Clustering_Time_Series-TSClust-Study-SatO2
Gonzalo Aris 16021
2023-03-05
Libraries
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: Heart Rate and SatO2
SatO2_TS_HR_P2 = data.frame(read_xlsx("../../data/clean-data/BoxBasedImputation/SatO2_valid_patients_input_P2.xlsx", sheet = "SatO2_valid_patients_input_P2" ))
# 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,]SatO2_TS_HR_P2 <- SatO2_TS_HR_P2[,valid_patients_P2]Create a dataframe with ACF [Heart Rate]
dimension_col <- dim(SatO2_TS_HR_P2)[2]
dimension_row <- 480 #lag.max -1
# SatO2
SatO2_TS_HR_P2_ACF <- data.frame(matrix(nrow = dimension_row, ncol = dimension_col))
colnames(SatO2_TS_HR_P2_ACF) <- names(SatO2_TS_HR_P2)[1:dimension_col]for (i in names(SatO2_TS_HR_P2_ACF)) {
acf_result_SatO2 <- forecast::Acf(SatO2_TS_HR_P2[[i]], lag.max = (dimension_row - 1), plot = FALSE)
SatO2_TS_HR_P2_ACF[, i] <- acf_result_SatO2$acf
}Create a dataframe with peridiogram
# Generar un dataset con varias series temporales
df <- SatO2_TS_HR_P2
# Crear una matriz para almacenar los periodogramas
pg_mat <- data.frame(matrix(nrow = nrow(df), ncol = ncol(df)))
colnames(pg_mat) = colnames(SatO2_TS_HR_P2)
# Calcular el periodograma de cada serie temporal y almacenarlo en la matriz
library(stats)
# Calcular el periodograma de cada serie temporal y almacenarlo en la matriz
for (i in colnames(SatO2_TS_HR_P2)) {
pg_mat[,i] <- stats::spec.pgram(SatO2_TS_HR_P2[,i], plot = FALSE)$spec
}TsClust Comprobation
datos <- SatO2_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_TS_HR_P2_ACF[c(1:51),])
distance <- dist(t(SatO2_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 <- cutree( hclust(DD_ACF, "ward.D2"), k = 2)fviz_cluster(list(data = t(datos), cluster = DDclust_ACF_SatO2))
fviz_silhouette(silhouette(DDclust_ACF_SatO2, 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[DDclust_ACF_SatO2 == 2]),names(DDclust_ACF_SatO2[DDclust_ACF_SatO2 == 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)
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_CRUCES_INGRESO | 6.9567255 |
| SCORE_WOOD_DOWNES_INGRESO | 5.6871218 |
| SAPI_0_8h | 5.5863809 |
| ALIMENTACION | 3.2721843 |
| EDAD | 3.1737649 |
| ENFERMEDAD_BASE | 3.0453651 |
| DIAS_GN | 2.4931429 |
| FLUJO2_0_8H | 1.9797567 |
| ETIOLOGIA | 1.6401817 |
| DIAS_O2_TOTAL | 1.4355993 |
| PESO | 1.3241418 |
| EG | 1.2454329 |
| SUERO | 1.1090003 |
| SEXO | 0.9962267 |
| FR_0_8h | 0.9814377 |
| ANALITICA | 0.8497344 |
| TABACO | 0.7928605 |
| PALIVIZUMAB | 0.7516826 |
| ALERGIAS | 0.7394366 |
| RADIOGRAFIA | 0.6213666 |
| LM | 0.6198586 |
| PREMATURIDAD | 0.6033484 |
| SNG | 0.2078559 |
| DIAS_OAF | 0.1251547 |
| DETERIORO | 0.0898222 |
| OAF_TRAS_INGRESO | 0.0840110 |
| GN_INGRESO | 0.0807368 |
| OAF | 0.0694922 |
| PAUSAS_APNEA | 0.0261236 |
| UCIP | 0.0166883 |
| DERMATITIS | 0.0120801 |
| OAF_AL_INGRESO | 0.0000000 |
Importance of first 50 ACF
data_frame1_ACF = data.frame("CLUSTER" = DDclust_ACF_SatO2)
data_frame2_ACF = data.frame(t(SatO2_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)
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
## 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), mean)
row.names(rp_tbl_ACF) <- paste0("Group",rp_tbl_ACF$DDclust_ACF_SatO2)
rp_tbl_ACF <- rp_tbl_ACF %>%
select(starts_with('X'))
rp_tbl_ACF <- data.frame(t(rp_tbl_ACF))
head(rp_tbl_ACF)## Group1 Group2
## X1 1.0000000 1.0000000
## X2 0.4160338 0.7830190
## X3 0.3241161 0.7392115
## X4 0.2856382 0.7003584
## X5 0.2544281 0.6769579
## X6 0.2261663 0.6447044# Create plotting data-frame
ACF_values_by_group <- data.frame("value_ACF" = c(rp_tbl_ACF$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
## 0.7278723 0.6767669 0.8093860 0.9053869NbClust 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$All.index## 2 3 4 5
## 0.1239 0.1163 0.1370 0.1778res$Best.nc## Number_clusters Value_Index
## 5.0000 0.1778#res$Best.partitionhcintper_EUCL <- hclust(DD_EUCL, "ward.D2")
fviz_dend(hcintper_EUCL, palette = "jco",
rect = TRUE, show_labels = FALSE, k = 5)
DDclust_EUCL_SatO2 <- cutree( hclust(DD_EUCL, "ward.D2"), k = 5)fviz_cluster(list(data = t(datos), cluster = DDclust_EUCL_SatO2))
fviz_silhouette(silhouette(DDclust_EUCL_SatO2, DD_EUCL))## cluster size ave.sil.width
## 1 1 28 0.00
## 2 2 27 0.27
## 3 3 1 0.00
## 4 4 1 0.00
## 5 5 1 0.00
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[DDclust_EUCL_SatO2 == 2]),names(DDclust_EUCL_SatO2[DDclust_EUCL_SatO2 == 1]))
fviz_dend(hcintper_EUCL, k = 2,
k_colors = c("blue", "green"),
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 | 28 | 24 |
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 | 0.0666667 | 0.1428571 |
| NO DETERIORO | 0.9333333 | 0.8571429 |
Random Forest: Discriminant TSCLust EUCL
data_frame1_EUCL = data.frame("CLUSTER" = DDclust_EUCL_SatO2)
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
## 30 28data_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$CLUSTER)##
## 1 2
## 30 28set.seed(123)
pos_1 = get_column_position(newSMOTE_EUCL, "SAPI_0_8h")
pos_2 = get_column_position(newSMOTE_EUCL, "PAUSAS_APNEA")
col_names_factor <- names(newSMOTE_EUCL[pos_1:pos_2])
newSMOTE_EUCL[col_names_factor] <- lapply(newSMOTE_EUCL[col_names_factor] , factor)
RF_EUCL <- randomForest(CLUSTER ~ ., data = newSMOTE_EUCL)
print(RF_EUCL)##
## Call:
## randomForest(formula = CLUSTER ~ ., data = newSMOTE_EUCL)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 5
##
## OOB estimate of error rate: 48.28%
## Confusion matrix:
## 1 2 class.error
## 1 16 14 0.4666667
## 2 14 14 0.5000000Importance
kable(RF_EUCL$importance[order(RF_EUCL$importance, decreasing = TRUE),])| x | |
|---|---|
| SCORE_WOOD_DOWNES_INGRESO | 3.5645422 |
| FR_0_8h | 2.7355615 |
| PESO | 2.6841347 |
| SCORE_CRUCES_INGRESO | 2.6712216 |
| EDAD | 2.3318932 |
| SAPI_0_8h | 1.9271213 |
| EG | 1.4555086 |
| RADIOGRAFIA | 1.3770331 |
| DIAS_GN | 1.3365956 |
| DIAS_O2_TOTAL | 1.1951206 |
| FLUJO2_0_8H | 1.0780517 |
| LM | 0.6672803 |
| ETIOLOGIA | 0.5385464 |
| SEXO | 0.4922139 |
| ALIMENTACION | 0.3855488 |
| SUERO | 0.3691580 |
| TABACO | 0.3576500 |
| ANALITICA | 0.3412550 |
| GN_INGRESO | 0.3342309 |
| PREMATURIDAD | 0.3243099 |
| DERMATITIS | 0.2830174 |
| ENFERMEDAD_BASE | 0.2724867 |
| ALERGIAS | 0.2240857 |
| DIAS_OAF | 0.2143303 |
| SNG | 0.2097880 |
| OAF_TRAS_INGRESO | 0.1796964 |
| PAUSAS_APNEA | 0.1443769 |
| OAF | 0.1438204 |
| PALIVIZUMAB | 0.1363593 |
| DETERIORO | 0.0984227 |
| UCIP | 0.0609929 |
| OAF_AL_INGRESO | 0.0000000 |
Importance of the TS-data
data_frame1_EUCL = data.frame("CLUSTER" = DDclust_EUCL_SatO2)
data_frame2_EUCL = data.frame(datos_EUCL)
data_frame_merge_EUCL <-
merge(data_frame1_EUCL, data_frame2_EUCL, by = 'row.names', all = TRUE)
data_frame_merge_EUCL <- data_frame_merge_EUCL[, 2:dim(data_frame_merge_EUCL)[2]]set.seed(123)
data_frame_merge_EUCL$CLUSTER <- as.factor(data_frame_merge_EUCL$CLUSTER)
RF_0_EUCL <- randomForest(CLUSTER ~ ., data = data_frame_merge_EUCL)
print(RF_0_EUCL)##
## Call:
## randomForest(formula = CLUSTER ~ ., data = data_frame_merge_EUCL)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 21
##
## OOB estimate of error rate: 15.52%
## Confusion matrix:
## 1 2 class.error
## 1 25 5 0.1666667
## 2 4 24 0.1428571plot(RF_0_EUCL$importance, type = "h")
EUCL by clusters
plot_data_EUCL <- data.frame(t(datos))
cluster_data_EUCL <- data.frame(DDclust_EUCL_SatO2)
plotting_EUCL <- cbind(plot_data_EUCL, cluster_data_EUCL)
head(plotting_EUCL)## X1 X2 X3 X4 X5 X6 X7
## ACR_11231843 100.0000 100.00000 97.00000 100.00000 96.00000 95.00000 99.00000
## ADAO_11159808 98.0000 97.00000 97.00000 98.00000 98.00000 98.00000 98.00000
## AGG_11236448 97.2421 97.32759 96.82957 97.75063 96.80206 97.63722 96.92675
## AHL_11239959 98.0000 98.00000 98.00000 98.00000 96.00000 98.00000 99.00000
## AJGD_11119689 99.6000 100.00000 99.40000 99.40000 97.40000 99.40000 100.00000
## AMP_11228639 73.0000 74.00000 83.10561 80.00000 77.00000 90.00000 88.00000
## X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20 X21 X22
## ACR_11231843 80 100 96.0 99.0 99.0 100 94.0 99.0 99 97 100 100 100 100 100
## ADAO_11159808 98 97 97.0 97.0 97.0 97 98.0 97.0 96 98 99 98 98 97 98
## AGG_11236448 99 99 95.0 88.0 99.0 98 100.0 97.0 97 94 99 96 99 98 98
## AHL_11239959 98 96 99.0 98.0 98.0 98 98.0 98.0 98 99 96 96 96 100 98
## AJGD_11119689 100 100 99.2 99.6 99.6 100 99.6 99.2 100 100 100 100 89 100 100
## AMP_11228639 86 91 91.0 93.0 90.0 85 90.0 88.0 83 94 91 87 90 91 82
## X23 X24 X25 X26 X27 X28 X29 X30 X31 X32 X33 X34 X35 X36 X37 X38
## ACR_11231843 100 100 98 100 98 100 100 97 100 100 100 99 100 100 96 99
## ADAO_11159808 98 99 98 97 97 98 97 99 98 96 98 97 98 97 98 97
## AGG_11236448 96 99 95 99 90 95 90 97 89 100 98 98 99 99 97 99
## AHL_11239959 97 96 97 99 99 97 98 98 96 98 100 99 99 97 96 97
## AJGD_11119689 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100
## AMP_11228639 93 94 84 88 94 94 91 89 90 100 92 95 89 91 100 89
## X39 X40 X41 X42 X43 X44 X45 X46 X47 X48 X49 X50 X51 X52 X53 X54
## ACR_11231843 100 100 99 95 98 100 97 98 97 99 100 99 95 99 100 100
## ADAO_11159808 95 95 97 97 97 96 97 96 98 96 96 96 97 97 97 97
## AGG_11236448 97 91 97 100 98 96 97 100 97 100 95 96 99 98 96 96
## AHL_11239959 95 98 97 98 97 96 96 98 97 97 98 98 96 96 96 97
## AJGD_11119689 100 99 100 100 100 99 96 98 100 98 100 100 100 100 100 100
## AMP_11228639 88 90 86 91 94 99 91 90 82 55 88 89 93 90 96 89
## X55 X56 X57 X58 X59 X60 X61 X62 X63 X64 X65 X66 X67 X68 X69 X70
## ACR_11231843 99 99 99 98 98 98 97 99 98 98 99 96 94 95 92 94
## ADAO_11159808 96 97 97 96 96 96 98 96 96 97 97 96 96 96 96 96
## AGG_11236448 97 99 95 96 96 95 95 97 98 98 96 95 97 100 98 97
## AHL_11239959 97 96 96 96 96 96 96 96 95 96 96 98 97 98 97 96
## AJGD_11119689 100 100 100 100 100 100 100 100 100 97 98 97 100 100 100 100
## AMP_11228639 89 86 89 89 90 91 90 90 93 92 94 91 95 94 89 87
## X71 X72 X73 X74 X75 X76 X77 X78 X79 X80 X81 X82 X83 X84 X85 X86
## ACR_11231843 98 96 94 89 92 92 92 94 91 93 93 93 94 95 94 96
## ADAO_11159808 94 95 95 95 97 97 97 97 97 97 97 96 97 96 96 96
## AGG_11236448 96 97 98 95 94 98 95 96 97 95 95 100 97 97 100 99
## AHL_11239959 98 96 99 98 99 98 98 97 97 97 98 99 98 97 98 97
## AJGD_11119689 100 100 100 100 100 100 100 99 100 100 100 100 100 100 97 98
## AMP_11228639 91 89 91 91 84 87 85 83 90 88 88 88 83 91 93 91
## X87 X88 X89 X90 X91 X92 X93 X94 X95 X96 X97 X98 X99 X100 X101
## ACR_11231843 94 95 94 94 96 96 96 96 97 98 97 96 97 96 95
## ADAO_11159808 96 96 96 96 96 96 97 96 96 95 96 96 96 96 96
## AGG_11236448 99 99 98 100 98 98 99 98 97 97 97 98 98 98 96
## AHL_11239959 96 98 96 97 97 97 97 97 99 98 96 95 96 97 97
## AJGD_11119689 98 100 99 100 100 100 100 100 99 100 100 96 98 96 97
## AMP_11228639 93 93 96 89 87 95 97 95 91 88 92 91 91 89 90
## X102 X103 X104 X105 X106 X107 X108 X109 X110 X111 X112 X113 X114
## ACR_11231843 96 95 95 94 95 90 92 94 93 92 93 91 92
## ADAO_11159808 96 95 95 96 96 96 95 94 96 96 96 96 96
## AGG_11236448 97 98 99 98 97 98 96 98 97 99 97 96 98
## AHL_11239959 98 97 99 97 97 96 94 94 92 95 94 93 94
## AJGD_11119689 94 98 96 93 98 99 100 97 100 100 99 96 99
## AMP_11228639 76 91 90 90 89 91 91 95 97 95 94 92 90
## X115 X116 X117 X118 X119 X120 X121 X122 X123 X124 X125 X126 X127
## ACR_11231843 94 95 94 90 93 93 90 95 97 95 97 97 96
## ADAO_11159808 97 97 97 98 96 97 96 96 94 94 95 94 95
## AGG_11236448 98 91 96 100 97 97 98 96 97 95 97 97 96
## AHL_11239959 95 93 94 94 94 94 95 94 94 94 94 94 95
## AJGD_11119689 100 99 100 100 91 95 99 100 99 100 100 94 95
## AMP_11228639 89 89 91 87 92 88 87 94 82 90 90 84 91
## X128 X129 X130 X131 X132 X133 X134 X135 X136 X137 X138 X139 X140
## ACR_11231843 97 95 92 97 96 95 94 95 96 94 93 97 96
## ADAO_11159808 95 97 96 95 97 97 97 98 98 98 97 98 98
## AGG_11236448 94 96 97 97 96 98 96 95 95 95 96 98 96
## AHL_11239959 94 95 93 93 95 95 94 95 96 98 84 79 88
## AJGD_11119689 94 93 92 92 95 96 96 95 92 92 98 98 97
## AMP_11228639 89 90 91 88 89 91 90 92 93 91 90 90 88
## X141 X142 X143 X144 X145 X146 X147 X148
## ACR_11231843 94.00000 96.00000 96.00000 94.00000 94.00000 95 96 95
## ADAO_11159808 98.00000 98.00000 98.00000 98.00000 98.00000 98 98 98
## AGG_11236448 97.00000 96.00000 96.00000 96.00000 96.00000 95 99 96
## AHL_11239959 97.51849 96.64464 96.80635 96.23432 96.14719 97 98 100
## AJGD_11119689 98.00000 99.00000 100.00000 100.00000 100.00000 99 100 100
## AMP_11228639 88.00000 77.00000 84.00000 79.00000 85.00000 75 83 89
## X149 X150 X151 X152 X153 X154 X155 X156 X157 X158 X159 X160 X161
## ACR_11231843 94 93 89 92 92 94 97 94 94 97 97 97 95
## ADAO_11159808 98 98 98 98 98 98 97 98 98 97 97 96 95
## AGG_11236448 97 94 95 96 94 93 95 95 94 95 94 97 95
## AHL_11239959 98 100 95 96 97 97 98 98 97 97 96 96 95
## AJGD_11119689 100 100 100 99 100 100 100 100 98 99 96 93 95
## AMP_11228639 82 90 88 90 93 87 89 81 88 84 87 88 87
## X162 X163 X164 X165 X166 X167 X168 X169 X170 X171 X172 X173 X174
## ACR_11231843 96 92 97 96 97 96 93 99 96 98 97 95 95
## ADAO_11159808 97 97 97 97 97 97 98 98 97 98 97 96 95
## AGG_11236448 98 95 96 95 96 96 96 96 96 95 96 96 96
## AHL_11239959 96 97 97 97 97 97 96 98 98 97 96 96 96
## AJGD_11119689 92 91 94 96 96 93 93 100 99 100 100 98 96
## AMP_11228639 92 85 87 89 83 80 81 88 88 87 86 90 93
## X175 X176 X177 X178 X179 X180 X181 X182 X183 X184 X185 X186 X187
## ACR_11231843 98 95 98 100 98 98 98 97 95 99 98 97 87
## ADAO_11159808 96 97 95 99 99 98 99 99 99 99 99 99 99
## AGG_11236448 96 96 95 96 96 96 95 96 95 96 96 95 87
## AHL_11239959 99 97 95 95 96 96 97 96 95 96 97 96 97
## AJGD_11119689 99 96 100 100 98 97 99 100 100 100 100 100 100
## AMP_11228639 91 89 89 81 72 77 82 80 87 76 71 86 82
## X188 X189 X190 X191 X192 X193 X194 X195 X196 X197 X198 X199 X200
## ACR_11231843 99 96 96 96 98 96 97 95 96 99 94 93 96
## ADAO_11159808 98 99 99 99 99 99 99 99 99 99 100 99 99
## AGG_11236448 96 92 95 95 87 96 96 97 98 96 95 98 96
## AHL_11239959 97 97 95 97 96 95 95 94 95 95 95 95 96
## AJGD_11119689 100 100 100 100 100 100 100 100 100 100 100 100 85
## AMP_11228639 86 77 85 77 83 86 88 84 85 88 87 82 83
## X201 X202 X203 X204 X205 X206 X207 X208 X209 X210 X211 X212 X213
## ACR_11231843 95 98 97 97 99 98 97 92 93 95 100 99 99
## ADAO_11159808 99 99 99 99 99 99 100 99 99 99 99 100 100
## AGG_11236448 95 96 97 98 97 95 96 96 96 95 96 95 97
## AHL_11239959 95 95 93 94 95 95 95 96 95 95 96 95 97
## AJGD_11119689 100 100 100 100 100 95 97 96 96 95 96 96 100
## AMP_11228639 84 87 87 82 86 81 73 87 70 85 79 89 79
## X214 X215 X216 X217 X218 X219 X220 X221 X222 X223 X224 X225 X226
## ACR_11231843 99 98 97 98 98 98 98 98 97 98 98 97 96
## ADAO_11159808 100 100 100 100 100 100 100 99 99 100 100 100 99
## AGG_11236448 97 97 96 95 95 95 96 96 94 94 95 93 94
## AHL_11239959 96 96 98 96 97 97 97 98 98 98 98 97 95
## AJGD_11119689 99 98 99 98 97 98 99 97 100 96 94 98 100
## AMP_11228639 88 88 88 80 87 87 87 87 87 87 88 90 87
## X227 X228 X229 X230 X231 X232 X233 X234 X235 X236 X237 X238 X239
## ACR_11231843 95 96 88 89 95 92 95 90 95 93 95 98 96
## ADAO_11159808 100 100 100 100 100 100 100 100 100 99 100 100 100
## AGG_11236448 94 94 96 91 89 89 92 90 93 91 91 94 94
## AHL_11239959 96 96 97 96 96 99 97 97 96 96 97 97 96
## AJGD_11119689 100 99 99 100 100 100 100 100 100 100 100 100 100
## AMP_11228639 88 89 89 88 88 90 88 89 89 87 87 90 85
## X240 X241 X242 X243 X244 X245 X246 X247 X248 X249 X250 X251 X252
## ACR_11231843 95 95 98 95 94 94 87 88 92 86 92 93 86
## ADAO_11159808 100 100 100 100 100 100 100 100 100 99 100 99 100
## AGG_11236448 90 92 93 97 95 91 93 91 92 92 94 94 93
## AHL_11239959 97 96 97 97 97 98 97 97 97 97 97 98 97
## AJGD_11119689 100 100 100 100 100 97 99 100 100 100 100 100 100
## AMP_11228639 92 91 91 90 84 88 84 88 94 85 88 89 89
## X253 X254 X255 X256 X257 X258 X259 X260 X261 X262 X263 X264 X265
## ACR_11231843 68 84 94 97 96 94 95 95 96 95 95 96 97
## ADAO_11159808 99 99 99 100 99 99 100 100 99 100 99 98 99
## AGG_11236448 93 96 96 94 94 93 92 93 92 92 91 92 91
## AHL_11239959 97 97 98 97 99 98 98 98 98 98 98 98 98
## AJGD_11119689 100 98 99 95 100 100 100 98 100 100 97 99 98
## AMP_11228639 89 88 84 88 82 86 84 80 87 82 89 89 91
## X266 X267 X268 X269 X270 X271 X272 X273 X274 X275 X276 X277 X278
## ACR_11231843 98 99 98 94 97 96 97 97 98 97 97 96 97
## ADAO_11159808 97 97 97 98 98 98 99 100 99 98 98 98 97
## AGG_11236448 91 91 94 92 92 91 92 92 92 92 92 93 92
## AHL_11239959 98 98 98 98 94 97 98 98 97 97 97 97 97
## AJGD_11119689 99 100 99 99 99 98 98 98 98 97 96 96 95
## AMP_11228639 92 72 85 92 95 91 84 91 91 91 92 94 83
## X279 X280 X281 X282 X283 X284 X285 X286 X287 X288 X289 X290 X291
## ACR_11231843 96 96 97 97 96 97 97 96 96 95 97 96 97
## ADAO_11159808 99 99 98 99 100 99 100 98 99 98 97 98 98
## AGG_11236448 91 91 87 93 85 97 98 94 95 96 94 96 94
## AHL_11239959 97 97 97 98 97 97 97 97 96 98 99 97 96
## AJGD_11119689 94 94 94 94 97 95 95 94 94 95 95 96 96
## AMP_11228639 87 89 88 88 85 86 81 83 93 76 73 85 88
## X292 X293 X294 X295 X296 X297 X298 X299 X300 X301 X302 X303 X304
## ACR_11231843 95 95 96 95 96 96 96 96 95 96 95 95 97
## ADAO_11159808 99 99 99 99 97 99 99 99 95 96 97 97 97
## AGG_11236448 97 97 97 96 97 87 85 87 98 99 100 98 98
## AHL_11239959 97 98 98 98 99 98 98 97 97 95 97 97 97
## AJGD_11119689 96 97 97 96 97 96 96 96 95 95 94 94 95
## AMP_11228639 86 86 82 85 88 89 86 80 78 87 86 86 88
## X305 X306 X307 X308 X309 X310 X311 X312 X313 X314 X315 X316 X317
## ACR_11231843 96 95 95 96 96 97 96 97 96 97 97 96 96
## ADAO_11159808 98 99 97 97 98 98 99 99 99 98 99 99 98
## AGG_11236448 97 98 98 97 91 98 97 97 96 94 96 98 96
## AHL_11239959 98 97 97 97 96 97 97 98 97 97 97 97 98
## AJGD_11119689 95 93 93 92 92 92 92 92 92 92 93 93 92
## AMP_11228639 89 89 89 89 90 91 91 89 90 90 90 92 89
## X318 X319 X320 X321 X322 X323 X324 X325 X326 X327 X328 X329 X330
## ACR_11231843 97 97 95 97 98 97 96 96 94 97 95 95 94
## ADAO_11159808 99 99 99 99 99 100 98 97 97 96 97 97 98
## AGG_11236448 96 95 98 96 95 94 95 98 96 95 97 97 95
## AHL_11239959 98 97 97 97 97 97 97 98 99 98 99 99 99
## AJGD_11119689 93 96 94 93 95 97 91 100 94 99 100 100 100
## AMP_11228639 90 90 90 92 91 91 89 90 89 88 89 90 90
## X331 X332 X333 X334 X335 X336 X337 X338 X339 X340 X341 X342 X343
## ACR_11231843 94 95 95 93 95 95 96 97 93 95 94 96 97
## ADAO_11159808 99 98 97 97 97 97 98 98 98 98 98 98 98
## AGG_11236448 96 96 95 95 95 96 95 96 94 94 96 95 96
## AHL_11239959 99 99 99 99 98 99 99 98 98 98 97 98 98
## AJGD_11119689 100 98 100 100 99 99 100 99 100 98 100 99 100
## AMP_11228639 89 90 92 90 89 90 91 89 90 88 90 83 88
## X344 X345 X346 X347 X348 X349 X350 X351 X352 X353 X354 X355 X356
## ACR_11231843 97 97 96 98 95 95 96 96 95 97 94 93 95
## ADAO_11159808 98 98 98 98 98 98 98 98 98 98 98 98 98
## AGG_11236448 99 94 97 95 94 94 93 93 93 94 93 97 94
## AHL_11239959 98 97 97 97 97 97 96 96 97 96 97 96 97
## AJGD_11119689 99 100 99 97 98 100 97 100 100 100 100 99 98
## AMP_11228639 86 88 73 85 78 87 75 84 87 77 88 79 87
## X357 X358 X359 X360 X361 X362 X363 X364 X365 X366 X367 X368 X369
## ACR_11231843 96 94 95 95 95 97 95 96 95 97 96 96 97
## ADAO_11159808 98 98 98 100 98 97 99 99 99 96 99 98 98
## AGG_11236448 96 94 96 95 95 97 100 97 95 95 95 95 96
## AHL_11239959 99 97 97 97 97 97 96 98 98 98 98 98 98
## AJGD_11119689 100 100 99 100 100 100 99 100 99 99 99 98 98
## AMP_11228639 80 89 81 83 88 84 88 87 87 90 88 90 89
## X370 X371 X372 X373 X374 X375 X376 X377 X378 X379 X380 X381 X382
## ACR_11231843 97 97 98 97 97 98 97 97 96 96 94 95 94
## ADAO_11159808 92 98 99 100 99 99 99 99 100 100 99 100 100
## AGG_11236448 95 96 96 94 94 93 97 93 94 94 94 94 96
## AHL_11239959 98 98 97 98 98 98 98 98 98 97 98 99 98
## AJGD_11119689 98 96 99 100 99 100 99 98 98 100 100 99 100
## AMP_11228639 89 88 89 89 88 89 90 89 89 89 87 89 91
## X383 X384 X385 X386 X387 X388 X389 X390 X391 X392 X393 X394 X395
## ACR_11231843 95 93 98 95 90 90 89 92 90 92 95 95 94
## ADAO_11159808 100 100 100 100 100 100 100 99 99 99 100 99 98
## AGG_11236448 95 95 96 94 95 95 94 94 93 93 93 94 95
## AHL_11239959 98 98 98 98 97 100 96 97 98 98 96 98 97
## AJGD_11119689 100 100 97 100 100 100 100 100 100 100 100 100 100
## AMP_11228639 88 89 90 87 84 85 88 86 86 84 75 88 75
## X396 X397 X398 X399 X400 X401 X402 X403 X404 X405 X406 X407 X408
## ACR_11231843 93 94 96 94 93 97 94 95 91 100 97 96 97
## ADAO_11159808 98 99 100 100 100 100 100 100 100 100 100 99 100
## AGG_11236448 94 94 95 95 94 94 94 95 95 94 96 95 96
## AHL_11239959 98 97 99 98 98 97 97 97 97 96 97 97 97
## AJGD_11119689 100 97 100 100 100 100 99 100 100 99 100 99 99
## AMP_11228639 88 76 76 89 89 87 89 87 90 81 90 87 87
## X409 X410 X411 X412 X413 X414 X415 X416
## ACR_11231843 97 97 95 98.00000 96.0000 96.00000 98.00000 94.00000
## ADAO_11159808 100 100 99 99.00000 99.0000 99.00000 99.00000 99.00000
## AGG_11236448 96 93 96 94.00000 96.0000 95.00000 94.00000 85.00000
## AHL_11239959 97 98 98 98.00000 98.0000 98.00000 98.00000 98.00000
## AJGD_11119689 99 99 100 100.00000 100.0000 100.00000 96.00000 100.00000
## AMP_11228639 87 88 84 88.90209 87.9594 87.85945 89.36001 88.19118
## X417 X418 X419 X420 X421 X422
## ACR_11231843 96.0000 98.00000 96.00000 96.00000 96.00000 97.00000
## ADAO_11159808 99.0000 99.00000 99.00000 99.00000 99.00000 99.00000
## AGG_11236448 96.0000 97.00000 94.00000 100.00000 98.00000 99.00000
## AHL_11239959 98.0000 98.00000 99.00000 98.00000 99.00000 99.00000
## AJGD_11119689 100.0000 100.00000 100.00000 99.00000 100.00000 100.00000
## AMP_11228639 89.0267 88.36341 88.52669 87.74906 87.39831 89.78503
## X423 X424 X425 X426 X427 X428 X429 X430 X431 X432 X433 X434
## ACR_11231843 95.00000 96 96 97 95 97 93 83 80 89 92 95
## ADAO_11159808 99.00000 99 99 100 100 100 100 100 99 98 99 98
## AGG_11236448 98.00000 96 98 100 99 100 99 93 99 98 97 97
## AHL_11239959 99.00000 99 99 99 97 98 98 99 98 96 98 97
## AJGD_11119689 100.00000 100 99 100 100 100 100 100 100 100 100 100
## AMP_11228639 84.69023 89 78 81 87 85 88 91 87 89 95 91
## X435 X436 X437 X438 X439 X440 X441 X442 X443 X444 X445 X446 X447
## ACR_11231843 97 95 96 95 95 94 93 95 95 94 95 96 96
## ADAO_11159808 99 99 99 98 99 99 99 99 99 99 99 98 98
## AGG_11236448 97 97 99 97 97 96 97 100 97 97 99 99 99
## AHL_11239959 97 98 96 96 96 96 96 96 97 96 96 96 96
## AJGD_11119689 100 99 100 100 100 100 98 100 98 100 100 100 100
## AMP_11228639 91 90 94 86 91 95 79 86 88 86 90 89 92
## X448 X449 X450 X451 X452 X453 X454 X455 X456 X457 X458 X459 X460
## ACR_11231843 95 94 96 95 99 96 98 98 86 98 89 91 90
## ADAO_11159808 100 98 98 98 99 99 99 99 99 98 99 100 99
## AGG_11236448 97 96 97 87 100 94 93 93 97 95 94 96 93
## AHL_11239959 95 96 96 95 96 96 96 97 95 95 95 94 95
## AJGD_11119689 100 100 100 100 100 100 100 100 100 100 100 98 99
## AMP_11228639 89 87 90 88 92 88 84 87 90 89 90 88 89
## X461 X462 X463 X464 X465 X466 X467 X468 X469 X470 X471 X472 X473
## ACR_11231843 98 95 94 81 93 92 94 93 93 93 95 94 94
## ADAO_11159808 99 99 99 99 98 98 99 99 99 100 99 99 99
## AGG_11236448 92 95 95 96 93 94 95 93 93 97 94 93 92
## AHL_11239959 96 97 98 98 98 97 99 98 100 98 95 96 96
## AJGD_11119689 100 100 100 100 100 100 100 100 100 100 100 100 100
## AMP_11228639 84 81 81 81 81 80 78 80 84 83 84 85 86
## X474 X475 X476 X477 X478 X479 X480 DDclust_EUCL_SatO2
## ACR_11231843 94 94 93 93 95 93 95 1
## ADAO_11159808 98 99 99 99 99 99 99 2
## AGG_11236448 92 91 91 94 91 92 95 1
## AHL_11239959 96 97 96 98 98 98 99 2
## AJGD_11119689 100 100 100 100 100 100 100 2
## AMP_11228639 85 85 86 84 86 87 86 1## Mean by groups
rp_tbl_EUCL <- aggregate(plotting_EUCL, by = list(plotting_EUCL$DDclust_EUCL_SatO2), mean)
row.names(rp_tbl_EUCL) <- paste0("Group",rp_tbl_EUCL$DDclust_EUCL_SatO2)
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 94.03836 96.49842
## X2 93.87824 97.90154
## X3 94.64947 96.79130
## X4 94.86153 94.88320
## X5 94.57652 95.85469
## X6 95.14732 96.99904# 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)$ac}
map_dbl(m,ac)## average single complete ward.D2
## 0.8148798 0.7824204 0.8259267 0.8807069NbClust study
This package will help us identify the optimum number of clusters
based our criteria in the silhouette index
diss_matrix<- DD_PER
res<-NbClust(datos_PER, diss=diss_matrix, distance = NULL, min.nc=2, max.nc=5, method = "ward.D2", index = "silhouette")
res$All.index## 2 3 4 5
## 0.5690 0.5654 0.5544 0.5918res$Best.nc## Number_clusters Value_Index
## 5.0000 0.5918#res$Best.partitionhcintper_PER <- hclust(DD_PER, "ward.D2")
fviz_dend(hcintper_PER, palette = "jco",
rect = TRUE, show_labels = FALSE, k = 5)
DDclust_PER_SatO2 <- cutree( hclust(DD_PER, "ward.D2"), k = 5)fviz_cluster(list(data = t(datos), cluster = DDclust_PER_SatO2))
fviz_silhouette(silhouette(DDclust_PER_SatO2, DD_PER))## cluster size ave.sil.width
## 1 1 50 0.61
## 2 2 1 0.00
## 3 3 5 0.12
## 4 4 1 0.00
## 5 5 1 0.00
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[DDclust_PER_SatO2 == 2]),names(DDclust_PER_SatO2[DDclust_PER_SatO2 == 1]))
fviz_dend(hcintper_PER, k = 2,
k_colors = c("blue", "green"),
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 | 4 | 2 |
| NO DETERIORO | 46 | 6 |
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 | 0.08 | 0.25 |
| NO DETERIORO | 0.92 | 0.75 |
Random Forest: Discriminant TSCLust PER
data_frame1_PER = data.frame("CLUSTER" = DDclust_PER_SatO2)
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$CLUSTER)##
## 1 2
## 50 8data_frame_merge_PER[,c(1:dim(data_frame_merge_PER)[2])]<- lapply(data_frame_merge_PER[,c(1:dim(data_frame_merge_PER)[2])], as.numeric)
head(data_frame_merge_PER)## CLUSTER EDAD PESO EG FR_0_8h FLUJO2_0_8H DIAS_GN DIAS_O2_TOTAL DIAS_OAF
## 1 1 10.0 8.20 41 48 2.00 3 3 0
## 2 1 13.0 7.78 40 56 2.00 2 2 0
## 3 1 3.1 5.66 37 44 1.00 4 4 0
## 4 1 5.3 8.44 38 65 0.40 3 3 0
## 5 1 15.0 7.00 34 37 2.00 4 4 0
## 6 1 1.6 3.80 37 42 0.94 4 4 0
## SAPI_0_8h SCORE_CRUCES_INGRESO SCORE_WOOD_DOWNES_INGRESO SEXO PALIVIZUMAB LM
## 1 3 3 6 1 1 2
## 2 4 4 8 1 1 1
## 3 3 3 7 1 1 2
## 4 4 3 6 1 1 2
## 5 1 3 6 1 2 1
## 6 2 4 7 1 1 2
## DERMATITIS ALERGIAS TABACO ENFERMEDAD_BASE RADIOGRAFIA ANALITICA SUERO
## 1 1 2 1 1 1 1 1
## 2 1 2 2 2 1 1 2
## 3 1 1 1 1 1 1 1
## 4 1 1 1 1 1 1 1
## 5 1 1 2 2 1 1 2
## 6 1 1 2 2 1 1 1
## ETIOLOGIA PREMATURIDAD ALIMENTACION SNG GN_INGRESO OAF OAF_AL_INGRESO
## 1 2 1 2 1 2 1 1
## 2 1 1 1 1 2 1 1
## 3 2 1 2 1 2 1 1
## 4 2 1 2 1 1 1 1
## 5 2 2 2 1 2 1 1
## 6 1 1 2 1 1 1 1
## OAF_TRAS_INGRESO UCIP DETERIORO PAUSAS_APNEA
## 1 1 1 1 1
## 2 1 1 1 1
## 3 1 1 1 1
## 4 1 1 1 1
## 5 1 1 1 1
## 6 1 1 1 1data_frame_merge_PER$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
## 50 43set.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: 7.53%
## Confusion matrix:
## 1 2 class.error
## 1 48 2 0.0400000
## 2 5 38 0.1162791Importance
kable(RF_PER$importance[order(RF_PER$importance, decreasing = TRUE),])| x | |
|---|---|
| SCORE_WOOD_DOWNES_INGRESO | 9.4039903 |
| SCORE_CRUCES_INGRESO | 5.8936796 |
| SAPI_0_8h | 2.9363664 |
| ETIOLOGIA | 2.5904366 |
| TABACO | 2.4803961 |
| FR_0_8h | 2.3003699 |
| LM | 2.0204451 |
| ENFERMEDAD_BASE | 1.9248365 |
| FLUJO2_0_8H | 1.8666065 |
| SEXO | 1.7931860 |
| ALIMENTACION | 1.6796836 |
| EDAD | 1.6575682 |
| PESO | 1.4126419 |
| DIAS_O2_TOTAL | 1.1191715 |
| EG | 0.9606597 |
| DIAS_GN | 0.8283240 |
| RADIOGRAFIA | 0.5729906 |
| DIAS_OAF | 0.5506047 |
| OAF_TRAS_INGRESO | 0.4321288 |
| ALERGIAS | 0.4264464 |
| PREMATURIDAD | 0.4191550 |
| OAF | 0.4126883 |
| SUERO | 0.4012926 |
| DETERIORO | 0.3708729 |
| ANALITICA | 0.2953936 |
| PAUSAS_APNEA | 0.2135549 |
| SNG | 0.2074607 |
| PALIVIZUMAB | 0.1622264 |
| DERMATITIS | 0.1246912 |
| GN_INGRESO | 0.1227308 |
| UCIP | 0.0366706 |
| OAF_AL_INGRESO | 0.0000000 |
Importance of the PER
data_frame1_PER = data.frame("CLUSTER" = DDclust_PER_SatO2)
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: 8.62%
## Confusion matrix:
## 1 2 class.error
## 1 50 0 0.000
## 2 5 3 0.625plot(RF_0_PER$importance, type = "h")
### PER by clusters
plot_data_PER <- data.frame(datos_PER)
cluster_data_PER <- data.frame(DDclust_PER_SatO2)
plotting_PER <- cbind(plot_data_PER, cluster_data_PER)
head(plotting_PER)## X1 X2 X3 X4 X5 X6
## ACR_11231843 63.34321 8.804109 149.970718 88.401531 22.05811 81.060151
## ADAO_11159808 36.84221 105.380705 27.414295 5.535132 8.37439 6.107621
## AGG_11236448 176.78687 82.545494 9.218756 141.274964 22.83899 9.740057
## AHL_11239959 58.08849 32.944168 22.670332 18.290623 11.66146 62.481321
## AJGD_11119689 132.52475 65.461483 235.745768 15.417801 93.53463 45.924171
## AMP_11228639 196.20848 210.962281 183.269971 43.081685 121.12504 23.070540
## X7 X8 X9 X10 X11 X12
## ACR_11231843 10.8683632 46.083096 21.235818 70.633662 81.459096 47.815877
## ADAO_11159808 0.3710764 4.747506 3.881014 4.622159 2.135861 3.020235
## AGG_11236448 22.6968927 15.621177 29.235335 5.688367 23.933592 17.277122
## AHL_11239959 20.3166148 10.377407 9.446535 8.092775 11.019441 5.183153
## AJGD_11119689 7.0683209 2.347478 21.169545 40.257378 9.556339 21.825076
## AMP_11228639 132.4421506 37.821216 143.958943 97.958445 58.395636 12.544805
## X13 X14 X15 X16 X17 X18
## ACR_11231843 11.9393303 80.316026 18.7317304 12.268367 17.989420 28.605029
## ADAO_11159808 0.9250451 7.958751 4.9832235 3.494020 1.921946 2.634994
## AGG_11236448 10.9448371 1.051881 3.4795239 1.145955 6.251074 6.934874
## AHL_11239959 8.9161978 9.135399 0.3164635 6.935179 2.137869 7.139524
## AJGD_11119689 64.1971923 28.204735 14.2932550 71.565628 7.538120 9.683059
## AMP_11228639 11.0915093 223.071204 13.6449688 24.140913 15.940775 145.194931
## X19 X20 X21 X22 X23 X24
## ACR_11231843 38.257693 2.824034 32.4229967 15.463484 12.602955 49.849574
## ADAO_11159808 1.353506 2.880389 0.5462331 5.123970 1.321674 2.864160
## AGG_11236448 3.686626 1.582984 13.4532152 1.680307 3.488362 20.912284
## AHL_11239959 4.637904 9.697475 13.9209315 2.368095 4.817511 3.985751
## AJGD_11119689 7.230665 6.366921 2.8536385 1.437046 1.986260 12.630199
## AMP_11228639 9.688285 4.921903 156.7317278 30.682827 60.149055 96.023021
## X25 X26 X27 X28 X29 X30
## ACR_11231843 10.6267902 7.90961069 6.930867 2.870991 15.254742 7.293742
## ADAO_11159808 0.5636077 1.12310701 2.847475 2.247278 2.957144 2.072708
## AGG_11236448 4.4554290 26.46785475 9.272394 14.027219 2.488577 8.518406
## AHL_11239959 3.7550746 3.41534065 2.365421 0.311328 5.290309 1.215995
## AJGD_11119689 4.7499956 0.08805868 1.137881 1.903208 3.517403 10.900673
## AMP_11228639 18.4405841 2.14397397 5.678396 9.817580 38.111001 5.701528
## X31 X32 X33 X34 X35 X36
## ACR_11231843 13.2323247 9.515291 2.8172371 13.3551221 7.4129249 4.5593280
## ADAO_11159808 0.9646299 1.822462 0.4988853 0.5931173 1.1582696 0.1939320
## AGG_11236448 16.9771896 10.790757 2.2824382 3.4707868 0.4036638 10.4557534
## AHL_11239959 0.4291100 3.160707 4.7306339 6.7084776 2.2831056 6.2989200
## AJGD_11119689 11.0199016 5.875834 8.7029281 1.2572906 1.3788886 0.2296991
## AMP_11228639 39.3275497 11.746390 5.6081134 25.8939664 5.8960381 62.5939122
## X37 X38 X39 X40 X41 X42
## ACR_11231843 10.0132018 10.2899680 1.146353 0.2884168 5.1318245 2.419986
## ADAO_11159808 6.7964561 2.0170858 0.252977 1.1664212 1.6280188 4.222803
## AGG_11236448 15.5905162 0.4347703 2.471872 9.1131598 6.6324975 2.535306
## AHL_11239959 2.0153037 5.4197216 3.054855 0.2835575 0.9750323 1.180768
## AJGD_11119689 0.1783269 0.6666589 5.574594 3.4846162 6.8549208 9.240505
## AMP_11228639 51.7879115 6.9963574 16.832360 32.8022234 0.8702049 10.396920
## X43 X44 X45 X46 X47 X48
## ACR_11231843 26.6837983 5.01786799 21.1691428 8.233676 0.1769897 14.6585402
## ADAO_11159808 0.5067394 0.58412914 0.2864248 1.193573 1.3968459 0.1549375
## AGG_11236448 0.7008720 2.44399796 5.6885786 1.415491 2.4528260 9.6034709
## AHL_11239959 3.4925288 3.87413108 8.6126877 1.738123 5.2796679 7.6317663
## AJGD_11119689 6.7544966 0.03581313 1.5948770 4.370756 1.6471838 2.9019614
## AMP_11228639 30.7002967 79.02008325 15.7850884 13.308740 6.7816018 54.0875809
## X49 X50 X51 X52 X53 X54
## ACR_11231843 5.5479897 2.2447384 4.26475409 10.5414486 2.1880920 29.4269164
## ADAO_11159808 0.6748814 0.9956261 0.06366724 0.2028089 0.1008996 1.5827742
## AGG_11236448 1.5689776 14.2444263 0.13461373 0.7938609 6.3006843 6.3469789
## AHL_11239959 2.7727955 3.4933547 1.50818075 0.5917350 0.3230123 0.7723974
## AJGD_11119689 1.2145541 0.5081058 1.95224027 2.3062108 6.0323330 11.3875319
## AMP_11228639 4.6863463 12.6834981 40.49270750 11.4440069 3.7968497 11.9888700
## X55 X56 X57 X58 X59 X60
## ACR_11231843 0.4547142 7.8853524 26.6905483 2.2193812 10.8856793 8.1272113
## ADAO_11159808 1.6882057 0.8671078 1.5904964 0.2044874 0.4996113 0.1452373
## AGG_11236448 2.9325977 12.2179192 16.9900184 12.7981143 4.3336262 10.5372949
## AHL_11239959 1.1189397 2.2168669 0.7253355 3.2628869 2.3493105 4.1425600
## AJGD_11119689 1.9043719 0.0995816 1.8773444 1.7359038 7.1133271 8.6060255
## AMP_11228639 10.9223057 17.1030156 28.0324155 10.7626707 9.8027188 1.1235034
## X61 X62 X63 X64 X65 X66
## ACR_11231843 10.4285895 22.1553621 0.1873565 6.7557521 15.9172563 1.3383732
## ADAO_11159808 0.3807241 0.1103688 0.1542464 0.8262244 0.2052170 0.5759879
## AGG_11236448 4.6652361 1.4212415 0.5211929 2.9851455 9.2788022 2.6763260
## AHL_11239959 1.6583981 8.5784697 3.2512996 2.0283131 6.3612701 7.4985686
## AJGD_11119689 11.5300195 4.4089149 1.8512887 1.3122566 0.9470481 1.8046908
## AMP_11228639 18.4759156 15.2608004 18.9140909 8.3750294 9.2441257 68.0419853
## X67 X68 X69 X70 X71 X72
## ACR_11231843 14.8319760 1.8529143 4.5736285 8.7093139 9.4925877 3.0539801
## ADAO_11159808 0.7864593 0.5142474 0.9645969 0.5114217 1.6905453 0.5653769
## AGG_11236448 5.8855064 3.3628887 9.2170083 2.5109460 0.8513963 1.5081150
## AHL_11239959 2.8645084 1.6744746 2.7033955 4.8981839 9.0780969 7.6970677
## AJGD_11119689 1.0604933 0.1682712 1.9213682 2.0826333 5.8017148 2.0327891
## AMP_11228639 8.2843640 12.1028460 8.4742292 44.9636426 28.8303098 7.6021069
## X73 X74 X75 X76 X77
## ACR_11231843 16.5850018 3.986366690 14.0521031 7.4049430 0.3028456
## ADAO_11159808 0.4381563 0.007834983 0.6567072 0.5202654 2.5031132
## AGG_11236448 3.8567234 0.392288569 5.6216801 2.1835732 5.3898056
## AHL_11239959 4.3483046 2.071081928 4.5028410 3.9851043 2.7108724
## AJGD_11119689 1.3753797 1.439396938 4.4613088 5.2693486 3.6992390
## AMP_11228639 13.7584895 20.924489732 3.6480345 14.2416241 4.2564746
## X78 X79 X80 X81 X82
## ACR_11231843 13.7117019 8.1660841 13.19886911 17.8335260 1.1715776
## ADAO_11159808 0.8378059 0.3020811 0.07218197 0.1140574 0.8103329
## AGG_11236448 2.6245558 1.1381900 0.05285022 0.9724201 6.9019854
## AHL_11239959 0.7990704 1.3357630 2.79213385 1.2819393 2.5894579
## AJGD_11119689 7.9400047 1.3048949 0.57357780 0.4621902 0.1677105
## AMP_11228639 8.4223137 16.0665829 30.95569242 41.3571903 18.0118633
## X83 X84 X85 X86 X87
## ACR_11231843 2.8906789 5.8848036 2.5171602 24.8705703 0.8022650
## ADAO_11159808 0.5883821 0.9382038 0.1214446 0.2262521 0.6956008
## AGG_11236448 2.6723793 0.8663802 7.7130087 2.3752326 4.5435013
## AHL_11239959 2.4948705 5.9151523 3.3235673 0.7645684 1.0193560
## AJGD_11119689 3.3450485 1.0753882 1.6431958 1.3957595 1.6681565
## AMP_11228639 11.8566067 17.6291154 36.8284536 4.5769514 65.1525810
## X88 X89 X90 X91 X92
## ACR_11231843 2.42420922 19.368988055 0.7525603 3.9629429 0.1518997
## ADAO_11159808 0.02661939 0.002379936 1.0380908 0.4694261 0.2321663
## AGG_11236448 1.70330872 1.381016327 3.8609216 11.5354294 2.2803819
## AHL_11239959 6.93332156 3.456882070 1.5162512 1.2561523 2.0712678
## AJGD_11119689 8.49678981 6.008529503 5.9882392 1.2924799 0.8959029
## AMP_11228639 64.35819291 3.135586452 7.4492678 8.4624308 30.6671089
## X93 X94 X95 X96 X97 X98
## ACR_11231843 3.1612271 1.9525130 11.3414677 2.3828966 2.4897002 1.52363944
## ADAO_11159808 0.3744431 0.5348566 0.6057966 0.9631920 0.7629763 0.04930203
## AGG_11236448 5.5330310 4.2055970 18.4963326 1.3286518 2.7821870 2.88508934
## AHL_11239959 0.3440545 0.8770131 4.5450189 2.4381883 0.9398997 3.65638291
## AJGD_11119689 1.3901661 0.5679558 2.0044142 7.2327525 0.4658782 0.73126479
## AMP_11228639 14.5571460 1.1264306 7.0231739 0.4990224 9.6231026 11.91901382
## X99 X100 X101 X102 X103 X104
## ACR_11231843 3.4700734 25.4142877 6.6240795 4.0316520 0.360054 1.6819318
## ADAO_11159808 0.1778209 0.4192574 0.6817611 0.2559650 2.095070 0.2536700
## AGG_11236448 9.4098213 4.4195116 6.8624776 1.8621123 5.310998 0.1874846
## AHL_11239959 1.0831158 2.5898990 1.2791752 0.7278099 1.634212 2.3196570
## AJGD_11119689 2.2925140 0.6395044 0.4506342 1.3070641 1.151868 1.7485166
## AMP_11228639 4.4280782 1.1406044 4.9663945 24.7404825 14.140511 4.6686136
## X105 X106 X107 X108 X109 X110
## ACR_11231843 7.372784 3.1574862 7.4832447 6.6589537 6.6027316 7.4118830
## ADAO_11159808 0.637686 1.0561880 0.3838186 0.7872085 0.2739714 0.5478482
## AGG_11236448 1.866520 0.5413669 6.9513357 1.9318438 1.4363975 5.7628482
## AHL_11239959 3.011525 1.6195227 1.6337904 0.2771766 2.8620438 2.8980697
## AJGD_11119689 3.399601 1.9653717 1.1430034 1.6740247 1.3173545 2.4158176
## AMP_11228639 16.134162 1.7038167 37.7748773 5.9230075 14.2035836 11.0575510
## X111 X112 X113 X114 X115
## ACR_11231843 3.591789e-01 0.7386980 0.066717668 4.6723432 5.21670459
## ADAO_11159808 5.005781e-01 0.4243604 0.003422015 0.5845329 1.11601782
## AGG_11236448 9.586251e+00 2.8340960 1.700439614 4.5672717 3.62361616
## AHL_11239959 2.845085e+00 1.7205604 3.745760267 2.8239057 2.51623331
## AJGD_11119689 4.014607e-04 1.1548911 0.060699404 1.3999257 0.02210621
## AMP_11228639 3.109086e+01 7.1852090 16.326723830 13.1666892 15.78390072
## X116 X117 X118 X119 X120
## ACR_11231843 9.17126396 1.6990968 8.76846745 2.0808144 0.29367873
## ADAO_11159808 1.55189386 1.2924043 0.09221122 1.0289680 0.09169136
## AGG_11236448 2.71638254 0.3236832 0.22050096 3.9605813 0.07860607
## AHL_11239959 3.54546398 1.9548379 0.19585927 0.8602858 3.07059521
## AJGD_11119689 0.04558142 3.5221383 3.69106596 1.4529946 3.82614053
## AMP_11228639 18.38168650 4.3062201 0.61368231 12.1714935 8.21020057
## X121 X122 X123 X124 X125
## ACR_11231843 13.98285065 4.5115484 4.06381395 12.01751907 10.2641113
## ADAO_11159808 0.04523647 0.4989915 0.04744316 0.15752631 2.1232122
## AGG_11236448 2.13101322 3.7272558 5.11416651 2.42337543 0.4076115
## AHL_11239959 2.79692512 0.8588625 0.22931248 0.41052815 1.1354936
## AJGD_11119689 2.95561111 0.9775363 0.75516116 0.04392191 1.2702708
## AMP_11228639 1.93314720 13.6245868 1.36635045 8.46387899 1.2520937
## X126 X127 X128 X129 X130 X131
## ACR_11231843 1.2433863 0.237207 10.1048106 1.1885266 1.161114 1.4732604
## ADAO_11159808 0.4857401 1.183417 0.2494351 0.2465117 1.267549 1.1118693
## AGG_11236448 5.5489916 3.217711 1.2308394 0.8857493 5.481754 0.3795889
## AHL_11239959 2.0416424 1.543890 2.3398673 0.5438318 3.108199 0.4936366
## AJGD_11119689 1.0257717 3.066184 0.3712139 0.2985001 3.968729 3.0523974
## AMP_11228639 1.8725832 6.998584 11.9561922 9.1589403 2.986988 1.4951705
## X132 X133 X134 X135 X136 X137
## ACR_11231843 2.226283 0.6303585 2.3684273 1.49857078 3.62321741 2.16137457
## ADAO_11159808 1.255284 0.8883771 0.5407307 0.02712975 1.39178882 1.14452678
## AGG_11236448 4.475602 0.1879412 1.5107810 9.68412840 0.08558425 1.40952758
## AHL_11239959 1.917680 0.5286069 0.1456123 0.57136932 0.87325821 0.05234215
## AJGD_11119689 2.019123 1.9463560 4.0602182 0.21594428 5.41680094 3.41086884
## AMP_11228639 4.156780 53.1739739 47.1664594 16.53741783 13.43917900 9.07463475
## X138 X139 X140 X141 X142 X143
## ACR_11231843 12.1231774 0.3573286 0.8549416 6.3498232 1.6759142 4.5722421
## ADAO_11159808 0.5386929 0.1480573 0.2168780 0.2976941 0.3238780 0.3184413
## AGG_11236448 5.4215972 6.2604711 1.7454461 0.6220094 0.3011754 2.0521386
## AHL_11239959 1.0095620 1.5512634 0.8063490 0.9730691 1.0051741 0.7053865
## AJGD_11119689 3.0831632 0.2758462 0.4687653 10.0297407 2.7791707 6.2941515
## AMP_11228639 14.4683935 0.7875425 34.1932873 12.0507516 7.2043588 22.0022401
## X144 X145 X146 X147 X148
## ACR_11231843 15.4419587 8.70393119 17.08545179 0.83822780 1.7811044
## ADAO_11159808 0.3064871 0.55521192 0.06188036 1.30127700 0.1289914
## AGG_11236448 3.3929363 1.60387233 0.60178844 2.96187896 1.3145196
## AHL_11239959 0.1613510 0.06303558 1.38611369 0.61107906 1.8098455
## AJGD_11119689 1.9705500 0.53601831 0.65501580 1.05185627 0.8895971
## AMP_11228639 16.7606420 7.65842652 0.35917826 0.06093996 39.1774178
## X149 X150 X151 X152 X153
## ACR_11231843 3.63996398 4.21539559 3.5037041 3.84029023 2.7514662
## ADAO_11159808 0.43246580 0.02994306 0.3587978 0.01610157 0.1093731
## AGG_11236448 0.83818314 0.95073711 4.2411586 4.72859472 0.2055687
## AHL_11239959 2.39615970 0.20649998 1.8521309 1.42834956 0.6711869
## AJGD_11119689 0.07325307 1.73634212 1.6078250 0.68868328 1.0097763
## AMP_11228639 0.15799054 17.35544456 24.2779640 0.43035483 1.0665819
## X154 X155 X156 X157 X158 X159
## ACR_11231843 0.02432612 3.3019360 0.6178072 2.7362999 0.6312085 3.9810301
## ADAO_11159808 0.35082811 0.1865965 0.3350513 0.1490592 0.1764462 0.0530203
## AGG_11236448 3.89878641 0.4936731 1.5367124 0.6327673 2.7508028 5.8271653
## AHL_11239959 0.11486818 0.1732010 0.1040641 0.6730607 0.1690408 0.1297706
## AJGD_11119689 1.28212483 0.7713180 1.1798139 0.1315944 0.5003738 2.8960695
## AMP_11228639 16.17873195 2.4725937 9.8980723 33.2699882 10.7776720 10.6611539
## X160 X161 X162 X163 X164
## ACR_11231843 4.30033713 1.16901693 0.02281927 0.21960310 1.179263270
## ADAO_11159808 0.45096512 0.09021543 0.05737048 0.08659491 0.008181057
## AGG_11236448 0.09272221 1.38011747 3.60282405 10.55519870 7.641763534
## AHL_11239959 0.36416392 0.16710781 0.09311028 0.36228685 1.621167944
## AJGD_11119689 8.91576020 3.69217344 2.64331881 4.79507728 6.580576771
## AMP_11228639 36.28968889 2.91378082 24.21241915 3.58376529 0.492945912
## X165 X166 X167 X168 X169 X170
## ACR_11231843 3.1396557 2.1683665 0.1250389 2.8256115 5.5947647 3.1100863
## ADAO_11159808 0.4273584 0.2771944 0.5334110 0.1598546 0.3858205 0.1582067
## AGG_11236448 2.7509132 4.4901924 2.6386424 0.8560534 1.6217626 5.0306564
## AHL_11239959 0.9681020 0.4756748 0.2781810 0.3449813 0.8562805 0.5352752
## AJGD_11119689 0.5421299 3.4884060 3.7096413 2.2215766 0.3176552 0.1345843
## AMP_11228639 10.2306950 14.3870829 2.7665920 0.7050920 4.4458964 0.4796000
## X171 X172 X173 X174 X175 X176
## ACR_11231843 0.4734626 5.7096548 1.7173744 5.69739671 6.0164241 2.5347856
## ADAO_11159808 0.1078960 0.8940043 0.2118074 0.14131343 0.4299174 0.6827341
## AGG_11236448 0.4996926 6.6760153 7.2639651 9.10910768 0.7728034 14.5309942
## AHL_11239959 0.0951920 0.6950725 0.5633619 0.09770190 2.5491493 0.8471888
## AJGD_11119689 1.0273650 2.0187771 0.5813334 0.03346851 0.5892873 1.9809981
## AMP_11228639 19.6883796 0.6314290 5.0525320 5.82877160 16.7620028 8.4460127
## X177 X178 X179 X180 X181 X182
## ACR_11231843 0.9190560 0.1120314 0.2530291 3.36161006 0.61098739 3.4261742
## ADAO_11159808 0.4042994 0.1015104 0.1641715 0.34164335 0.06392122 0.3730584
## AGG_11236448 3.5583855 6.3374178 0.1741007 0.66359234 0.19718988 4.7810208
## AHL_11239959 0.4878427 0.8233419 0.2983574 0.10363495 0.45896505 0.3680708
## AJGD_11119689 0.6905091 1.4569077 3.2326004 0.09489134 2.45122724 4.7921832
## AMP_11228639 11.8617528 8.5256958 19.7339069 56.07438587 1.74175493 18.8805353
## X183 X184 X185 X186 X187
## ACR_11231843 5.98141383 0.59938340 2.7341921 2.303690541 1.05861813
## ADAO_11159808 0.08575015 0.03267463 0.5400481 0.003856769 0.05452006
## AGG_11236448 0.54698325 4.54995654 6.1644218 0.653157321 3.87045316
## AHL_11239959 2.03671170 1.15512844 0.2099878 0.972032293 0.37519734
## AJGD_11119689 5.56125854 0.78066563 1.4656292 2.240882442 0.25841834
## AMP_11228639 5.96846568 19.24522118 7.0724204 14.487507009 24.41712360
## X188 X189 X190 X191 X192
## ACR_11231843 0.24995075 1.7660546 1.57118912 2.06752604 2.73636618
## ADAO_11159808 0.06516697 0.4209274 0.34283816 0.08832234 0.39733856
## AGG_11236448 2.50103226 9.5319824 0.15295773 0.81337072 1.07729377
## AHL_11239959 1.65231317 0.5014524 0.05099315 0.06888119 0.07886296
## AJGD_11119689 0.65588138 1.4214110 2.79663670 0.20407543 0.74860380
## AMP_11228639 19.62368972 13.4268022 26.14932446 82.11575613 3.57215962
## X193 X194 X195 X196 X197 X198
## ACR_11231843 0.3971704 1.1240872 7.8058416 0.2860373 5.0488452 2.9077658
## ADAO_11159808 0.4338560 0.2276968 0.5215772 0.7594468 0.1758369 1.3389821
## AGG_11236448 1.1167107 0.1950759 5.8292275 0.3914546 0.2124345 0.4648302
## AHL_11239959 0.7589685 0.1295652 0.4607371 0.2824607 0.2874024 0.9345262
## AJGD_11119689 0.1495800 0.2569443 1.1866951 0.5354896 2.8890761 0.4303639
## AMP_11228639 7.9217845 11.8611717 8.0128693 1.3785162 4.6957924 3.5117605
## X199 X200 X201 X202 X203
## ACR_11231843 0.7475640 0.07616359 5.0735746 6.00405755 5.96237794
## ADAO_11159808 0.3322807 0.29574768 0.2580489 0.05884420 0.03554613
## AGG_11236448 1.2127478 5.48633189 1.7100444 1.23345534 0.81774085
## AHL_11239959 1.0552976 1.26629801 0.6963185 0.09203756 1.55871335
## AJGD_11119689 0.4371993 1.88658001 2.4122339 2.59882023 0.27622353
## AMP_11228639 10.7430628 30.17546195 16.3856587 3.00272383 86.65419234
## X204 X205 X206 X207 X208 X209
## ACR_11231843 5.9558555 4.7932228 1.4213538 1.7124957 0.13953801 1.7473419
## ADAO_11159808 0.7867290 0.2375591 0.6401114 0.5359071 0.06544673 0.6475573
## AGG_11236448 1.7770559 3.3276966 1.7759371 5.8729541 2.46283975 7.5967716
## AHL_11239959 0.2240975 0.6980473 0.1572846 0.5498278 0.19101544 0.1099240
## AJGD_11119689 0.5135189 5.8247671 2.4952644 0.8592207 0.41722224 1.3363842
## AMP_11228639 13.0206981 23.8205253 22.9303584 38.6428509 12.78483804 1.6502920
## X210 X211 X212 X213 X214
## ACR_11231843 0.09644472 0.1779691 2.2905939 1.85104372 0.32301662
## ADAO_11159808 0.84555262 0.0800038 0.5133744 0.85873960 0.51399803
## AGG_11236448 2.65962642 0.1296346 1.8061166 3.20909638 0.29920766
## AHL_11239959 0.94384548 1.0420692 0.4324950 0.03046616 0.03779672
## AJGD_11119689 2.02579633 0.0304233 1.7086539 1.77783344 0.33679009
## AMP_11228639 4.25153894 18.0082387 16.9204894 13.44245276 28.54682363
## X215 X216 X217 X218 X219 X220
## ACR_11231843 4.21980130 2.5827710 2.37474050 2.8558115 3.7578991 1.3699196
## ADAO_11159808 0.07301315 0.1292316 0.23353910 0.6059223 0.3525472 0.3284839
## AGG_11236448 1.76609610 4.0283101 2.68889285 5.1444444 0.5805841 0.6531493
## AHL_11239959 1.02617704 0.6726032 0.04039316 1.9829224 0.5479089 0.1204094
## AJGD_11119689 0.25753796 0.8938505 0.41919061 1.4629935 0.5161031 0.6202013
## AMP_11228639 3.26978709 16.4744810 36.10439988 3.5903544 12.7169771 24.5197832
## X221 X222 X223 X224 X225 X226
## ACR_11231843 1.2117059 0.7480923 15.21042240 4.3575008 4.04697330 2.6545317
## ADAO_11159808 0.6602280 0.1822880 0.06374832 0.3118209 0.51729799 0.6438665
## AGG_11236448 0.6248186 8.2796934 2.32674759 2.5848339 4.42916370 0.1979652
## AHL_11239959 0.0330523 0.4901177 0.63444898 0.8258034 0.02232097 0.3104947
## AJGD_11119689 8.8914160 2.3021882 2.03235272 1.2078564 1.90485330 1.0240980
## AMP_11228639 16.9472960 2.0922122 3.69846096 39.3293051 2.66581814 5.8968017
## X227 X228 X229 X230 X231 X232
## ACR_11231843 1.7309899 1.4066905 0.06545876 3.0106503 2.3203072 4.0214309
## ADAO_11159808 0.0911962 0.1038657 0.16279743 1.3659718 0.1844041 0.0284395
## AGG_11236448 0.1092725 2.5959079 1.05933343 0.3745092 3.0631672 2.5686140
## AHL_11239959 0.1808797 0.7054476 0.01446630 2.6825362 0.3477136 1.8902592
## AJGD_11119689 3.5762388 1.1112865 1.00392098 0.4650652 0.5122473 2.2102840
## AMP_11228639 11.1945644 18.9597862 2.93204549 37.4800277 4.8883690 11.9982069
## X233 X234 X235 X236 X237 X238
## ACR_11231843 0.6157965 5.90435293 1.28498899 1.5905659 0.3947477 0.39491075
## ADAO_11159808 0.6032443 1.33715813 0.20732848 0.1290961 0.3697631 0.53614415
## AGG_11236448 5.1433290 0.08151819 4.80912184 8.4908668 4.0288281 14.27906879
## AHL_11239959 0.2294777 0.85067112 0.42182514 0.4342760 0.7703858 0.07511138
## AJGD_11119689 2.1864561 1.04038158 0.07550684 3.0983477 7.3318743 0.47104296
## AMP_11228639 2.0684866 0.04048644 31.96350278 0.9582038 22.4130041 63.80501519
## X239 X240 X241 X242 X243 X244
## ACR_11231843 2.9587937 0.4930386 63.34321 8.804109 149.970718 88.401531
## ADAO_11159808 0.2510000 1.3184045 36.84221 105.380705 27.414295 5.535132
## AGG_11236448 0.4329341 10.3397142 176.78687 82.545494 9.218756 141.274964
## AHL_11239959 1.2055245 0.1083238 58.08849 32.944168 22.670332 18.290623
## AJGD_11119689 3.2897818 5.1960287 132.52475 65.461483 235.745768 15.417801
## AMP_11228639 13.8662683 0.8309796 196.20848 210.962281 183.269971 43.081685
## X245 X246 X247 X248 X249 X250
## ACR_11231843 22.05811 81.060151 10.8683632 46.083096 21.235818 70.633662
## ADAO_11159808 8.37439 6.107621 0.3710764 4.747506 3.881014 4.622159
## AGG_11236448 22.83899 9.740057 22.6968927 15.621177 29.235335 5.688367
## AHL_11239959 11.66146 62.481321 20.3166148 10.377407 9.446535 8.092775
## AJGD_11119689 93.53463 45.924171 7.0683209 2.347478 21.169545 40.257378
## AMP_11228639 121.12504 23.070540 132.4421506 37.821216 143.958943 97.958445
## X251 X252 X253 X254 X255 X256
## ACR_11231843 81.459096 47.815877 11.9393303 80.316026 18.7317304 12.268367
## ADAO_11159808 2.135861 3.020235 0.9250451 7.958751 4.9832235 3.494020
## AGG_11236448 23.933592 17.277122 10.9448371 1.051881 3.4795239 1.145955
## AHL_11239959 11.019441 5.183153 8.9161978 9.135399 0.3164635 6.935179
## AJGD_11119689 9.556339 21.825076 64.1971923 28.204735 14.2932550 71.565628
## AMP_11228639 58.395636 12.544805 11.0915093 223.071204 13.6449688 24.140913
## X257 X258 X259 X260 X261 X262
## ACR_11231843 17.989420 28.605029 38.257693 2.824034 32.4229967 15.463484
## ADAO_11159808 1.921946 2.634994 1.353506 2.880389 0.5462331 5.123970
## AGG_11236448 6.251074 6.934874 3.686626 1.582984 13.4532152 1.680307
## AHL_11239959 2.137869 7.139524 4.637904 9.697475 13.9209315 2.368095
## AJGD_11119689 7.538120 9.683059 7.230665 6.366921 2.8536385 1.437046
## AMP_11228639 15.940775 145.194931 9.688285 4.921903 156.7317278 30.682827
## X263 X264 X265 X266 X267 X268
## ACR_11231843 12.602955 49.849574 10.6267902 7.90961069 6.930867 2.870991
## ADAO_11159808 1.321674 2.864160 0.5636077 1.12310701 2.847475 2.247278
## AGG_11236448 3.488362 20.912284 4.4554290 26.46785475 9.272394 14.027219
## AHL_11239959 4.817511 3.985751 3.7550746 3.41534065 2.365421 0.311328
## AJGD_11119689 1.986260 12.630199 4.7499956 0.08805868 1.137881 1.903208
## AMP_11228639 60.149055 96.023021 18.4405841 2.14397397 5.678396 9.817580
## X269 X270 X271 X272 X273 X274
## ACR_11231843 15.254742 7.293742 13.2323247 9.515291 2.8172371 13.3551221
## ADAO_11159808 2.957144 2.072708 0.9646299 1.822462 0.4988853 0.5931173
## AGG_11236448 2.488577 8.518406 16.9771896 10.790757 2.2824382 3.4707868
## AHL_11239959 5.290309 1.215995 0.4291100 3.160707 4.7306339 6.7084776
## AJGD_11119689 3.517403 10.900673 11.0199016 5.875834 8.7029281 1.2572906
## AMP_11228639 38.111001 5.701528 39.3275497 11.746390 5.6081134 25.8939664
## X275 X276 X277 X278 X279 X280
## ACR_11231843 7.4129249 4.5593280 10.0132018 10.2899680 1.146353 0.2884168
## ADAO_11159808 1.1582696 0.1939320 6.7964561 2.0170858 0.252977 1.1664212
## AGG_11236448 0.4036638 10.4557534 15.5905162 0.4347703 2.471872 9.1131598
## AHL_11239959 2.2831056 6.2989200 2.0153037 5.4197216 3.054855 0.2835575
## AJGD_11119689 1.3788886 0.2296991 0.1783269 0.6666589 5.574594 3.4846162
## AMP_11228639 5.8960381 62.5939122 51.7879115 6.9963574 16.832360 32.8022234
## X281 X282 X283 X284 X285 X286
## ACR_11231843 5.1318245 2.419986 26.6837983 5.01786799 21.1691428 8.233676
## ADAO_11159808 1.6280188 4.222803 0.5067394 0.58412914 0.2864248 1.193573
## AGG_11236448 6.6324975 2.535306 0.7008720 2.44399796 5.6885786 1.415491
## AHL_11239959 0.9750323 1.180768 3.4925288 3.87413108 8.6126877 1.738123
## AJGD_11119689 6.8549208 9.240505 6.7544966 0.03581313 1.5948770 4.370756
## AMP_11228639 0.8702049 10.396920 30.7002967 79.02008325 15.7850884 13.308740
## X287 X288 X289 X290 X291 X292
## ACR_11231843 0.1769897 14.6585402 5.5479897 2.2447384 4.26475409 10.5414486
## ADAO_11159808 1.3968459 0.1549375 0.6748814 0.9956261 0.06366724 0.2028089
## AGG_11236448 2.4528260 9.6034709 1.5689776 14.2444263 0.13461373 0.7938609
## AHL_11239959 5.2796679 7.6317663 2.7727955 3.4933547 1.50818075 0.5917350
## AJGD_11119689 1.6471838 2.9019614 1.2145541 0.5081058 1.95224027 2.3062108
## AMP_11228639 6.7816018 54.0875809 4.6863463 12.6834981 40.49270750 11.4440069
## X293 X294 X295 X296 X297 X298
## ACR_11231843 2.1880920 29.4269164 0.4547142 7.8853524 26.6905483 2.2193812
## ADAO_11159808 0.1008996 1.5827742 1.6882057 0.8671078 1.5904964 0.2044874
## AGG_11236448 6.3006843 6.3469789 2.9325977 12.2179192 16.9900184 12.7981143
## AHL_11239959 0.3230123 0.7723974 1.1189397 2.2168669 0.7253355 3.2628869
## AJGD_11119689 6.0323330 11.3875319 1.9043719 0.0995816 1.8773444 1.7359038
## AMP_11228639 3.7968497 11.9888700 10.9223057 17.1030156 28.0324155 10.7626707
## X299 X300 X301 X302 X303 X304
## ACR_11231843 10.8856793 8.1272113 10.4285895 22.1553621 0.1873565 6.7557521
## ADAO_11159808 0.4996113 0.1452373 0.3807241 0.1103688 0.1542464 0.8262244
## AGG_11236448 4.3336262 10.5372949 4.6652361 1.4212415 0.5211929 2.9851455
## AHL_11239959 2.3493105 4.1425600 1.6583981 8.5784697 3.2512996 2.0283131
## AJGD_11119689 7.1133271 8.6060255 11.5300195 4.4089149 1.8512887 1.3122566
## AMP_11228639 9.8027188 1.1235034 18.4759156 15.2608004 18.9140909 8.3750294
## X305 X306 X307 X308 X309 X310
## ACR_11231843 15.9172563 1.3383732 14.8319760 1.8529143 4.5736285 8.7093139
## ADAO_11159808 0.2052170 0.5759879 0.7864593 0.5142474 0.9645969 0.5114217
## AGG_11236448 9.2788022 2.6763260 5.8855064 3.3628887 9.2170083 2.5109460
## AHL_11239959 6.3612701 7.4985686 2.8645084 1.6744746 2.7033955 4.8981839
## AJGD_11119689 0.9470481 1.8046908 1.0604933 0.1682712 1.9213682 2.0826333
## AMP_11228639 9.2441257 68.0419853 8.2843640 12.1028460 8.4742292 44.9636426
## X311 X312 X313 X314 X315
## ACR_11231843 9.4925877 3.0539801 16.5850018 3.986366690 14.0521031
## ADAO_11159808 1.6905453 0.5653769 0.4381563 0.007834983 0.6567072
## AGG_11236448 0.8513963 1.5081150 3.8567234 0.392288569 5.6216801
## AHL_11239959 9.0780969 7.6970677 4.3483046 2.071081928 4.5028410
## AJGD_11119689 5.8017148 2.0327891 1.3753797 1.439396938 4.4613088
## AMP_11228639 28.8303098 7.6021069 13.7584895 20.924489732 3.6480345
## X316 X317 X318 X319 X320 X321
## ACR_11231843 7.4049430 0.3028456 13.7117019 8.1660841 13.19886911 17.8335260
## ADAO_11159808 0.5202654 2.5031132 0.8378059 0.3020811 0.07218197 0.1140574
## AGG_11236448 2.1835732 5.3898056 2.6245558 1.1381900 0.05285022 0.9724201
## AHL_11239959 3.9851043 2.7108724 0.7990704 1.3357630 2.79213385 1.2819393
## AJGD_11119689 5.2693486 3.6992390 7.9400047 1.3048949 0.57357780 0.4621902
## AMP_11228639 14.2416241 4.2564746 8.4223137 16.0665829 30.95569242 41.3571903
## X322 X323 X324 X325 X326 X327
## ACR_11231843 1.1715776 2.8906789 5.8848036 2.5171602 24.8705703 0.8022650
## ADAO_11159808 0.8103329 0.5883821 0.9382038 0.1214446 0.2262521 0.6956008
## AGG_11236448 6.9019854 2.6723793 0.8663802 7.7130087 2.3752326 4.5435013
## AHL_11239959 2.5894579 2.4948705 5.9151523 3.3235673 0.7645684 1.0193560
## AJGD_11119689 0.1677105 3.3450485 1.0753882 1.6431958 1.3957595 1.6681565
## AMP_11228639 18.0118633 11.8566067 17.6291154 36.8284536 4.5769514 65.1525810
## X328 X329 X330 X331 X332
## ACR_11231843 2.42420922 19.368988055 0.7525603 3.9629429 0.1518997
## ADAO_11159808 0.02661939 0.002379936 1.0380908 0.4694261 0.2321663
## AGG_11236448 1.70330872 1.381016327 3.8609216 11.5354294 2.2803819
## AHL_11239959 6.93332156 3.456882070 1.5162512 1.2561523 2.0712678
## AJGD_11119689 8.49678981 6.008529503 5.9882392 1.2924799 0.8959029
## AMP_11228639 64.35819291 3.135586452 7.4492678 8.4624308 30.6671089
## X333 X334 X335 X336 X337 X338
## ACR_11231843 3.1612271 1.9525130 11.3414677 2.3828966 2.4897002 1.52363944
## ADAO_11159808 0.3744431 0.5348566 0.6057966 0.9631920 0.7629763 0.04930203
## AGG_11236448 5.5330310 4.2055970 18.4963326 1.3286518 2.7821870 2.88508934
## AHL_11239959 0.3440545 0.8770131 4.5450189 2.4381883 0.9398997 3.65638291
## AJGD_11119689 1.3901661 0.5679558 2.0044142 7.2327525 0.4658782 0.73126479
## AMP_11228639 14.5571460 1.1264306 7.0231739 0.4990224 9.6231026 11.91901382
## X339 X340 X341 X342 X343 X344
## ACR_11231843 3.4700734 25.4142877 6.6240795 4.0316520 0.360054 1.6819318
## ADAO_11159808 0.1778209 0.4192574 0.6817611 0.2559650 2.095070 0.2536700
## AGG_11236448 9.4098213 4.4195116 6.8624776 1.8621123 5.310998 0.1874846
## AHL_11239959 1.0831158 2.5898990 1.2791752 0.7278099 1.634212 2.3196570
## AJGD_11119689 2.2925140 0.6395044 0.4506342 1.3070641 1.151868 1.7485166
## AMP_11228639 4.4280782 1.1406044 4.9663945 24.7404825 14.140511 4.6686136
## X345 X346 X347 X348 X349 X350
## ACR_11231843 7.372784 3.1574862 7.4832447 6.6589537 6.6027316 7.4118830
## ADAO_11159808 0.637686 1.0561880 0.3838186 0.7872085 0.2739714 0.5478482
## AGG_11236448 1.866520 0.5413669 6.9513357 1.9318438 1.4363975 5.7628482
## AHL_11239959 3.011525 1.6195227 1.6337904 0.2771766 2.8620438 2.8980697
## AJGD_11119689 3.399601 1.9653717 1.1430034 1.6740247 1.3173545 2.4158176
## AMP_11228639 16.134162 1.7038167 37.7748773 5.9230075 14.2035836 11.0575510
## X351 X352 X353 X354 X355
## ACR_11231843 3.591789e-01 0.7386980 0.066717668 4.6723432 5.21670459
## ADAO_11159808 5.005781e-01 0.4243604 0.003422015 0.5845329 1.11601782
## AGG_11236448 9.586251e+00 2.8340960 1.700439614 4.5672717 3.62361616
## AHL_11239959 2.845085e+00 1.7205604 3.745760267 2.8239057 2.51623331
## AJGD_11119689 4.014607e-04 1.1548911 0.060699404 1.3999257 0.02210621
## AMP_11228639 3.109086e+01 7.1852090 16.326723830 13.1666892 15.78390072
## X356 X357 X358 X359 X360
## ACR_11231843 9.17126396 1.6990968 8.76846745 2.0808144 0.29367873
## ADAO_11159808 1.55189386 1.2924043 0.09221122 1.0289680 0.09169136
## AGG_11236448 2.71638254 0.3236832 0.22050096 3.9605813 0.07860607
## AHL_11239959 3.54546398 1.9548379 0.19585927 0.8602858 3.07059521
## AJGD_11119689 0.04558142 3.5221383 3.69106596 1.4529946 3.82614053
## AMP_11228639 18.38168650 4.3062201 0.61368231 12.1714935 8.21020057
## X361 X362 X363 X364 X365
## ACR_11231843 13.98285065 4.5115484 4.06381395 12.01751907 10.2641113
## ADAO_11159808 0.04523647 0.4989915 0.04744316 0.15752631 2.1232122
## AGG_11236448 2.13101322 3.7272558 5.11416651 2.42337543 0.4076115
## AHL_11239959 2.79692512 0.8588625 0.22931248 0.41052815 1.1354936
## AJGD_11119689 2.95561111 0.9775363 0.75516116 0.04392191 1.2702708
## AMP_11228639 1.93314720 13.6245868 1.36635045 8.46387899 1.2520937
## X366 X367 X368 X369 X370 X371
## ACR_11231843 1.2433863 0.237207 10.1048106 1.1885266 1.161114 1.4732604
## ADAO_11159808 0.4857401 1.183417 0.2494351 0.2465117 1.267549 1.1118693
## AGG_11236448 5.5489916 3.217711 1.2308394 0.8857493 5.481754 0.3795889
## AHL_11239959 2.0416424 1.543890 2.3398673 0.5438318 3.108199 0.4936366
## AJGD_11119689 1.0257717 3.066184 0.3712139 0.2985001 3.968729 3.0523974
## AMP_11228639 1.8725832 6.998584 11.9561922 9.1589403 2.986988 1.4951705
## X372 X373 X374 X375 X376 X377
## ACR_11231843 2.226283 0.6303585 2.3684273 1.49857078 3.62321741 2.16137457
## ADAO_11159808 1.255284 0.8883771 0.5407307 0.02712975 1.39178882 1.14452678
## AGG_11236448 4.475602 0.1879412 1.5107810 9.68412840 0.08558425 1.40952758
## AHL_11239959 1.917680 0.5286069 0.1456123 0.57136932 0.87325821 0.05234215
## AJGD_11119689 2.019123 1.9463560 4.0602182 0.21594428 5.41680094 3.41086884
## AMP_11228639 4.156780 53.1739739 47.1664594 16.53741783 13.43917900 9.07463475
## X378 X379 X380 X381 X382 X383
## ACR_11231843 12.1231774 0.3573286 0.8549416 6.3498232 1.6759142 4.5722421
## ADAO_11159808 0.5386929 0.1480573 0.2168780 0.2976941 0.3238780 0.3184413
## AGG_11236448 5.4215972 6.2604711 1.7454461 0.6220094 0.3011754 2.0521386
## AHL_11239959 1.0095620 1.5512634 0.8063490 0.9730691 1.0051741 0.7053865
## AJGD_11119689 3.0831632 0.2758462 0.4687653 10.0297407 2.7791707 6.2941515
## AMP_11228639 14.4683935 0.7875425 34.1932873 12.0507516 7.2043588 22.0022401
## X384 X385 X386 X387 X388
## ACR_11231843 15.4419587 8.70393119 17.08545179 0.83822780 1.7811044
## ADAO_11159808 0.3064871 0.55521192 0.06188036 1.30127700 0.1289914
## AGG_11236448 3.3929363 1.60387233 0.60178844 2.96187896 1.3145196
## AHL_11239959 0.1613510 0.06303558 1.38611369 0.61107906 1.8098455
## AJGD_11119689 1.9705500 0.53601831 0.65501580 1.05185627 0.8895971
## AMP_11228639 16.7606420 7.65842652 0.35917826 0.06093996 39.1774178
## X389 X390 X391 X392 X393
## ACR_11231843 3.63996398 4.21539559 3.5037041 3.84029023 2.7514662
## ADAO_11159808 0.43246580 0.02994306 0.3587978 0.01610157 0.1093731
## AGG_11236448 0.83818314 0.95073711 4.2411586 4.72859472 0.2055687
## AHL_11239959 2.39615970 0.20649998 1.8521309 1.42834956 0.6711869
## AJGD_11119689 0.07325307 1.73634212 1.6078250 0.68868328 1.0097763
## AMP_11228639 0.15799054 17.35544456 24.2779640 0.43035483 1.0665819
## X394 X395 X396 X397 X398 X399
## ACR_11231843 0.02432612 3.3019360 0.6178072 2.7362999 0.6312085 3.9810301
## ADAO_11159808 0.35082811 0.1865965 0.3350513 0.1490592 0.1764462 0.0530203
## AGG_11236448 3.89878641 0.4936731 1.5367124 0.6327673 2.7508028 5.8271653
## AHL_11239959 0.11486818 0.1732010 0.1040641 0.6730607 0.1690408 0.1297706
## AJGD_11119689 1.28212483 0.7713180 1.1798139 0.1315944 0.5003738 2.8960695
## AMP_11228639 16.17873195 2.4725937 9.8980723 33.2699882 10.7776720 10.6611539
## X400 X401 X402 X403 X404
## ACR_11231843 4.30033713 1.16901693 0.02281927 0.21960310 1.179263270
## ADAO_11159808 0.45096512 0.09021543 0.05737048 0.08659491 0.008181057
## AGG_11236448 0.09272221 1.38011747 3.60282405 10.55519870 7.641763534
## AHL_11239959 0.36416392 0.16710781 0.09311028 0.36228685 1.621167944
## AJGD_11119689 8.91576020 3.69217344 2.64331881 4.79507728 6.580576771
## AMP_11228639 36.28968889 2.91378082 24.21241915 3.58376529 0.492945912
## X405 X406 X407 X408 X409 X410
## ACR_11231843 3.1396557 2.1683665 0.1250389 2.8256115 5.5947647 3.1100863
## ADAO_11159808 0.4273584 0.2771944 0.5334110 0.1598546 0.3858205 0.1582067
## AGG_11236448 2.7509132 4.4901924 2.6386424 0.8560534 1.6217626 5.0306564
## AHL_11239959 0.9681020 0.4756748 0.2781810 0.3449813 0.8562805 0.5352752
## AJGD_11119689 0.5421299 3.4884060 3.7096413 2.2215766 0.3176552 0.1345843
## AMP_11228639 10.2306950 14.3870829 2.7665920 0.7050920 4.4458964 0.4796000
## X411 X412 X413 X414 X415 X416
## ACR_11231843 0.4734626 5.7096548 1.7173744 5.69739671 6.0164241 2.5347856
## ADAO_11159808 0.1078960 0.8940043 0.2118074 0.14131343 0.4299174 0.6827341
## AGG_11236448 0.4996926 6.6760153 7.2639651 9.10910768 0.7728034 14.5309942
## AHL_11239959 0.0951920 0.6950725 0.5633619 0.09770190 2.5491493 0.8471888
## AJGD_11119689 1.0273650 2.0187771 0.5813334 0.03346851 0.5892873 1.9809981
## AMP_11228639 19.6883796 0.6314290 5.0525320 5.82877160 16.7620028 8.4460127
## X417 X418 X419 X420 X421 X422
## ACR_11231843 0.9190560 0.1120314 0.2530291 3.36161006 0.61098739 3.4261742
## ADAO_11159808 0.4042994 0.1015104 0.1641715 0.34164335 0.06392122 0.3730584
## AGG_11236448 3.5583855 6.3374178 0.1741007 0.66359234 0.19718988 4.7810208
## AHL_11239959 0.4878427 0.8233419 0.2983574 0.10363495 0.45896505 0.3680708
## AJGD_11119689 0.6905091 1.4569077 3.2326004 0.09489134 2.45122724 4.7921832
## AMP_11228639 11.8617528 8.5256958 19.7339069 56.07438587 1.74175493 18.8805353
## X423 X424 X425 X426 X427
## ACR_11231843 5.98141383 0.59938340 2.7341921 2.303690541 1.05861813
## ADAO_11159808 0.08575015 0.03267463 0.5400481 0.003856769 0.05452006
## AGG_11236448 0.54698325 4.54995654 6.1644218 0.653157321 3.87045316
## AHL_11239959 2.03671170 1.15512844 0.2099878 0.972032293 0.37519734
## AJGD_11119689 5.56125854 0.78066563 1.4656292 2.240882442 0.25841834
## AMP_11228639 5.96846568 19.24522118 7.0724204 14.487507009 24.41712360
## X428 X429 X430 X431 X432
## ACR_11231843 0.24995075 1.7660546 1.57118912 2.06752604 2.73636618
## ADAO_11159808 0.06516697 0.4209274 0.34283816 0.08832234 0.39733856
## AGG_11236448 2.50103226 9.5319824 0.15295773 0.81337072 1.07729377
## AHL_11239959 1.65231317 0.5014524 0.05099315 0.06888119 0.07886296
## AJGD_11119689 0.65588138 1.4214110 2.79663670 0.20407543 0.74860380
## AMP_11228639 19.62368972 13.4268022 26.14932446 82.11575613 3.57215962
## X433 X434 X435 X436 X437 X438
## ACR_11231843 0.3971704 1.1240872 7.8058416 0.2860373 5.0488452 2.9077658
## ADAO_11159808 0.4338560 0.2276968 0.5215772 0.7594468 0.1758369 1.3389821
## AGG_11236448 1.1167107 0.1950759 5.8292275 0.3914546 0.2124345 0.4648302
## AHL_11239959 0.7589685 0.1295652 0.4607371 0.2824607 0.2874024 0.9345262
## AJGD_11119689 0.1495800 0.2569443 1.1866951 0.5354896 2.8890761 0.4303639
## AMP_11228639 7.9217845 11.8611717 8.0128693 1.3785162 4.6957924 3.5117605
## X439 X440 X441 X442 X443
## ACR_11231843 0.7475640 0.07616359 5.0735746 6.00405755 5.96237794
## ADAO_11159808 0.3322807 0.29574768 0.2580489 0.05884420 0.03554613
## AGG_11236448 1.2127478 5.48633189 1.7100444 1.23345534 0.81774085
## AHL_11239959 1.0552976 1.26629801 0.6963185 0.09203756 1.55871335
## AJGD_11119689 0.4371993 1.88658001 2.4122339 2.59882023 0.27622353
## AMP_11228639 10.7430628 30.17546195 16.3856587 3.00272383 86.65419234
## X444 X445 X446 X447 X448 X449
## ACR_11231843 5.9558555 4.7932228 1.4213538 1.7124957 0.13953801 1.7473419
## ADAO_11159808 0.7867290 0.2375591 0.6401114 0.5359071 0.06544673 0.6475573
## AGG_11236448 1.7770559 3.3276966 1.7759371 5.8729541 2.46283975 7.5967716
## AHL_11239959 0.2240975 0.6980473 0.1572846 0.5498278 0.19101544 0.1099240
## AJGD_11119689 0.5135189 5.8247671 2.4952644 0.8592207 0.41722224 1.3363842
## AMP_11228639 13.0206981 23.8205253 22.9303584 38.6428509 12.78483804 1.6502920
## X450 X451 X452 X453 X454
## ACR_11231843 0.09644472 0.1779691 2.2905939 1.85104372 0.32301662
## ADAO_11159808 0.84555262 0.0800038 0.5133744 0.85873960 0.51399803
## AGG_11236448 2.65962642 0.1296346 1.8061166 3.20909638 0.29920766
## AHL_11239959 0.94384548 1.0420692 0.4324950 0.03046616 0.03779672
## AJGD_11119689 2.02579633 0.0304233 1.7086539 1.77783344 0.33679009
## AMP_11228639 4.25153894 18.0082387 16.9204894 13.44245276 28.54682363
## X455 X456 X457 X458 X459 X460
## ACR_11231843 4.21980130 2.5827710 2.37474050 2.8558115 3.7578991 1.3699196
## ADAO_11159808 0.07301315 0.1292316 0.23353910 0.6059223 0.3525472 0.3284839
## AGG_11236448 1.76609610 4.0283101 2.68889285 5.1444444 0.5805841 0.6531493
## AHL_11239959 1.02617704 0.6726032 0.04039316 1.9829224 0.5479089 0.1204094
## AJGD_11119689 0.25753796 0.8938505 0.41919061 1.4629935 0.5161031 0.6202013
## AMP_11228639 3.26978709 16.4744810 36.10439988 3.5903544 12.7169771 24.5197832
## X461 X462 X463 X464 X465 X466
## ACR_11231843 1.2117059 0.7480923 15.21042240 4.3575008 4.04697330 2.6545317
## ADAO_11159808 0.6602280 0.1822880 0.06374832 0.3118209 0.51729799 0.6438665
## AGG_11236448 0.6248186 8.2796934 2.32674759 2.5848339 4.42916370 0.1979652
## AHL_11239959 0.0330523 0.4901177 0.63444898 0.8258034 0.02232097 0.3104947
## AJGD_11119689 8.8914160 2.3021882 2.03235272 1.2078564 1.90485330 1.0240980
## AMP_11228639 16.9472960 2.0922122 3.69846096 39.3293051 2.66581814 5.8968017
## X467 X468 X469 X470 X471 X472
## ACR_11231843 1.7309899 1.4066905 0.06545876 3.0106503 2.3203072 4.0214309
## ADAO_11159808 0.0911962 0.1038657 0.16279743 1.3659718 0.1844041 0.0284395
## AGG_11236448 0.1092725 2.5959079 1.05933343 0.3745092 3.0631672 2.5686140
## AHL_11239959 0.1808797 0.7054476 0.01446630 2.6825362 0.3477136 1.8902592
## AJGD_11119689 3.5762388 1.1112865 1.00392098 0.4650652 0.5122473 2.2102840
## AMP_11228639 11.1945644 18.9597862 2.93204549 37.4800277 4.8883690 11.9982069
## X473 X474 X475 X476 X477 X478
## ACR_11231843 0.6157965 5.90435293 1.28498899 1.5905659 0.3947477 0.39491075
## ADAO_11159808 0.6032443 1.33715813 0.20732848 0.1290961 0.3697631 0.53614415
## AGG_11236448 5.1433290 0.08151819 4.80912184 8.4908668 4.0288281 14.27906879
## AHL_11239959 0.2294777 0.85067112 0.42182514 0.4342760 0.7703858 0.07511138
## AJGD_11119689 2.1864561 1.04038158 0.07550684 3.0983477 7.3318743 0.47104296
## AMP_11228639 2.0684866 0.04048644 31.96350278 0.9582038 22.4130041 63.80501519
## X479 X480 DDclust_PER_SatO2
## ACR_11231843 2.9587937 0.4930386 1
## ADAO_11159808 0.2510000 1.3184045 1
## AGG_11236448 0.4329341 10.3397142 1
## AHL_11239959 1.2055245 0.1083238 1
## AJGD_11119689 3.2897818 5.1960287 1
## AMP_11228639 13.8662683 0.8309796 1## Mean by groups
rp_tbl_PER <- aggregate(plotting_PER, by = list(plotting_PER$DDclust_PER_SatO2), mean)
row.names(rp_tbl_PER) <- paste0("Group",rp_tbl_PER$DDclust_PER_SatO2)
rp_tbl_PER <- rp_tbl_PER %>%
select(starts_with('X'))
rp_tbl_PER <- data.frame(t(rp_tbl_PER))
head(rp_tbl_PER)## Group1 Group2
## X1 107.47746 329.8271
## X2 79.43177 488.4544
## X3 71.33078 442.6777
## X4 52.06231 257.4408
## X5 43.83955 249.9436
## X6 36.75993 122.9238# Create plotting data-frame
PER_values_by_group <- data.frame("value_PER" = c(rp_tbl_PER$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 = data.frame(matrix(ncol = 3 , nrow = 3))
colnames(rand_index_table_SatO2) <- c("DDclust_ACF_SatO2", "DDclust_EUCL_SatO2", "DDclust_PER_SatO2")
rownames(rand_index_table_SatO2) <- c("DDclust_ACF_SatO2", "DDclust_EUCL_SatO2", "DDclust_PER_SatO2")
cluster_study_SatO2 <- list(DDclust_ACF_SatO2, DDclust_EUCL_SatO2, DDclust_PER_SatO2)for (i in c(1:length(cluster_study_SatO2))) {
for (j in c(1:length(cluster_study_SatO2))){
rand_index_table_SatO2[i,j] <- adjustedRandIndex(cluster_study_SatO2[[i]], cluster_study_SatO2[[j]])
}}head(rand_index_table_SatO2)## DDclust_ACF_SatO2 DDclust_EUCL_SatO2 DDclust_PER_SatO2
## DDclust_ACF_SatO2 1.00000000 0.010991521 0.115064289
## DDclust_EUCL_SatO2 0.01099152 1.000000000 -0.007839038
## DDclust_PER_SatO2 0.11506429 -0.007839038 1.000000000write.csv(cluster_study_SatO2, "../../data/clusters/cluster_study_SatO2.csv")