate of error rate: 55.17%
## Confusion matrix:
## 1 2 class.error
## 1 6 18 0.7500000
## 2 14 20 0.4117647Importance
kable(RF_EUCL$importance[order(RF_EUCL$importance, decreasing = TRUE),])| x | |
|---|---|
| SCORE_WOOD_DOWNES_INGRESO | 3.8160260 |
| FR_0_8h | 2.8111591 |
| PESO | 2.3188373 |
| SCORE_CRUCES_INGRESO | 2.2545591 |
| EDAD | 2.1933961 |
| EG | 1.8382975 |
| DIAS_GN | 1.4971681 |
| DIAS_O2_TOTAL | 1.4328313 |
| FLUJO2_0_8H | 1.2635127 |
| SAPI_0_8h | 1.2441751 |
| LM | 0.7158387 |
| SEXO | 0.5934293 |
| ALIMENTACION | 0.5507202 |
| PREMATURIDAD | 0.5224504 |
| RADIOGRAFIA | 0.4959604 |
| PALIVIZUMAB | 0.4041348 |
| TABACO | 0.3989201 |
| SUERO | 0.3927697 |
| ETIOLOGIA | 0.3575367 |
| ENFERMEDAD_BASE | 0.3208937 |
| ANALITICA | 0.2698763 |
| GN_INGRESO | 0.2665527 |
| DIAS_OAF | 0.2553120 |
| ALERGIAS | 0.2442751 |
| DERMATITIS | 0.1950109 |
| PAUSAS_APNEA | 0.1140586 |
| SNG | 0.1125693 |
| UCIP | 0.1083658 |
| DETERIORO | 0.0915780 |
| OAF_TRAS_INGRESO | 0.0886067 |
| OAF | 0.0816271 |
| OAF_AL_INGRESO | 0.0000000 |
Importance of the TS-data
data_frame1_EUCL = data.frame("CLUSTER" = DDclust_EUCL_SatO2_scaled)
data_frame2_EUCL = data.frame(datos_EUCL)
data_frame_merge_EUCL <-
merge(data_frame1_EUCL, data_frame2_EUCL, by = 'row.names', all = TRUE)
data_frame_merge_EUCL <- data_frame_merge_EUCL[, 2:dim(data_frame_merge_EUCL)[2]]set.seed(123)
data_frame_merge_EUCL$CLUSTER <- as.factor(data_frame_merge_EUCL$CLUSTER)
RF_0_EUCL <- randomForest(CLUSTER ~ ., data = data_frame_merge_EUCL)
print(RF_0_EUCL)##
## Call:
## randomForest(formula = CLUSTER ~ ., data = data_frame_merge_EUCL)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 21
##
## OOB estimate of error rate: 25.86%
## Confusion matrix:
## 1 2 class.error
## 1 15 9 0.3750000
## 2 6 28 0.1764706plot(RF_0_EUCL$importance, type = "h")
EUCL by clusters
plot_data_EUCL <- data.frame(t(datos))
cluster_data_EUCL <- data.frame(DDclust_EUCL_SatO2_scaled)
plotting_EUCL <- cbind(plot_data_EUCL, cluster_data_EUCL)
head(plotting_EUCL)## X1 X2 X3 X4 X5
## ACR_11231843 1.43850037 1.4385004 0.4887340 1.43850037 0.17214516
## ADAO_11159808 -0.05010484 -0.7372569 -0.7372569 -0.05010484 -0.05010484
## AGG_11236448 0.69244093 0.7254908 0.5329501 0.88904481 0.52231303
## AHL_11239959 0.65487426 0.6548743 0.6548743 0.65487426 -0.49940911
## AJGD_11119689 0.47590709 0.6443077 0.3917068 0.39170680 -0.45029617
## AMP_11228639 -2.93793849 -2.7326353 -0.8632242 -1.50081629 -2.11672581
## X6 X7 X8 X9 X10
## ACR_11231843 -0.14444364 1.12191157 -4.89327567 1.4385004 0.1721452
## ADAO_11159808 -0.05010484 -0.05010484 -0.05010484 -0.7372569 -0.7372569
## AGG_11236448 0.84519875 0.57051881 1.37207281 1.3720728 -0.1743929
## AHL_11239959 0.65487426 1.23201594 0.65487426 -0.4994091 1.2320159
## AJGD_11119689 0.39170680 0.64430769 0.64430769 0.6443077 0.3075065
## AMP_11228639 0.55221542 0.14160907 -0.26899727 0.7575186 0.7575186
## X11 X12 X13 X14 X15
## ACR_11231843 1.1219116 1.1219116 1.4385004 -0.46103244 1.1219116
## ADAO_11159808 -0.7372569 -0.7372569 -0.7372569 -0.05010484 -0.7372569
## AGG_11236448 -2.8807080 1.3720728 0.9854564 1.75868925 0.5988399
## AHL_11239959 0.6548743 0.6548743 0.6548743 0.65487426 0.6548743
## AJGD_11119689 0.4759071 0.4759071 0.6443077 0.47590709 0.3075065
## AMP_11228639 1.1681249 0.5522154 -0.4743004 0.55221542 0.1416091
## X16 X17 X18 X19 X20
## ACR_11231843 1.1219116 0.48873396 1.4385004 1.43850037 1.43850037
## ADAO_11159808 -1.4244090 -0.05010484 0.6370473 -0.05010484 -0.05010484
## AGG_11236448 0.5988399 -0.56100938 1.3720728 0.21222350 1.37207281
## AHL_11239959 0.6548743 1.23201594 -0.4994091 -0.49940911 -0.49940911
## AJGD_11119689 0.6443077 0.64430769 0.6443077 0.64430769 -3.98670863
## AMP_11228639 -0.8849068 1.37342810 0.7575186 -0.06369410 0.55221542
## X21 X22 X23 X24 X25
## ACR_11231843 1.4385004 1.43850037 1.43850037 1.4385004 0.80532277
## ADAO_11159808 -0.7372569 -0.05010484 -0.05010484 0.6370473 -0.05010484
## AGG_11236448 0.9854564 0.98545637 0.21222350 1.3720728 -0.17439294
## AHL_11239959 1.8091576 0.65487426 0.07773258 -0.4994091 0.07773258
## AJGD_11119689 0.6443077 0.64430769 0.64430769 0.6443077 0.64430769
## AMP_11228639 0.7575186 -1.09020995 1.16812493 1.3734281 -0.67960361
## X26 X27 X28 X29 X30
## ACR_11231843 1.4385004 0.8053228 1.43850037 1.4385004 0.4887340
## ADAO_11159808 -0.7372569 -0.7372569 -0.05010484 -0.7372569 0.6370473
## AGG_11236448 1.3720728 -2.1074751 -0.17439294 -2.1074751 0.5988399
## AHL_11239959 1.2320159 1.2320159 0.07773258 0.6548743 0.6548743
## AJGD_11119689 0.6443077 0.6443077 0.64430769 0.6443077 0.6443077
## AMP_11228639 0.1416091 1.3734281 1.37342810 0.7575186 0.3469122
## X31 X32 X33 X34 X35
## ACR_11231843 1.43850037 1.4385004 1.43850037 1.1219116 1.43850037
## ADAO_11159808 -0.05010484 -1.4244090 -0.05010484 -0.7372569 -0.05010484
## AGG_11236448 -2.49409157 1.7586892 0.98545637 0.9854564 1.37207281
## AHL_11239959 -0.49940911 0.6548743 1.80915763 1.2320159 1.23201594
## AJGD_11119689 0.64430769 0.6443077 0.64430769 0.6443077 0.64430769
## AMP_11228639 0.55221542 2.6052471 0.96282176 1.5787313 0.34691225
## X36 X37 X38 X39 X40
## ACR_11231843 1.43850037 0.17214516 1.12191157 1.4385004 1.4385004
## ADAO_11159808 -0.73725694 -0.05010484 -0.73725694 -2.1115611 -2.1115611
## AGG_11236448 1.37207281 0.59883993 1.37207281 0.5988399 -1.7208587
## AHL_11239959 0.07773258 -0.49940911 0.07773258 -1.0765508 0.6548743
## AJGD_11119689 0.64430769 0.64430769 0.64430769 0.6443077 0.2233062
## AMP_11228639 0.75751859 2.60524713 0.34691225 0.1416091 0.5522154
## X41 X42 X43 X44 X45
## ACR_11231843 1.12191157 -0.1444436 0.80532277 1.4385004 0.4887340
## ADAO_11159808 -0.73725694 -0.7372569 -0.73725694 -1.4244090 -0.7372569
## AGG_11236448 0.59883993 1.7586892 0.98545637 0.2122235 0.5988399
## AHL_11239959 0.07773258 0.6548743 0.07773258 -0.4994091 -0.4994091
## AJGD_11119689 0.64430769 0.6443077 0.64430769 0.2233062 -1.0396982
## AMP_11228639 -0.26899727 0.7575186 1.37342810 2.3999440 0.7575186
## X46 X47 X48 X49 X50
## ACR_11231843 0.8053228 0.48873396 1.12191157 1.4385004 1.1219116
## ADAO_11159808 -1.4244090 -0.05010484 -1.42440905 -1.4244090 -1.4244090
## AGG_11236448 1.7586892 0.59883993 1.75868925 -0.1743929 0.2122235
## AHL_11239959 0.6548743 0.07773258 0.07773258 0.6548743 0.6548743
## AJGD_11119689 -0.1976953 0.64430769 -0.19769528 0.6443077 0.6443077
## AMP_11228639 0.5522154 -1.09020995 -6.63339557 0.1416091 0.3469122
## X51 X52 X53 X54 X55
## ACR_11231843 -0.1444436 1.1219116 1.4385004 1.43850037 1.12191157
## ADAO_11159808 -0.7372569 -0.7372569 -0.7372569 -0.73725694 -1.42440905
## AGG_11236448 1.3720728 0.9854564 0.2122235 0.21222350 0.59883993
## AHL_11239959 -0.4994091 -0.4994091 -0.4994091 0.07773258 0.07773258
## AJGD_11119689 0.6443077 0.6443077 0.6443077 0.64430769 0.64430769
## AMP_11228639 1.1681249 0.5522154 1.7840344 0.34691225 0.34691225
## X56 X57 X58 X59 X60
## ACR_11231843 1.1219116 1.1219116 0.8053228 0.8053228 0.8053228
## ADAO_11159808 -0.7372569 -0.7372569 -1.4244090 -1.4244090 -1.4244090
## AGG_11236448 1.3720728 -0.1743929 0.2122235 0.2122235 -0.1743929
## AHL_11239959 -0.4994091 -0.4994091 -0.4994091 -0.4994091 -0.4994091
## AJGD_11119689 0.6443077 0.6443077 0.6443077 0.6443077 0.6443077
## AMP_11228639 -0.2689973 0.3469122 0.3469122 0.5522154 0.7575186
## X61 X62 X63 X64 X65
## ACR_11231843 0.48873396 1.1219116 0.8053228 0.8053228 1.1219116
## ADAO_11159808 -0.05010484 -1.4244090 -1.4244090 -0.7372569 -0.7372569
## AGG_11236448 -0.17439294 0.5988399 0.9854564 0.9854564 0.2122235
## AHL_11239959 -0.49940911 -0.4994091 -1.0765508 -0.4994091 -0.4994091
## AJGD_11119689 0.64430769 0.6443077 0.6443077 -0.6186968 -0.1976953
## AMP_11228639 0.55221542 0.5522154 1.1681249 0.9628218 1.3734281
## X66 X67 X68 X69 X70
## ACR_11231843 0.1721452 -0.46103244 -0.1444436 -1.09421005 -0.4610324
## ADAO_11159808 -1.4244090 -1.42440905 -1.4244090 -1.42440905 -1.4244090
## AGG_11236448 -0.1743929 0.59883993 1.7586892 0.98545637 0.5988399
## AHL_11239959 0.6548743 0.07773258 0.6548743 0.07773258 -0.4994091
## AJGD_11119689 -0.6186968 0.64430769 0.6443077 0.64430769 0.6443077
## AMP_11228639 0.7575186 1.57873127 1.3734281 0.34691225 -0.0636941
## X71 X72 X73 X74 X75 X76
## ACR_11231843 0.8053228 0.1721452 -0.4610324 -2.0439765 -1.0942100 -1.0942100
## ADAO_11159808 -2.7987132 -2.1115611 -2.1115611 -2.1115611 -0.7372569 -0.7372569
## AGG_11236448 0.2122235 0.5988399 0.9854564 -0.1743929 -0.5610094 0.9854564
## AHL_11239959 0.6548743 -0.4994091 1.2320159 0.6548743 1.2320159 0.6548743
## AJGD_11119689 0.6443077 0.6443077 0.6443077 0.6443077 0.6443077 0.6443077
## AMP_11228639 0.7575186 0.3469122 0.7575186 0.7575186 -0.6796036 -0.0636941
## X77 X78 X79 X80 X81
## ACR_11231843 -1.0942100 -0.46103244 -1.41079885 -0.77762125 -0.7776212
## ADAO_11159808 -0.7372569 -0.73725694 -0.73725694 -0.73725694 -0.7372569
## AGG_11236448 -0.1743929 0.21222350 0.59883993 -0.17439294 -0.1743929
## AHL_11239959 0.6548743 0.07773258 0.07773258 0.07773258 0.6548743
## AJGD_11119689 0.6443077 0.22330620 0.64430769 0.64430769 0.6443077
## AMP_11228639 -0.4743004 -0.88490678 0.55221542 0.14160907 0.1416091
## X82 X83 X84 X85 X86
## ACR_11231843 -0.7776212 -0.4610324 -0.14444364 -0.4610324 0.17214516
## ADAO_11159808 -1.4244090 -0.7372569 -1.42440905 -1.4244090 -1.42440905
## AGG_11236448 1.7586892 0.5988399 0.59883993 1.7586892 1.37207281
## AHL_11239959 1.2320159 0.6548743 0.07773258 0.6548743 0.07773258
## AJGD_11119689 0.6443077 0.6443077 0.64430769 -0.6186968 -0.19769528
## AMP_11228639 0.1416091 -0.8849068 0.75751859 1.1681249 0.75751859
## X87 X88 X89 X90 X91
## ACR_11231843 -0.4610324 -0.1444436 -0.4610324 -0.46103244 0.17214516
## ADAO_11159808 -1.4244090 -1.4244090 -1.4244090 -1.42440905 -1.42440905
## AGG_11236448 1.3720728 1.3720728 0.9854564 1.75868925 0.98545637
## AHL_11239959 -0.4994091 0.6548743 -0.4994091 0.07773258 0.07773258
## AJGD_11119689 -0.1976953 0.6443077 0.2233062 0.64430769 0.64430769
## AMP_11228639 1.1681249 1.1681249 1.7840344 0.34691225 -0.06369410
## X92 X93 X94 X95 X96
## ACR_11231843 0.17214516 0.17214516 0.17214516 0.4887340 0.8053228
## ADAO_11159808 -1.42440905 -0.73725694 -1.42440905 -1.4244090 -2.1115611
## AGG_11236448 0.98545637 1.37207281 0.98545637 0.5988399 0.5988399
## AHL_11239959 0.07773258 0.07773258 0.07773258 1.2320159 0.6548743
## AJGD_11119689 0.64430769 0.64430769 0.64430769 0.2233062 0.6443077
## AMP_11228639 1.57873127 1.98933761 1.57873127 0.7575186 0.1416091
## X97 X98 X99 X100 X101
## ACR_11231843 0.4887340 0.1721452 0.4887340 0.17214516 -0.14444364
## ADAO_11159808 -1.4244090 -1.4244090 -1.4244090 -1.42440905 -1.42440905
## AGG_11236448 0.5988399 0.9854564 0.9854564 0.98545637 0.21222350
## AHL_11239959 -0.4994091 -1.0765508 -0.4994091 0.07773258 0.07773258
## AJGD_11119689 0.6443077 -1.0396982 -0.1976953 -1.03969825 -0.61869676
## AMP_11228639 0.9628218 0.7575186 0.7575186 0.34691225 0.55221542
## X102 X103 X104 X105 X106
## ACR_11231843 0.1721452 -0.14444364 -0.1444436 -0.46103244 -0.14444364
## ADAO_11159808 -1.4244090 -2.11156115 -2.1115611 -1.42440905 -1.42440905
## AGG_11236448 0.5988399 0.98545637 1.3720728 0.98545637 0.59883993
## AHL_11239959 0.6548743 0.07773258 1.2320159 0.07773258 0.07773258
## AJGD_11119689 -1.8817012 -0.19769528 -1.0396982 -2.30270270 -0.19769528
## AMP_11228639 -2.3220290 0.75751859 0.5522154 0.55221542 0.34691225
## X107 X108 X109 X110 X111 X112
## ACR_11231843 -1.7273877 -1.0942100 -0.4610324 -0.7776212 -1.0942100 -0.7776212
## ADAO_11159808 -1.4244090 -2.1115611 -2.7987132 -1.4244090 -1.4244090 -1.4244090
## AGG_11236448 0.9854564 0.2122235 0.9854564 0.5988399 1.3720728 0.5988399
## AHL_11239959 -0.4994091 -1.6536925 -1.6536925 -2.8079758 -1.0765508 -1.6536925
## AJGD_11119689 0.2233062 0.6443077 -0.6186968 0.6443077 0.6443077 0.2233062
## AMP_11228639 0.7575186 0.7575186 1.5787313 1.9893376 1.5787313 1.3734281
## X113 X114 X115 X116 X117
## ACR_11231843 -1.4107988 -1.0942100 -0.4610324 -0.1444436 -0.4610324
## ADAO_11159808 -1.4244090 -1.4244090 -0.7372569 -0.7372569 -0.7372569
## AGG_11236448 0.2122235 0.9854564 0.9854564 -1.7208587 0.2122235
## AHL_11239959 -2.2308342 -1.6536925 -1.0765508 -2.2308342 -1.6536925
## AJGD_11119689 -1.0396982 0.2233062 0.6443077 0.2233062 0.6443077
## AMP_11228639 0.9628218 0.5522154 0.3469122 0.3469122 0.7575186
## X118 X119 X120 X121 X122
## ACR_11231843 -1.72738765 -0.7776212 -0.7776212 -1.7273877 -0.1444436
## ADAO_11159808 -0.05010484 -1.4244090 -0.7372569 -1.4244090 -1.4244090
## AGG_11236448 1.75868925 0.5988399 0.5988399 0.9854564 0.2122235
## AHL_11239959 -1.65369247 -1.6536925 -1.6536925 -1.0765508 -1.6536925
## AJGD_11119689 0.64430769 -3.1447057 -1.4606997 0.2233062 0.6443077
## AMP_11228639 -0.06369410 0.9628218 0.1416091 -0.0636941 1.3734281
## X123 X124 X125 X126 X127 X128
## ACR_11231843 0.4887340 -0.1444436 0.4887340 0.4887340 0.1721452 0.4887340
## ADAO_11159808 -2.7987132 -2.7987132 -2.1115611 -2.7987132 -2.1115611 -2.1115611
## AGG_11236448 0.5988399 -0.1743929 0.5988399 0.5988399 0.2122235 -0.5610094
## AHL_11239959 -1.6536925 -1.6536925 -1.6536925 -1.6536925 -1.0765508 -1.6536925
## AJGD_11119689 0.2233062 0.6443077 0.6443077 -1.8817012 -1.4606997 -1.8817012
## AMP_11228639 -1.0902100 0.5522154 0.5522154 -0.6796036 0.7575186 0.3469122
## X129 X130 X131 X132 X133 X134
## ACR_11231843 -0.1444436 -1.0942100 0.4887340 0.1721452 -0.1444436 -0.4610324
## ADAO_11159808 -0.7372569 -1.4244090 -2.1115611 -0.7372569 -0.7372569 -0.7372569
## AGG_11236448 0.2122235 0.5988399 0.5988399 0.2122235 0.9854564 0.2122235
## AHL_11239959 -1.0765508 -2.2308342 -2.2308342 -1.0765508 -1.0765508 -1.6536925
## AJGD_11119689 -2.3027027 -2.7237042 -2.7237042 -1.4606997 -1.0396982 -1.0396982
## AMP_11228639 0.5522154 0.7575186 0.1416091 0.3469122 0.7575186 0.5522154
## X135 X136 X137 X138 X139
## ACR_11231843 -0.14444364 0.17214516 -0.46103244 -0.7776212 0.48873396
## ADAO_11159808 -0.05010484 -0.05010484 -0.05010484 -0.7372569 -0.05010484
## AGG_11236448 -0.17439294 -0.17439294 -0.17439294 0.2122235 0.98545637
## AHL_11239959 -1.07655079 -0.49940911 0.65487426 -7.4251093 -10.31081772
## AJGD_11119689 -1.46069973 -2.72370418 -2.72370418 -0.1976953 -0.19769528
## AMP_11228639 0.96282176 1.16812493 0.75751859 0.5522154 0.55221542
## X140 X141 X142 X143 X144
## ACR_11231843 0.17214516 -0.46103244 0.17214516 0.17214516 -0.46103244
## ADAO_11159808 -0.05010484 -0.05010484 -0.05010484 -0.05010484 -0.05010484
## AGG_11236448 0.21222350 0.59883993 0.21222350 0.21222350 0.21222350
## AHL_11239959 -5.11654257 0.37697550 -0.12736062 -0.03402941 -0.36417213
## AJGD_11119689 -0.61869676 -0.19769528 0.22330620 0.64430769 0.64430769
## AMP_11228639 0.14160907 0.14160907 -2.11672581 -0.67960361 -1.70611947
## X145 X146 X147 X148 X149
## ACR_11231843 -0.46103244 -0.14444364 0.17214516 -0.14444364 -0.46103244
## ADAO_11159808 -0.05010484 -0.05010484 -0.05010484 -0.05010484 -0.05010484
## AGG_11236448 0.21222350 -0.17439294 1.37207281 0.21222350 0.59883993
## AHL_11239959 -0.41446107 0.07773258 0.65487426 1.80915763 0.65487426
## AJGD_11119689 0.64430769 0.22330620 0.64430769 0.64430769 0.64430769
## AMP_11228639 -0.47430044 -2.52733215 -0.88490678 0.34691225 -1.09020995
## X150 X151 X152 X153 X154
## ACR_11231843 -0.77762125 -2.04397645 -1.09421005 -1.09421005 -0.46103244
## ADAO_11159808 -0.05010484 -0.05010484 -0.05010484 -0.05010484 -0.05010484
## AGG_11236448 -0.56100938 -0.17439294 0.21222350 -0.56100938 -0.94762582
## AHL_11239959 1.80915763 -1.07655079 -0.49940911 0.07773258 0.07773258
## AJGD_11119689 0.64430769 0.64430769 0.22330620 0.64430769 0.64430769
## AMP_11228639 0.55221542 0.14160907 0.55221542 1.16812493 -0.06369410
## X155 X156 X157 X158 X159
## ACR_11231843 0.4887340 -0.46103244 -0.46103244 0.48873396 0.4887340
## ADAO_11159808 -0.7372569 -0.05010484 -0.05010484 -0.73725694 -0.7372569
## AGG_11236448 -0.1743929 -0.17439294 -0.56100938 -0.17439294 -0.5610094
## AHL_11239959 0.6548743 0.65487426 0.07773258 0.07773258 -0.4994091
## AJGD_11119689 0.6443077 0.64430769 -0.19769528 0.22330620 -1.0396982
## AMP_11228639 0.3469122 -1.29551312 0.14160907 -0.67960361 -0.0636941
## X160 X161 X162 X163 X164
## ACR_11231843 0.4887340 -0.1444436 0.1721452 -1.09421005 0.48873396
## ADAO_11159808 -1.4244090 -2.1115611 -0.7372569 -0.73725694 -0.73725694
## AGG_11236448 0.5988399 -0.1743929 0.9854564 -0.17439294 0.21222350
## AHL_11239959 -0.4994091 -1.0765508 -0.4994091 0.07773258 0.07773258
## AJGD_11119689 -2.3027027 -1.4606997 -2.7237042 -3.14470567 -1.88170122
## AMP_11228639 0.1416091 -0.0636941 0.9628218 -0.47430044 -0.06369410
## X165 X166 X167 X168 X169
## ACR_11231843 0.17214516 0.48873396 0.17214516 -0.77762125 1.12191157
## ADAO_11159808 -0.73725694 -0.73725694 -0.73725694 -0.05010484 -0.05010484
## AGG_11236448 -0.17439294 0.21222350 0.21222350 0.21222350 0.21222350
## AHL_11239959 0.07773258 0.07773258 0.07773258 -0.49940911 0.65487426
## AJGD_11119689 -1.03969825 -1.03969825 -2.30270270 -2.30270270 0.64430769
## AMP_11228639 0.34691225 -0.88490678 -1.50081629 -1.29551312 0.14160907
## X170 X171 X172 X173 X174
## ACR_11231843 0.1721452 0.80532277 0.4887340 -0.1444436 -0.1444436
## ADAO_11159808 -0.7372569 -0.05010484 -0.7372569 -1.4244090 -2.1115611
## AGG_11236448 0.2122235 -0.17439294 0.2122235 0.2122235 0.2122235
## AHL_11239959 0.6548743 0.07773258 -0.4994091 -0.4994091 -0.4994091
## AJGD_11119689 0.2233062 0.64430769 0.6443077 -0.1976953 -1.0396982
## AMP_11228639 0.1416091 -0.06369410 -0.2689973 0.5522154 1.1681249
## X175 X176 X177 X178 X179
## ACR_11231843 0.8053228 -0.14444364 0.8053228 1.4385004 0.8053228
## ADAO_11159808 -1.4244090 -0.73725694 -2.1115611 0.6370473 0.6370473
## AGG_11236448 0.2122235 0.21222350 -0.1743929 0.2122235 0.2122235
## AHL_11239959 1.2320159 0.07773258 -1.0765508 -1.0765508 -0.4994091
## AJGD_11119689 0.2233062 -1.03969825 0.6443077 0.6443077 -0.1976953
## AMP_11228639 0.7575186 0.34691225 0.3469122 -1.2955131 -3.1432417
## X180 X181 X182 X183 X184
## ACR_11231843 0.80532277 0.80532277 0.4887340 -0.1444436 1.1219116
## ADAO_11159808 -0.05010484 0.63704726 0.6370473 0.6370473 0.6370473
## AGG_11236448 0.21222350 -0.17439294 0.2122235 -0.1743929 0.2122235
## AHL_11239959 -0.49940911 0.07773258 -0.4994091 -1.0765508 -0.4994091
## AJGD_11119689 -0.61869676 0.22330620 0.6443077 0.6443077 0.6443077
## AMP_11228639 -2.11672581 -1.09020995 -1.5008163 -0.0636941 -2.3220290
## X185 X186 X187 X188 X189
## ACR_11231843 0.80532277 0.4887340 -2.67715406 1.12191157 0.17214516
## ADAO_11159808 0.63704726 0.6370473 0.63704726 -0.05010484 0.63704726
## AGG_11236448 0.21222350 -0.1743929 -3.26732444 0.21222350 -1.33424225
## AHL_11239959 0.07773258 -0.4994091 0.07773258 0.07773258 0.07773258
## AJGD_11119689 0.64430769 0.6443077 0.64430769 0.64430769 0.64430769
## AMP_11228639 -3.34854483 -0.2689973 -1.09020995 -0.26899727 -2.11672581
## X190 X191 X192 X193 X194
## ACR_11231843 0.1721452 0.17214516 0.8053228 0.1721452 0.4887340
## ADAO_11159808 0.6370473 0.63704726 0.6370473 0.6370473 0.6370473
## AGG_11236448 -0.1743929 -0.17439294 -3.2673244 0.2122235 0.2122235
## AHL_11239959 -1.0765508 0.07773258 -0.4994091 -1.0765508 -1.0765508
## AJGD_11119689 0.6443077 0.64430769 0.6443077 0.6443077 0.6443077
## AMP_11228639 -0.4743004 -2.11672581 -0.8849068 -0.2689973 0.1416091
## X195 X196 X197 X198 X199 X200
## ACR_11231843 -0.1444436 0.1721452 1.1219116 -0.4610324 -0.7776212 0.1721452
## ADAO_11159808 0.6370473 0.6370473 0.6370473 1.3241994 0.6370473 0.6370473
## AGG_11236448 0.5988399 0.9854564 0.2122235 -0.1743929 0.9854564 0.2122235
## AHL_11239959 -1.6536925 -1.0765508 -1.0765508 -1.0765508 -1.0765508 -0.4994091
## AJGD_11119689 0.6443077 0.6443077 0.6443077 0.6443077 0.6443077 -5.6707146
## AMP_11228639 -0.6796036 -0.4743004 0.1416091 -0.0636941 -1.0902100 -0.8849068
## X201 X202 X203 X204 X205 X206
## ACR_11231843 -0.1444436 0.8053228 0.4887340 0.4887340 1.1219116 0.8053228
## ADAO_11159808 0.6370473 0.6370473 0.6370473 0.6370473 0.6370473 0.6370473
## AGG_11236448 -0.1743929 0.2122235 0.5988399 0.9854564 0.5988399 -0.1743929
## AHL_11239959 -1.0765508 -1.0765508 -2.2308342 -1.6536925 -1.0765508 -1.0765508
## AJGD_11119689 0.6443077 0.6443077 0.6443077 0.6443077 0.6443077 -1.4606997
## AMP_11228639 -0.6796036 -0.0636941 -0.0636941 -1.0902100 -0.2689973 -1.2955131
## X207 X208 X209 X210 X211 X212
## ACR_11231843 0.4887340 -1.0942100 -0.7776212 -0.1444436 1.4385004 1.1219116
## ADAO_11159808 1.3241994 0.6370473 0.6370473 0.6370473 0.6370473 1.3241994
## AGG_11236448 0.2122235 0.2122235 0.2122235 -0.1743929 0.2122235 -0.1743929
## AHL_11239959 -1.0765508 -0.4994091 -1.0765508 -1.0765508 -0.4994091 -1.0765508
## AJGD_11119689 -0.6186968 -1.0396982 -1.0396982 -1.4606997 -1.0396982 -1.0396982
## AMP_11228639 -2.9379385 -0.0636941 -3.5538480 -0.4743004 -1.7061195 0.3469122
## X213 X214 X215 X216 X217
## ACR_11231843 1.12191157 1.1219116 0.8053228 0.4887340 0.8053228
## ADAO_11159808 1.32419936 1.3241994 1.3241994 1.3241994 1.3241994
## AGG_11236448 0.59883993 0.5988399 0.5988399 0.2122235 -0.1743929
## AHL_11239959 0.07773258 -0.4994091 -0.4994091 0.6548743 -0.4994091
## AJGD_11119689 0.64430769 0.2233062 -0.1976953 0.2233062 -0.1976953
## AMP_11228639 -1.70611947 0.1416091 0.1416091 0.1416091 -1.5008163
## X218 X219 X220 X221 X222
## ACR_11231843 0.80532277 0.80532277 0.80532277 0.8053228 0.4887340
## ADAO_11159808 1.32419936 1.32419936 1.32419936 0.6370473 0.6370473
## AGG_11236448 -0.17439294 -0.17439294 0.21222350 0.2122235 -0.5610094
## AHL_11239959 0.07773258 0.07773258 0.07773258 0.6548743 0.6548743
## AJGD_11119689 -0.61869676 -0.19769528 0.22330620 -0.6186968 0.6443077
## AMP_11228639 -0.06369410 -0.06369410 -0.06369410 -0.0636941 -0.0636941
## X223 X224 X225 X226 X227
## ACR_11231843 0.8053228 0.8053228 0.48873396 0.1721452 -0.1444436
## ADAO_11159808 1.3241994 1.3241994 1.32419936 0.6370473 1.3241994
## AGG_11236448 -0.5610094 -0.1743929 -0.94762582 -0.5610094 -0.5610094
## AHL_11239959 0.6548743 0.6548743 0.07773258 -1.0765508 -0.4994091
## AJGD_11119689 -1.0396982 -1.8817012 -0.19769528 0.6443077 0.6443077
## AMP_11228639 -0.0636941 0.1416091 0.55221542 -0.0636941 0.1416091
## X228 X229 X230 X231 X232
## ACR_11231843 0.1721452 -2.36056526 -2.0439765 -0.1444436 -1.0942100
## ADAO_11159808 1.3241994 1.32419936 1.3241994 1.3241994 1.3241994
## AGG_11236448 -0.5610094 0.21222350 -1.7208587 -2.4940916 -2.4940916
## AHL_11239959 -0.4994091 0.07773258 -0.4994091 -0.4994091 1.2320159
## AJGD_11119689 0.2233062 0.22330620 0.6443077 0.6443077 0.6443077
## AMP_11228639 0.3469122 0.34691225 0.1416091 0.1416091 0.5522154
## X233 X234 X235 X236 X237
## ACR_11231843 -0.14444364 -1.72738765 -0.1444436 -0.7776212 -0.14444364
## ADAO_11159808 1.32419936 1.32419936 1.3241994 0.6370473 1.32419936
## AGG_11236448 -1.33424225 -2.10747513 -0.9476258 -1.7208587 -1.72085869
## AHL_11239959 0.07773258 0.07773258 -0.4994091 -0.4994091 0.07773258
## AJGD_11119689 0.64430769 0.64430769 0.6443077 0.6443077 0.64430769
## AMP_11228639 0.14160907 0.34691225 0.3469122 -0.0636941 -0.06369410
## X238 X239 X240 X241 X242
## ACR_11231843 0.80532277 0.1721452 -0.14444364 -0.1444436 0.80532277
## ADAO_11159808 1.32419936 1.3241994 1.32419936 1.3241994 1.32419936
## AGG_11236448 -0.56100938 -0.5610094 -2.10747513 -1.3342423 -0.94762582
## AHL_11239959 0.07773258 -0.4994091 0.07773258 -0.4994091 0.07773258
## AJGD_11119689 0.64430769 0.6443077 0.64430769 0.6443077 0.64430769
## AMP_11228639 0.55221542 -0.4743004 0.96282176 0.7575186 0.75751859
## X243 X244 X245 X246 X247
## ACR_11231843 -0.14444364 -0.46103244 -0.4610324 -2.67715406 -2.36056526
## ADAO_11159808 1.32419936 1.32419936 1.3241994 1.32419936 1.32419936
## AGG_11236448 0.59883993 -0.17439294 -1.7208587 -0.94762582 -1.72085869
## AHL_11239959 0.07773258 0.07773258 0.6548743 0.07773258 0.07773258
## AJGD_11119689 0.64430769 0.64430769 -0.6186968 0.22330620 0.64430769
## AMP_11228639 0.55221542 -0.67960361 0.1416091 -0.67960361 0.14160907
## X248 X249 X250 X251 X252
## ACR_11231843 -1.09421005 -2.99374286 -1.09421005 -0.7776212 -2.99374286
## ADAO_11159808 1.32419936 0.63704726 1.32419936 0.6370473 1.32419936
## AGG_11236448 -1.33424225 -1.33424225 -0.56100938 -0.5610094 -0.94762582
## AHL_11239959 0.07773258 0.07773258 0.07773258 0.6548743 0.07773258
## AJGD_11119689 0.64430769 0.64430769 0.64430769 0.6443077 0.64430769
## AMP_11228639 1.37342810 -0.47430044 0.14160907 0.3469122 0.34691225
## X253 X254 X255 X256 X257
## ACR_11231843 -8.69234130 -3.62692046 -0.4610324 0.48873396 0.1721452
## ADAO_11159808 0.63704726 0.63704726 0.6370473 1.32419936 0.6370473
## AGG_11236448 -0.94762582 0.21222350 0.2122235 -0.56100938 -0.5610094
## AHL_11239959 0.07773258 0.07773258 0.6548743 0.07773258 1.2320159
## AJGD_11119689 0.64430769 -0.19769528 0.2233062 -1.46069973 0.6443077
## AMP_11228639 0.34691225 0.14160907 -0.6796036 0.14160907 -1.0902100
## X258 X259 X260 X261 X262 X263
## ACR_11231843 -0.4610324 -0.1444436 -0.1444436 0.1721452 -0.1444436 -0.1444436
## ADAO_11159808 0.6370473 1.3241994 1.3241994 0.6370473 1.3241994 0.6370473
## AGG_11236448 -0.9476258 -1.3342423 -0.9476258 -1.3342423 -1.3342423 -1.7208587
## AHL_11239959 0.6548743 0.6548743 0.6548743 0.6548743 0.6548743 0.6548743
## AJGD_11119689 0.6443077 0.6443077 -0.1976953 0.6443077 0.6443077 -0.6186968
## AMP_11228639 -0.2689973 -0.6796036 -1.5008163 -0.0636941 -1.0902100 0.3469122
## X264 X265 X266 X267 X268
## ACR_11231843 0.17214516 0.4887340 0.8053228 1.1219116 0.8053228
## ADAO_11159808 -0.05010484 0.6370473 -0.7372569 -0.7372569 -0.7372569
## AGG_11236448 -1.33424225 -1.7208587 -1.7208587 -1.7208587 -0.5610094
## AHL_11239959 0.65487426 0.6548743 0.6548743 0.6548743 0.6548743
## AJGD_11119689 0.22330620 -0.1976953 0.2233062 0.6443077 0.2233062
## AMP_11228639 0.34691225 0.7575186 0.9628218 -3.1432417 -0.4743004
## X269 X270 X271 X272 X273
## ACR_11231843 -0.46103244 0.48873396 0.17214516 0.4887340 0.4887340
## ADAO_11159808 -0.05010484 -0.05010484 -0.05010484 0.6370473 1.3241994
## AGG_11236448 -1.33424225 -1.33424225 -1.72085869 -1.3342423 -1.3342423
## AHL_11239959 0.65487426 -1.65369247 0.07773258 0.6548743 0.6548743
## AJGD_11119689 0.22330620 0.22330620 -0.19769528 -0.1976953 -0.1976953
## AMP_11228639 0.96282176 1.57873127 0.75751859 -0.6796036 0.7575186
## X274 X275 X276 X277 X278
## ACR_11231843 0.80532277 0.48873396 0.48873396 0.17214516 0.48873396
## ADAO_11159808 0.63704726 -0.05010484 -0.05010484 -0.05010484 -0.73725694
## AGG_11236448 -1.33424225 -1.33424225 -1.33424225 -0.94762582 -1.33424225
## AHL_11239959 0.07773258 0.07773258 0.07773258 0.07773258 0.07773258
## AJGD_11119689 -0.19769528 -0.61869676 -1.03969825 -1.03969825 -1.46069973
## AMP_11228639 0.75751859 0.75751859 0.96282176 1.37342810 -0.88490678
## X279 X280 X281 X282 X283
## ACR_11231843 0.17214516 0.17214516 0.48873396 0.4887340 0.17214516
## ADAO_11159808 0.63704726 0.63704726 -0.05010484 0.6370473 1.32419936
## AGG_11236448 -1.72085869 -1.72085869 -3.26732444 -0.9476258 -4.04055732
## AHL_11239959 0.07773258 0.07773258 0.07773258 0.6548743 0.07773258
## AJGD_11119689 -1.88170122 -1.88170122 -1.88170122 -1.8817012 -0.61869676
## AMP_11228639 -0.06369410 0.34691225 0.14160907 0.1416091 -0.47430044
## X284 X285 X286 X287 X288
## ACR_11231843 0.48873396 0.48873396 0.17214516 0.1721452 -0.14444364
## ADAO_11159808 0.63704726 1.32419936 -0.05010484 0.6370473 -0.05010484
## AGG_11236448 0.59883993 0.98545637 -0.56100938 -0.1743929 0.21222350
## AHL_11239959 0.07773258 0.07773258 0.07773258 -0.4994091 0.65487426
## AJGD_11119689 -1.46069973 -1.46069973 -1.88170122 -1.8817012 -1.46069973
## AMP_11228639 -0.26899727 -1.29551312 -0.88490678 1.1681249 -2.32202898
## X289 X290 X291 X292 X293
## ACR_11231843 0.4887340 0.17214516 0.48873396 -0.14444364 -0.1444436
## ADAO_11159808 -0.7372569 -0.05010484 -0.05010484 0.63704726 0.6370473
## AGG_11236448 -0.5610094 0.21222350 -0.56100938 0.59883993 0.5988399
## AHL_11239959 1.2320159 0.07773258 -0.49940911 0.07773258 0.6548743
## AJGD_11119689 -1.4606997 -1.03969825 -1.03969825 -1.03969825 -0.6186968
## AMP_11228639 -2.9379385 -0.47430044 0.14160907 -0.26899727 -0.2689973
## X294 X295 X296 X297 X298
## ACR_11231843 0.1721452 -0.1444436 0.1721452 0.1721452 0.1721452
## ADAO_11159808 0.6370473 0.6370473 -0.7372569 0.6370473 0.6370473
## AGG_11236448 0.5988399 0.2122235 0.5988399 -3.2673244 -4.0405573
## AHL_11239959 0.6548743 0.6548743 1.2320159 0.6548743 0.6548743
## AJGD_11119689 -0.6186968 -1.0396982 -0.6186968 -1.0396982 -1.0396982
## AMP_11228639 -1.0902100 -0.4743004 0.1416091 0.3469122 -0.2689973
## X299 X300 X301 X302 X303
## ACR_11231843 0.17214516 -0.14444364 0.1721452 -0.14444364 -0.14444364
## ADAO_11159808 0.63704726 -2.11156115 -1.4244090 -0.73725694 -0.73725694
## AGG_11236448 -3.26732444 0.98545637 1.3720728 1.75868925 0.98545637
## AHL_11239959 0.07773258 0.07773258 -1.0765508 0.07773258 0.07773258
## AJGD_11119689 -1.03969825 -1.46069973 -1.4606997 -1.88170122 -1.88170122
## AMP_11228639 -1.50081629 -1.91142264 -0.0636941 -0.26899727 -0.26899727
## X304 X305 X306 X307 X308
## ACR_11231843 0.48873396 0.17214516 -0.14444364 -0.14444364 0.17214516
## ADAO_11159808 -0.73725694 -0.05010484 0.63704726 -0.73725694 -0.73725694
## AGG_11236448 0.98545637 0.59883993 0.98545637 0.98545637 0.59883993
## AHL_11239959 0.07773258 0.65487426 0.07773258 0.07773258 0.07773258
## AJGD_11119689 -1.46069973 -1.46069973 -2.30270270 -2.30270270 -2.72370418
## AMP_11228639 0.14160907 0.34691225 0.34691225 0.34691225 0.34691225
## X309 X310 X311 X312 X313
## ACR_11231843 0.17214516 0.48873396 0.17214516 0.4887340 0.17214516
## ADAO_11159808 -0.05010484 -0.05010484 0.63704726 0.6370473 0.63704726
## AGG_11236448 -1.72085869 0.98545637 0.59883993 0.5988399 0.21222350
## AHL_11239959 -0.49940911 0.07773258 0.07773258 0.6548743 0.07773258
## AJGD_11119689 -2.72370418 -2.72370418 -2.72370418 -2.7237042 -2.72370418
## AMP_11228639 0.55221542 0.75751859 0.75751859 0.3469122 0.55221542
## X314 X315 X316 X317 X318
## ACR_11231843 0.48873396 0.48873396 0.17214516 0.17214516 0.4887340
## ADAO_11159808 -0.05010484 0.63704726 0.63704726 -0.05010484 0.6370473
## AGG_11236448 -0.56100938 0.21222350 0.98545637 0.21222350 0.2122235
## AHL_11239959 0.07773258 0.07773258 0.07773258 0.65487426 0.6548743
## AJGD_11119689 -2.72370418 -2.30270270 -2.30270270 -2.72370418 -2.3027027
## AMP_11228639 0.55221542 0.55221542 0.96282176 0.34691225 0.5522154
## X319 X320 X321 X322 X323
## ACR_11231843 0.48873396 -0.14444364 0.48873396 0.80532277 0.48873396
## ADAO_11159808 0.63704726 0.63704726 0.63704726 0.63704726 1.32419936
## AGG_11236448 -0.17439294 0.98545637 0.21222350 -0.17439294 -0.56100938
## AHL_11239959 0.07773258 0.07773258 0.07773258 0.07773258 0.07773258
## AJGD_11119689 -1.03969825 -1.88170122 -2.30270270 -1.46069973 -0.61869676
## AMP_11228639 0.55221542 0.55221542 0.96282176 0.75751859 0.75751859
## X324 X325 X326 X327 X328
## ACR_11231843 0.17214516 0.1721452 -0.4610324 0.4887340 -0.1444436
## ADAO_11159808 -0.05010484 -0.7372569 -0.7372569 -1.4244090 -0.7372569
## AGG_11236448 -0.17439294 0.9854564 0.2122235 -0.1743929 0.5988399
## AHL_11239959 0.07773258 0.6548743 1.2320159 0.6548743 1.2320159
## AJGD_11119689 -3.14470567 0.6443077 -1.8817012 0.2233062 0.6443077
## AMP_11228639 0.34691225 0.5522154 0.3469122 0.1416091 0.3469122
## X329 X330 X331 X332 X333
## ACR_11231843 -0.1444436 -0.46103244 -0.4610324 -0.14444364 -0.1444436
## ADAO_11159808 -0.7372569 -0.05010484 0.6370473 -0.05010484 -0.7372569
## AGG_11236448 0.5988399 -0.17439294 0.2122235 0.21222350 -0.1743929
## AHL_11239959 1.2320159 1.23201594 1.2320159 1.23201594 1.2320159
## AJGD_11119689 0.6443077 0.64430769 0.6443077 -0.19769528 0.6443077
## AMP_11228639 0.5522154 0.55221542 0.3469122 0.55221542 0.9628218
## X334 X335 X336 X337 X338
## ACR_11231843 -0.7776212 -0.1444436 -0.1444436 0.17214516 0.48873396
## ADAO_11159808 -0.7372569 -0.7372569 -0.7372569 -0.05010484 -0.05010484
## AGG_11236448 -0.1743929 -0.1743929 0.2122235 -0.17439294 0.21222350
## AHL_11239959 1.2320159 0.6548743 1.2320159 1.23201594 0.65487426
## AJGD_11119689 0.6443077 0.2233062 0.2233062 0.64430769 0.22330620
## AMP_11228639 0.5522154 0.3469122 0.5522154 0.75751859 0.34691225
## X339 X340 X341 X342 X343
## ACR_11231843 -0.77762125 -0.14444364 -0.46103244 0.17214516 0.48873396
## ADAO_11159808 -0.05010484 -0.05010484 -0.05010484 -0.05010484 -0.05010484
## AGG_11236448 -0.56100938 -0.56100938 0.21222350 -0.17439294 0.21222350
## AHL_11239959 0.65487426 0.65487426 0.07773258 0.65487426 0.65487426
## AJGD_11119689 0.64430769 -0.19769528 0.64430769 0.22330620 0.64430769
## AMP_11228639 0.55221542 0.14160907 0.55221542 -0.88490678 0.14160907
## X344 X345 X346 X347 X348
## ACR_11231843 0.48873396 0.48873396 0.17214516 0.80532277 -0.14444364
## ADAO_11159808 -0.05010484 -0.05010484 -0.05010484 -0.05010484 -0.05010484
## AGG_11236448 1.37207281 -0.56100938 0.59883993 -0.17439294 -0.56100938
## AHL_11239959 0.65487426 0.07773258 0.07773258 0.07773258 0.07773258
## AJGD_11119689 0.22330620 0.64430769 0.22330620 -0.61869676 -0.19769528
## AMP_11228639 -0.26899727 0.14160907 -2.93793849 -0.47430044 -1.91142264
## X349 X350 X351 X352 X353
## ACR_11231843 -0.14444364 0.17214516 0.17214516 -0.14444364 0.48873396
## ADAO_11159808 -0.05010484 -0.05010484 -0.05010484 -0.05010484 -0.05010484
## AGG_11236448 -0.56100938 -0.94762582 -0.94762582 -0.94762582 -0.56100938
## AHL_11239959 0.07773258 -0.49940911 -0.49940911 0.07773258 -0.49940911
## AJGD_11119689 0.64430769 -0.61869676 0.64430769 0.64430769 0.64430769
## AMP_11228639 -0.06369410 -2.52733215 -0.67960361 -0.06369410 -2.11672581
## X354 X355 X356 X357 X358
## ACR_11231843 -0.46103244 -0.77762125 -0.14444364 0.17214516 -0.46103244
## ADAO_11159808 -0.05010484 -0.05010484 -0.05010484 -0.05010484 -0.05010484
## AGG_11236448 -0.94762582 0.59883993 -0.56100938 0.21222350 -0.56100938
## AHL_11239959 0.07773258 -0.49940911 0.07773258 1.23201594 0.07773258
## AJGD_11119689 0.64430769 0.22330620 -0.19769528 0.64430769 0.64430769
## AMP_11228639 0.14160907 -1.70611947 -0.06369410 -1.50081629 0.34691225
## X359 X360 X361 X362 X363
## ACR_11231843 -0.14444364 -0.14444364 -0.14444364 0.48873396 -0.1444436
## ADAO_11159808 -0.05010484 1.32419936 -0.05010484 -0.73725694 0.6370473
## AGG_11236448 0.21222350 -0.17439294 -0.17439294 0.59883993 1.7586892
## AHL_11239959 0.07773258 0.07773258 0.07773258 0.07773258 -0.4994091
## AJGD_11119689 0.22330620 0.64430769 0.64430769 0.64430769 0.2233062
## AMP_11228639 -1.29551312 -0.88490678 0.14160907 -0.67960361 0.1416091
## X364 X365 X366 X367 X368
## ACR_11231843 0.1721452 -0.1444436 0.4887340 0.1721452 0.17214516
## ADAO_11159808 0.6370473 0.6370473 -1.4244090 0.6370473 -0.05010484
## AGG_11236448 0.5988399 -0.1743929 -0.1743929 -0.1743929 -0.17439294
## AHL_11239959 0.6548743 0.6548743 0.6548743 0.6548743 0.65487426
## AJGD_11119689 0.6443077 0.2233062 0.2233062 0.2233062 -0.19769528
## AMP_11228639 -0.0636941 -0.0636941 0.5522154 0.1416091 0.55221542
## X369 X370 X371 X372 X373
## ACR_11231843 0.48873396 0.4887340 0.48873396 0.80532277 0.4887340
## ADAO_11159808 -0.05010484 -4.1730175 -0.05010484 0.63704726 1.3241994
## AGG_11236448 0.21222350 -0.1743929 0.21222350 0.21222350 -0.5610094
## AHL_11239959 0.65487426 0.6548743 0.65487426 0.07773258 0.6548743
## AJGD_11119689 -0.19769528 -0.1976953 -1.03969825 0.22330620 0.6443077
## AMP_11228639 0.34691225 0.3469122 0.14160907 0.34691225 0.3469122
## X374 X375 X376 X377 X378 X379
## ACR_11231843 0.4887340 0.8053228 0.4887340 0.4887340 0.1721452 0.17214516
## ADAO_11159808 0.6370473 0.6370473 0.6370473 0.6370473 1.3241994 1.32419936
## AGG_11236448 -0.5610094 -0.9476258 0.5988399 -0.9476258 -0.5610094 -0.56100938
## AHL_11239959 0.6548743 0.6548743 0.6548743 0.6548743 0.6548743 0.07773258
## AJGD_11119689 0.2233062 0.6443077 0.2233062 -0.1976953 -0.1976953 0.64430769
## AMP_11228639 0.1416091 0.3469122 0.5522154 0.3469122 0.3469122 0.34691225
## X380 X381 X382 X383 X384 X385
## ACR_11231843 -0.4610324 -0.1444436 -0.4610324 -0.1444436 -0.7776212 0.8053228
## ADAO_11159808 0.6370473 1.3241994 1.3241994 1.3241994 1.3241994 1.3241994
## AGG_11236448 -0.5610094 -0.5610094 0.2122235 -0.1743929 -0.1743929 0.2122235
## AHL_11239959 0.6548743 1.2320159 0.6548743 0.6548743 0.6548743 0.6548743
## AJGD_11119689 0.6443077 0.2233062 0.6443077 0.6443077 0.6443077 -0.6186968
## AMP_11228639 -0.0636941 0.3469122 0.7575186 0.1416091 0.3469122 0.5522154
## X386 X387 X388 X389 X390
## ACR_11231843 -0.1444436 -1.72738765 -1.7273877 -2.0439765 -1.09421005
## ADAO_11159808 1.3241994 1.32419936 1.3241994 1.3241994 0.63704726
## AGG_11236448 -0.5610094 -0.17439294 -0.1743929 -0.5610094 -0.56100938
## AHL_11239959 0.6548743 0.07773258 1.8091576 -0.4994091 0.07773258
## AJGD_11119689 0.6443077 0.64430769 0.6443077 0.6443077 0.64430769
## AMP_11228639 -0.0636941 -0.67960361 -0.4743004 0.1416091 -0.26899727
## X391 X392 X393 X394 X395
## ACR_11231843 -1.7273877 -1.0942100 -0.1444436 -0.1444436 -0.46103244
## ADAO_11159808 0.6370473 0.6370473 1.3241994 0.6370473 -0.05010484
## AGG_11236448 -0.9476258 -0.9476258 -0.9476258 -0.5610094 -0.17439294
## AHL_11239959 0.6548743 0.6548743 -0.4994091 0.6548743 0.07773258
## AJGD_11119689 0.6443077 0.6443077 0.6443077 0.6443077 0.64430769
## AMP_11228639 -0.2689973 -0.6796036 -2.5273322 0.1416091 -2.52733215
## X396 X397 X398 X399 X400
## ACR_11231843 -0.77762125 -0.46103244 0.1721452 -0.4610324 -0.7776212
## ADAO_11159808 -0.05010484 0.63704726 1.3241994 1.3241994 1.3241994
## AGG_11236448 -0.56100938 -0.56100938 -0.1743929 -0.1743929 -0.5610094
## AHL_11239959 0.65487426 0.07773258 1.2320159 0.6548743 0.6548743
## AJGD_11119689 0.64430769 -0.61869676 0.6443077 0.6443077 0.6443077
## AMP_11228639 0.14160907 -2.32202898 -2.3220290 0.3469122 0.3469122
## X401 X402 X403 X404 X405
## ACR_11231843 0.48873396 -0.46103244 -0.14444364 -1.41079885 1.4385004
## ADAO_11159808 1.32419936 1.32419936 1.32419936 1.32419936 1.3241994
## AGG_11236448 -0.56100938 -0.56100938 -0.17439294 -0.17439294 -0.5610094
## AHL_11239959 0.07773258 0.07773258 0.07773258 0.07773258 -0.4994091
## AJGD_11119689 0.64430769 0.22330620 0.64430769 0.64430769 0.2233062
## AMP_11228639 -0.06369410 0.34691225 -0.06369410 0.55221542 -1.2955131
## X406 X407 X408 X409 X410
## ACR_11231843 0.48873396 0.17214516 0.48873396 0.48873396 0.4887340
## ADAO_11159808 1.32419936 0.63704726 1.32419936 1.32419936 1.3241994
## AGG_11236448 0.21222350 -0.17439294 0.21222350 0.21222350 -0.9476258
## AHL_11239959 0.07773258 0.07773258 0.07773258 0.07773258 0.6548743
## AJGD_11119689 0.64430769 0.22330620 0.22330620 0.22330620 0.2233062
## AMP_11228639 0.55221542 -0.06369410 -0.06369410 -0.06369410 0.1416091
## X411 X412 X413 X414 X415 X416
## ACR_11231843 -0.1444436 0.8053228 0.1721452 0.1721452 0.8053228 -0.4610324
## ADAO_11159808 0.6370473 0.6370473 0.6370473 0.6370473 0.6370473 0.6370473
## AGG_11236448 0.2122235 -0.5610094 0.2122235 -0.1743929 -0.5610094 -4.0405573
## AHL_11239959 0.6548743 0.6548743 0.6548743 0.6548743 0.6548743 0.6548743
## AJGD_11119689 0.6443077 0.6443077 0.6443077 0.6443077 -1.0396982 0.6443077
## AMP_11228639 -0.6796036 0.3268101 0.1332740 0.1127541 0.4208230 0.1808587
## X417 X418 X419 X420 X421 X422
## ACR_11231843 0.1721452 0.8053228 0.1721452 0.1721452 0.1721452 0.4887340
## ADAO_11159808 0.6370473 0.6370473 0.6370473 0.6370473 0.6370473 0.6370473
## AGG_11236448 0.2122235 0.5988399 -0.5610094 1.7586892 0.9854564 1.3720728
## AHL_11239959 0.6548743 0.6548743 1.2320159 0.6548743 1.2320159 1.2320159
## AJGD_11119689 0.6443077 0.6443077 0.6443077 0.2233062 0.6443077 0.6443077
## AMP_11228639 0.3523946 0.2162184 0.2497394 0.0900907 0.0180795 0.5080804
## X423 X424 X425 X426 X427 X428
## ACR_11231843 -0.1444436 0.1721452 0.1721452 0.4887340 -0.14444364 0.4887340
## ADAO_11159808 0.6370473 0.6370473 0.6370473 1.3241994 1.32419936 1.3241994
## AGG_11236448 0.9854564 0.2122235 0.9854564 1.7586892 1.37207281 1.7586892
## AHL_11239959 1.2320159 1.2320159 1.2320159 1.2320159 0.07773258 0.6548743
## AJGD_11119689 0.6443077 0.6443077 0.2233062 0.6443077 0.64430769 0.6443077
## AMP_11228639 -0.5378962 0.3469122 -1.9114226 -1.2955131 -0.06369410 -0.4743004
## X429 X430 X431 X432 X433
## ACR_11231843 -0.7776212 -3.9435093 -4.8932757 -2.04397645 -1.0942100
## ADAO_11159808 1.3241994 1.3241994 0.6370473 -0.05010484 0.6370473
## AGG_11236448 1.3720728 -0.9476258 1.3720728 0.98545637 0.5988399
## AHL_11239959 0.6548743 1.2320159 0.6548743 -0.49940911 0.6548743
## AJGD_11119689 0.6443077 0.6443077 0.6443077 0.64430769 0.6443077
## AMP_11228639 0.1416091 0.7575186 -0.0636941 0.34691225 1.5787313
## X434 X435 X436 X437 X438
## ACR_11231843 -0.14444364 0.48873396 -0.1444436 0.1721452 -0.14444364
## ADAO_11159808 -0.05010484 0.63704726 0.6370473 0.6370473 -0.05010484
## AGG_11236448 0.59883993 0.59883993 0.5988399 1.3720728 0.59883993
## AHL_11239959 0.07773258 0.07773258 0.6548743 -0.4994091 -0.49940911
## AJGD_11119689 0.64430769 0.64430769 0.2233062 0.6443077 0.64430769
## AMP_11228639 0.75751859 0.75751859 0.5522154 1.3734281 -0.26899727
## X439 X440 X441 X442 X443
## ACR_11231843 -0.1444436 -0.4610324 -0.7776212 -0.1444436 -0.14444364
## ADAO_11159808 0.6370473 0.6370473 0.6370473 0.6370473 0.63704726
## AGG_11236448 0.5988399 0.2122235 0.5988399 1.7586892 0.59883993
## AHL_11239959 -0.4994091 -0.4994091 -0.4994091 -0.4994091 0.07773258
## AJGD_11119689 0.6443077 0.6443077 -0.1976953 0.6443077 -0.19769528
## AMP_11228639 0.7575186 1.5787313 -1.7061195 -0.2689973 0.14160907
## X444 X445 X446 X447 X448
## ACR_11231843 -0.4610324 -0.1444436 0.17214516 0.17214516 -0.1444436
## ADAO_11159808 0.6370473 0.6370473 -0.05010484 -0.05010484 1.3241994
## AGG_11236448 0.5988399 1.3720728 1.37207281 1.37207281 0.5988399
## AHL_11239959 -0.4994091 -0.4994091 -0.49940911 -0.49940911 -1.0765508
## AJGD_11119689 0.6443077 0.6443077 0.64430769 0.64430769 0.6443077
## AMP_11228639 -0.2689973 0.5522154 0.34691225 0.96282176 0.3469122
## X449 X450 X451 X452 X453
## ACR_11231843 -0.46103244 0.17214516 -0.14444364 1.1219116 0.1721452
## ADAO_11159808 -0.05010484 -0.05010484 -0.05010484 0.6370473 0.6370473
## AGG_11236448 0.21222350 0.59883993 -3.26732444 1.7586892 -0.5610094
## AHL_11239959 -0.49940911 -0.49940911 -1.07655079 -0.4994091 -0.4994091
## AJGD_11119689 0.64430769 0.64430769 0.64430769 0.6443077 0.6443077
## AMP_11228639 -0.06369410 0.55221542 0.14160907 0.9628218 0.1416091
## X454 X455 X456 X457 X458
## ACR_11231843 0.8053228 0.80532277 -2.9937429 0.80532277 -2.0439765
## ADAO_11159808 0.6370473 0.63704726 0.6370473 -0.05010484 0.6370473
## AGG_11236448 -0.9476258 -0.94762582 0.5988399 -0.17439294 -0.5610094
## AHL_11239959 -0.4994091 0.07773258 -1.0765508 -1.07655079 -1.0765508
## AJGD_11119689 0.6443077 0.64430769 0.6443077 0.64430769 0.6443077
## AMP_11228639 -0.6796036 -0.06369410 0.5522154 0.34691225 0.5522154
## X459 X460 X461 X462 X463
## ACR_11231843 -1.4107988 -1.7273877 0.8053228 -0.14444364 -0.4610324
## ADAO_11159808 1.3241994 0.6370473 0.6370473 0.63704726 0.6370473
## AGG_11236448 0.2122235 -0.9476258 -1.3342423 -0.17439294 -0.1743929
## AHL_11239959 -1.6536925 -1.0765508 -0.4994091 0.07773258 0.6548743
## AJGD_11119689 -0.1976953 0.2233062 0.6443077 0.64430769 0.6443077
## AMP_11228639 0.1416091 0.3469122 -0.6796036 -1.29551312 -1.2955131
## X464 X465 X466 X467 X468
## ACR_11231843 -4.5766869 -0.77762125 -1.09421005 -0.4610324 -0.7776212
## ADAO_11159808 0.6370473 -0.05010484 -0.05010484 0.6370473 0.6370473
## AGG_11236448 0.2122235 -0.94762582 -0.56100938 -0.1743929 -0.9476258
## AHL_11239959 0.6548743 0.65487426 0.07773258 1.2320159 0.6548743
## AJGD_11119689 0.6443077 0.64430769 0.64430769 0.6443077 0.6443077
## AMP_11228639 -1.2955131 -1.29551312 -1.50081629 -1.9114226 -1.5008163
## X469 X470 X471 X472 X473
## ACR_11231843 -0.7776212 -0.7776212 -0.1444436 -0.4610324 -0.4610324
## ADAO_11159808 0.6370473 1.3241994 0.6370473 0.6370473 0.6370473
## AGG_11236448 -0.9476258 0.5988399 -0.5610094 -0.9476258 -1.3342423
## AHL_11239959 1.8091576 0.6548743 -1.0765508 -0.4994091 -0.4994091
## AJGD_11119689 0.6443077 0.6443077 0.6443077 0.6443077 0.6443077
## AMP_11228639 -0.6796036 -0.8849068 -0.6796036 -0.4743004 -0.2689973
## X474 X475 X476 X477 X478
## ACR_11231843 -0.46103244 -0.46103244 -0.7776212 -0.7776212 -0.1444436
## ADAO_11159808 -0.05010484 0.63704726 0.6370473 0.6370473 0.6370473
## AGG_11236448 -1.33424225 -1.72085869 -1.7208587 -0.5610094 -1.7208587
## AHL_11239959 -0.49940911 0.07773258 -0.4994091 0.6548743 0.6548743
## AJGD_11119689 0.64430769 0.64430769 0.6443077 0.6443077 0.6443077
## AMP_11228639 -0.47430044 -0.47430044 -0.2689973 -0.6796036 -0.2689973
## X479 X480 DDclust_EUCL_SatO2_scaled
## ACR_11231843 -0.7776212 -0.1444436 1
## ADAO_11159808 0.6370473 0.6370473 2
## AGG_11236448 -1.3342423 -0.1743929 1
## AHL_11239959 0.6548743 1.2320159 2
## AJGD_11119689 0.6443077 0.6443077 2
## AMP_11228639 -0.0636941 -0.2689973 1## Mean by groups
rp_tbl_EUCL <- aggregate(plotting_EUCL, by = list(plotting_EUCL$DDclust_EUCL_SatO2_scaled), mean)
row.names(rp_tbl_EUCL) <- paste0("Group",rp_tbl_EUCL$DDclust_EUCL_SatO2_scaled)
rp_tbl_EUCL <- rp_tbl_EUCL %>%
select(starts_with('X'))
rp_tbl_EUCL <- data.frame(t(rp_tbl_EUCL))
head(rp_tbl_EUCL)## Group1 Group2
## X1 0.009127268 -0.4454704
## X2 0.209442524 -0.3333026
## X3 -0.076872070 -0.2964212
## X4 -0.104239051 -0.7216093
## X5 -0.081223130 -0.4984848
## X6 0.163388039 -0.2444696# Create plotting data-frame
EUCL_values_by_group <- data.frame("value_EUCL" = c(rp_tbl_EUCL$Group1,rp_tbl_EUCL$Group2),
"cluster" = c(rep("Group1", times = length(rp_tbl_EUCL$Group1)),
rep("Group2", times = length(rp_tbl_EUCL$Group2))),
"index" = c(c(1:length(rp_tbl_EUCL$Group1)),c(1:length(rp_tbl_EUCL$Group2))))
p <- ggplot(EUCL_values_by_group, aes(x = index, y = value_EUCL, group = cluster)) +
geom_line(aes(color=cluster)) +
scale_color_brewer(palette="Paired") + theme_minimal()
p
PER TSclust
# DD_PER <- diss(datos, "PER")
DD_PER <- distance_PERAgnes study
To find which hierarchical clustering methods that can identify stronger clustering structures. Here we see that Ward’s method identifies the strongest clustering structure of the four methods assessed.
#method to assess
m <- c("average", "single","complete","ward")
names(m) <- c("average", "single","complete","ward.D2")
#function to compute coefficient
ac <- function(x){agnes(datos_PER, method = x)$ac}
map_dbl(m,ac)## average single complete ward.D2
## 0.6054100 0.4748166 0.7390853 0.8575876NbClust study
This package will help us identify the optimum number of clusters
based our criteria in the silhouette index
diss_matrix<- DD_PER
res<-NbClust(datos_PER, diss=diss_matrix, distance = NULL, min.nc=2, max.nc=5, method = "ward.D2", index = "silhouette")
res$All.index## 2 3 4 5
## 0.3524 0.3244 0.1186 0.1285res$Best.nc## Number_clusters Value_Index
## 2.0000 0.3524#res$Best.partitionhcintper_PER <- hclust(DD_PER, "ward.D2")
fviz_dend(hcintper_PER, palette = "jco",
rect = TRUE, show_labels = FALSE, k = 2)
DDclust_PER_SatO2_scaled <- cutree( hclust(DD_PER, "ward.D2"), k = 2)fviz_cluster(list(data = t(datos), cluster = DDclust_PER_SatO2_scaled))
fviz_silhouette(silhouette(DDclust_PER_SatO2_scaled, DD_PER))## cluster size ave.sil.width
## 1 1 45 0.44
## 2 2 13 0.03
Contingency PER
DETERIORO_CLUST <- union(intersect(file_patient_name_DETERIORO,names_1),intersect(file_patient_name_DETERIORO,names_2))
NO_DETERIORO_CLUST <- union(intersect(file_patient_name_NO_DETERIORO,names_1),intersect(file_patient_name_NO_DETERIORO,names_2))#DETERIORO
DETERIORO_patients = data.frame(t(rep("#4A235A", length(DETERIORO_CLUST))))
colnames(DETERIORO_patients)<- DETERIORO_CLUST
#NO DETERIORO
NO_DETERIORO_patients = data.frame(t(rep("#117864", length(NO_DETERIORO_CLUST))))
colnames(NO_DETERIORO_patients)<- NO_DETERIORO_CLUST
COLOR_PER <- cbind(NO_DETERIORO_patients,DETERIORO_patients)order_PER <- union(names(DDclust_PER_SatO2_scaled[DDclust_PER_SatO2_scaled == 2]),names(DDclust_PER_SatO2_scaled[DDclust_PER_SatO2_scaled == 1]))
fviz_dend(hcintper_PER, k = 2,
k_colors = c("blue", "green3"),
label_cols = as.vector(COLOR_PER[,order_PER]), cex = 0.6) 
n1 = length(intersect(file_patient_name_DETERIORO,names_1))
n2 = length(intersect(file_patient_name_DETERIORO,names_2))
n3 = length(intersect(file_patient_name_NO_DETERIORO,names_1))
n4 = length(intersect(file_patient_name_NO_DETERIORO,names_2))
conttingency_table <- data.frame("CLust1" = c(n1,n3), "Clust2" = c(n2,n4))
rownames(conttingency_table) <- c("DETERIORO","NO DETERIORO")
knitr::kable(conttingency_table, align = "lccrr")| CLust1 | Clust2 | |
|---|---|---|
| DETERIORO | 5 | 1 |
| NO DETERIORO | 40 | 12 |
conttingency_table_prop <- data.frame(c(n1,n3)/(n1+n3),c(n2,n4)/(n2+n4))
rownames(conttingency_table_prop) <- c("DETERIORO","NO DETERIORO")
colnames(conttingency_table_prop) <- c("Clust1","Clust2")
knitr::kable(conttingency_table_prop, align = "lccrr")| Clust1 | Clust2 | |
|---|---|---|
| DETERIORO | 0.1111111 | 0.0769231 |
| NO DETERIORO | 0.8888889 | 0.9230769 |
Random Forest: Discriminant TSCLust PER
data_frame1_PER = data.frame("CLUSTER" = DDclust_PER_SatO2_scaled)
data_frame2_PER = df_descriptive
data_frame_merge_PER <-
merge(data_frame1_PER, data_frame2_PER, by = 'row.names', all = TRUE)
data_frame_merge_PER <- data_frame_merge_PER[, 2:dim(data_frame_merge_PER)[2]]
data_frame_merge_PER$CLUSTER = factor(data_frame_merge_PER$CLUSTER)table(data_frame_merge_PER$CLUSTER)##
## 1 2
## 45 13data_frame_merge_PER[,c(1:dim(data_frame_merge_PER)[2])]<- lapply(data_frame_merge_PER[,c(1:dim(data_frame_merge_PER)[2])], as.numeric)
head(data_frame_merge_PER)## CLUSTER EDAD PESO EG FR_0_8h FLUJO2_0_8H DIAS_GN DIAS_O2_TOTAL DIAS_OAF
## 1 1 10.0 8.20 41 48 2.00 3 3 0
## 2 2 13.0 7.78 40 56 2.00 2 2 0
## 3 1 3.1 5.66 37 44 1.00 4 4 0
## 4 1 5.3 8.44 38 65 0.40 3 3 0
## 5 2 15.0 7.00 34 37 2.00 4 4 0
## 6 1 1.6 3.80 37 42 0.94 4 4 0
## SAPI_0_8h SCORE_CRUCES_INGRESO SCORE_WOOD_DOWNES_INGRESO SEXO PALIVIZUMAB LM
## 1 3 3 6 1 1 2
## 2 4 4 8 1 1 1
## 3 3 3 7 1 1 2
## 4 4 3 6 1 1 2
## 5 1 3 6 1 2 1
## 6 2 4 7 1 1 2
## DERMATITIS ALERGIAS TABACO ENFERMEDAD_BASE RADIOGRAFIA ANALITICA SUERO
## 1 1 2 1 1 1 1 1
## 2 1 2 2 2 1 1 2
## 3 1 1 1 1 1 1 1
## 4 1 1 1 1 1 1 1
## 5 1 1 2 2 1 1 2
## 6 1 1 2 2 1 1 1
## ETIOLOGIA PREMATURIDAD ALIMENTACION SNG GN_INGRESO OAF OAF_AL_INGRESO
## 1 2 1 2 1 2 1 1
## 2 1 1 1 1 2 1 1
## 3 2 1 2 1 2 1 1
## 4 2 1 2 1 1 1 1
## 5 2 2 2 1 2 1 1
## 6 1 1 2 1 1 1 1
## OAF_TRAS_INGRESO UCIP DETERIORO PAUSAS_APNEA
## 1 1 1 1 1
## 2 1 1 1 1
## 3 1 1 1 1
## 4 1 1 1 1
## 5 1 1 1 1
## 6 1 1 1 1data_frame_merge_PER$CLUSTER <- factor(data_frame_merge_PER$CLUSTER)
newSMOTE_PER <- oversample(data_frame_merge_PER, ratio = 0.85, method = "SMOTE", classAttr = "CLUSTER")
newSMOTE_PER <- data.frame(newSMOTE_PER)
pos_1 <- get_column_position(newSMOTE_PER, "SAPI_0_8h")
pos_2 <- get_column_position(newSMOTE_PER, "PAUSAS_APNEA")
columns_to_round <- c(pos_1:pos_2)
newSMOTE_PER[, columns_to_round] <- lapply(newSMOTE_PER[, columns_to_round], function(x) round(x, 1))
table(newSMOTE_PER$CLUSTER)##
## 1 2
## 45 39set.seed(123)
pos_1 = get_column_position(newSMOTE_PER, "SAPI_0_8h")
pos_2 = get_column_position(newSMOTE_PER, "PAUSAS_APNEA")
col_names_factor <- names(newSMOTE_PER[pos_1:pos_2])
newSMOTE_PER[col_names_factor] <- lapply(newSMOTE_PER[col_names_factor] , factor)
RF_PER <- randomForest(CLUSTER ~ ., data = newSMOTE_PER)
print(RF_PER)##
## Call:
## randomForest(formula = CLUSTER ~ ., data = newSMOTE_PER)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 5
##
## OOB estimate of error rate: 14.29%
## Confusion matrix:
## 1 2 class.error
## 1 41 4 0.08888889
## 2 8 31 0.20512821Importance
kable(RF_PER$importance[order(RF_PER$importance, decreasing = TRUE),])| x | |
|---|---|
| SAPI_0_8h | 5.9478329 |
| EDAD | 4.8844865 |
| PESO | 4.4966094 |
| SCORE_WOOD_DOWNES_INGRESO | 3.8820332 |
| SCORE_CRUCES_INGRESO | 3.3041235 |
| ENFERMEDAD_BASE | 1.5700888 |
| TABACO | 1.5195153 |
| FR_0_8h | 1.4079287 |
| ALIMENTACION | 1.3278857 |
| EG | 1.3219039 |
| DERMATITIS | 1.2832285 |
| DIAS_O2_TOTAL | 1.1556829 |
| ETIOLOGIA | 1.1548707 |
| SEXO | 1.0651162 |
| DIAS_GN | 0.9976891 |
| FLUJO2_0_8H | 0.9747712 |
| RADIOGRAFIA | 0.8607499 |
| PALIVIZUMAB | 0.8097352 |
| SUERO | 0.7347771 |
| LM | 0.7249721 |
| ANALITICA | 0.5863486 |
| PREMATURIDAD | 0.2275032 |
| ALERGIAS | 0.2096151 |
| DIAS_OAF | 0.1638123 |
| OAF | 0.1521106 |
| GN_INGRESO | 0.1079112 |
| DETERIORO | 0.1027632 |
| SNG | 0.0938089 |
| OAF_TRAS_INGRESO | 0.0799879 |
| UCIP | 0.0488113 |
| PAUSAS_APNEA | 0.0286631 |
| OAF_AL_INGRESO | 0.0000000 |
Importance of the PER
data_frame1_PER = data.frame("CLUSTER" = DDclust_PER_SatO2_scaled)
data_frame2_PER = data.frame(datos_PER)
data_frame_merge_PER <-
merge(data_frame1_PER, data_frame2_PER, by = 'row.names', all = TRUE)
data_frame_merge_PER <- data_frame_merge_PER[, 2:dim(data_frame_merge_PER)[2]]set.seed(123)
data_frame_merge_PER$CLUSTER <- as.factor(data_frame_merge_PER$CLUSTER)
RF_0_PER <- randomForest(CLUSTER ~ ., data = data_frame_merge_PER)
print(RF_0_PER)##
## Call:
## randomForest(formula = CLUSTER ~ ., data = data_frame_merge_PER)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 21
##
## OOB estimate of error rate: 12.07%
## Confusion matrix:
## 1 2 class.error
## 1 45 0 0.0000000
## 2 7 6 0.5384615plot(RF_0_PER$importance, type = "h")
### PER by clusters
plot_data_PER <- data.frame(datos_PER)
cluster_data_PER <- data.frame(DDclust_PER_SatO2_scaled)
plotting_PER <- cbind(plot_data_PER, cluster_data_PER)
head(plotting_PER)## X1 X2 X3 X4 X5 X6
## ACR_11231843 6.348793 0.8824224 15.031336 8.860350 2.210851 8.1245349
## ADAO_11159808 17.396080 49.7584515 12.944428 2.613567 3.954203 2.8838844
## AGG_11236448 26.424735 12.3382623 1.377948 21.116690 3.413796 1.4558684
## AHL_11239959 19.348843 10.9734559 7.551318 6.092470 3.884345 20.8120609
## AJGD_11119689 23.488985 11.6025404 41.784110 2.732686 16.578289 8.1397034
## AMP_11228639 8.270068 8.8919319 7.724718 1.815867 5.105347 0.9724093
## X7 X8 X9 X10 X11 X12
## ACR_11231843 1.0893194 4.6188381 2.128434 7.0795038 8.164521 4.7925122
## ADAO_11159808 0.1752141 2.2416678 1.832529 2.1824821 1.008507 1.4260886
## AGG_11236448 3.3925561 2.3349328 4.369872 0.8502531 3.577408 2.5824506
## AHL_11239959 6.7673125 3.4566368 3.146570 2.6956428 3.670493 1.7264694
## AJGD_11119689 1.2528051 0.4160723 3.752138 7.1353083 1.693787 3.8683255
## AMP_11228639 5.5823561 1.5941413 6.067782 4.1288889 2.461341 0.5287559
## X13 X14 X15 X16 X17 X18
## ACR_11231843 1.1966608 8.049952 1.8774527 1.2296397 1.8030521 2.867038
## ADAO_11159808 0.4367859 3.757947 2.3529685 1.6497995 0.9075008 1.244186
## AGG_11236448 1.6359496 0.157227 0.5200923 0.1712884 0.9343623 1.036571
## AHL_11239959 2.9699188 3.042933 0.1054116 2.3100562 0.7121081 2.378122
## AJGD_11119689 11.3784548 4.999071 2.5333687 12.6844528 1.3360734 1.716247
## AMP_11228639 0.4675004 9.402316 0.5751271 1.0175248 0.6718940 6.119878
## X19 X20 X21 X22 X23 X24
## ACR_11231843 3.8345100 0.2830486 3.2497073 1.5498814 1.2631749 4.996347
## ADAO_11159808 0.6390957 1.3600563 0.2579193 2.4194261 0.6240652 1.352393
## AGG_11236448 0.5510484 0.2366121 2.0108826 0.2511593 0.5214134 3.125807
## AHL_11239959 1.5448511 3.2301563 4.6369582 0.7887947 1.6046767 1.327624
## AJGD_11119689 1.2815792 1.1284874 0.5057853 0.2547052 0.3520491 2.238605
## AMP_11228639 0.4083553 0.2074552 6.6061470 1.2932625 2.5352461 4.047312
## X25 X26 X27 X28 X29 X30
## ACR_11231843 1.0651069 0.79276818 0.6946702 0.2877550 1.5289594 0.7310406
## ADAO_11159808 0.2661232 0.53030644 1.3445150 1.0611150 1.3962986 0.9786870
## AGG_11236448 0.6659631 3.95621033 1.3859658 2.0966802 0.3719732 1.2732655
## AHL_11239959 1.2507873 1.13762443 0.7879042 0.1037010 1.7621625 0.4050390
## AJGD_11119689 0.8418999 0.01560772 0.2016805 0.3373289 0.6234324 1.9320597
## AMP_11228639 0.7772594 0.09036720 0.2393410 0.4138050 1.6063555 0.2403159
## X31 X32 X33 X34 X35 X36
## ACR_11231843 1.3262557 0.9537030 0.2823674 1.3385635 0.74298612 0.45697447
## ADAO_11159808 0.4554770 0.8605264 0.2355627 0.2800569 0.54690942 0.09157041
## AGG_11236448 2.5376191 1.6129190 0.3411612 0.5187864 0.06033654 1.56284519
## AHL_11239959 0.1429333 1.0528079 1.5757388 2.2345437 0.76048539 2.09812314
## AJGD_11119689 1.9531922 1.0414460 1.5425265 0.2228450 0.24439732 0.04071238
## AMP_11228639 1.6576323 0.4951032 0.2363786 1.0914149 0.24851442 2.63829534
## X37 X38 X39 X40 X41 X42
## ACR_11231843 1.00360789 1.0313477 0.1148972 0.02890758 0.51435492 0.2425515
## ADAO_11159808 3.20913711 0.9524235 0.1194502 0.55075843 0.76871470 1.9939146
## AGG_11236448 2.33034984 0.0649861 0.3694764 1.36216468 0.99137445 0.3789579
## AHL_11239959 0.67128259 1.8052687 1.0175493 0.09445088 0.32477596 0.3933050
## AJGD_11119689 0.03160705 0.1181601 0.9880536 0.61762121 1.21498159 1.6378079
## AMP_11228639 2.18282898 0.2948922 0.7094737 1.38259378 0.03667861 0.4382239
## X43 X44 X45 X46 X47 X48
## ACR_11231843 2.6744763 0.502933230 2.1217508 0.8252488 0.0177394 1.46920306
## ADAO_11159808 0.2392712 0.275812936 0.1352435 0.5635791 0.6595599 0.07315809
## AGG_11236448 0.1047609 0.365309922 0.8502848 0.2115767 0.3666295 1.43545259
## AHL_11239959 1.1633352 1.290444095 2.8688219 0.5789557 1.7586179 2.54208427
## AJGD_11119689 1.1971822 0.006347599 0.2826796 0.7746827 0.2919506 0.51435017
## AMP_11228639 1.2939988 3.330648469 0.6653319 0.5609553 0.2858404 2.27975865
## X49 X50 X51 X52 X53 X54
## ACR_11231843 0.5560665 0.22498670 0.42744978 1.05655327 0.21930911 2.9494148
## ADAO_11159808 0.3186641 0.47011277 0.03006227 0.09576189 0.04764259 0.7473512
## AGG_11236448 0.2345186 2.12914673 0.02012102 0.11866019 0.94177758 0.9486973
## AHL_11239959 0.9235975 1.16361033 0.50236373 0.19710250 0.10759298 0.2572798
## AJGD_11119689 0.2152703 0.09005781 0.34601946 0.40875798 1.06918428 2.0183518
## AMP_11228639 0.1975266 0.53460173 1.70674300 0.48235793 0.16003491 0.5053236
## X55 X56 X57 X58 X59 X60
## ACR_11231843 0.0455753 0.79033680 2.6751528 0.22244518 1.0910550 0.81457795
## ADAO_11159808 0.7971336 0.40942926 0.7509974 0.09655444 0.2359055 0.06857787
## AGG_11236448 0.4383420 1.82624012 2.5395366 1.91296319 0.6477569 1.57503339
## AHL_11239959 0.3727104 0.73842178 0.2416038 1.08684323 0.7825378 1.37985577
## AJGD_11119689 0.3375352 0.01765007 0.3327447 0.30767550 1.2607821 1.52535132
## AMP_11228639 0.4603685 0.72088171 1.1815493 0.45364003 0.4131786 0.04735498
## X61 X62 X63 X64 X65 X66
## ACR_11231843 1.0452416 2.22059804 0.01877845 0.6771187 1.59536224 0.1341431
## ADAO_11159808 0.1797696 0.05211371 0.07283177 0.3901250 0.09689898 0.2719688
## AGG_11236448 0.6973234 0.21243619 0.07790388 0.4461965 1.38692363 0.4000365
## AHL_11239959 0.5524000 2.85742411 1.08298358 0.6756159 2.11889150 2.4977171
## AJGD_11119689 2.0436066 0.78144599 0.32812657 0.2325873 0.16785693 0.3198675
## AMP_11228639 0.7787486 0.64323346 0.79721743 0.3530024 0.38963428 2.8679283
## X67 X68 X69 X70 X71 X72
## ACR_11231843 1.4865863 0.18571476 0.4584078 0.8729212 0.9514275 0.3060958
## ADAO_11159808 0.3713488 0.24281633 0.4554614 0.2414821 0.7982383 0.2669585
## AGG_11236448 0.8797200 0.50265861 1.3776871 0.3753168 0.1272601 0.2254214
## AHL_11239959 0.9541463 0.55775497 0.9004808 1.6315484 3.0238462 2.5638357
## AJGD_11119689 0.1879642 0.02982476 0.3405476 0.3691306 1.0283090 0.3602961
## AMP_11228639 0.3491809 0.51012760 0.3571836 1.8951902 1.2151800 0.3204242
## X73 X74 X75 X76 X77 X78
## ACR_11231843 1.6622894 0.399547433 1.4084208 0.7421861 0.03035375 1.3743029
## ADAO_11159808 0.2068878 0.003699507 0.3100827 0.2456579 1.18191503 0.3955935
## AGG_11236448 0.5764732 0.058636263 0.8402853 0.3263836 0.80562648 0.3922983
## AHL_11239959 1.4483877 0.689861904 1.4998627 1.3274084 0.90297132 0.2661644
## AJGD_11119689 0.2437754 0.255121951 0.7907324 0.9339512 0.65566145 1.4073043
## AMP_11228639 0.5799120 0.881954521 0.1537624 0.6002758 0.17940782 0.3549954
## X79 X80 X81 X82 X83 X84
## ACR_11231843 0.8184741 1.322902452 1.78742702 0.11742543 0.2897283 0.5898249
## ADAO_11159808 0.1426360 0.034082740 0.05385541 0.38262140 0.2778211 0.4429992
## AGG_11236448 0.1701278 0.007899642 0.14534983 1.03165543 0.3994466 0.1294998
## AHL_11239959 0.4449327 0.930038906 0.42700438 0.86252906 0.8310227 1.9702930
## AJGD_11119689 0.2312825 0.101662219 0.08191963 0.02972539 0.5928839 0.1906042
## AMP_11228639 0.6771967 1.304763616 1.74318043 0.75918909 0.4997488 0.7430565
## X85 X86 X87 X88 X89 X90
## ACR_11231843 0.25229112 2.4927392 0.08040979 0.24297478 1.941324031 0.07542797
## ADAO_11159808 0.05734347 0.1068313 0.32844742 0.01256909 0.001123753 0.49016365
## AGG_11236448 1.15288092 0.3550314 0.67912745 0.25459742 0.206423645 0.57710071
## AHL_11239959 1.10705540 0.2546720 0.33953986 2.30943757 1.151461569 0.50505194
## AJGD_11119689 0.29124372 0.2473875 0.29566780 1.50599014 1.064965284 1.06136899
## AMP_11228639 1.55229693 0.1929157 2.74614168 2.71265871 0.132163063 0.31398211
## X91 X92 X93 X94 X95 X96
## ACR_11231843 0.3971997 0.01522468 0.3168450 0.19569739 1.1367379 0.23883407
## ADAO_11159808 0.2216527 0.10962381 0.1768038 0.25254751 0.2860438 0.45479807
## AGG_11236448 1.7242268 0.34085386 0.8270347 0.62862012 2.7646888 0.19859660
## AHL_11239959 0.4184149 0.68992381 0.1146020 0.29212651 1.5139118 0.81214229
## AJGD_11119689 0.2290820 0.15879185 0.2463962 0.10066577 0.3552669 1.28194932
## AMP_11228639 0.3566863 1.29260000 0.6135749 0.04747837 0.2960225 0.02103349
## X97 X98 X99 X100 X101 X102
## ACR_11231843 0.2495388 0.15271205 0.34780014 2.54723516 0.66392135 0.4040863
## ADAO_11159808 0.3602606 0.02327933 0.08396312 0.19796414 0.32191258 0.1208610
## AGG_11236448 0.4158598 0.43124085 1.40650734 0.66059443 1.02575010 0.2783342
## AHL_11239959 0.3130736 1.21791381 0.36077779 0.86267599 0.42608371 0.2424280
## AJGD_11119689 0.0825733 0.12961102 0.40633033 0.11334720 0.07987141 0.2316670
## AMP_11228639 0.4056079 0.50237919 0.18664080 0.04807578 0.20933051 1.0427963
## X103 X104 X105 X106 X107 X108
## ACR_11231843 0.03608767 0.16857745 0.7389629 0.31647001 0.7500342 0.66741674
## ADAO_11159808 0.98924620 0.11977738 0.3011013 0.49870875 0.1812307 0.37170253
## AGG_11236448 0.79384688 0.02802375 0.2789929 0.08091934 1.0390319 0.28875708
## AHL_11239959 0.54434377 0.77266041 1.0031163 0.53945090 0.5442034 0.09232544
## AJGD_11119689 0.20415959 0.30991101 0.6025530 0.34834691 0.2025885 0.29670790
## AMP_11228639 0.59601392 0.19677922 0.6800451 0.07181484 1.5921881 0.24965117
## X109 X110 X111 X112 X113 X114
## ACR_11231843 0.6617817 0.7428817 3.599995e-02 0.07403857 0.00668701 0.4683018
## ADAO_11159808 0.1293633 0.2586819 2.363620e-01 0.20037365 0.00161580 0.2760036
## AGG_11236448 0.2147016 0.8613860 1.432879e+00 0.42361877 0.25416857 0.6826805
## AHL_11239959 0.9533254 0.9653254 9.476766e-01 0.57310579 1.24768474 0.9406219
## AJGD_11119689 0.2334909 0.4281849 7.115579e-05 0.20469550 0.01075850 0.2481260
## AMP_11228639 0.5986724 0.4660691 1.310461e+00 0.30285219 0.68816148 0.5549679
## X115 X116 X117 X118 X119
## ACR_11231843 0.522862318 0.919221751 0.17029787 0.87885007 0.2085568
## ADAO_11159808 0.526959076 0.732770157 0.61024489 0.04354011 0.4858561
## AGG_11236448 0.541630132 0.406023864 0.04838166 0.03295878 0.5919971
## AHL_11239959 0.838138502 1.180967539 0.65114190 0.06523926 0.2865548
## AJGD_11119689 0.003918154 0.008078954 0.62427172 0.65421283 0.2575320
## AMP_11228639 0.665281820 0.774776911 0.18150456 0.02586634 0.5130211
## X120 X121 X122 X123 X124 X125
## ACR_11231843 0.02943497 1.40147972 0.4521856 0.40730985 1.204497545 1.02875617
## ADAO_11159808 0.04329464 0.02135967 0.2356128 0.02240162 0.074380459 1.00253412
## AGG_11236448 0.01174943 0.31852738 0.5571214 0.76442608 0.362227426 0.06092662
## AHL_11239959 1.02279230 0.93163484 0.2860807 0.07638227 0.136743856 0.37822444
## AJGD_11119689 0.67815375 0.52385916 0.1732607 0.13384646 0.007784818 0.22514565
## AMP_11228639 0.34605496 0.08148098 0.5742681 0.05759084 0.356747354 0.05277499
## X126 X127 X128 X129 X130 X131
## ACR_11231843 0.12462271 0.02377489 1.0127897 0.11912420 0.1163767 0.14766264
## ADAO_11159808 0.22935578 0.55878364 0.1177778 0.11639738 0.5985089 0.52500026
## AGG_11236448 0.82942037 0.48095856 0.1839764 0.13239496 0.8193702 0.05673802
## AHL_11239959 0.68005582 0.51425829 0.7793923 0.18114629 1.0353180 0.16442668
## AJGD_11119689 0.18181008 0.54345734 0.0657948 0.05290683 0.7034265 0.54101378
## AMP_11228639 0.07892824 0.29498608 0.5039462 0.38604376 0.1258997 0.06302053
## X132 X133 X134 X135 X136 X137
## ACR_11231843 0.2231370 0.06317987 0.23738384 0.15019946 0.36314954 0.21663127
## ADAO_11159808 0.5927175 0.41947213 0.25532116 0.01281007 0.65717208 0.54042038
## AGG_11236448 0.6689784 0.02809200 0.22581987 1.44750865 0.01279247 0.21068529
## AHL_11239959 0.6387647 0.17607502 0.04850236 0.19031885 0.29087578 0.01743478
## AJGD_11119689 0.3578739 0.34497652 0.71964220 0.03827445 0.96008598 0.60455007
## AMP_11228639 0.1752058 2.24125067 1.98803759 0.69704211 0.56645322 0.38249034
## X138 X139 X140 X141 X142 X143
## ACR_11231843 1.2150875 0.03581450 0.08568949 0.63643306 0.16797431 0.4582688
## ADAO_11159808 0.2543590 0.06990940 0.10240501 0.14056461 0.15292808 0.1503610
## AGG_11236448 0.8103784 0.93576683 0.26089579 0.09297315 0.04501737 0.3067378
## AHL_11239959 0.3362775 0.51671425 0.26858883 0.32412205 0.33481597 0.2349590
## AJGD_11119689 0.5464668 0.04889159 0.08308501 1.77769380 0.49258646 1.1155896
## AMP_11228639 0.6098340 0.03319444 1.44122627 0.50793185 0.30365934 0.9273810
## X144 X145 X146 X147 X148
## ACR_11231843 1.54772389 0.87238170 1.71244869 0.084014290 0.17851737
## ADAO_11159808 0.14471645 0.26215886 0.02921854 0.614434388 0.06090689
## AGG_11236448 0.50714989 0.23973444 0.08995068 0.442718772 0.19648423
## AHL_11239959 0.05374483 0.02099668 0.46170411 0.203545864 0.60284599
## AJGD_11119689 0.34926472 0.09500509 0.11609647 0.186433370 0.15767419
## AMP_11228639 0.70645087 0.32279802 0.01513915 0.002568582 1.65130434
## X149 X150 X151 X152 X153
## ACR_11231843 0.364828020 0.42250265 0.3511709 0.384906413 0.27577525
## ADAO_11159808 0.204200840 0.01413845 0.1694164 0.007602808 0.05164355
## AGG_11236448 0.125285136 0.14210883 0.6339356 0.706793786 0.03072682
## AHL_11239959 0.798142880 0.06878360 0.6169310 0.475772557 0.22356734
## AJGD_11119689 0.012983538 0.30775318 0.2849745 0.122063774 0.17897502
## AMP_11228639 0.006659205 0.73152144 1.0233014 0.018139194 0.04495578
## X154 X155 X156 X157 X158 X159
## ACR_11231843 0.00243817 0.33094799 0.06192187 0.27425515 0.06326506 0.39901255
## ADAO_11159808 0.16565332 0.08810677 0.15820385 0.07038247 0.08331402 0.02503502
## AGG_11236448 0.58276045 0.07379043 0.22969589 0.09458116 0.41116874 0.87099963
## AHL_11239959 0.03826173 0.05769197 0.03466299 0.22419148 0.05630622 0.04322563
## AJGD_11119689 0.22724669 0.13671013 0.20911288 0.02332409 0.08868737 0.51330588
## AMP_11228639 0.68192372 0.10421832 0.41719773 1.40230978 0.45427232 0.44936115
## X160 X161 X162 X163 X164
## ACR_11231843 0.4310162 0.11716878 0.002287141 0.02201048 0.118195753
## ADAO_11159808 0.2129358 0.04259774 0.027089081 0.04088821 0.003862915
## AGG_11236448 0.0138594 0.20628929 0.538522289 1.57770951 1.142231741
## AHL_11239959 0.1213003 0.05566236 0.031014337 0.12067504 0.539998920
## AJGD_11119689 1.5802494 0.65440913 0.468507771 0.84989028 1.166356229
## AMP_11228639 1.5295883 0.12281409 1.020538748 0.15105353 0.020777371
## X165 X166 X167 X168 X169 X170
## ACR_11231843 0.31468289 0.2173321 0.01253246 0.28320671 0.56075471 0.31171919
## ADAO_11159808 0.20178926 0.1308851 0.25186496 0.07547981 0.18217595 0.07470175
## AGG_11236448 0.41118525 0.6711593 0.39440387 0.12795625 0.24240853 0.75194362
## AHL_11239959 0.32246755 0.1584437 0.09266000 0.11491070 0.28522062 0.17829618
## AJGD_11119689 0.09608833 0.6182929 0.65750518 0.39375723 0.05630193 0.02385402
## AMP_11228639 0.43121758 0.6064068 0.11661017 0.02971920 0.18739183 0.02021485
## X171 X172 X173 X174 X175 X176
## ACR_11231843 0.04745443 0.57226997 0.1721298 0.571041354 0.6030170 0.2540577
## ADAO_11159808 0.05094611 0.42212916 0.1000108 0.066725093 0.2029976 0.3223721
## AGG_11236448 0.07469018 0.99787916 1.0857614 1.361559000 0.1155127 2.1719807
## AHL_11239959 0.03170774 0.23152345 0.1876516 0.032543773 0.8491026 0.2821922
## AJGD_11119689 0.18209249 0.35781259 0.1030368 0.005932034 0.1044466 0.3511166
## AMP_11228639 0.82985323 0.02661435 0.2129612 0.245679179 0.7065082 0.3559943
## X177 X178 X179 X180 X181 X182
## ACR_11231843 0.09211557 0.01122874 0.02536072 0.33692903 0.06123833 0.3434002
## ADAO_11159808 0.19090129 0.04793096 0.07751819 0.16131648 0.03018219 0.1761500
## AGG_11236448 0.53187995 0.94726822 0.02602323 0.09918865 0.02947442 0.7146300
## AHL_11239959 0.16249675 0.27424902 0.09938061 0.03452003 0.15287783 0.1226016
## AJGD_11119689 0.12238739 0.25822560 0.57295336 0.01681876 0.43446103 0.8493773
## AMP_11228639 0.49996567 0.35935289 0.83177218 2.36350127 0.07341391 0.7958031
## X183 X184 X185 X186 X187
## ACR_11231843 0.59950795 0.06007528 0.27404389 0.230895377 0.10610367
## ADAO_11159808 0.04048933 0.01542824 0.25499883 0.001821081 0.02574317
## AGG_11236448 0.08175883 0.68009233 0.92141012 0.097628907 0.57852542
## AHL_11239959 0.67841344 0.38476465 0.06994537 0.323776688 0.12497543
## AJGD_11119689 0.98568997 0.13836693 0.25977142 0.397179045 0.04580265
## AMP_11228639 0.25156720 0.81117437 0.29809822 0.610639613 1.02916692
## X188 X189 X190 X191 X192 X193
## ACR_11231843 0.02505218 0.1770090 0.15747788 0.20722497 0.27426179 0.03980778
## ADAO_11159808 0.03077041 0.1987527 0.16188064 0.04170387 0.18761453 0.20485728
## AGG_11236448 0.37383497 1.4247670 0.02286294 0.12157637 0.16102554 0.16691728
## AHL_11239959 0.55037316 0.1670300 0.01698544 0.02294381 0.02626866 0.25280673
## AJGD_11119689 0.11624989 0.2519341 0.49568218 0.03617079 0.13268422 0.02651190
## AMP_11228639 0.82712659 0.5659315 1.10217813 3.46112920 0.15056436 0.33389840
## X194 X195 X196 X197 X198 X199
## ACR_11231843 0.11266554 0.7823676 0.02866908 0.50603803 0.29144092 0.07492719
## ADAO_11159808 0.10751342 0.2462773 0.35859410 0.08302634 0.63223792 0.15689565
## AGG_11236448 0.02915843 0.8713079 0.05851160 0.03175306 0.06947922 0.18127217
## AHL_11239959 0.04315721 0.1534681 0.09408553 0.09573158 0.31128368 0.35151173
## AJGD_11119689 0.04554138 0.2103325 0.09491138 0.51206634 0.07627866 0.07749018
## AMP_11228639 0.49994117 0.3377376 0.05810362 0.19792480 0.14801857 0.45281357
## X200 X201 X202 X203 X204 X205
## ACR_11231843 0.00763376 0.5085166 0.60177750 0.59760002 0.59694628 0.4804174
## ADAO_11159808 0.13964555 0.1218450 0.02778494 0.01678410 0.37147612 0.1121702
## AGG_11236448 0.82005448 0.2556042 0.18436737 0.12222958 0.26562058 0.4973984
## AHL_11239959 0.42179440 0.2319385 0.03065702 0.51919576 0.07464520 0.2325143
## AJGD_11119689 0.33438169 0.4275498 0.46062074 0.04895848 0.09101724 1.0323948
## AMP_11228639 1.27187738 0.6906456 0.12656298 3.65242153 0.54881451 1.0040207
## X206 X207 X208 X209 X210
## ACR_11231843 0.14246012 0.1716408 0.01398568 0.17513341 0.009666507
## ADAO_11159808 0.30224651 0.2530436 0.03090251 0.30576233 0.399251353
## AGG_11236448 0.26545336 0.8778438 0.36812625 1.13550669 0.397540398
## AHL_11239959 0.05239033 0.1831435 0.06362581 0.03661487 0.314387873
## AJGD_11119689 0.44226627 0.1522902 0.07394941 0.23686375 0.359056698
## AMP_11228639 0.96650067 1.6287727 0.53887315 0.06955880 0.179199782
## X211 X212 X213 X214 X215 X216
## ACR_11231843 0.017837575 0.2295827 0.18552728 0.03237546 0.42294423 0.25886718
## ADAO_11159808 0.037776035 0.2424041 0.40547796 0.24269857 0.03447520 0.06102033
## AGG_11236448 0.019376777 0.2699643 0.47967092 0.04472325 0.26398239 0.60212065
## AHL_11239959 0.347105443 0.1440609 0.01014805 0.01258980 0.34181190 0.22403910
## AJGD_11119689 0.005392294 0.3028457 0.31510720 0.05969343 0.04564661 0.15842807
## AMP_11228639 0.759036312 0.7131883 0.56659121 1.20323126 0.13781954 0.69438936
## X217 X218 X219 X220 X221 X222
## ACR_11231843 0.23801661 0.2862336 0.37664848 0.13730494 0.12144742 0.07498014
## ADAO_11159808 0.11027203 0.2861032 0.16646503 0.15510289 0.31174515 0.08607239
## AGG_11236448 0.40191492 0.7689518 0.08678123 0.09762770 0.09339306 1.23758457
## AHL_11239959 0.01345466 0.6604966 0.18250434 0.04010745 0.01100947 0.16325454
## AJGD_11119689 0.07429829 0.2593043 0.09147527 0.10992588 1.57593458 0.40804501
## AMP_11228639 1.52177851 0.1513313 0.53601285 1.03349395 0.71431823 0.08818547
## X223 X224 X225 X226 X227 X228
## ACR_11231843 1.52451736 0.4367456 0.405621940 0.26605965 0.17349447 0.14099043
## ADAO_11159808 0.03010055 0.1472350 0.244256738 0.30401962 0.04306084 0.04904309
## AGG_11236448 0.34778424 0.3863610 0.662037151 0.02959031 0.01633321 0.38801624
## AHL_11239959 0.21133021 0.2750689 0.007434949 0.10342347 0.06024967 0.23497932
## AJGD_11119689 0.36021877 0.2140832 0.337620483 0.18151343 0.63386061 0.19696692
## AMP_11228639 0.15588788 1.6577063 0.112362614 0.24854661 0.47184409 0.79914346
## X229 X230 X231 X232 X233
## ACR_11231843 0.006560831 0.30175288 0.23256084 0.4030619 0.06172034
## ADAO_11159808 0.076869368 0.64498187 0.08707156 0.0134285 0.28483871
## AGG_11236448 0.158340973 0.05597874 0.45785856 0.3839366 0.76878506
## AHL_11239959 0.004818617 0.89353275 0.11582079 0.6296312 0.07643730
## AJGD_11119689 0.177937213 0.08242920 0.09079186 0.3917557 0.38753240
## AMP_11228639 0.123583935 1.57976038 0.20604178 0.5057171 0.08718545
## X234 X235 X236 X237 X238
## ACR_11231843 0.591784258 0.12879248 0.15941998 0.03956496 0.03958130
## ADAO_11159808 0.631376666 0.09789595 0.06095633 0.17459402 0.25315548
## AGG_11236448 0.012184709 0.71883036 1.26914914 0.60219808 2.13432482
## AHL_11239959 0.283352188 0.14050680 0.14465409 0.25660975 0.02501904
## AJGD_11119689 0.184399571 0.01338300 0.54915811 1.29951789 0.08348871
## AMP_11228639 0.001706479 1.34724221 0.04038771 0.94469450 2.68934260
## X239 X240 X241 X242 X243 X244
## ACR_11231843 0.29655536 0.04941650 6.348793 0.8824224 15.031336 8.860350
## ADAO_11159808 0.11851667 0.62252162 17.396080 49.7584515 12.944428 2.613567
## AGG_11236448 0.06471164 1.54550055 26.424735 12.3382623 1.377948 21.116690
## AHL_11239959 0.40155119 0.03608186 19.348843 10.9734559 7.551318 6.092470
## AJGD_11119689 0.58308832 0.92095582 23.488985 11.6025404 41.784110 2.732686
## AMP_11228639 0.58445478 0.03502529 8.270068 8.8919319 7.724718 1.815867
## X245 X246 X247 X248 X249 X250
## ACR_11231843 2.210851 8.1245349 1.0893194 4.6188381 2.128434 7.0795038
## ADAO_11159808 3.954203 2.8838844 0.1752141 2.2416678 1.832529 2.1824821
## AGG_11236448 3.413796 1.4558684 3.3925561 2.3349328 4.369872 0.8502531
## AHL_11239959 3.884345 20.8120609 6.7673125 3.4566368 3.146570 2.6956428
## AJGD_11119689 16.578289 8.1397034 1.2528051 0.4160723 3.752138 7.1353083
## AMP_11228639 5.105347 0.9724093 5.5823561 1.5941413 6.067782 4.1288889
## X251 X252 X253 X254 X255 X256
## ACR_11231843 8.164521 4.7925122 1.1966608 8.049952 1.8774527 1.2296397
## ADAO_11159808 1.008507 1.4260886 0.4367859 3.757947 2.3529685 1.6497995
## AGG_11236448 3.577408 2.5824506 1.6359496 0.157227 0.5200923 0.1712884
## AHL_11239959 3.670493 1.7264694 2.9699188 3.042933 0.1054116 2.3100562
## AJGD_11119689 1.693787 3.8683255 11.3784548 4.999071 2.5333687 12.6844528
## AMP_11228639 2.461341 0.5287559 0.4675004 9.402316 0.5751271 1.0175248
## X257 X258 X259 X260 X261 X262
## ACR_11231843 1.8030521 2.867038 3.8345100 0.2830486 3.2497073 1.5498814
## ADAO_11159808 0.9075008 1.244186 0.6390957 1.3600563 0.2579193 2.4194261
## AGG_11236448 0.9343623 1.036571 0.5510484 0.2366121 2.0108826 0.2511593
## AHL_11239959 0.7121081 2.378122 1.5448511 3.2301563 4.6369582 0.7887947
## AJGD_11119689 1.3360734 1.716247 1.2815792 1.1284874 0.5057853 0.2547052
## AMP_11228639 0.6718940 6.119878 0.4083553 0.2074552 6.6061470 1.2932625
## X263 X264 X265 X266 X267 X268
## ACR_11231843 1.2631749 4.996347 1.0651069 0.79276818 0.6946702 0.2877550
## ADAO_11159808 0.6240652 1.352393 0.2661232 0.53030644 1.3445150 1.0611150
## AGG_11236448 0.5214134 3.125807 0.6659631 3.95621033 1.3859658 2.0966802
## AHL_11239959 1.6046767 1.327624 1.2507873 1.13762443 0.7879042 0.1037010
## AJGD_11119689 0.3520491 2.238605 0.8418999 0.01560772 0.2016805 0.3373289
## AMP_11228639 2.5352461 4.047312 0.7772594 0.09036720 0.2393410 0.4138050
## X269 X270 X271 X272 X273 X274
## ACR_11231843 1.5289594 0.7310406 1.3262557 0.9537030 0.2823674 1.3385635
## ADAO_11159808 1.3962986 0.9786870 0.4554770 0.8605264 0.2355627 0.2800569
## AGG_11236448 0.3719732 1.2732655 2.5376191 1.6129190 0.3411612 0.5187864
## AHL_11239959 1.7621625 0.4050390 0.1429333 1.0528079 1.5757388 2.2345437
## AJGD_11119689 0.6234324 1.9320597 1.9531922 1.0414460 1.5425265 0.2228450
## AMP_11228639 1.6063555 0.2403159 1.6576323 0.4951032 0.2363786 1.0914149
## X275 X276 X277 X278 X279 X280
## ACR_11231843 0.74298612 0.45697447 1.00360789 1.0313477 0.1148972 0.02890758
## ADAO_11159808 0.54690942 0.09157041 3.20913711 0.9524235 0.1194502 0.55075843
## AGG_11236448 0.06033654 1.56284519 2.33034984 0.0649861 0.3694764 1.36216468
## AHL_11239959 0.76048539 2.09812314 0.67128259 1.8052687 1.0175493 0.09445088
## AJGD_11119689 0.24439732 0.04071238 0.03160705 0.1181601 0.9880536 0.61762121
## AMP_11228639 0.24851442 2.63829534 2.18282898 0.2948922 0.7094737 1.38259378
## X281 X282 X283 X284 X285 X286
## ACR_11231843 0.51435492 0.2425515 2.6744763 0.502933230 2.1217508 0.8252488
## ADAO_11159808 0.76871470 1.9939146 0.2392712 0.275812936 0.1352435 0.5635791
## AGG_11236448 0.99137445 0.3789579 0.1047609 0.365309922 0.8502848 0.2115767
## AHL_11239959 0.32477596 0.3933050 1.1633352 1.290444095 2.8688219 0.5789557
## AJGD_11119689 1.21498159 1.6378079 1.1971822 0.006347599 0.2826796 0.7746827
## AMP_11228639 0.03667861 0.4382239 1.2939988 3.330648469 0.6653319 0.5609553
## X287 X288 X289 X290 X291 X292
## ACR_11231843 0.0177394 1.46920306 0.5560665 0.22498670 0.42744978 1.05655327
## ADAO_11159808 0.6595599 0.07315809 0.3186641 0.47011277 0.03006227 0.09576189
## AGG_11236448 0.3666295 1.43545259 0.2345186 2.12914673 0.02012102 0.11866019
## AHL_11239959 1.7586179 2.54208427 0.9235975 1.16361033 0.50236373 0.19710250
## AJGD_11119689 0.2919506 0.51435017 0.2152703 0.09005781 0.34601946 0.40875798
## AMP_11228639 0.2858404 2.27975865 0.1975266 0.53460173 1.70674300 0.48235793
## X293 X294 X295 X296 X297 X298
## ACR_11231843 0.21930911 2.9494148 0.0455753 0.79033680 2.6751528 0.22244518
## ADAO_11159808 0.04764259 0.7473512 0.7971336 0.40942926 0.7509974 0.09655444
## AGG_11236448 0.94177758 0.9486973 0.4383420 1.82624012 2.5395366 1.91296319
## AHL_11239959 0.10759298 0.2572798 0.3727104 0.73842178 0.2416038 1.08684323
## AJGD_11119689 1.06918428 2.0183518 0.3375352 0.01765007 0.3327447 0.30767550
## AMP_11228639 0.16003491 0.5053236 0.4603685 0.72088171 1.1815493 0.45364003
## X299 X300 X301 X302 X303 X304
## ACR_11231843 1.0910550 0.81457795 1.0452416 2.22059804 0.01877845 0.6771187
## ADAO_11159808 0.2359055 0.06857787 0.1797696 0.05211371 0.07283177 0.3901250
## AGG_11236448 0.6477569 1.57503339 0.6973234 0.21243619 0.07790388 0.4461965
## AHL_11239959 0.7825378 1.37985577 0.5524000 2.85742411 1.08298358 0.6756159
## AJGD_11119689 1.2607821 1.52535132 2.0436066 0.78144599 0.32812657 0.2325873
## AMP_11228639 0.4131786 0.04735498 0.7787486 0.64323346 0.79721743 0.3530024
## X305 X306 X307 X308 X309 X310
## ACR_11231843 1.59536224 0.1341431 1.4865863 0.18571476 0.4584078 0.8729212
## ADAO_11159808 0.09689898 0.2719688 0.3713488 0.24281633 0.4554614 0.2414821
## AGG_11236448 1.38692363 0.4000365 0.8797200 0.50265861 1.3776871 0.3753168
## AHL_11239959 2.11889150 2.4977171 0.9541463 0.55775497 0.9004808 1.6315484
## AJGD_11119689 0.16785693 0.3198675 0.1879642 0.02982476 0.3405476 0.3691306
## AMP_11228639 0.38963428 2.8679283 0.3491809 0.51012760 0.3571836 1.8951902
## X311 X312 X313 X314 X315 X316
## ACR_11231843 0.9514275 0.3060958 1.6622894 0.399547433 1.4084208 0.7421861
## ADAO_11159808 0.7982383 0.2669585 0.2068878 0.003699507 0.3100827 0.2456579
## AGG_11236448 0.1272601 0.2254214 0.5764732 0.058636263 0.8402853 0.3263836
## AHL_11239959 3.0238462 2.5638357 1.4483877 0.689861904 1.4998627 1.3274084
## AJGD_11119689 1.0283090 0.3602961 0.2437754 0.255121951 0.7907324 0.9339512
## AMP_11228639 1.2151800 0.3204242 0.5799120 0.881954521 0.1537624 0.6002758
## X317 X318 X319 X320 X321 X322
## ACR_11231843 0.03035375 1.3743029 0.8184741 1.322902452 1.78742702 0.11742543
## ADAO_11159808 1.18191503 0.3955935 0.1426360 0.034082740 0.05385541 0.38262140
## AGG_11236448 0.80562648 0.3922983 0.1701278 0.007899642 0.14534983 1.03165543
## AHL_11239959 0.90297132 0.2661644 0.4449327 0.930038906 0.42700438 0.86252906
## AJGD_11119689 0.65566145 1.4073043 0.2312825 0.101662219 0.08191963 0.02972539
## AMP_11228639 0.17940782 0.3549954 0.6771967 1.304763616 1.74318043 0.75918909
## X323 X324 X325 X326 X327 X328
## ACR_11231843 0.2897283 0.5898249 0.25229112 2.4927392 0.08040979 0.24297478
## ADAO_11159808 0.2778211 0.4429992 0.05734347 0.1068313 0.32844742 0.01256909
## AGG_11236448 0.3994466 0.1294998 1.15288092 0.3550314 0.67912745 0.25459742
## AHL_11239959 0.8310227 1.9702930 1.10705540 0.2546720 0.33953986 2.30943757
## AJGD_11119689 0.5928839 0.1906042 0.29124372 0.2473875 0.29566780 1.50599014
## AMP_11228639 0.4997488 0.7430565 1.55229693 0.1929157 2.74614168 2.71265871
## X329 X330 X331 X332 X333 X334
## ACR_11231843 1.941324031 0.07542797 0.3971997 0.01522468 0.3168450 0.19569739
## ADAO_11159808 0.001123753 0.49016365 0.2216527 0.10962381 0.1768038 0.25254751
## AGG_11236448 0.206423645 0.57710071 1.7242268 0.34085386 0.8270347 0.62862012
## AHL_11239959 1.151461569 0.50505194 0.4184149 0.68992381 0.1146020 0.29212651
## AJGD_11119689 1.064965284 1.06136899 0.2290820 0.15879185 0.2463962 0.10066577
## AMP_11228639 0.132163063 0.31398211 0.3566863 1.29260000 0.6135749 0.04747837
## X335 X336 X337 X338 X339 X340
## ACR_11231843 1.1367379 0.23883407 0.2495388 0.15271205 0.34780014 2.54723516
## ADAO_11159808 0.2860438 0.45479807 0.3602606 0.02327933 0.08396312 0.19796414
## AGG_11236448 2.7646888 0.19859660 0.4158598 0.43124085 1.40650734 0.66059443
## AHL_11239959 1.5139118 0.81214229 0.3130736 1.21791381 0.36077779 0.86267599
## AJGD_11119689 0.3552669 1.28194932 0.0825733 0.12961102 0.40633033 0.11334720
## AMP_11228639 0.2960225 0.02103349 0.4056079 0.50237919 0.18664080 0.04807578
## X341 X342 X343 X344 X345 X346
## ACR_11231843 0.66392135 0.4040863 0.03608767 0.16857745 0.7389629 0.31647001
## ADAO_11159808 0.32191258 0.1208610 0.98924620 0.11977738 0.3011013 0.49870875
## AGG_11236448 1.02575010 0.2783342 0.79384688 0.02802375 0.2789929 0.08091934
## AHL_11239959 0.42608371 0.2424280 0.54434377 0.77266041 1.0031163 0.53945090
## AJGD_11119689 0.07987141 0.2316670 0.20415959 0.30991101 0.6025530 0.34834691
## AMP_11228639 0.20933051 1.0427963 0.59601392 0.19677922 0.6800451 0.07181484
## X347 X348 X349 X350 X351 X352
## ACR_11231843 0.7500342 0.66741674 0.6617817 0.7428817 3.599995e-02 0.07403857
## ADAO_11159808 0.1812307 0.37170253 0.1293633 0.2586819 2.363620e-01 0.20037365
## AGG_11236448 1.0390319 0.28875708 0.2147016 0.8613860 1.432879e+00 0.42361877
## AHL_11239959 0.5442034 0.09232544 0.9533254 0.9653254 9.476766e-01 0.57310579
## AJGD_11119689 0.2025885 0.29670790 0.2334909 0.4281849 7.115579e-05 0.20469550
## AMP_11228639 1.5921881 0.24965117 0.5986724 0.4660691 1.310461e+00 0.30285219
## X353 X354 X355 X356 X357
## ACR_11231843 0.00668701 0.4683018 0.522862318 0.919221751 0.17029787
## ADAO_11159808 0.00161580 0.2760036 0.526959076 0.732770157 0.61024489
## AGG_11236448 0.25416857 0.6826805 0.541630132 0.406023864 0.04838166
## AHL_11239959 1.24768474 0.9406219 0.838138502 1.180967539 0.65114190
## AJGD_11119689 0.01075850 0.2481260 0.003918154 0.008078954 0.62427172
## AMP_11228639 0.68816148 0.5549679 0.665281820 0.774776911 0.18150456
## X358 X359 X360 X361 X362 X363
## ACR_11231843 0.87885007 0.2085568 0.02943497 1.40147972 0.4521856 0.40730985
## ADAO_11159808 0.04354011 0.4858561 0.04329464 0.02135967 0.2356128 0.02240162
## AGG_11236448 0.03295878 0.5919971 0.01174943 0.31852738 0.5571214 0.76442608
## AHL_11239959 0.06523926 0.2865548 1.02279230 0.93163484 0.2860807 0.07638227
## AJGD_11119689 0.65421283 0.2575320 0.67815375 0.52385916 0.1732607 0.13384646
## AMP_11228639 0.02586634 0.5130211 0.34605496 0.08148098 0.5742681 0.05759084
## X364 X365 X366 X367 X368 X369
## ACR_11231843 1.204497545 1.02875617 0.12462271 0.02377489 1.0127897 0.11912420
## ADAO_11159808 0.074380459 1.00253412 0.22935578 0.55878364 0.1177778 0.11639738
## AGG_11236448 0.362227426 0.06092662 0.82942037 0.48095856 0.1839764 0.13239496
## AHL_11239959 0.136743856 0.37822444 0.68005582 0.51425829 0.7793923 0.18114629
## AJGD_11119689 0.007784818 0.22514565 0.18181008 0.54345734 0.0657948 0.05290683
## AMP_11228639 0.356747354 0.05277499 0.07892824 0.29498608 0.5039462 0.38604376
## X370 X371 X372 X373 X374 X375
## ACR_11231843 0.1163767 0.14766264 0.2231370 0.06317987 0.23738384 0.15019946
## ADAO_11159808 0.5985089 0.52500026 0.5927175 0.41947213 0.25532116 0.01281007
## AGG_11236448 0.8193702 0.05673802 0.6689784 0.02809200 0.22581987 1.44750865
## AHL_11239959 1.0353180 0.16442668 0.6387647 0.17607502 0.04850236 0.19031885
## AJGD_11119689 0.7034265 0.54101378 0.3578739 0.34497652 0.71964220 0.03827445
## AMP_11228639 0.1258997 0.06302053 0.1752058 2.24125067 1.98803759 0.69704211
## X376 X377 X378 X379 X380 X381
## ACR_11231843 0.36314954 0.21663127 1.2150875 0.03581450 0.08568949 0.63643306
## ADAO_11159808 0.65717208 0.54042038 0.2543590 0.06990940 0.10240501 0.14056461
## AGG_11236448 0.01279247 0.21068529 0.8103784 0.93576683 0.26089579 0.09297315
## AHL_11239959 0.29087578 0.01743478 0.3362775 0.51671425 0.26858883 0.32412205
## AJGD_11119689 0.96008598 0.60455007 0.5464668 0.04889159 0.08308501 1.77769380
## AMP_11228639 0.56645322 0.38249034 0.6098340 0.03319444 1.44122627 0.50793185
## X382 X383 X384 X385 X386 X387
## ACR_11231843 0.16797431 0.4582688 1.54772389 0.87238170 1.71244869 0.084014290
## ADAO_11159808 0.15292808 0.1503610 0.14471645 0.26215886 0.02921854 0.614434388
## AGG_11236448 0.04501737 0.3067378 0.50714989 0.23973444 0.08995068 0.442718772
## AHL_11239959 0.33481597 0.2349590 0.05374483 0.02099668 0.46170411 0.203545864
## AJGD_11119689 0.49258646 1.1155896 0.34926472 0.09500509 0.11609647 0.186433370
## AMP_11228639 0.30365934 0.9273810 0.70645087 0.32279802 0.01513915 0.002568582
## X388 X389 X390 X391 X392
## ACR_11231843 0.17851737 0.364828020 0.42250265 0.3511709 0.384906413
## ADAO_11159808 0.06090689 0.204200840 0.01413845 0.1694164 0.007602808
## AGG_11236448 0.19648423 0.125285136 0.14210883 0.6339356 0.706793786
## AHL_11239959 0.60284599 0.798142880 0.06878360 0.6169310 0.475772557
## AJGD_11119689 0.15767419 0.012983538 0.30775318 0.2849745 0.122063774
## AMP_11228639 1.65130434 0.006659205 0.73152144 1.0233014 0.018139194
## X393 X394 X395 X396 X397 X398
## ACR_11231843 0.27577525 0.00243817 0.33094799 0.06192187 0.27425515 0.06326506
## ADAO_11159808 0.05164355 0.16565332 0.08810677 0.15820385 0.07038247 0.08331402
## AGG_11236448 0.03072682 0.58276045 0.07379043 0.22969589 0.09458116 0.41116874
## AHL_11239959 0.22356734 0.03826173 0.05769197 0.03466299 0.22419148 0.05630622
## AJGD_11119689 0.17897502 0.22724669 0.13671013 0.20911288 0.02332409 0.08868737
## AMP_11228639 0.04495578 0.68192372 0.10421832 0.41719773 1.40230978 0.45427232
## X399 X400 X401 X402 X403
## ACR_11231843 0.39901255 0.4310162 0.11716878 0.002287141 0.02201048
## ADAO_11159808 0.02503502 0.2129358 0.04259774 0.027089081 0.04088821
## AGG_11236448 0.87099963 0.0138594 0.20628929 0.538522289 1.57770951
## AHL_11239959 0.04322563 0.1213003 0.05566236 0.031014337 0.12067504
## AJGD_11119689 0.51330588 1.5802494 0.65440913 0.468507771 0.84989028
## AMP_11228639 0.44936115 1.5295883 0.12281409 1.020538748 0.15105353
## X404 X405 X406 X407 X408 X409
## ACR_11231843 0.118195753 0.31468289 0.2173321 0.01253246 0.28320671 0.56075471
## ADAO_11159808 0.003862915 0.20178926 0.1308851 0.25186496 0.07547981 0.18217595
## AGG_11236448 1.142231741 0.41118525 0.6711593 0.39440387 0.12795625 0.24240853
## AHL_11239959 0.539998920 0.32246755 0.1584437 0.09266000 0.11491070 0.28522062
## AJGD_11119689 1.166356229 0.09608833 0.6182929 0.65750518 0.39375723 0.05630193
## AMP_11228639 0.020777371 0.43121758 0.6064068 0.11661017 0.02971920 0.18739183
## X410 X411 X412 X413 X414 X415
## ACR_11231843 0.31171919 0.04745443 0.57226997 0.1721298 0.571041354 0.6030170
## ADAO_11159808 0.07470175 0.05094611 0.42212916 0.1000108 0.066725093 0.2029976
## AGG_11236448 0.75194362 0.07469018 0.99787916 1.0857614 1.361559000 0.1155127
## AHL_11239959 0.17829618 0.03170774 0.23152345 0.1876516 0.032543773 0.8491026
## AJGD_11119689 0.02385402 0.18209249 0.35781259 0.1030368 0.005932034 0.1044466
## AMP_11228639 0.02021485 0.82985323 0.02661435 0.2129612 0.245679179 0.7065082
## X416 X417 X418 X419 X420 X421
## ACR_11231843 0.2540577 0.09211557 0.01122874 0.02536072 0.33692903 0.06123833
## ADAO_11159808 0.3223721 0.19090129 0.04793096 0.07751819 0.16131648 0.03018219
## AGG_11236448 2.1719807 0.53187995 0.94726822 0.02602323 0.09918865 0.02947442
## AHL_11239959 0.2821922 0.16249675 0.27424902 0.09938061 0.03452003 0.15287783
## AJGD_11119689 0.3511166 0.12238739 0.25822560 0.57295336 0.01681876 0.43446103
## AMP_11228639 0.3559943 0.49996567 0.35935289 0.83177218 2.36350127 0.07341391
## X422 X423 X424 X425 X426 X427
## ACR_11231843 0.3434002 0.59950795 0.06007528 0.27404389 0.230895377 0.10610367
## ADAO_11159808 0.1761500 0.04048933 0.01542824 0.25499883 0.001821081 0.02574317
## AGG_11236448 0.7146300 0.08175883 0.68009233 0.92141012 0.097628907 0.57852542
## AHL_11239959 0.1226016 0.67841344 0.38476465 0.06994537 0.323776688 0.12497543
## AJGD_11119689 0.8493773 0.98568997 0.13836693 0.25977142 0.397179045 0.04580265
## AMP_11228639 0.7958031 0.25156720 0.81117437 0.29809822 0.610639613 1.02916692
## X428 X429 X430 X431 X432 X433
## ACR_11231843 0.02505218 0.1770090 0.15747788 0.20722497 0.27426179 0.03980778
## ADAO_11159808 0.03077041 0.1987527 0.16188064 0.04170387 0.18761453 0.20485728
## AGG_11236448 0.37383497 1.4247670 0.02286294 0.12157637 0.16102554 0.16691728
## AHL_11239959 0.55037316 0.1670300 0.01698544 0.02294381 0.02626866 0.25280673
## AJGD_11119689 0.11624989 0.2519341 0.49568218 0.03617079 0.13268422 0.02651190
## AMP_11228639 0.82712659 0.5659315 1.10217813 3.46112920 0.15056436 0.33389840
## X434 X435 X436 X437 X438 X439
## ACR_11231843 0.11266554 0.7823676 0.02866908 0.50603803 0.29144092 0.07492719
## ADAO_11159808 0.10751342 0.2462773 0.35859410 0.08302634 0.63223792 0.15689565
## AGG_11236448 0.02915843 0.8713079 0.05851160 0.03175306 0.06947922 0.18127217
## AHL_11239959 0.04315721 0.1534681 0.09408553 0.09573158 0.31128368 0.35151173
## AJGD_11119689 0.04554138 0.2103325 0.09491138 0.51206634 0.07627866 0.07749018
## AMP_11228639 0.49994117 0.3377376 0.05810362 0.19792480 0.14801857 0.45281357
## X440 X441 X442 X443 X444 X445
## ACR_11231843 0.00763376 0.5085166 0.60177750 0.59760002 0.59694628 0.4804174
## ADAO_11159808 0.13964555 0.1218450 0.02778494 0.01678410 0.37147612 0.1121702
## AGG_11236448 0.82005448 0.2556042 0.18436737 0.12222958 0.26562058 0.4973984
## AHL_11239959 0.42179440 0.2319385 0.03065702 0.51919576 0.07464520 0.2325143
## AJGD_11119689 0.33438169 0.4275498 0.46062074 0.04895848 0.09101724 1.0323948
## AMP_11228639 1.27187738 0.6906456 0.12656298 3.65242153 0.54881451 1.0040207
## X446 X447 X448 X449 X450
## ACR_11231843 0.14246012 0.1716408 0.01398568 0.17513341 0.009666507
## ADAO_11159808 0.30224651 0.2530436 0.03090251 0.30576233 0.399251353
## AGG_11236448 0.26545336 0.8778438 0.36812625 1.13550669 0.397540398
## AHL_11239959 0.05239033 0.1831435 0.06362581 0.03661487 0.314387873
## AJGD_11119689 0.44226627 0.1522902 0.07394941 0.23686375 0.359056698
## AMP_11228639 0.96650067 1.6287727 0.53887315 0.06955880 0.179199782
## X451 X452 X453 X454 X455 X456
## ACR_11231843 0.017837575 0.2295827 0.18552728 0.03237546 0.42294423 0.25886718
## ADAO_11159808 0.037776035 0.2424041 0.40547796 0.24269857 0.03447520 0.06102033
## AGG_11236448 0.019376777 0.2699643 0.47967092 0.04472325 0.26398239 0.60212065
## AHL_11239959 0.347105443 0.1440609 0.01014805 0.01258980 0.34181190 0.22403910
## AJGD_11119689 0.005392294 0.3028457 0.31510720 0.05969343 0.04564661 0.15842807
## AMP_11228639 0.759036312 0.7131883 0.56659121 1.20323126 0.13781954 0.69438936
## X457 X458 X459 X460 X461 X462
## ACR_11231843 0.23801661 0.2862336 0.37664848 0.13730494 0.12144742 0.07498014
## ADAO_11159808 0.11027203 0.2861032 0.16646503 0.15510289 0.31174515 0.08607239
## AGG_11236448 0.40191492 0.7689518 0.08678123 0.09762770 0.09339306 1.23758457
## AHL_11239959 0.01345466 0.6604966 0.18250434 0.04010745 0.01100947 0.16325454
## AJGD_11119689 0.07429829 0.2593043 0.09147527 0.10992588 1.57593458 0.40804501
## AMP_11228639 1.52177851 0.1513313 0.53601285 1.03349395 0.71431823 0.08818547
## X463 X464 X465 X466 X467 X468
## ACR_11231843 1.52451736 0.4367456 0.405621940 0.26605965 0.17349447 0.14099043
## ADAO_11159808 0.03010055 0.1472350 0.244256738 0.30401962 0.04306084 0.04904309
## AGG_11236448 0.34778424 0.3863610 0.662037151 0.02959031 0.01633321 0.38801624
## AHL_11239959 0.21133021 0.2750689 0.007434949 0.10342347 0.06024967 0.23497932
## AJGD_11119689 0.36021877 0.2140832 0.337620483 0.18151343 0.63386061 0.19696692
## AMP_11228639 0.15588788 1.6577063 0.112362614 0.24854661 0.47184409 0.79914346
## X469 X470 X471 X472 X473
## ACR_11231843 0.006560831 0.30175288 0.23256084 0.4030619 0.06172034
## ADAO_11159808 0.076869368 0.64498187 0.08707156 0.0134285 0.28483871
## AGG_11236448 0.158340973 0.05597874 0.45785856 0.3839366 0.76878506
## AHL_11239959 0.004818617 0.89353275 0.11582079 0.6296312 0.07643730
## AJGD_11119689 0.177937213 0.08242920 0.09079186 0.3917557 0.38753240
## AMP_11228639 0.123583935 1.57976038 0.20604178 0.5057171 0.08718545
## X474 X475 X476 X477 X478
## ACR_11231843 0.591784258 0.12879248 0.15941998 0.03956496 0.03958130
## ADAO_11159808 0.631376666 0.09789595 0.06095633 0.17459402 0.25315548
## AGG_11236448 0.012184709 0.71883036 1.26914914 0.60219808 2.13432482
## AHL_11239959 0.283352188 0.14050680 0.14465409 0.25660975 0.02501904
## AJGD_11119689 0.184399571 0.01338300 0.54915811 1.29951789 0.08348871
## AMP_11228639 0.001706479 1.34724221 0.04038771 0.94469450 2.68934260
## X479 X480 DDclust_PER_SatO2_scaled
## ACR_11231843 0.29655536 0.04941650 1
## ADAO_11159808 0.11851667 0.62252162 2
## AGG_11236448 0.06471164 1.54550055 1
## AHL_11239959 0.40155119 0.03608186 1
## AJGD_11119689 0.58308832 0.92095582 2
## AMP_11228639 0.58445478 0.03502529 1## Mean by groups
rp_tbl_PER <- aggregate(plotting_PER, by = list(plotting_PER$DDclust_PER_SatO2_scaled), mean)
row.names(rp_tbl_PER) <- paste0("Group",rp_tbl_PER$DDclust_PER_SatO2_scaled)
rp_tbl_PER <- rp_tbl_PER %>%
select(starts_with('X'))
rp_tbl_PER <- data.frame(t(rp_tbl_PER))
head(rp_tbl_PER)## Group1 Group2
## X1 7.094070 33.150320
## X2 7.784602 28.970721
## X3 7.261647 12.342896
## X4 5.623319 8.448607
## X5 5.266168 4.720825
## X6 4.081840 4.633952# Create plotting data-frame
PER_values_by_group <- data.frame("value_PER" = c(rp_tbl_PER$Group1,rp_tbl_PER$Group2),
"cluster" = c(rep("Group1", times = length(rp_tbl_PER$Group1)),
rep("Group2", times = length(rp_tbl_PER$Group2))),
"index" = c(c(1:length(rp_tbl_PER$Group1)),c(1:length(rp_tbl_PER$Group2))))
p <- ggplot(PER_values_by_group, aes(x = index, y = value_PER, group = cluster)) +
geom_line(aes(color=cluster)) +
scale_color_brewer(palette="Paired") + theme_minimal()
p
Adjusted Rand Index
rand_index_table_SatO2_scaled = data.frame(matrix(ncol = 3 , nrow = 3))
colnames(rand_index_table_SatO2_scaled) <- c("DDclust_ACF_SatO2_scaled", "DDclust_EUCL_SatO2_scaled", "DDclust_PER_SatO2_scaled")
rownames(rand_index_table_SatO2_scaled) <- c("DDclust_ACF_SatO2_scaled", "DDclust_EUCL_SatO2_scaled", "DDclust_PER_SatO2_scaled")
cluster_study_SatO2_scaled <- list(DDclust_ACF_SatO2_scaled, DDclust_EUCL_SatO2_scaled, DDclust_PER_SatO2_scaled)for (i in c(1:length(cluster_study_SatO2_scaled))) {
for (j in c(1:length(cluster_study_SatO2_scaled))){
rand_index_table_SatO2_scaled[i,j] <- adjustedRandIndex(cluster_study_SatO2_scaled[[i]], cluster_study_SatO2_scaled[[j]])
}}head(rand_index_table_SatO2_scaled)## DDclust_ACF_SatO2_scaled DDclust_EUCL_SatO2_scaled
## DDclust_ACF_SatO2_scaled 1.00000000 -0.02006569
## DDclust_EUCL_SatO2_scaled -0.02006569 1.00000000
## DDclust_PER_SatO2_scaled 0.54737607 -0.01647745
## DDclust_PER_SatO2_scaled
## DDclust_ACF_SatO2_scaled 0.54737607
## DDclust_EUCL_SatO2_scaled -0.01647745
## DDclust_PER_SatO2_scaled 1.00000000write.csv(cluster_study_SatO2_scaled, "../../data/clusters/cluster_study_SatO2_scaled.csv")