Clustering_Time_Series-TSClust-Study-02-HRScaled
Gonzalo Aris 16021
2023-03-05
Libraries
library(TSA) # time series
library(TSclust)
library(tidyr)
library(readr)
library(ggplot2) # ggplot graphs
library(knitr)
library(readxl)
library(xlsx)
library(openxlsx)
library(reactable) # reactable(df)
library(naniar) # miss_case_summary
library(dplyr)
## KNN imputation
library(caret)
library(RANN)
# CLustering
library(factoextra) # Clustering visualization
library(cluster) # Clustering algorithms
library(dendextend) # For comparing two dendrograms
library(corrplot) # Corelation between dendrograms
library(tidyverse) # Data manupulation
library(NbClust) # Determine optimal no. of clusters [not working...]
library(TSclust)
library(mclust) # Adjusted Rand index
#RandomForest
library(randomForest) # RandomForest Discrete Classification
library(imbalance) # To create a more balanced datasetFunctions
source("../../scripts/useful-functions/get_column_position.R")
# In a normal script it will be: source("./scripts/useful-functions/get_column_position.R")Reading Data
Time Series Data: FC_scaled Heart Rate
FC_TS_HR_P2 = data.frame(read_xlsx("../../data/clean-data/BoxBasedImputation/FC_valid_patients_input_P2.xlsx", sheet = "FC_valid_patients_input_P2" ))
FC_scaled_TS_HR_P2<- as.data.frame(lapply(FC_TS_HR_P2, scale)) # Scaled Data
# First patients with OAF
name_patients_DETERIORO_OAF_0 <- data.frame(read_csv("../../data/clean-data/name_patients_DETERIORO_OAF_0.csv"))
name_patients_DETERIORO_OAF_0 <- name_patients_DETERIORO_OAF_0$x
name_patients_DETERIORO_OAF_0_8 <- data.frame(read_csv("../../data/clean-data/name_patients_DETERIORO_OAF_0_8.csv"))
name_patients_DETERIORO_OAF_0_8 <- name_patients_DETERIORO_OAF_0_8$x
## Deterioro and Not deterioro
file_patient_name_NO_DETERIORO <- data.frame(read_csv("../../data/info-patients/file_patient_name_NO_DETERIORO.csv"))
file_patient_name_NO_DETERIORO <- file_patient_name_NO_DETERIORO$x
file_patient_name_DETERIORO <- data.frame(read_csv("../../data/info-patients/file_patient_name_DETERIORO.csv"))
file_patient_name_DETERIORO <- file_patient_name_DETERIORO$xDescriptive Data for Discriminant analysis
valid_patients_P2 <- data.frame(read_xlsx("../../data/clean-data/valid_patients_P2.xlsx"))
valid_patients_P2 <- valid_patients_P2$x
valid_patients_P2 <- valid_patients_P2[! valid_patients_P2 %in% union(name_patients_DETERIORO_OAF_0,name_patients_DETERIORO_OAF_0_8)]
file_patient_name <- data.frame(read_csv("../../data/clean-data/file_patient_name.csv", show_col_types = FALSE))
file_patient_name <- file_patient_name$x
df1 <- data.frame(read_xlsx("../../data/clean-data/descriptive-data/descriptive_data.xlsx"))
rownames(df1) <- file_patient_name
df1 <- df1[valid_patients_P2,]df_descriptive <- data.frame(read_xlsx("../../data/clean-data/descriptive-data/descriptive_data_imputed.xlsx"), row.names = TRUE)
rownames(df_descriptive) <- file_patient_namedf_descriptive <- df_descriptive %>% select(-c(FR_8_16h, FR_16_24h, FLUJO2_8_16h,FLUJO2_16_24h,SCORE_WOOD_DOWNES_24H,SAPI_16_24h, SAPI_8_16h))# Class
pos_1 = get_column_position(df_descriptive,"SAPI_0_8h")
pos_2 = get_column_position(df_descriptive,"PAUSAS_APNEA")
df_descriptive[,c(pos_1:pos_2)] <- lapply(df_descriptive[,c(pos_1:pos_2)], as.factor)
#lapply(df_descriptive,class)
df_descriptive <- df_descriptive[valid_patients_P2,]Create a dataframe with ACF [Heart Rate ]
FC_scaled_TS_HR_P2 <- FC_scaled_TS_HR_P2[,valid_patients_P2]Restando Media
#FC_scaled_TS_HR_P2 = data.frame(scale(FC_scaled_TS_HR_P2))dimension_col <- dim(FC_scaled_TS_HR_P2)[2]
dimension_row <- 480 #lag.max -1
# Heart Rate
FC_scaled_TS_HR_P2_ACF <- data.frame(matrix(nrow = dimension_row, ncol = dimension_col))
colnames(FC_scaled_TS_HR_P2_ACF) <- names(FC_scaled_TS_HR_P2)[1:dimension_col]for (i in names(FC_scaled_TS_HR_P2_ACF)) {
acf_result_FC_scaled <- forecast::Acf(FC_scaled_TS_HR_P2[[i]], lag.max = (dimension_row - 1), plot = FALSE, drop.lag.0 = FALSE)
FC_scaled_TS_HR_P2_ACF[, i] <- acf_result_FC_scaled$acf
}Create a dataframe with peridiogram
# Generar un dataset con varias series temporales
df <- FC_scaled_TS_HR_P2
# Crear una matriz para almacenar los periodogramas
pg_mat <- data.frame(matrix(nrow = nrow(df), ncol = ncol(df)))
colnames(pg_mat) = colnames(FC_scaled_TS_HR_P2)
# Calcular el periodograma de cada serie temporal y almacenarlo en la matriz
library(stats)
# Calcular el periodograma de cada serie temporal y almacenarlo en la matriz
for (i in colnames(FC_scaled_TS_HR_P2)) {
pg_mat[,i] <- stats::spec.pgram(FC_scaled_TS_HR_P2[,i], plot = FALSE)$spec
}TsClust Comprobation
datos <- FC_scaled_TS_HR_P2diss.ACF Computes the dissimilarity between two time
series as the distance between their estimated simple (ACF) or partial
(PACF) autocorrelation coefficients
DD_ACF <- diss(datos, "ACF", lag.max = 50)
DD_ACF_matrix <- as.matrix(DD_ACF)diss.EUCL
DD_EUCL <- diss(datos, "EUCL")
DD_EUCL_matrix <- as.matrix(DD_EUCL)diss.PER
DD_PER <- diss(datos, "PER")
DD_PER_matrix <- as.matrix(DD_PER)Euclidean Distance first 50 ACF
datos_ACF = t(FC_scaled_TS_HR_P2_ACF[c(1:51),])
distance <- dist(t(FC_scaled_TS_HR_P2_ACF[c(1:51),]), method = "euclidean")
distance_matrix_ACF <- as.matrix(distance)Euclidean Distance
datos_EUCL <- t(datos)
distance <- dist(datos_EUCL, method = "euclidean")
distance_matrix_EUCL <- as.matrix(distance)Eculidean PER Distance
datos_PER <- t(pg_mat)
distance_PER <- dist(t(pg_mat), method = "euclidean")
distance_matrix_PER <- as.matrix(distance_PER)
distance_matrix_PER_NORM = distance_matrix_PER / 480TSCLust in Action
ACF TSclust
# DD_ACF <- diss(datos, "ACF", lag.max = 50)Agnes study
To find which hierarchical clustering methods that can identify stronger clustering structures. Here we see that Ward’s method identifies the strongest clustering structure of the four methods assessed.
#method to assess
m <- c("average", "single","complete","ward")
names(m) <- c("average", "single","complete","ward.D2")
#function to compute coefficient
ac <- function(x){agnes(datos_ACF, method = x)$ac}
map_dbl(m,ac)## average single complete ward.D2
## 0.8704366 0.6767717 0.9196194 0.9584022NbClust study
This package will help us identify the optimum number of clusters
based our criteria in the silhouette index
diss_matrix<- DD_ACF
res<-NbClust(datos_ACF, diss=diss_matrix, distance = NULL, min.nc=2, max.nc=5, method = "ward.D2", index = "silhouette")
res$All.index## 2 3 4 5
## 0.4944 0.3520 0.3354 0.2960res$Best.nc## Number_clusters Value_Index
## 2.0000 0.4944#res$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_FC_scaled <- cutree( hclust(DD_ACF, "ward.D2"), k = 2)fviz_cluster(list(data = t(datos), cluster = DDclust_ACF_FC_scaled))
fviz_silhouette(silhouette(DDclust_ACF_FC_scaled, DD_ACF))## cluster size ave.sil.width
## 1 1 37 0.45
## 2 2 21 0.58
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_FC_scaled[DDclust_ACF_FC_scaled == 2]),names(DDclust_ACF_FC_scaled[DDclust_ACF_FC_scaled == 1]))
fviz_dend(hcintper_ACF, k = 2,
k_colors = c("blue", "green3"),
label_cols = as.vector(COLOR_ACF[,order_ACF]), cex = 0.6) 
n1 = length(intersect(file_patient_name_DETERIORO,names_1))
n2 = length(intersect(file_patient_name_DETERIORO,names_2))
n3 = length(intersect(file_patient_name_NO_DETERIORO,names_1))
n4 = length(intersect(file_patient_name_NO_DETERIORO,names_2))
conttingency_table <- data.frame("CLust1" = c(n1,n3), "Clust2" = c(n2,n4))
rownames(conttingency_table) <- c("DETERIORO","NO DETERIORO")
knitr::kable(conttingency_table, align = "lccrr")| CLust1 | Clust2 | |
|---|---|---|
| DETERIORO | 4 | 2 |
| NO DETERIORO | 33 | 19 |
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 | 0.1081081 | 0.0952381 |
| NO DETERIORO | 0.8918919 | 0.9047619 |
Random Forest: Discriminant TSCLust ACF
data_frame1_ACF = data.frame("CLUSTER" = DDclust_ACF_FC_scaled)
data_frame2 = df_descriptive
data_frame_merge_ACF <-
merge(data_frame1_ACF, data_frame2, by = 'row.names', all = TRUE)
data_frame_merge_ACF <- data_frame_merge_ACF[, 2:dim(data_frame_merge_ACF)[2]]
data_frame_merge_ACF$CLUSTER = factor(data_frame_merge_ACF$CLUSTER)table(data_frame_merge_ACF$CLUSTER)##
## 1 2
## 37 21data_frame_merge_ACF[,c(1:dim(data_frame_merge_ACF)[2])]<- lapply(data_frame_merge_ACF[,c(1:dim(data_frame_merge_ACF)[2])], as.numeric)
head(data_frame_merge_ACF)## CLUSTER EDAD PESO EG FR_0_8h FLUJO2_0_8H DIAS_GN DIAS_O2_TOTAL DIAS_OAF
## 1 1 10.0 8.20 41 48 2.00 3 3 0
## 2 1 13.0 7.78 40 56 2.00 2 2 0
## 3 1 3.1 5.66 37 44 1.00 4 4 0
## 4 1 5.3 8.44 38 65 0.40 3 3 0
## 5 1 15.0 7.00 34 37 2.00 4 4 0
## 6 2 1.6 3.80 37 42 0.94 4 4 0
## SAPI_0_8h SCORE_CRUCES_INGRESO SCORE_WOOD_DOWNES_INGRESO SEXO PALIVIZUMAB LM
## 1 3 3 6 1 1 2
## 2 4 4 8 1 1 1
## 3 3 3 7 1 1 2
## 4 4 3 6 1 1 2
## 5 1 3 6 1 2 1
## 6 2 4 7 1 1 2
## DERMATITIS ALERGIAS TABACO ENFERMEDAD_BASE RADIOGRAFIA ANALITICA SUERO
## 1 1 2 1 1 1 1 1
## 2 1 2 2 2 1 1 2
## 3 1 1 1 1 1 1 1
## 4 1 1 1 1 1 1 1
## 5 1 1 2 2 1 1 2
## 6 1 1 2 2 1 1 1
## ETIOLOGIA PREMATURIDAD ALIMENTACION SNG GN_INGRESO OAF OAF_AL_INGRESO
## 1 2 1 2 1 2 1 1
## 2 1 1 1 1 2 1 1
## 3 2 1 2 1 2 1 1
## 4 2 1 2 1 1 1 1
## 5 2 2 2 1 2 1 1
## 6 1 1 2 1 1 1 1
## OAF_TRAS_INGRESO UCIP DETERIORO PAUSAS_APNEA
## 1 1 1 1 1
## 2 1 1 1 1
## 3 1 1 1 1
## 4 1 1 1 1
## 5 1 1 1 1
## 6 1 1 1 1data_frame_merge_ACF$CLUSTER <- factor(data_frame_merge_ACF$CLUSTER)
newSMOTE_ACF <-data_frame_merge_ACFset.seed(123)
pos_1 = get_column_position(newSMOTE_ACF, "SAPI_0_8h")
pos_2 = get_column_position(newSMOTE_ACF, "PAUSAS_APNEA")
col_names_factor <- names(newSMOTE_ACF[pos_1:pos_2])
newSMOTE_ACF[col_names_factor] <- lapply(newSMOTE_ACF[col_names_factor] , factor)
RF_ACF <- randomForest(CLUSTER ~ ., data = newSMOTE_ACF)
print(RF_ACF)##
## Call:
## randomForest(formula = CLUSTER ~ ., data = newSMOTE_ACF)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 5
##
## OOB estimate of error rate: 48.28%
## Confusion matrix:
## 1 2 class.error
## 1 26 11 0.2972973
## 2 17 4 0.8095238Importance
kable(RF_ACF$importance[order(RF_ACF$importance, decreasing = TRUE),])| x | |
|---|---|
| EDAD | 2.6328715 |
| SCORE_WOOD_DOWNES_INGRESO | 2.5338316 |
| PESO | 2.3967813 |
| SCORE_CRUCES_INGRESO | 2.3630299 |
| FR_0_8h | 1.7007907 |
| SAPI_0_8h | 1.6392267 |
| DIAS_O2_TOTAL | 1.5501284 |
| EG | 1.4672375 |
| DIAS_GN | 1.3476104 |
| FLUJO2_0_8H | 1.3447355 |
| RADIOGRAFIA | 1.0722420 |
| SEXO | 0.7386009 |
| ETIOLOGIA | 0.7294733 |
| ALIMENTACION | 0.6162708 |
| LM | 0.4676251 |
| ANALITICA | 0.3376106 |
| DIAS_OAF | 0.3297077 |
| PREMATURIDAD | 0.3288944 |
| TABACO | 0.3274340 |
| ENFERMEDAD_BASE | 0.3257954 |
| SUERO | 0.3135917 |
| ALERGIAS | 0.3089164 |
| GN_INGRESO | 0.2477224 |
| SNG | 0.2476302 |
| DERMATITIS | 0.1752274 |
| OAF | 0.1405188 |
| PALIVIZUMAB | 0.1345768 |
| OAF_TRAS_INGRESO | 0.1273862 |
| DETERIORO | 0.1157549 |
| PAUSAS_APNEA | 0.0759802 |
| UCIP | 0.0517007 |
| OAF_AL_INGRESO | 0.0000000 |
Importance of first 50 ACF
data_frame1_ACF = data.frame("CLUSTER" = DDclust_ACF_FC_scaled)
data_frame2_ACF = data.frame(t(FC_scaled_TS_HR_P2_ACF[c(1:51),]))
data_frame_merge_ACF <-
merge(data_frame1_ACF, data_frame2_ACF, by = 'row.names', all = TRUE)
data_frame_merge_ACF <- data_frame_merge_ACF[, 2:dim(data_frame_merge_ACF)[2]]set.seed(123)
data_frame_merge_ACF$CLUSTER <- as.factor(data_frame_merge_ACF$CLUSTER)
RF_0_ACF <- randomForest(CLUSTER ~ ., data = data_frame_merge_ACF)
print(RF_0_ACF)##
## Call:
## randomForest(formula = CLUSTER ~ ., data = data_frame_merge_ACF)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 7
##
## OOB estimate of error rate: 1.72%
## Confusion matrix:
## 1 2 class.error
## 1 37 0 0.00000000
## 2 1 20 0.04761905plot(RF_0_ACF$importance, type = "h")
### ACF by clusters
plot_data_ACF <- data.frame(datos_ACF)
cluster_data_ACF <- data.frame(DDclust_ACF_FC_scaled)
plotting_ACF <- cbind(plot_data_ACF, cluster_data_ACF)
head(plotting_ACF)## X1 X2 X3 X4 X5 X6 X7
## ACR_11231843 1 0.5747954 0.4244149 0.3898310 0.3054550 0.2987537 0.2466085
## ADAO_11159808 1 0.6805727 0.5935279 0.5085316 0.4365390 0.3660983 0.3061974
## AGG_11236448 1 0.7659893 0.6522822 0.5752187 0.5026580 0.4312281 0.4003839
## AHL_11239959 1 0.7330013 0.6576631 0.6158813 0.5836972 0.5097892 0.4615090
## AJGD_11119689 1 0.4856503 0.4165173 0.3766304 0.3176037 0.3071462 0.2873193
## AMP_11228639 1 0.6595950 0.6178051 0.6037129 0.5651124 0.5755787 0.5525003
## X8 X9 X10 X11 X12 X13
## ACR_11231843 0.1833401 0.1800060 0.1590625 0.1193108 0.1028016 0.08907378
## ADAO_11159808 0.2645815 0.2238202 0.1822452 0.1658125 0.1617351 0.14100383
## AGG_11236448 0.3616214 0.3484616 0.3680116 0.3937240 0.3530302 0.34635666
## AHL_11239959 0.4253346 0.3663603 0.3350366 0.3211704 0.3012808 0.29708129
## AJGD_11119689 0.2504552 0.2382239 0.2213956 0.1841389 0.1555994 0.19398733
## AMP_11228639 0.5484309 0.5151089 0.5260231 0.5356568 0.5412524 0.53812733
## X14 X15 X16 X17 X18
## ACR_11231843 0.02692387 0.02098007 0.01292424 0.0006154294 0.004233393
## ADAO_11159808 0.13026706 0.13321015 0.13066704 0.1285902414 0.110254753
## AGG_11236448 0.35754880 0.32532530 0.27518679 0.2299503431 0.206868669
## AHL_11239959 0.26719489 0.25676612 0.24100162 0.2420480921 0.198732377
## AJGD_11119689 0.15437946 0.18278084 0.17649073 0.1750699756 0.190130736
## AMP_11228639 0.53846799 0.54239706 0.55188989 0.5360942030 0.531989902
## X19 X20 X21 X22 X23
## ACR_11231843 -0.006757205 -0.007036055 -0.01496791 -0.02636549 -0.001402886
## ADAO_11159808 0.127414548 0.087625607 0.07760646 0.09671878 0.104156536
## AGG_11236448 0.184265903 0.151694667 0.14302509 0.12456011 0.131032068
## AHL_11239959 0.197691766 0.174912058 0.19319050 0.21205775 0.206312141
## AJGD_11119689 0.148089255 0.169290584 0.18741791 0.23496989 0.197737591
## AMP_11228639 0.471925620 0.484153622 0.49282071 0.49739289 0.498560780
## X24 X25 X26 X27 X28
## ACR_11231843 0.01324269 0.02086305 0.02388871 -0.01035748 -0.03510893
## ADAO_11159808 0.10125899 0.07996171 0.07672970 0.09223638 0.06817663
## AGG_11236448 0.15374218 0.13654930 0.11173266 0.10480140 0.09948863
## AHL_11239959 0.18457331 0.17656272 0.19223655 0.17530399 0.13561442
## AJGD_11119689 0.19868995 0.18905268 0.22099258 0.18826589 0.20099552
## AMP_11228639 0.47651016 0.47433498 0.49124269 0.46617148 0.47121446
## X29 X30 X31 X32 X33
## ACR_11231843 -0.04784269 -0.05506440 -0.008593307 0.04976843 0.09740572
## ADAO_11159808 0.09274641 0.06786673 0.071169346 0.07319753 0.07839764
## AGG_11236448 0.06725227 0.07242530 0.075139440 0.09715155 0.11831388
## AHL_11239959 0.14198217 0.15516364 0.104568650 0.08413169 0.10251633
## AJGD_11119689 0.18028969 0.14769550 0.165832019 0.11516172 0.12166889
## AMP_11228639 0.49358464 0.45935156 0.460468415 0.43672572 0.43495816
## X34 X35 X36 X37 X38 X39
## ACR_11231843 0.06713914 0.04049820 0.01295611 0.03353650 0.02670075 0.05435805
## ADAO_11159808 0.04368361 0.05899972 0.06957130 0.06502349 0.04428139 0.08720689
## AGG_11236448 0.15119247 0.16481099 0.17926504 0.17486282 0.17939105 0.16038387
## AHL_11239959 0.09851785 0.13082361 0.13043217 0.13487428 0.11851440 0.13412057
## AJGD_11119689 0.13947771 0.13992780 0.11603972 0.12556575 0.13822745 0.08786577
## AMP_11228639 0.42293683 0.41076414 0.39748507 0.38724889 0.38415061 0.37327204
## X40 X41 X42 X43 X44
## ACR_11231843 0.05163509 0.04581221 0.03625811 0.006093607 0.015911426
## ADAO_11159808 0.08245975 0.07325789 0.02199331 0.010335726 0.005374176
## AGG_11236448 0.14700455 0.14244461 0.15426774 0.162140790 0.167102275
## AHL_11239959 0.14253952 0.14241159 0.13874827 0.152605118 0.122308403
## AJGD_11119689 0.11241510 0.12355936 0.14482627 0.151343806 0.125769167
## AMP_11228639 0.38356428 0.34057877 0.35432214 0.350061870 0.362196920
## X45 X46 X47 X48 X49
## ACR_11231843 0.065718135 -0.044529286 0.02727482 0.03598471 0.01538854
## ADAO_11159808 0.008207619 0.005354707 0.04384913 0.07181361 0.07248583
## AGG_11236448 0.193588145 0.189538586 0.17553872 0.17449402 0.19356802
## AHL_11239959 0.116989766 0.097048192 0.09234721 0.08137636 0.05817498
## AJGD_11119689 0.114462985 0.147833838 0.11542335 0.14404906 0.07111346
## AMP_11228639 0.348115576 0.328500927 0.32673600 0.28805596 0.28570880
## X50 X51 DDclust_ACF_FC_scaled
## ACR_11231843 0.03556238 0.03345494 1
## ADAO_11159808 0.09748668 0.12009315 1
## AGG_11236448 0.19300176 0.17783030 1
## AHL_11239959 0.05753825 0.06746329 1
## AJGD_11119689 0.10372669 0.07286803 1
## AMP_11228639 0.27435691 0.26466781 2## Mean by groups
rp_tbl_ACF <- aggregate(plotting_ACF, by = list(plotting_ACF$DDclust_ACF_FC_scaled), mean)
row.names(rp_tbl_ACF) <- paste0("Group",rp_tbl_ACF$DDclust_ACF_FC_scaled)
rp_tbl_ACF <- rp_tbl_ACF %>%
select(starts_with('X'))
rp_tbl_ACF <- data.frame(t(rp_tbl_ACF))
head(rp_tbl_ACF)## Group1 Group2
## X1 1.0000000 1.0000000
## X2 0.7226921 0.8428105
## X3 0.6451763 0.7923087
## X4 0.5790438 0.7612588
## X5 0.5311166 0.7314445
## X6 0.4874229 0.7109206# 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)$ac}
map_dbl(m,ac)## average single complete ward.D2
## 0.6500106 0.4878584 0.7349317 0.9441717NbClust study
This package will help us identify the optimum number of clusters
based our criteria in the silhouette index
diss_matrix<- DD_EUCL
res<-NbClust(datos, diss=diss_matrix, distance = NULL, min.nc=2, max.nc=5, method = "ward.D2", index = "silhouette")
res$All.index## 2 3 4 5
## 0.0671 0.0702 0.0615 0.0707res$Best.nc## Number_clusters Value_Index
## 5.0000 0.0707#res$Best.partitionhcintper_EUCL <- hclust(DD_EUCL, "ward.D2")
fviz_dend(hcintper_EUCL, palette = "jco",
rect = TRUE, show_labels = FALSE, k = 5)
DDclust_EUCL_FC_scaled <- cutree( hclust(DD_EUCL, "ward.D2"), k = 5)fviz_cluster(list(data = t(datos), cluster = DDclust_EUCL_FC_scaled))
fviz_silhouette(silhouette(DDclust_EUCL_FC_scaled, DD_EUCL))## cluster size ave.sil.width
## 1 1 12 0.12
## 2 2 10 0.03
## 3 3 18 0.08
## 4 4 12 -0.02
## 5 5 6 0.18
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_FC_scaled[DDclust_EUCL_FC_scaled == 2]),names(DDclust_EUCL_FC_scaled[DDclust_EUCL_FC_scaled == 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 | 1 | 5 |
| NO DETERIORO | 21 | 31 |
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 | 0.0454545 | 0.1388889 |
| NO DETERIORO | 0.9545455 | 0.8611111 |
Random Forest: Discriminant TSCLust EUCL
data_frame1_EUCL = data.frame("CLUSTER" = DDclust_EUCL_FC_scaled)
data_frame2 = df_descriptive
data_frame_merge_EUCL <-
merge(data_frame1_EUCL, data_frame2, by = 'row.names', all = TRUE)
data_frame_merge_EUCL <- data_frame_merge_EUCL[, 2:dim(data_frame_merge_EUCL)[2]]
data_frame_merge_EUCL$CLUSTER = factor(data_frame_merge_EUCL$CLUSTER)table(data_frame_merge_EUCL$CLUSTER)##
## 1 2
## 22 36data_frame_merge_EUCL[,c(1:dim(data_frame_merge_EUCL)[2])]<- lapply(data_frame_merge_EUCL[,c(1:dim(data_frame_merge_EUCL)[2])], as.numeric)
head(data_frame_merge_EUCL)## CLUSTER EDAD PESO EG FR_0_8h FLUJO2_0_8H DIAS_GN DIAS_O2_TOTAL DIAS_OAF
## 1 1 10.0 8.20 41 48 2.00 3 3 0
## 2 1 13.0 7.78 40 56 2.00 2 2 0
## 3 2 3.1 5.66 37 44 1.00 4 4 0
## 4 1 5.3 8.44 38 65 0.40 3 3 0
## 5 2 15.0 7.00 34 37 2.00 4 4 0
## 6 1 1.6 3.80 37 42 0.94 4 4 0
## SAPI_0_8h SCORE_CRUCES_INGRESO SCORE_WOOD_DOWNES_INGRESO SEXO PALIVIZUMAB LM
## 1 3 3 6 1 1 2
## 2 4 4 8 1 1 1
## 3 3 3 7 1 1 2
## 4 4 3 6 1 1 2
## 5 1 3 6 1 2 1
## 6 2 4 7 1 1 2
## DERMATITIS ALERGIAS TABACO ENFERMEDAD_BASE RADIOGRAFIA ANALITICA SUERO
## 1 1 2 1 1 1 1 1
## 2 1 2 2 2 1 1 2
## 3 1 1 1 1 1 1 1
## 4 1 1 1 1 1 1 1
## 5 1 1 2 2 1 1 2
## 6 1 1 2 2 1 1 1
## ETIOLOGIA PREMATURIDAD ALIMENTACION SNG GN_INGRESO OAF OAF_AL_INGRESO
## 1 2 1 2 1 2 1 1
## 2 1 1 1 1 2 1 1
## 3 2 1 2 1 2 1 1
## 4 2 1 2 1 1 1 1
## 5 2 2 2 1 2 1 1
## 6 1 1 2 1 1 1 1
## OAF_TRAS_INGRESO UCIP DETERIORO PAUSAS_APNEA
## 1 1 1 1 1
## 2 1 1 1 1
## 3 1 1 1 1
## 4 1 1 1 1
## 5 1 1 1 1
## 6 1 1 1 1data_frame_merge_EUCL$CLUSTER <- factor(data_frame_merge_EUCL$CLUSTER)
newSMOTE_EUCL <- data_frame_merge_EUCLset.seed(123)
pos_1 = get_column_position(newSMOTE_EUCL, "SAPI_0_8h")
pos_2 = get_column_position(newSMOTE_EUCL, "PAUSAS_APNEA")
col_names_factor <- names(newSMOTE_EUCL[pos_1:pos_2])
newSMOTE_EUCL[col_names_factor] <- lapply(newSMOTE_EUCL[col_names_factor] , factor)
RF_EUCL <- randomForest(CLUSTER ~ ., data = newSMOTE_EUCL)
print(RF_EUCL)##
## Call:
## randomForest(formula = CLUSTER ~ ., data = newSMOTE_EUCL)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 5
##
## OOB estimate of error rate: 43.1%
## Confusion matrix:
## 1 2 class.error
## 1 6 16 0.7272727
## 2 9 27 0.2500000Importance
kable(RF_EUCL$importance[order(RF_EUCL$importance, decreasing = TRUE),])| x | |
|---|---|
| FR_0_8h | 3.2107589 |
| SCORE_CRUCES_INGRESO | 2.8801769 |
| SCORE_WOOD_DOWNES_INGRESO | 2.8575468 |
| PESO | 2.3468291 |
| EDAD | 2.3045016 |
| FLUJO2_0_8H | 1.7144879 |
| DIAS_GN | 1.5597942 |
| EG | 1.4840537 |
| DIAS_O2_TOTAL | 1.2860841 |
| SAPI_0_8h | 1.0594950 |
| ALERGIAS | 0.7448684 |
| ETIOLOGIA | 0.6850869 |
| RADIOGRAFIA | 0.5925099 |
| TABACO | 0.4099089 |
| LM | 0.3672648 |
| ENFERMEDAD_BASE | 0.3633714 |
| ALIMENTACION | 0.3546204 |
| SEXO | 0.3073980 |
| ANALITICA | 0.3038037 |
| PREMATURIDAD | 0.2709365 |
| DIAS_OAF | 0.2251811 |
| GN_INGRESO | 0.2208036 |
| SUERO | 0.1705668 |
| OAF | 0.1271875 |
| DERMATITIS | 0.1168093 |
| PALIVIZUMAB | 0.1089798 |
| PAUSAS_APNEA | 0.1049091 |
| OAF_TRAS_INGRESO | 0.0995509 |
| UCIP | 0.0855689 |
| SNG | 0.0678775 |
| DETERIORO | 0.0667034 |
| OAF_AL_INGRESO | 0.0000000 |
Importance of the TS-data
data_frame1_EUCL = data.frame("CLUSTER" = DDclust_EUCL_FC_scaled)
data_frame2_EUCL = data.frame(datos_EUCL)
data_frame_merge_EUCL <-
merge(data_frame1_EUCL, data_frame2_EUCL, by = 'row.names', all = TRUE)
data_frame_merge_EUCL <- data_frame_merge_EUCL[, 2:dim(data_frame_merge_EUCL)[2]]set.seed(123)
data_frame_merge_EUCL$CLUSTER <- as.factor(data_frame_merge_EUCL$CLUSTER)
RF_0_EUCL <- randomForest(CLUSTER ~ ., data = data_frame_merge_EUCL)
print(RF_0_EUCL)##
## Call:
## randomForest(formula = CLUSTER ~ ., data = data_frame_merge_EUCL)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 21
##
## OOB estimate of error rate: 15.52%
## Confusion matrix:
## 1 2 class.error
## 1 15 7 0.31818182
## 2 2 34 0.05555556plot(RF_0_EUCL$importance, type = "h")
EUCL by clusters
plot_data_EUCL <- data.frame(t(datos))
cluster_data_EUCL <- data.frame(DDclust_EUCL_FC_scaled)
plotting_EUCL <- cbind(plot_data_EUCL, cluster_data_EUCL)
head(plotting_EUCL)## X1 X2 X3 X4 X5 X6
## ACR_11231843 -1.5078367 -2.0261049 -0.9895684 1.3426387 2.8974434 1.3426387
## ADAO_11159808 -0.1643746 0.1260541 -0.4548032 0.1260541 -0.4548032 -0.9388510
## AGG_11236448 0.7783241 0.3728698 0.2071795 0.5914196 1.3204655 0.2339430
## AHL_11239959 0.7958952 0.7958952 -0.3920616 1.0598856 1.3898736 1.1258832
## AJGD_11119689 0.2134007 -0.1056155 0.2040179 0.7013079 -0.4340145 -0.2557408
## AMP_11228639 2.0594676 -0.2860160 -0.3893368 -1.1420189 -0.7539599 -0.4629157
## X7 X8 X9 X10 X11
## ACR_11231843 0.4356692 0.9539375 1.0835046 0.1765351 -0.73043431
## ADAO_11159808 -0.7452319 -0.7452319 -0.9388510 -1.0356606 -0.84204144
## AGG_11236448 0.2336995 0.7102987 0.6493752 0.4056814 1.19768631
## AHL_11239959 1.3898736 0.5979024 1.5218688 1.7198616 1.58786644
## AJGD_11119689 0.6731594 0.4855028 0.1477209 0.3072290 -0.01178718
## AMP_11228639 -1.6270926 1.5743939 -0.0748567 1.0893202 -0.07485670
## X12 X13 X14 X15 X16
## ACR_11231843 -0.8600014 -0.7304343 -0.9895684 0.95393749 0.9539375
## ADAO_11159808 -1.2292797 -1.4228988 -0.9388510 -1.13247011 -0.8420414
## AGG_11236448 1.0149160 0.5275283 1.3195332 1.74599740 2.2943085
## AHL_11239959 1.6538640 2.1158473 2.3138401 2.04984966 1.7198616
## AJGD_11119689 -0.3214206 -0.1243811 0.6168624 -0.08684982 -1.0720470
## AMP_11228639 0.3132023 -0.4629157 -0.9479894 -0.36590093 -0.3659009
## X17 X18 X19 X20 X21
## ACR_11231843 -0.08259900 -0.86000137 -0.7304343 -0.9895684 -1.1191355
## ADAO_11159808 0.99734010 -0.06756500 -0.3579937 -0.8420414 -0.5516128
## AGG_11236448 2.29430848 1.68507394 1.5023036 0.5275283 0.2229111
## AHL_11239959 1.38987364 0.06992159 -0.3920616 -0.5900544 0.7298976
## AJGD_11119689 -0.22759227 -0.69673376 0.1008068 -0.8843904 -1.2597036
## AMP_11228639 0.02215804 0.79827598 0.3132023 0.7012612 0.7982760
## X22 X23 X24 X25 X26 X27
## ACR_11231843 -0.7304343 -1.2487026 -0.7304343 -0.0825990 -0.9895684 -0.8600014
## ADAO_11159808 -1.3260892 -0.4548032 -1.0356606 -0.5516128 -0.8420414 -1.2292797
## AGG_11236448 1.3804567 2.7816961 2.8426196 3.3300072 3.3300072 3.2690837
## AHL_11239959 -0.5900544 -0.2600664 -0.4580592 1.5878664 0.5319048 0.7958952
## AJGD_11119689 1.1798322 -0.2275923 -1.0251328 -0.2275923 -0.3683347 -0.5090772
## AMP_11228639 0.7012612 0.7012612 1.0893202 1.8654382 1.0893202 1.9624529
## X28 X29 X30 X31 X32
## ACR_11231843 -1.378270 -1.1191355 -0.8600014 0.3061022 0.56523631
## ADAO_11159808 -1.326089 -1.2292797 0.4164828 -0.1643746 0.02924455
## AGG_11236448 3.025390 3.3909306 2.4770788 2.2943085 3.81739482
## AHL_11239959 1.983852 0.9278904 1.9838521 2.0498497 2.11584726
## AJGD_11119689 -1.259704 1.1798322 -0.8374762 0.7106907 -1.35353185
## AMP_11228639 1.089320 0.2161875 -0.0748567 0.3132023 1.28334970
## X33 X34 X35 X36 X37
## ACR_11231843 -0.60086725 -0.8600014 -0.2121661 -0.7304343 0.04696806
## ADAO_11159808 -0.45480322 0.1260541 0.1260541 0.7069114 0.51329233
## AGG_11236448 1.25860977 0.6493752 1.4413801 0.6493752 0.34475796
## AHL_11239959 2.57783048 1.9838521 0.9278904 1.4558712 0.79589522
## AJGD_11119689 -0.08684982 1.5551454 -1.3535319 0.1477209 2.02428690
## AMP_11228639 0.31320227 -0.2688862 -0.0748567 0.1191728 -0.17187144
## X38 X39 X40 X41 X42 X43
## ACR_11231843 0.3061022 -1.2487026 -0.6008672 -0.98956843 1.0835046 0.4356692
## ADAO_11159808 0.9005305 1.5781974 0.9005305 0.80372099 1.4813879 0.4164828
## AGG_11236448 0.6493752 0.2838345 1.3195332 0.89306904 1.3195332 -0.1426297
## AHL_11239959 0.8618928 1.7858593 1.0598856 1.19188083 1.1258832 0.9938880
## AJGD_11119689 2.2588577 1.6958879 2.0242869 -0.18067812 -0.4152489 0.3822917
## AMP_11228639 0.5072318 0.5072318 0.3132023 0.02215804 0.5072318 0.4102170
## X44 X45 X46 X47 X48 X49
## ACR_11231843 0.4356692 1.3426387 1.7313399 2.1200410 2.1200410 2.7678764
## ADAO_11159808 0.4164828 0.9973401 0.1260541 1.6750070 0.2228637 0.2228637
## AGG_11236448 -0.9346346 -0.1426297 0.9539925 -0.3863235 0.1010641 0.1619876
## AHL_11239959 0.7958952 0.4659072 1.2578784 0.2679144 0.5319048 0.7298976
## AJGD_11119689 -0.9782187 -0.7905621 -0.2275923 -0.4621630 0.5699483 -1.6350167
## AMP_11228639 0.5072318 0.9923055 1.0893202 0.8952907 0.8952907 0.9923055
## X50 X51 X52 X53 X54
## ACR_11231843 1.8609069 1.60177280 1.3426387 1.21307161 0.9539375
## ADAO_11159808 -0.4548032 -0.06756500 0.9005305 -0.84204144 0.2228637
## AGG_11236448 0.1010641 -0.02078276 0.5275283 -0.32540003 -0.6909408
## AHL_11239959 0.3339120 0.46590720 0.3339120 0.33391200 1.9178545
## AJGD_11119689 -1.3066177 -1.30661770 -0.8374762 -0.03993567 -0.7436479
## AMP_11228639 0.7982760 1.18633495 0.8952907 1.67140867 1.1863350
## X55 X56 X57 X58 X59
## ACR_11231843 0.8243704 0.694803367 0.306102183 0.30610218 0.3061022
## ADAO_11159808 1.1909592 0.222863663 -0.357993667 -0.26118411 0.1260541
## AGG_11236448 -0.5690938 1.014915952 -0.264476577 0.77122214 0.9539925
## AHL_11239959 -0.1280712 0.003923986 0.003923986 0.06992159 0.2679144
## AJGD_11119689 -1.6350167 -0.931304508 -0.790562060 -0.83747621 1.6020596
## AMP_11228639 1.0893202 1.186334954 0.701261241 0.50723176 0.4102170
## X60 X61 X62 X63 X64
## ACR_11231843 -0.0825990 0.046968060 0.1765351 0.3061022 0.30610218
## ADAO_11159808 0.3196732 1.287768769 1.8686261 0.9005305 0.22286366
## AGG_11236448 0.2838345 0.527528322 0.1010641 -0.3863235 -0.02078276
## AHL_11239959 0.1359192 0.003923986 0.1359192 0.2019168 0.06992159
## AJGD_11119689 2.1181152 0.757604870 2.4934284 0.4761200 2.58725670
## AMP_11228639 0.7012612 1.186334954 0.7982760 0.7982760 1.18633495
## X65 X66 X67 X68 X69
## ACR_11231843 0.176535121 -0.4713002 -0.08259900 -0.0825990 0.5652363
## ADAO_11159808 1.868626099 0.4164828 0.90053055 0.5132923 0.6101019
## AGG_11236448 1.014915952 -0.2035531 0.04014069 0.7102987 1.1976863
## AHL_11239959 0.003923986 0.7958952 1.25787843 1.5878664 1.1258832
## AJGD_11119689 -0.133763968 -1.2127894 -0.88439036 -0.9782187 -0.8843904
## AMP_11228639 1.283349697 1.4773792 0.89529073 0.7012612 1.4773792
## X70 X71 X72 X73 X74 X75
## ACR_11231843 0.6948034 0.9539375 1.3426387 0.9539375 1.4722057 0.8243704
## ADAO_11159808 2.2558643 1.0941497 0.4164828 1.5781974 1.0941497 0.4164828
## AGG_11236448 0.7102987 1.2586098 0.8321456 0.2229111 0.5275283 2.1115381
## AHL_11239959 2.3138401 1.3898736 2.4458353 1.7198616 1.7858593 2.1818449
## AJGD_11119689 -0.9782187 -1.0251328 -0.8843904 -0.5559913 -0.9313045 -1.1189611
## AMP_11228639 0.9923055 0.8952907 1.4773792 0.7012612 1.1863350 0.9923055
## X76 X77 X78 X79 X80 X81
## ACR_11231843 0.6948034 1.2130716 1.2130716 1.6017728 1.3426387 1.3426387
## ADAO_11159808 0.7069114 0.9973401 0.8037210 -0.1643746 -0.0675650 0.7069114
## AGG_11236448 1.2586098 1.0758394 0.7712221 1.0758394 0.6493752 0.8321456
## AHL_11239959 2.1818449 1.5218688 1.5218688 2.0498497 1.5878664 2.1818449
## AJGD_11119689 0.8983473 -0.9782187 0.7576049 0.8983473 1.5082313 2.3526859
## AMP_11228639 0.4102170 0.5072318 1.8654382 1.2833497 0.2161875 0.4102170
## X82 X83 X84 X85 X86 X87
## ACR_11231843 0.8243704 1.3426387 1.3426387 1.3426387 0.9539375 0.4356692
## ADAO_11159808 0.5132923 0.8037210 0.5132923 0.5132923 0.5132923 0.4164828
## AGG_11236448 0.6493752 0.8930690 1.3195332 1.6241505 0.2229111 -0.6909408
## AHL_11239959 2.5118329 1.4558712 1.3238760 0.6639000 1.1918808 0.5979024
## AJGD_11119689 1.8835445 1.4144030 1.6958879 2.3526859 2.4465142 0.9921756
## AMP_11228639 0.5072318 0.2161875 2.1564824 0.8952907 1.8654382 0.9923055
## X88 X89 X90 X91 X92
## ACR_11231843 0.8243704 0.5652363 0.30610218 0.1765351 0.17653512
## ADAO_11159808 1.1909592 0.3196732 0.02924455 0.4164828 1.28776877
## AGG_11236448 -0.6909408 -1.6047926 0.28383451 -0.9346346 -0.87371111
## AHL_11239959 1.1918808 0.4659072 0.79589522 0.7958952 0.46590720
## AJGD_11119689 1.6489737 2.2119435 0.19463508 0.6637766 0.05389263
## AMP_11228639 2.6415561 1.5743939 2.25349712 2.4475266 0.50723176
## X93 X94 X95 X96 X97
## ACR_11231843 0.30610218 0.3061022 0.4356692 0.1765351 0.306102183
## ADAO_11159808 0.02924455 0.9005305 0.4164828 0.7069114 0.610101883
## AGG_11236448 -1.36109874 -1.1783284 -0.3254000 -0.8737111 -1.300175290
## AHL_11239959 0.33391200 2.3138401 2.2478425 0.2679144 0.003923986
## AJGD_11119689 0.24154923 0.9452615 -0.1337640 2.4465142 2.305771800
## AMP_11228639 1.08932021 1.4773792 1.5743939 1.0893202 1.962452896
## X98 X99 X100 X101 X102
## ACR_11231843 0.04696806 0.69480337 -0.3417331 0.1765351 0.046968060
## ADAO_11159808 0.41648277 1.28776877 0.2228637 0.1260541 0.610101883
## AGG_11236448 -1.11740493 -1.11740493 -0.9955580 -1.3001753 -1.239251837
## AHL_11239959 0.20191679 0.06992159 -0.1280712 0.2019168 0.003923986
## AJGD_11119689 0.52303412 -1.35353185 -0.8843904 -0.6967338 0.100806779
## AMP_11228639 0.89529073 1.08932021 0.8952907 1.7684234 1.768423410
## X103 X104 X105 X106 X107
## ACR_11231843 -0.0825990 0.3061022 0.04696806 0.43566924 -0.3417331
## ADAO_11159808 0.4164828 0.1260541 -0.16437456 -0.26118411 -0.4548032
## AGG_11236448 -1.2392518 -1.4220222 -1.48294565 -1.36109874 -1.6657160
## AHL_11239959 -0.2600664 1.4558712 0.33391200 -0.06207362 0.2679144
## AJGD_11119689 1.2267464 0.8045190 2.07120105 0.19463508 -0.6498196
## AMP_11228639 1.5743939 1.3803644 1.57439393 1.08932021 1.7684234
## X108 X109 X110 X111 X112
## ACR_11231843 0.95393749 0.6948034 0.3061022 0.9539375 0.6948034
## ADAO_11159808 1.38457832 0.1260541 0.2228637 0.2228637 0.7069114
## AGG_11236448 -0.99555802 -2.0921802 -1.3001753 -2.0312567 -1.1783284
## AHL_11239959 -0.06207362 -0.1940688 1.5878664 0.5979024 -0.2600664
## AJGD_11119689 -0.64981961 -0.5090772 1.0390898 1.7428020 0.1008068
## AMP_11228639 1.28334970 1.0893202 0.9923055 1.0893202 1.4773792
## X113 X114 X115 X116 X117 X118
## ACR_11231843 0.5652363 0.9539375 0.5652363 0.8243704 1.3426387 1.3426387
## ADAO_11159808 0.3196732 0.1260541 -0.4548032 -0.2611841 -0.3579937 0.3196732
## AGG_11236448 -1.0564815 -1.5438691 -1.2392518 1.6241505 2.5989257 2.2943085
## AHL_11239959 -0.3920616 -0.3920616 -0.4580592 -0.3920616 -0.4580592 -0.3920616
## AJGD_11119689 1.6020596 0.2884634 -0.1337640 1.8835445 2.2588577 2.0242869
## AMP_11228639 1.2833497 1.1863350 0.4102170 1.5743939 0.7012612 1.3803644
## X119 X120 X121 X122 X123 X124
## ACR_11231843 0.5652363 0.3061022 0.5652363 0.6948034 1.0835046 0.4356692
## ADAO_11159808 1.2877688 0.8037210 1.5781974 1.5781974 1.3845783 2.1590548
## AGG_11236448 2.3552319 2.2333850 -0.2644766 -0.8737111 0.5884518 -0.5081704
## AHL_11239959 -0.2600664 -0.4580592 -0.3920616 -0.1280712 -0.6560520 -0.4580592
## AJGD_11119689 0.5699483 0.4292058 2.1650294 2.1181152 1.8366303 1.8366303
## AMP_11228639 1.7684234 0.6042465 0.4102170 1.7684234 2.3505119 1.0893202
## X125 X126 X127 X128 X129
## ACR_11231843 0.8243704 0.1765351 0.6948034 0.1765351 0.04696806
## ADAO_11159808 0.9973401 5.3537701 4.6761032 5.3537701 1.86862610
## AGG_11236448 -0.9955580 -0.5690938 0.3447580 -0.2644766 0.34475796
## AHL_11239959 -0.3920616 -0.4580592 -0.4580592 -0.4580592 -0.52405683
## AJGD_11119689 1.8366303 1.6020596 1.3205747 0.9921756 0.24154923
## AMP_11228639 0.9923055 1.0893202 1.6714087 0.4102170 1.76842341
## X130 X131 X132 X133 X134 X135
## ACR_11231843 0.9539375 0.3061022 0.1765351 0.4356692 1.2130716 0.8243704
## ADAO_11159808 1.5781974 1.8686261 0.3196732 0.7069114 0.6101019 0.5132923
## AGG_11236448 0.7712221 -0.5081704 -0.5690938 -0.2035531 -0.4472469 -0.2035531
## AHL_11239959 -0.9200424 -1.3160281 -0.7880472 -0.7880472 -0.9200424 -0.9860400
## AJGD_11119689 0.8983473 0.8983473 0.6168624 0.4761200 0.8514332 0.5699483
## AMP_11228639 1.1863350 0.2161875 1.2833497 0.7012612 2.0594676 1.5743939
## X136 X137 X138 X139 X140
## ACR_11231843 0.04696806 0.04696806 0.95393749 0.56523631 -0.08259900
## ADAO_11159808 0.02924455 -0.06756500 0.12605411 0.02924455 0.02924455
## AGG_11236448 -0.32540003 -0.44724694 -0.44724694 -0.20355312 -0.08170622
## AHL_11239959 -0.72204964 -0.72204964 0.92789042 1.52186884 0.17936370
## AJGD_11119689 0.71069072 0.56994827 0.05389263 0.10080678 0.19463508
## AMP_11228639 1.86543815 1.08932021 1.96245290 1.28334970 -0.94798938
## X141 X142 X143 X144 X145
## ACR_11231843 0.43566924 -0.2121661 0.1765351 0.04696806 0.4356692
## ADAO_11159808 -0.45480322 -0.6484223 -0.1643746 0.12605411 -0.3579937
## AGG_11236448 0.10106415 -0.2644766 -0.4472469 -0.20355312 -0.3254000
## AHL_11239959 0.05097887 0.2152986 0.2626828 0.44898558 0.3485986
## AJGD_11119689 1.55514541 0.1477209 0.3353775 0.14772093 0.5699483
## AMP_11228639 0.41021701 -0.3659009 -0.2688862 -0.46291567 -0.9479894
## X146 X147 X148 X149 X150
## ACR_11231843 -0.0825990 -0.3417331 -0.0825990 0.30610218 0.5652363
## ADAO_11159808 -1.1324701 -1.0356606 -0.2611841 -0.26118411 -1.2292797
## AGG_11236448 0.1010641 -0.5081704 -0.9346346 -0.56909385 -0.4472469
## AHL_11239959 1.5218688 1.8518569 0.7298976 0.06992159 1.0598856
## AJGD_11119689 0.2415492 0.4761200 1.5082313 1.13291807 0.5230341
## AMP_11228639 0.2161875 0.5072318 -0.1718714 -0.94798938 1.1863350
## X151 X152 X153 X154 X155
## ACR_11231843 -0.0825990 -0.73043431 0.3061022 2.7678764 3.28614460
## ADAO_11159808 -0.1643746 0.02924455 -0.2611841 0.7069114 -0.06756500
## AGG_11236448 0.2838345 -0.75186421 -0.3254000 -0.3254000 -0.56909385
## AHL_11239959 1.4558712 1.78585925 0.8618928 1.2578784 -1.44802327
## AJGD_11119689 0.8983473 2.25885765 0.8045190 -0.1337640 0.00697848
## AMP_11228639 -0.1718714 0.21618753 1.5743939 -0.3659009 -0.55993041
## X156 X157 X158 X159 X160
## ACR_11231843 0.9539375 -0.0825990 -0.2121661 -0.6008672 -0.73043431
## ADAO_11159808 0.2228637 0.4164828 0.5132923 0.1260541 0.90053055
## AGG_11236448 -0.5081704 -0.8737111 -0.8737111 -1.1174049 -1.05648148
## AHL_11239959 -1.9760041 -0.9200424 -0.9200424 -0.1940688 -0.06207362
## AJGD_11119689 -0.5559913 0.2884634 2.3996001 -0.1337640 0.38229167
## AMP_11228639 -0.5599304 -1.2390336 -1.7241073 -0.0748567 -0.26888619
## X161 X162 X163 X164 X165
## ACR_11231843 -0.86000137 -1.248702555 -0.8600014 -0.4713002 -0.8600014
## ADAO_11159808 0.02924455 0.900530548 -0.7452319 -0.8420414 -0.4548032
## AGG_11236448 -0.69094075 -1.604792559 -0.8127877 -0.8127877 -0.9346346
## AHL_11239959 0.72989761 0.003923986 0.2679144 0.5979024 0.3999096
## AJGD_11119689 2.49342840 0.945261468 1.9773728 2.1181152 0.5699483
## AMP_11228639 -0.17187144 0.216187528 0.7982760 1.1863350 -0.5599304
## X166 X167 X168 X169 X170
## ACR_11231843 -0.7304343 -0.9895684 -0.60086725 -0.2121661 -0.0825990
## ADAO_11159808 1.2877688 -0.8420414 0.02924455 -0.0675650 -0.0675650
## AGG_11236448 -1.0564815 -0.8737111 -0.93463457 -0.8127877 -1.1174049
## AHL_11239959 -0.3260640 0.2679144 1.38987364 -0.1940688 -0.3260640
## AJGD_11119689 -0.2745064 0.1946351 1.78971616 -0.3683347 1.3205747
## AMP_11228639 -0.1718714 -0.5599304 -0.55993041 -0.5599304 -0.8509746
## X171 X172 X173 X174 X175
## ACR_11231843 -0.3417331 0.30610218 0.30610218 0.8243704 0.5652363
## ADAO_11159808 -0.4548032 0.02924455 0.51329233 2.9335312 3.3207694
## AGG_11236448 -0.8127877 -0.69094075 -1.23925184 -0.7518642 -0.9346346
## AHL_11239959 0.3339120 0.72989761 0.72989761 -0.4580592 1.3238760
## AJGD_11119689 1.3205747 1.13291807 0.00697848 1.9773728 0.5699483
## AMP_11228639 -0.7539599 -1.04500413 -0.07485670 1.0893202 0.7982760
## X176 X177 X178 X179 X180 X181
## ACR_11231843 -0.2121661 -0.2121661 1.0835046 -0.0825990 -0.0825990 -1.7669708
## ADAO_11159808 2.9335312 3.0303408 0.7069114 1.2877688 0.9973401 0.9973401
## AGG_11236448 -0.9346346 -1.1174049 -1.0564815 -0.9346346 -0.7518642 -1.3001753
## AHL_11239959 -1.5140209 -0.8540448 -0.2600664 -1.0520377 -0.7880472 -1.0520377
## AJGD_11119689 1.8366303 -0.2275923 0.5230341 -0.6029055 1.2267464 -0.5559913
## AMP_11228639 -0.3659009 0.5072318 -0.7539599 -0.6569452 -0.8509746 0.6042465
## X182 X183 X184 X185 X186
## ACR_11231843 -0.7304343 0.04696806 -0.4713002 -0.2121661 -0.6008672
## ADAO_11159808 0.5132923 0.02924455 2.6431025 1.2877688 0.4164828
## AGG_11236448 -0.8737111 -1.17832838 -1.3610987 -0.9346346 -1.2392518
## AHL_11239959 -0.7880472 -0.19406882 -0.5900544 -0.5900544 -0.3260640
## AJGD_11119689 -0.8374762 -0.46216301 1.3205747 -0.1337640 0.2415492
## AMP_11228639 -1.4330631 -0.85097464 -0.7539599 -0.0748567 -1.3360484
## X187 X188 X189 X190 X191
## ACR_11231843 1.4722057 0.8243704 0.3061022 -0.60086725 -0.7304343
## ADAO_11159808 0.8037210 1.6750070 1.1909592 0.02924455 -0.6484223
## AGG_11236448 0.7102987 -0.3254000 0.8321456 -0.38632348 -0.9955580
## AHL_11239959 -0.3920616 -0.1940688 -0.6560520 -0.45805923 -0.4580592
## AJGD_11119689 -0.7905621 -0.5090772 -1.0720470 -0.88439036 0.2884634
## AMP_11228639 -0.8509746 -0.7539599 -0.2688862 -0.17187144 0.1191728
## X192 X193 X194 X195 X196 X197
## ACR_11231843 -0.4713002 -1.2487026 -0.8600014 -0.3417331 -0.3417331 -0.4713002
## ADAO_11159808 -0.9388510 -1.3260892 -0.2611841 1.2877688 -0.6484223 -0.6484223
## AGG_11236448 -0.2644766 -0.6300173 -1.1783284 -1.1783284 -1.1783284 -0.9346346
## AHL_11239959 -0.3920616 -0.4580592 -0.3920616 -0.6560520 -0.9200424 -0.1940688
## AJGD_11119689 -0.7436479 -0.6029055 -0.8374762 -0.8374762 -0.6029055 1.9304586
## AMP_11228639 -1.3360484 -0.4629157 -0.8509746 -0.5599304 0.9923055 -0.6569452
## X198 X199 X200 X201 X202 X203
## ACR_11231843 0.8243704 0.4356692 1.8609069 1.3426387 1.3426387 1.3426387
## ADAO_11159808 -0.0675650 -0.8420414 -0.6484223 -0.8420414 -0.8420414 -1.1324701
## AGG_11236448 -0.9955580 -0.8737111 -0.7518642 -0.7518642 -0.9346346 -1.0564815
## AHL_11239959 -0.4580592 -0.4580592 -0.4580592 -0.7220496 -0.6560520 -0.5900544
## AJGD_11119689 0.1008068 0.8983473 -0.6498196 -0.6967338 -0.8843904 -0.8374762
## AMP_11228639 -0.3659009 -0.1718714 -1.3360484 -1.6270926 -0.3659009 -1.3360484
## X204 X205 X206 X207 X208 X209
## ACR_11231843 1.8609069 2.2496081 1.8609069 1.4722057 1.0835046 1.3426387
## ADAO_11159808 -0.7452319 -0.6484223 -1.0356606 -1.1324701 -0.8420414 -1.2292797
## AGG_11236448 -1.3001753 -1.2392518 -1.1174049 -0.8737111 -0.9955580 -0.9346346
## AHL_11239959 -0.3260640 -0.7220496 -0.6560520 -0.4580592 -0.4580592 -0.3260640
## AJGD_11119689 1.7897162 0.5230341 -1.1189611 -1.2127894 -0.8374762 -0.9782187
## AMP_11228639 -1.2390336 -0.7539599 -0.6569452 -1.2390336 -1.4330631 -1.1420189
## X210 X211 X212 X213 X214
## ACR_11231843 1.6017728 1.2130716 0.8243704 0.3061022 0.04696806
## ADAO_11159808 -1.5197083 -1.2292797 -0.5516128 -0.8420414 -1.71332744
## AGG_11236448 -0.8737111 -0.9955580 -0.6300173 0.4666049 -0.56909385
## AHL_11239959 -0.3260640 -0.7220496 -0.9860400 -0.9200424 -0.85404485
## AJGD_11119689 -0.7436479 -0.8374762 -0.4621630 0.7576049 0.66377657
## AMP_11228639 -1.8211221 -1.8211221 -1.4330631 -1.8211221 -1.43306310
## X215 X216 X217 X218 X219
## ACR_11231843 -0.2121661 -0.21216606 -0.4713002 -0.7304343 -0.3417331
## ADAO_11159808 -1.4228988 -1.32608922 -0.9388510 -1.4228988 -1.2292797
## AGG_11236448 -1.1783284 -0.63001730 -0.9346346 -1.3001753 -0.8127877
## AHL_11239959 -0.5900544 -0.59005444 -0.4580592 -0.3920616 -0.2600664
## AJGD_11119689 -0.2275923 0.00697848 1.3674888 0.4292058 -0.9313045
## AMP_11228639 -1.2390336 -1.33604835 -1.6270926 -2.1121663 -1.2390336
## X220 X221 X222 X223 X224
## ACR_11231843 -0.4713002 -0.4713002 -0.4713002 -0.86000137 -0.2121661
## ADAO_11159808 -1.1324701 -1.4228988 -1.7133274 0.61010188 -1.8101370
## AGG_11236448 -0.4472469 -0.1426297 -0.3254000 -0.50817039 -0.4472469
## AHL_11239959 -0.3260640 -0.6560520 -0.6560520 -0.39206163 -0.6560520
## AJGD_11119689 -0.6029055 -0.1337640 -0.8843904 -0.03993567 -0.2745064
## AMP_11228639 -1.1420189 -1.4330631 -1.3360484 -1.23903361 -1.3360484
## X225 X226 X227 X228 X229
## ACR_11231843 -0.8600014 -0.6008672 -0.9895684 -1.7669708 -0.47130019
## ADAO_11159808 -1.2292797 -0.7452319 -0.6484223 -0.4548032 -1.22927966
## AGG_11236448 -0.3863235 -0.7518642 -0.4472469 0.2229111 -0.02078276
## AHL_11239959 -0.9860400 -0.3920616 -0.4580592 -0.2600664 -0.39206163
## AJGD_11119689 1.3674888 -0.4152489 0.8983473 0.1477209 1.78971616
## AMP_11228639 -1.5300778 -1.4330631 -1.3360484 -1.0450041 -1.72410733
## X230 X231 X232 X233 X234
## ACR_11231843 0.1765351 0.43566924 0.04696806 0.04696806 0.04696806
## ADAO_11159808 -1.2292797 -0.74523189 -0.84204144 -0.74523189 0.51329233
## AGG_11236448 -0.7518642 0.04014069 0.22291105 -0.32540003 0.10106415
## AHL_11239959 -0.5900544 -0.52405683 0.53190481 -0.45805923 -0.39206163
## AJGD_11119689 -0.6967338 0.24154923 1.69588786 0.10080678 -0.60290546
## AMP_11228639 -1.0450041 -1.53007784 -1.14201887 -1.43306310 -1.43306310
## X235 X236 X237 X238 X239
## ACR_11231843 -0.3417331 1.3426387 0.04696806 -0.47130019 0.1765351
## ADAO_11159808 0.5132923 -1.1324701 -0.93885100 -0.84204144 -1.2292797
## AGG_11236448 -0.3863235 -0.5081704 -0.14262967 -0.02078276 -0.5081704
## AHL_11239959 -0.1940688 0.3999096 -0.52405683 -0.26006642 -0.3920616
## AJGD_11119689 0.8045190 -0.3683347 -1.58810260 1.08600392 1.3674888
## AMP_11228639 -1.0450041 -0.9479894 0.21618753 -0.75395990 -1.6270926
## X240 X241 X242 X243 X244
## ACR_11231843 -0.7304343 -0.3417331 0.43566924 0.1765351 -0.3417331
## ADAO_11159808 -0.6484223 -1.3260892 -0.84204144 -0.9388510 -1.1324701
## AGG_11236448 -0.8127877 -0.6909408 -0.38632348 -0.7518642 -0.5690938
## AHL_11239959 1.5218688 -1.0520377 -0.78804724 -0.5900544 0.7298976
## AJGD_11119689 -0.9782187 1.9773728 0.05389263 -0.3683347 2.0242869
## AMP_11228639 -0.4629157 -1.8211221 -1.82112207 -1.2390336 -2.1121663
## X245 X246 X247 X248 X249
## ACR_11231843 0.1765351 0.5652363 0.04696806 0.3061022 -0.9895684
## ADAO_11159808 -1.3260892 -1.5197083 -1.42289877 -1.4228988 -1.3260892
## AGG_11236448 -0.3254000 -0.5690938 -0.44724694 -0.5081704 -0.6909408
## AHL_11239959 -0.7220496 -0.5900544 -0.45805923 -0.4580592 -0.5240568
## AJGD_11119689 -0.5090772 2.0242869 -0.69673376 -1.2597036 -0.5090772
## AMP_11228639 -2.3061958 -2.2091810 -1.33604835 0.1191728 -0.8509746
## X250 X251 X252 X253 X254 X255
## ACR_11231843 0.4356692 0.6948034 -2.6739402 -2.0261049 1.6017728 1.0835046
## ADAO_11159808 -1.6165179 -1.4228988 -1.5197083 -1.5197083 -1.5197083 -1.4228988
## AGG_11236448 -0.8737111 -0.8127877 -0.6909408 -1.0564815 -0.8737111 -1.0564815
## AHL_11239959 -0.5240568 0.7958952 -0.9200424 -1.0520377 -0.7880472 -0.4580592
## AJGD_11119689 1.7897162 -0.6967338 -0.8374762 -0.7905621 -0.7905621 -0.7905621
## AMP_11228639 -1.3360484 -0.5599304 -0.7539599 -2.2091810 -1.9181368 -1.3360484
## X256 X257 X258 X259 X260
## ACR_11231843 0.56523631 0.1765351 -0.2121661 -0.7304343 -0.9895684
## ADAO_11159808 -1.51970833 -1.6165179 -1.5197083 -0.3579937 0.6101019
## AGG_11236448 -0.56909385 -0.6300173 -0.5690938 -0.4472469 -0.8127877
## AHL_11239959 -0.65605204 -0.6560520 -0.5240568 -0.5240568 -0.4580592
## AJGD_11119689 -0.03993567 -0.9782187 -1.1189611 1.0390898 1.0860039
## AMP_11228639 -1.53007784 -0.4629157 -1.5300778 -1.0450041 -1.5300778
## X261 X262 X263 X264 X265
## ACR_11231843 -0.9895684 0.04696806 -0.47130019 -0.6008672 -0.8600014
## ADAO_11159808 1.7718165 1.57819743 0.70691144 0.2228637 1.0941497
## AGG_11236448 -0.8127877 -1.11740493 -0.81278766 -0.6300173 -0.3863235
## AHL_11239959 -0.5240568 -0.26006642 -0.32606403 -0.7220496 -0.3260640
## AJGD_11119689 0.4761200 0.19463508 0.00697848 0.2415492 0.6637766
## AMP_11228639 -1.5300778 0.11917279 0.60424650 0.1191728 0.6042465
## X266 X267 X268 X269 X270
## ACR_11231843 0.5652363 0.4356692 0.5652363 0.69480337 0.4356692
## ADAO_11159808 0.9973401 -0.4548032 -1.1324701 -1.13247011 -1.1324701
## AGG_11236448 -0.8737111 -0.5690938 -0.9346346 -0.93463457 -0.8127877
## AHL_11239959 -0.1940688 -0.3920616 -0.2600664 1.78585925 2.1158473
## AJGD_11119689 0.3353775 -0.3683347 -0.5559913 -0.83747621 -0.6498196
## AMP_11228639 0.5072318 -0.8509746 0.5072318 0.02215804 1.0893202
## X271 X272 X273 X274 X275
## ACR_11231843 -0.4713002 -0.47130019 -0.4713002 -0.2121661 -0.4713002
## ADAO_11159808 -1.0356606 0.02924455 0.2228637 -0.6484223 0.2228637
## AGG_11236448 -0.7518642 -0.63001730 -0.6909408 -0.7518642 -1.1174049
## AHL_11239959 1.7198616 0.66390001 1.3238760 1.7858593 -0.4580592
## AJGD_11119689 -0.6967338 -0.55599131 -0.8843904 -1.0720470 -0.9313045
## AMP_11228639 1.3803644 -0.65694516 -1.2390336 -1.0450041 -1.8211221
## X276 X277 X278 X279 X280
## ACR_11231843 -0.2121661 0.04696806 0.04696806 0.1765351 -0.2121661
## ADAO_11159808 -0.1643746 -0.93885100 -0.55161278 -0.2611841 -0.9388510
## AGG_11236448 -0.3863235 -0.87371111 -0.81278766 -0.8737111 -1.4220222
## AHL_11239959 -0.7880472 -0.32606403 -0.59005444 -0.3260640 1.2578784
## AJGD_11119689 -0.5090772 -0.79056206 -0.55599131 -0.6967338 -0.6498196
## AMP_11228639 -1.7241073 0.41021701 -0.26888619 -0.7539599 -1.9181368
## X281 X282 X283 X284 X285
## ACR_11231843 -0.2121661 0.04696806 -0.3417331 -0.7304343 -0.3417331
## ADAO_11159808 0.6101019 -0.26118411 -0.1643746 2.0622452 2.6431025
## AGG_11236448 -1.6047926 0.71029868 0.4056814 2.3552319 0.8930690
## AHL_11239959 -0.4580592 -0.45805923 -0.4580592 -0.1280712 0.5319048
## AJGD_11119689 -0.8374762 -0.88439036 -1.0251328 -1.1189611 -1.0720470
## AMP_11228639 -1.3360484 0.89529073 -0.8509746 -0.3659009 -0.0748567
## X286 X287 X288 X289 X290
## ACR_11231843 0.17653512 -0.3417331 -0.2121661 -0.2121661 0.04696806
## ADAO_11159808 0.02924455 0.7069114 -0.0675650 0.6101019 -0.74523189
## AGG_11236448 -0.02078276 0.2229111 -0.3254000 -0.9955580 0.46660487
## AHL_11239959 2.44583528 1.9178545 0.1359192 0.1359192 1.38987364
## AJGD_11119689 -0.74364791 -0.8374762 -0.9782187 -0.9782187 -1.11896111
## AMP_11228639 -0.55993041 1.5743939 -1.1420189 -1.3360484 0.02215804
## X291 X292 X293 X294 X295
## ACR_11231843 -0.7304343 -0.9895684 0.1765351 -0.34173312 -0.0825990
## ADAO_11159808 -0.0675650 -0.7452319 -0.5516128 -0.64842233 -0.5516128
## AGG_11236448 -0.6909408 -0.9955580 -0.3863235 0.04014069 -0.2644766
## AHL_11239959 1.5218688 1.4558712 1.0598856 1.78585925 2.5118329
## AJGD_11119689 -1.0251328 -0.9313045 -1.0720470 -1.16587526 -1.1189611
## AMP_11228639 -0.3659009 -0.6569452 -0.3659009 -0.17187144 -0.3659009
## X296 X297 X298 X299 X300
## ACR_11231843 -0.08259900 -0.0825990 0.8243704 -0.0825990 -0.21216606
## ADAO_11159808 1.09414966 0.1260541 -0.2611841 0.6101019 1.09414966
## AGG_11236448 1.98969121 3.1472368 2.4161554 2.8426196 1.56322704
## AHL_11239959 2.11584726 0.9278904 1.9178545 0.3339120 -0.06207362
## AJGD_11119689 -1.30661770 -1.3066177 -1.4004460 -1.4004460 -1.16587526
## AMP_11228639 0.02215804 -0.5599304 -0.3659009 0.5072318 0.11917279
## X301 X302 X303 X304 X305
## ACR_11231843 -0.7304343 -0.2121661 -0.0825990 -0.34173312 0.04696806
## ADAO_11159808 2.1590548 1.0941497 0.3196732 -0.74523189 -0.35799367
## AGG_11236448 1.6850739 1.1367629 0.4056814 0.04014069 -0.38632348
## AHL_11239959 -0.5240568 -0.5240568 -0.5240568 -0.65605204 -0.72204964
## AJGD_11119689 -1.2597036 -1.4473602 -1.1658753 -1.68193090 -1.44736015
## AMP_11228639 -0.5599304 -0.3659009 -0.4629157 -0.85097464 -0.17187144
## X306 X307 X308 X309 X310 X311
## ACR_11231843 -0.6008672 -0.0825990 -0.0825990 0.3061022 -0.4713002 -0.3417331
## ADAO_11159808 -0.2611841 -0.7452319 -0.6484223 -1.7133274 -0.6484223 -0.0675650
## AGG_11236448 -0.2035531 -0.9955580 -1.0564815 1.0149160 -0.5690938 -0.6909408
## AHL_11239959 -0.6560520 -0.5900544 1.2578784 -1.1180353 -0.6560520 -0.5900544
## AJGD_11119689 -1.5411885 -1.3066177 -1.3066177 -1.2597036 -1.2597036 -1.3535319
## AMP_11228639 -0.4629157 -0.3659009 -1.1420189 -0.4629157 -0.3659009 -0.1718714
## X312 X313 X314 X315 X316
## ACR_11231843 0.04696806 -0.08259900 0.4356692 0.04696806 -0.47130019
## ADAO_11159808 0.02924455 0.02924455 -0.0675650 0.41648277 0.12605411
## AGG_11236448 -0.56909385 0.77122214 1.0758394 -0.26447658 -0.38632348
## AHL_11239959 1.91785446 -0.72204964 -0.7220496 0.20191679 -0.98604005
## AJGD_11119689 -1.21278940 -1.11896111 -1.3066177 -1.35353185 -1.35353185
## AMP_11228639 -0.17187144 -0.36590093 -0.4629157 -0.36590093 0.02215804
## X317 X318 X319 X320 X321
## ACR_11231843 -0.3417331 -0.0825990 -0.6008672 -0.6008672 -0.08259900
## ADAO_11159808 0.5132923 0.4164828 0.6101019 0.3196732 0.22286366
## AGG_11236448 0.2229111 -0.5690938 0.1010641 0.1619876 -0.02078276
## AHL_11239959 -0.7220496 -0.6560520 -0.7880472 -0.6560520 -0.78804724
## AJGD_11119689 -1.3535319 -1.3535319 -1.4004460 -1.5411885 -1.40044600
## AMP_11228639 -0.6569452 -0.5599304 -0.4629157 -0.4629157 -0.65694516
## X322 X323 X324 X325 X326
## ACR_11231843 -0.7304343 -0.3417331 -0.3417331 -0.6008672 0.04696806
## ADAO_11159808 0.3196732 1.1909592 0.3196732 0.4164828 0.12605411
## AGG_11236448 0.1619876 -0.2644766 -0.1426297 -0.2644766 0.10106415
## AHL_11239959 -0.3920616 -0.6560520 0.6639000 -0.9200424 -0.78804724
## AJGD_11119689 -1.4004460 -1.1658753 -0.4621630 0.1477209 0.80451902
## AMP_11228639 -0.4629157 -0.3659009 -0.5599304 -0.3659009 -0.46291567
## X327 X328 X329 X330 X331 X332
## ACR_11231843 -0.3417331 -1.3782696 -0.2121661 -0.7304343 -0.8600014 -1.3782696
## ADAO_11159808 -0.0675650 0.5132923 0.3196732 -0.5516128 0.4164828 -0.3579937
## AGG_11236448 0.1010641 0.2229111 0.2229111 -0.2644766 0.4056814 -0.8127877
## AHL_11239959 -0.9860400 -0.3260640 -0.5900544 -0.4580592 -0.3920616 -0.2600664
## AJGD_11119689 0.3822917 -0.9313045 -1.2597036 0.5699483 0.3353775 -0.6029055
## AMP_11228639 -0.1718714 -0.7539599 -0.7539599 -0.3659009 -0.5599304 -0.5599304
## X333 X334 X335 X336 X337
## ACR_11231843 -1.2487026 0.04696806 -0.860001371 -1.2487026 -0.7304343
## ADAO_11159808 -0.2611841 -0.55161278 -0.648422332 -0.9388510 -0.8420414
## AGG_11236448 -0.2035531 0.58845178 0.101064146 -0.1426297 0.1010641
## AHL_11239959 -0.3920616 0.06992159 0.003923986 -0.5900544 -0.1280712
## AJGD_11119689 -0.9782187 -2.01032994 0.945261468 -0.5559913 1.0860039
## AMP_11228639 -0.8509746 -0.75395990 -1.045004127 -1.4330631 0.2161875
## X338 X339 X340 X341 X342 X343
## ACR_11231843 -0.9895684 -0.9895684 -1.2487026 -0.4713002 -1.2487026 -0.6008672
## ADAO_11159808 -1.0356606 -0.2611841 -0.6484223 -0.7452319 -0.8420414 -0.8420414
## AGG_11236448 -0.3254000 0.1010641 0.1010641 -0.8127877 -0.3254000 -0.5081704
## AHL_11239959 -0.3260640 -0.7880472 -0.9200424 -0.9200424 -0.9200424 -0.9860400
## AJGD_11119689 -0.8843904 0.5699483 1.5551454 -0.8374762 -0.3683347 -0.4621630
## AMP_11228639 -0.2688862 -0.0748567 -0.1718714 0.1191728 0.9923055 -1.3360484
## X344 X345 X346 X347 X348
## ACR_11231843 -1.1191355 -0.47130019 -0.6008672 1.47220574 1.0835046
## ADAO_11159808 -0.9388510 -0.74523189 -0.7452319 -0.74523189 -0.7452319
## AGG_11236448 -0.3863235 -0.08170622 0.1010641 0.22291105 0.1010641
## AHL_11239959 -0.6560520 -0.65605204 -0.7880472 -0.32606403 -0.4580592
## AJGD_11119689 0.1008068 0.89834732 -1.0720470 -0.03993567 -0.6029055
## AMP_11228639 -0.5599304 -0.55993041 -1.1420189 0.21618753 -0.3659009
## X349 X350 X351 X352 X353
## ACR_11231843 -0.0825990 -0.47130019 -0.73043431 -0.86000137 0.43566924
## ADAO_11159808 -0.7452319 -0.55161278 -0.84204144 -0.84204144 -0.93885100
## AGG_11236448 0.1010641 -0.02078276 -0.02078276 -0.08170622 -0.08170622
## AHL_11239959 -0.3260640 -0.39206163 -0.45805923 -0.45805923 -0.39206163
## AJGD_11119689 -0.6029055 -0.64981961 -1.40044600 -0.18067812 0.85143317
## AMP_11228639 -1.2390336 -1.04500413 -0.75395990 0.60424650 -1.23903361
## X354 X355 X356 X357 X358
## ACR_11231843 -0.86000137 -0.9895684 -1.24870256 -1.24870256 -1.11913549
## ADAO_11159808 -0.93885100 -1.0356606 -0.93885100 -0.93885100 -0.84204144
## AGG_11236448 -0.08170622 -0.3254000 -0.08170622 -0.08170622 -0.02078276
## AHL_11239959 0.99388802 -1.0520377 -0.45805923 0.26791440 -0.65605204
## AJGD_11119689 -0.46216301 0.4292058 0.14772093 0.28846338 0.28846338
## AMP_11228639 -1.04500413 -1.2390336 0.21618753 -1.43306310 -0.55993041
## X359 X360 X361 X362 X363
## ACR_11231843 -0.86000137 -1.2487026 -1.3782696 -0.7304343 -1.2487026
## ADAO_11159808 -0.84204144 0.6101019 0.3196732 0.2228637 1.5781974
## AGG_11236448 -0.44724694 -1.1174049 -0.2644766 -0.8737111 -0.6300173
## AHL_11239959 0.06992159 -1.2500305 -1.0520377 -0.6560520 0.7958952
## AJGD_11119689 -0.03993567 0.7106907 -0.5559913 0.2884634 0.1008068
## AMP_11228639 -1.43306310 -1.0450041 -0.4629157 0.6042465 -1.5300778
## X364 X365 X366 X367 X368
## ACR_11231843 -0.7304343 -0.8600014 -1.1191355 -0.9895684 -1.11913549
## ADAO_11159808 -0.8420414 0.5132923 -0.4548032 0.1260541 -0.35799367
## AGG_11236448 -0.6300173 -0.2644766 0.1010641 -0.6909408 0.04014069
## AHL_11239959 -0.3260640 -1.1840329 -0.9860400 -0.7880472 -0.98604005
## AJGD_11119689 -0.4152489 0.2415492 -0.2745064 -0.2275923 -0.13376397
## AMP_11228639 -1.0450041 -0.8509746 -1.1420189 -0.6569452 -0.55993041
## X369 X370 X371 X372 X373
## ACR_11231843 -0.9895684 -0.9895684 -0.73043431 -0.73043431 -0.34173312
## ADAO_11159808 0.7069114 -0.3579937 -0.26118411 -1.13247011 -0.93885100
## AGG_11236448 -0.3254000 0.6493752 -0.08170622 0.04014069 -0.08170622
## AHL_11239959 -0.6560520 -0.7880472 -1.44802327 -0.78804724 -1.18403286
## AJGD_11119689 0.6637766 0.5230341 0.56994827 -0.36833472 -0.50907716
## AMP_11228639 -0.5599304 -0.4629157 -0.46291567 -0.36590093 -0.55993041
## X374 X375 X376 X377 X378
## ACR_11231843 -0.73043431 -0.73043431 -0.7304343 -0.7304343 -0.86000137
## ADAO_11159808 -0.45480322 -0.74523189 -0.6484223 -0.9388510 -0.06756500
## AGG_11236448 0.28383451 0.04014069 0.1619876 -0.1426297 -0.69094075
## AHL_11239959 0.79589522 -0.59005444 -0.8540448 -0.7220496 -0.65605204
## AJGD_11119689 0.00697848 -0.93130451 -0.3214206 0.3353775 -0.03993567
## AMP_11228639 -0.46291567 -0.36590093 -0.4629157 -0.1718714 -0.65694516
## X379 X380 X381 X382 X383
## ACR_11231843 -0.4713002 -0.8600014 -0.3417331 -0.3417331 -0.47130019
## ADAO_11159808 -0.6484223 -0.8420414 -0.3579937 -1.0356606 -0.93885100
## AGG_11236448 0.5275283 0.1619876 0.2838345 -0.1426297 -0.02078276
## AHL_11239959 -0.6560520 -0.9200424 -1.1180353 -1.3820257 -0.98604005
## AJGD_11119689 -1.2127894 -0.4621630 -0.8843904 0.1477209 0.14772093
## AMP_11228639 -0.6569452 -0.8509746 -0.7539599 -1.0450041 -1.23903361
## X384 X385 X386 X387 X388
## ACR_11231843 -0.86000137 3.15657754 -0.0825990 0.1765351 0.30610218
## ADAO_11159808 -0.93885100 -0.93885100 -0.9388510 -0.8420414 0.02924455
## AGG_11236448 -0.08170622 -0.50817039 -0.2035531 0.2229111 0.10106415
## AHL_11239959 -0.98604005 -0.98604005 0.3999096 0.6639000 1.38987364
## AJGD_11119689 0.19463508 0.05389263 -0.3683347 -0.2275923 -0.55599131
## AMP_11228639 0.11917279 0.21618753 -0.8509746 -1.3360484 0.02215804
## X389 X390 X391 X392 X393
## ACR_11231843 -0.21216606 -0.73043431 -0.21216606 -0.4713002 0.30610218
## ADAO_11159808 0.31967322 -0.35799367 2.35267387 0.4164828 1.28776877
## AGG_11236448 0.04014069 -0.02078276 -0.08170622 -0.1426297 0.04014069
## AHL_11239959 0.92789042 0.66390001 1.45587124 2.1158473 2.57783048
## AJGD_11119689 -0.60290546 -1.11896111 -0.79056206 -0.3683347 -0.83747621
## AMP_11228639 -0.65694516 -1.14201887 0.21618753 0.1191728 0.11917279
## X394 X395 X396 X397 X398
## ACR_11231843 1.08350455 0.8243704 1.2130716 1.34263867 1.3426387
## ADAO_11159808 2.06224521 1.5781974 0.5132923 -0.06756500 0.3196732
## AGG_11236448 -0.02078276 -0.6300173 -0.2644766 -0.02078276 -0.5081704
## AHL_11239959 1.45587124 2.9738161 2.2478425 2.57783048 1.7858593
## AJGD_11119689 -0.74364791 -0.8843904 -0.8374762 1.17983221 -0.6498196
## AMP_11228639 0.31320227 1.3803644 -0.1718714 0.21618753 0.7012612
## X399 X400 X401 X402 X403
## ACR_11231843 1.99047398 1.73133986 1.860906921 2.12004104 1.4722057
## ADAO_11159808 -0.26118411 -0.64842233 0.222863663 0.02924455 -0.7452319
## AGG_11236448 0.04014069 -0.08170622 -0.081706215 -0.81278766 -0.4472469
## AHL_11239959 0.79589522 0.26791440 0.003923986 0.86189282 0.9278904
## AJGD_11119689 0.61686242 -0.46216301 0.851433169 1.17983221 0.7576049
## AMP_11228639 -0.94798938 1.28334970 -0.656945156 -0.65694516 -0.9479894
## X404 X405 X406 X407 X408
## ACR_11231843 1.0835046 0.6948034 0.3061022 -0.3417331 -0.08259900
## ADAO_11159808 -0.3579937 -0.0675650 0.1260541 -0.3579937 -0.35799367
## AGG_11236448 -0.3863235 -0.1426297 -0.3863235 0.3447580 -0.69094075
## AHL_11239959 -0.6560520 0.3999096 0.2019168 -0.6560520 0.79589522
## AJGD_11119689 1.0390898 1.0860039 0.8514332 0.6168624 0.71069072
## AMP_11228639 -0.4629157 1.4773792 1.3803644 -0.6569452 0.02215804
## X409 X410 X411 X412 X413
## ACR_11231843 0.04696806 0.30610218 -0.2121661 -0.98956843 -0.47130019
## ADAO_11159808 -0.26118411 0.51329233 -0.6484223 -0.26118411 -0.35799367
## AGG_11236448 0.10106415 0.71029868 -0.3254000 0.46660487 -0.38632348
## AHL_11239959 0.06992159 0.06992159 -1.7120137 -1.18403286 -1.31602806
## AJGD_11119689 0.71069072 -0.08684982 0.3822917 -0.03993567 -0.03993567
## AMP_11228639 -0.55993041 1.96245290 1.4773792 0.53463942 0.71190224
## X414 X415 X416 X417 X418 X419
## ACR_11231843 -0.3417331 0.3061022 0.3061022 -0.0825990 -0.4713002 -0.0825990
## ADAO_11159808 -0.2611841 -0.4548032 -0.5516128 -0.4548032 -0.3579937 -0.5516128
## AGG_11236448 0.4666049 -0.3254000 0.7102987 2.1115381 1.8069209 3.2081603
## AHL_11239959 -1.7120137 -1.4480233 -1.3820257 -1.4480233 -0.9200424 -1.3160281
## AJGD_11119689 1.7428020 -0.7436479 -1.3535319 -0.8843904 -0.6498196 -0.6967338
## AMP_11228639 0.5170155 1.1302690 0.4679746 0.6766096 0.1522742 0.2495247
## X420 X421 X422 X423 X424
## ACR_11231843 0.04696806 0.04696806 -0.7304343 -0.2121661 -0.21216606
## ADAO_11159808 -0.35799367 -0.64842233 -0.7452319 -0.7452319 -0.64842233
## AGG_11236448 2.05061467 1.62415049 0.8930690 0.5884518 -0.08170622
## AHL_11239959 -0.98604005 -1.05203765 -0.7880472 -0.7220496 -0.65605204
## AJGD_11119689 -0.36833472 -0.46216301 -0.5559913 -1.1189611 -0.69673376
## AMP_11228639 0.65513880 0.69495541 0.4055839 0.1612500 0.31320227
## X425 X426 X427 X428 X429
## ACR_11231843 -0.2121661 0.4356692 -0.4713002 -0.08259900 1.7313399
## ADAO_11159808 -0.7452319 1.4813879 0.5132923 0.70691144 0.1260541
## AGG_11236448 -0.2035531 -0.2035531 -0.5690938 0.22291105 0.1619876
## AHL_11239959 -1.1180353 -1.5140209 -1.0520377 -0.92004245 -1.1840329
## AJGD_11119689 -0.6498196 -0.5559913 -0.6029055 -0.55599131 -0.5559913
## AMP_11228639 0.6042465 0.7982760 0.9923055 0.02215804 -0.5599304
## X430 X431 X432 X433 X434
## ACR_11231843 -8.8931592 -1.76697080 -0.08259900 -1.1191355 -0.4713002
## ADAO_11159808 0.6101019 0.99734010 1.48138788 2.2558643 1.4813879
## AGG_11236448 1.7459974 1.98969121 1.44138013 -0.1426297 -0.6909408
## AHL_11239959 -1.3160281 -0.98604005 -1.05203765 0.2019168 -0.8540448
## AJGD_11119689 -0.5559913 -0.03993567 -0.74364791 0.1008068 -0.8374762
## AMP_11228639 1.3803644 0.02215804 0.02215804 -0.1718714 0.4102170
## X435 X436 X437 X438 X439
## ACR_11231843 -1.24870256 -1.7669708 -1.5078367 -1.5078367 -1.5078367
## ADAO_11159808 -0.55161278 -0.0675650 -0.6484223 -0.8420414 -0.5516128
## AGG_11236448 -0.02078276 0.3447580 -0.5690938 -1.0564815 -0.2644766
## AHL_11239959 -0.65605204 -0.6560520 -0.6560520 -0.3260640 -0.9860400
## AJGD_11119689 -0.50907716 -0.2275923 -0.6498196 -0.4621630 -0.9782187
## AMP_11228639 0.60424650 0.5072318 1.0893202 0.8952907 0.8952907
## X440 X441 X442 X443 X444
## ACR_11231843 -1.2487026 -0.7304343 -0.60086725 0.04696806 -0.3417331
## ADAO_11159808 -0.7452319 -0.4548032 -0.06756500 0.12605411 0.5132923
## AGG_11236448 0.2229111 2.0506147 -0.08170622 -0.81278766 -0.7518642
## AHL_11239959 -0.7220496 -0.8540448 -0.78804724 -1.18403286 -0.7220496
## AJGD_11119689 -0.3214206 -0.1806781 0.33537753 0.85143317 -0.3214206
## AMP_11228639 -0.0748567 1.1863350 0.99230547 2.15648238 0.4102170
## X445 X446 X447 X448 X449
## ACR_11231843 -0.7304343 0.5652363 -0.4713002 -0.2121661 -0.6008672
## ADAO_11159808 1.3845783 0.1260541 0.6101019 1.1909592 1.6750070
## AGG_11236448 1.8069209 0.7712221 0.5275283 0.5275283 0.5884518
## AHL_11239959 -0.6560520 -0.9860400 -0.7880472 -1.0520377 -0.7880472
## AJGD_11119689 0.4292058 -0.5090772 -0.4621630 -0.4152489 -0.2745064
## AMP_11228639 -0.9479894 -0.7539599 -0.0748567 -0.6569452 0.7012612
## X450 X451 X452 X453 X454
## ACR_11231843 -0.34173312 0.5652363 -0.60086725 -0.6008672 -0.0825990
## ADAO_11159808 2.54629298 0.3196732 0.80372099 -0.1643746 0.5132923
## AGG_11236448 0.04014069 1.7459974 0.04014069 1.9896912 2.8426196
## AHL_11239959 -1.05203765 -1.1840329 -0.78804724 -0.6560520 -0.7220496
## AJGD_11119689 -0.13376397 -0.8374762 -1.16587526 0.1008068 1.6489737
## AMP_11228639 0.31320227 0.6042465 0.11917279 0.4102170 2.5445414
## X455 X456 X457 X458 X459
## ACR_11231843 -0.3417331 1.0835046 2.76787635 2.7678764 3.54527872
## ADAO_11159808 -0.2611841 0.3196732 0.90053055 0.5132923 0.02924455
## AGG_11236448 3.0253899 1.4413801 0.95399250 0.7102987 0.71029868
## AHL_11239959 -0.7880472 -0.7880472 -0.52405683 -0.8540448 -0.78804724
## AJGD_11119689 0.8983473 0.1008068 -0.03993567 -0.2275923 0.28846338
## AMP_11228639 0.2161875 1.0893202 0.60424650 -0.2688862 1.08932021
## X460 X461 X462 X463 X464
## ACR_11231843 3.0270105 0.4356692 0.04696806 0.04696806 0.04696806
## ADAO_11159808 0.3196732 -0.0675650 -0.06756500 -0.55161278 0.02924455
## AGG_11236448 0.4666049 -0.2035531 0.10106415 1.44138013 0.77122214
## AHL_11239959 -0.3920616 -1.5140209 -1.31602806 -0.85404485 -1.05203765
## AJGD_11119689 0.3353775 1.0390898 0.42920582 0.47611997 0.75760487
## AMP_11228639 0.7012612 0.2161875 0.70126124 0.60424650 0.50723176
## X465 X466 X467 X468 X469
## ACR_11231843 0.9539375 -0.6008672 0.1765351 0.1765351 -0.9895684
## ADAO_11159808 -1.3260892 0.5132923 0.5132923 -0.5516128 -0.1643746
## AGG_11236448 0.9539925 0.1619876 0.7712221 1.5023036 0.4666049
## AHL_11239959 -1.1840329 -0.7220496 -0.5900544 -0.5240568 1.0598856
## AJGD_11119689 -0.1806781 -0.7436479 -0.8374762 1.6958879 0.1477209
## AMP_11228639 -0.1718714 1.1863350 -0.0748567 -0.2688862 0.1191728
## X470 X471 X472 X473 X474
## ACR_11231843 -0.98956843 -1.1191355 -0.47130019 -0.8600014 -1.3782696
## ADAO_11159808 0.99734010 -0.2611841 -0.35799367 0.2228637 -0.8420414
## AGG_11236448 0.04014069 0.5884518 0.22291105 0.2838345 0.1010641
## AHL_11239959 -0.98604005 -0.1280712 -0.78804724 -0.9200424 -1.0520377
## AJGD_11119689 1.60205956 1.5082313 1.32057466 -0.6967338 -0.3214206
## AMP_11228639 -0.17187144 0.2161875 0.02215804 -0.1718714 0.1191728
## X475 X476 X477 X478 X479
## ACR_11231843 -0.9895684 -0.9895684 -1.11913549 -0.47130019 -0.86000137
## ADAO_11159808 0.8037210 -0.7452319 -0.26118411 0.51329233 -0.16437456
## AGG_11236448 0.2229111 0.2229111 -0.08170622 -0.02078276 -0.26447658
## AHL_11239959 -0.7880472 -1.6460161 -1.11803525 -1.51402087 -1.11803525
## AJGD_11119689 -0.2745064 -0.8843904 -0.79056206 -1.49427430 -0.60290546
## AMP_11228639 0.3132023 0.1191728 0.11917279 -0.36590093 0.02215804
## X480 DDclust_EUCL_FC_scaled
## ACR_11231843 -0.4713002 1
## ADAO_11159808 -0.5516128 1
## AGG_11236448 -0.3863235 2
## AHL_11239959 -0.9200424 1
## AJGD_11119689 1.2736605 2
## AMP_11228639 -0.0748567 1## Mean by groups
rp_tbl_EUCL <- aggregate(plotting_EUCL, by = list(plotting_EUCL$DDclust_EUCL_FC_scaled), mean)
row.names(rp_tbl_EUCL) <- paste0("Group",rp_tbl_EUCL$DDclust_EUCL_FC_scaled)
rp_tbl_EUCL <- rp_tbl_EUCL %>%
select(starts_with('X'))
rp_tbl_EUCL <- data.frame(t(rp_tbl_EUCL))
head(rp_tbl_EUCL)## Group1 Group2
## X1 0.7730007 0.9892621
## X2 0.5968431 1.0466806
## X3 0.5931450 1.0043540
## X4 0.8236536 1.0393478
## X5 0.8212286 0.8774817
## X6 0.7224673 0.6958067# 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)$ac}
map_dbl(m,ac)## average single complete ward.D2
## 0.7026757 0.5218905 0.7879160 0.9027988NbClust study
This package will help us identify the optimum number of clusters
based our criteria in the silhouette index
diss_matrix<- DD_PER
res<-NbClust(datos_PER, diss=diss_matrix, distance = NULL, min.nc=2, max.nc=5, method = "ward.D2", index = "silhouette")
res$All.index## 2 3 4 5
## 0.3480 0.2958 0.3049 0.2227res$Best.nc## Number_clusters Value_Index
## 2.000 0.348#res$Best.partitionhcintper_PER <- hclust(DD_PER, "ward.D2")
fviz_dend(hcintper_PER, palette = "jco",
rect = TRUE, show_labels = FALSE, k = 2)
DDclust_PER_FC_scaled <- cutree( hclust(DD_PER, "ward.D2"), k = 2)fviz_cluster(list(data = t(datos), cluster = DDclust_PER_FC_scaled))
fviz_silhouette(silhouette(DDclust_PER_FC_scaled, DD_PER))## cluster size ave.sil.width
## 1 1 36 0.39
## 2 2 22 0.27
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_FC_scaled[DDclust_PER_FC_scaled == 2]),names(DDclust_PER_FC_scaled[DDclust_PER_FC_scaled == 1]))
fviz_dend(hcintper_PER, k = 2,
k_colors = c("blue", "green3"),
label_cols = as.vector(COLOR_PER[,order_PER]), cex = 0.6) 
n1 = length(intersect(file_patient_name_DETERIORO,names_1))
n2 = length(intersect(file_patient_name_DETERIORO,names_2))
n3 = length(intersect(file_patient_name_NO_DETERIORO,names_1))
n4 = length(intersect(file_patient_name_NO_DETERIORO,names_2))
conttingency_table <- data.frame("CLust1" = c(n1,n3), "Clust2" = c(n2,n4))
rownames(conttingency_table) <- c("DETERIORO","NO DETERIORO")
knitr::kable(conttingency_table, align = "lccrr")| CLust1 | Clust2 | |
|---|---|---|
| DETERIORO | 3 | 3 |
| NO DETERIORO | 33 | 19 |
conttingency_table_prop <- data.frame(c(n1,n3)/(n1+n3),c(n2,n4)/(n2+n4))
rownames(conttingency_table_prop) <- c("DETERIORO","NO DETERIORO")
colnames(conttingency_table_prop) <- c("Clust1","Clust2")
knitr::kable(conttingency_table_prop, align = "lccrr")| Clust1 | Clust2 | |
|---|---|---|
| DETERIORO | 0.0833333 | 0.1363636 |
| NO DETERIORO | 0.9166667 | 0.8636364 |
Random Forest: Discriminant TSCLust PER
data_frame1_PER = data.frame("CLUSTER" = DDclust_PER_FC_scaled)
data_frame2_PER = df_descriptive
data_frame_merge_PER <-
merge(data_frame1_PER, data_frame2_PER, by = 'row.names', all = TRUE)
data_frame_merge_PER <- data_frame_merge_PER[, 2:dim(data_frame_merge_PER)[2]]
data_frame_merge_PER$CLUSTER = factor(data_frame_merge_PER$CLUSTER)table(data_frame_merge_PER$CLUSTER)##
## 1 2
## 36 22data_frame_merge_PER[,c(1:dim(data_frame_merge_PER)[2])]<- lapply(data_frame_merge_PER[,c(1:dim(data_frame_merge_PER)[2])], as.numeric)
head(data_frame_merge_PER)## CLUSTER EDAD PESO EG FR_0_8h FLUJO2_0_8H DIAS_GN DIAS_O2_TOTAL DIAS_OAF
## 1 1 10.0 8.20 41 48 2.00 3 3 0
## 2 1 13.0 7.78 40 56 2.00 2 2 0
## 3 2 3.1 5.66 37 44 1.00 4 4 0
## 4 1 5.3 8.44 38 65 0.40 3 3 0
## 5 1 15.0 7.00 34 37 2.00 4 4 0
## 6 2 1.6 3.80 37 42 0.94 4 4 0
## SAPI_0_8h SCORE_CRUCES_INGRESO SCORE_WOOD_DOWNES_INGRESO SEXO PALIVIZUMAB LM
## 1 3 3 6 1 1 2
## 2 4 4 8 1 1 1
## 3 3 3 7 1 1 2
## 4 4 3 6 1 1 2
## 5 1 3 6 1 2 1
## 6 2 4 7 1 1 2
## DERMATITIS ALERGIAS TABACO ENFERMEDAD_BASE RADIOGRAFIA ANALITICA SUERO
## 1 1 2 1 1 1 1 1
## 2 1 2 2 2 1 1 2
## 3 1 1 1 1 1 1 1
## 4 1 1 1 1 1 1 1
## 5 1 1 2 2 1 1 2
## 6 1 1 2 2 1 1 1
## ETIOLOGIA PREMATURIDAD ALIMENTACION SNG GN_INGRESO OAF OAF_AL_INGRESO
## 1 2 1 2 1 2 1 1
## 2 1 1 1 1 2 1 1
## 3 2 1 2 1 2 1 1
## 4 2 1 2 1 1 1 1
## 5 2 2 2 1 2 1 1
## 6 1 1 2 1 1 1 1
## OAF_TRAS_INGRESO UCIP DETERIORO PAUSAS_APNEA
## 1 1 1 1 1
## 2 1 1 1 1
## 3 1 1 1 1
## 4 1 1 1 1
## 5 1 1 1 1
## 6 1 1 1 1data_frame_merge_PER$CLUSTER <- factor(data_frame_merge_PER$CLUSTER)
newSMOTE_PER <- data_frame_merge_PERset.seed(123)
pos_1 = get_column_position(newSMOTE_PER, "SAPI_0_8h")
pos_2 = get_column_position(newSMOTE_PER, "PAUSAS_APNEA")
col_names_factor <- names(newSMOTE_PER[pos_1:pos_2])
newSMOTE_PER[col_names_factor] <- lapply(newSMOTE_PER[col_names_factor] , factor)
RF_PER <- randomForest(CLUSTER ~ ., data = newSMOTE_PER)
print(RF_PER)##
## Call:
## randomForest(formula = CLUSTER ~ ., data = newSMOTE_PER)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 5
##
## OOB estimate of error rate: 37.93%
## Confusion matrix:
## 1 2 class.error
## 1 28 8 0.2222222
## 2 14 8 0.6363636Importance
kable(RF_PER$importance[order(RF_PER$importance, decreasing = TRUE),])| x | |
|---|---|
| PESO | 2.8009743 |
| RADIOGRAFIA | 2.6058470 |
| EDAD | 2.4925543 |
| FR_0_8h | 2.4681832 |
| SCORE_WOOD_DOWNES_INGRESO | 2.0989243 |
| SCORE_CRUCES_INGRESO | 2.0854249 |
| FLUJO2_0_8H | 1.7598084 |
| DIAS_O2_TOTAL | 1.4238796 |
| DIAS_GN | 1.3531194 |
| SAPI_0_8h | 1.1630736 |
| EG | 1.1371458 |
| ALIMENTACION | 0.6379475 |
| TABACO | 0.6009182 |
| SEXO | 0.4571970 |
| ETIOLOGIA | 0.4570916 |
| ALERGIAS | 0.3514291 |
| ENFERMEDAD_BASE | 0.2993693 |
| LM | 0.2986870 |
| ANALITICA | 0.2643668 |
| DIAS_OAF | 0.2631015 |
| GN_INGRESO | 0.1941786 |
| PREMATURIDAD | 0.1858932 |
| SUERO | 0.1725155 |
| DERMATITIS | 0.1503197 |
| SNG | 0.1355917 |
| DETERIORO | 0.1343044 |
| OAF | 0.1279795 |
| UCIP | 0.1225009 |
| PALIVIZUMAB | 0.1056559 |
| OAF_TRAS_INGRESO | 0.0950935 |
| PAUSAS_APNEA | 0.0658861 |
| OAF_AL_INGRESO | 0.0000000 |
Importance of the PER
data_frame1_PER = data.frame("CLUSTER" = DDclust_PER_FC_scaled)
data_frame2_PER = data.frame(datos_PER)
data_frame_merge_PER <-
merge(data_frame1_PER, data_frame2_PER, by = 'row.names', all = TRUE)
data_frame_merge_PER <- data_frame_merge_PER[, 2:dim(data_frame_merge_PER)[2]]set.seed(123)
data_frame_merge_PER$CLUSTER <- as.factor(data_frame_merge_PER$CLUSTER)
RF_0_PER <- randomForest(CLUSTER ~ ., data = data_frame_merge_PER)
print(RF_0_PER)##
## Call:
## randomForest(formula = CLUSTER ~ ., data = data_frame_merge_PER)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 21
##
## OOB estimate of error rate: 13.79%
## Confusion matrix:
## 1 2 class.error
## 1 35 1 0.02777778
## 2 7 15 0.31818182plot(RF_0_PER$importance, type = "h")
### PER by clusters
plot_data_PER <- data.frame(datos_PER)
cluster_data_PER <- data.frame(DDclust_PER_FC_scaled)
plotting_PER <- cbind(plot_data_PER, cluster_data_PER)
head(plotting_PER)## X1 X2 X3 X4 X5 X6
## ACR_11231843 5.848578 0.3743415 6.8715853 1.212893 5.4053599 7.119084
## ADAO_11159808 19.161627 11.8601407 12.6707788 11.342341 2.4783679 1.319596
## AGG_11236448 41.825617 7.5375310 0.5631459 3.883392 6.8843685 3.681852
## AHL_11239959 9.799312 8.8503584 3.5331319 14.026002 8.4455885 8.689473
## AJGD_11119689 15.614663 21.7300554 2.3159148 13.376941 2.6553456 4.381623
## AMP_11228639 74.278454 21.2659958 28.3545140 4.785927 0.3193887 2.712271
## X7 X8 X9 X10 X11 X12
## ACR_11231843 15.28118156 2.2116713 4.0278251 4.230968 0.1883137 4.4361328
## ADAO_11159808 7.78966139 4.3803474 10.9436912 7.742754 6.0392558 2.5368184
## AGG_11236448 2.86468612 3.5267080 12.2953089 5.469311 9.8786332 0.8261306
## AHL_11239959 2.24797526 11.5702112 10.6558848 1.958953 1.4145088 8.6658881
## AJGD_11119689 0.02934979 2.9018657 1.6380244 3.544227 0.5145252 6.3450522
## AMP_11228639 1.07321517 0.1119197 0.2932176 0.157840 0.8138084 0.3929538
## X13 X14 X15 X16 X17 X18
## ACR_11231843 4.9348069 4.4093973 6.7331185 1.9762133 0.93579178 6.0100283
## ADAO_11159808 0.3145458 4.4979165 5.0708085 4.7334760 6.44443545 9.4783702
## AGG_11236448 4.0094456 0.9770059 0.7133863 4.4751416 0.34515817 0.4843672
## AHL_11239959 4.0880628 1.0348649 9.4089391 0.4852065 0.35446548 0.2574401
## AJGD_11119689 1.6824053 3.2126460 0.6232166 1.0946418 4.12262590 4.1668995
## AMP_11228639 1.0801008 3.1056244 0.2646312 0.4916785 0.07103315 0.7199646
## X19 X20 X21 X22 X23 X24
## ACR_11231843 3.0571351 1.54232147 0.3842215 2.6002511 1.10430444 0.02336846
## ADAO_11159808 0.2898114 0.33698581 0.9009742 6.7098349 2.21177265 0.71826102
## AGG_11236448 1.0754701 0.06283354 3.8517427 0.5734194 0.13949199 1.11299942
## AHL_11239959 3.8265333 2.95356184 0.8691741 1.1501982 2.88962546 5.32419393
## AJGD_11119689 1.5698349 0.22788057 6.9034674 2.5985036 1.04006215 5.43264599
## AMP_11228639 0.4103392 1.08671137 0.2895402 2.7390024 0.04809891 0.27554454
## X25 X26 X27 X28 X29 X30
## ACR_11231843 2.3374742 0.5025686 5.0378185 0.5831323 0.9783577 2.0479549
## ADAO_11159808 2.4029995 2.2759813 3.7789734 3.3253260 1.1003379 3.4222632
## AGG_11236448 0.1314794 3.3877322 3.6114150 0.9049440 3.1764513 0.3158165
## AHL_11239959 0.3643765 1.0724044 0.2690623 0.8720393 0.2882118 0.2864212
## AJGD_11119689 1.2104396 0.5301348 0.1577908 0.6199841 1.4541586 1.3212389
## AMP_11228639 0.1999019 1.1189641 0.6687096 0.3893545 0.1820798 1.8752743
## X31 X32 X33 X34 X35 X36
## ACR_11231843 0.9238922 0.7557598 0.29081018 0.89996994 0.434101445 0.17706965
## ADAO_11159808 1.7187118 0.3116339 2.78544583 0.10297563 0.005523373 3.44773877
## AGG_11236448 0.1746159 3.9563430 0.54172820 1.76530074 1.310928721 1.42964220
## AHL_11239959 0.4383296 0.9872661 0.03147537 1.05700756 1.019235571 2.70338438
## AJGD_11119689 1.6111188 0.5216999 0.71336370 1.44464965 1.022303502 0.11925587
## AMP_11228639 0.7205132 0.9431357 3.46603021 0.08606386 1.224937812 0.05180415
## X37 X38 X39 X40 X41 X42
## ACR_11231843 1.114130034 0.818201 0.1243323 0.4194054 2.38229839 1.7365795
## ADAO_11159808 1.184151623 2.011015 3.1392105 0.7270018 0.01130704 0.5212688
## AGG_11236448 3.024306932 0.401883 0.8281645 5.9207354 0.65580562 0.8074447
## AHL_11239959 0.002951126 1.110485 1.3243382 0.5503924 0.70264608 0.5572120
## AJGD_11119689 0.404745802 2.039838 0.7185712 0.8071540 0.04378581 0.7365631
## AMP_11228639 0.168620077 1.701726 1.2528504 0.4970656 0.31376147 0.1540637
## X43 X44 X45 X46 X47 X48
## ACR_11231843 1.7978597 0.5066648 0.50289164 0.73233210 0.08888497 2.26401020
## ADAO_11159808 1.0388283 0.9493673 0.62700018 0.08173826 0.14420578 0.31672438
## AGG_11236448 3.1525108 0.8490893 0.01835407 2.21523030 0.03900382 1.25560813
## AHL_11239959 1.6333344 0.4172136 0.30559030 0.33378635 0.34429828 0.06036797
## AJGD_11119689 2.3245802 1.9796980 0.27089096 0.21498381 0.40268939 0.33478576
## AMP_11228639 0.7114498 0.3954184 0.47672667 0.46731205 0.49128565 0.09666285
## X49 X50 X51 X52 X53 X54
## ACR_11231843 0.34543451 0.18619840 0.11415485 0.31159461 0.09302105 0.05915665
## ADAO_11159808 2.47634017 2.00092175 0.62268402 0.59616769 0.03502095 0.01228136
## AGG_11236448 0.40837894 0.01399644 0.41369583 0.26496712 0.66927353 0.23219247
## AHL_11239959 0.82313582 0.02939305 0.02025657 0.05367467 0.20684620 0.39182557
## AJGD_11119689 0.01749701 0.76442488 0.44458857 1.00991198 0.89924717 0.45429739
## AMP_11228639 0.02221005 0.29742321 0.10835145 0.35436989 0.21721122 0.16910957
## X55 X56 X57 X58 X59 X60
## ACR_11231843 0.25901322 0.4771633 1.05067514 0.9321941 0.648464481 1.01425475
## ADAO_11159808 0.23902748 0.1155511 0.09665106 1.8965789 0.599167360 0.35297847
## AGG_11236448 0.80816201 1.2789929 0.64002047 0.7709875 0.009326569 0.07763641
## AHL_11239959 0.48229323 0.0372571 0.80085676 0.2620627 0.239919218 0.33732714
## AJGD_11119689 0.08590092 0.9762465 0.27207320 1.1704495 1.229149415 0.77594042
## AMP_11228639 0.01759638 0.2481277 0.31268859 0.5058534 0.131831439 0.11627900
## X61 X62 X63 X64 X65 X66
## ACR_11231843 0.7315768 2.72857701 0.07153099 0.1639048 0.04842124 0.0703432
## ADAO_11159808 2.1246968 0.00449535 0.45493334 0.9414528 0.11256975 0.1694315
## AGG_11236448 0.6863714 0.11182585 0.10970842 0.3049607 0.63158206 0.2006222
## AHL_11239959 0.2343275 0.27443549 0.05932641 0.1907896 0.29974900 0.8147033
## AJGD_11119689 0.8182855 0.10231199 0.12014327 0.2716897 1.92578803 0.5990802
## AMP_11228639 0.5801604 0.07452320 0.05325495 1.2552320 0.88748663 0.1914041
## X67 X68 X69 X70 X71 X72
## ACR_11231843 0.4060085 0.07509483 0.04447808 0.46672033 0.071477462 0.2201671
## ADAO_11159808 1.0382281 0.23265890 0.37949181 0.24789428 0.270079732 1.1028110
## AGG_11236448 0.3728191 0.17447169 0.51896106 0.47299067 0.001271438 0.2545867
## AHL_11239959 0.6298898 1.19600632 0.93066032 0.03460972 0.077448599 0.3563844
## AJGD_11119689 2.4382180 0.53577310 0.27124141 0.60776532 1.779895580 1.6305871
## AMP_11228639 0.9140920 0.62565336 0.43974751 0.68536920 0.562934256 0.4314150
## X73 X74 X75 X76 X77 X78
## ACR_11231843 1.34872679 0.51943395 0.3915925 1.20368518 0.798697223 1.52675332
## ADAO_11159808 0.37235084 0.13846721 0.7887426 0.09448861 0.258291569 0.09216212
## AGG_11236448 0.08332572 0.37890282 1.0209840 0.29150520 0.139753006 0.17857195
## AHL_11239959 0.81215793 0.02453207 0.4363844 0.18546077 0.008480168 0.18849292
## AJGD_11119689 0.19769748 0.65524263 0.4509812 0.77369087 0.334620132 0.39313970
## AMP_11228639 0.03511920 0.00295760 0.9003414 0.24516452 0.126962347 0.62068025
## X79 X80 X81 X82 X83 X84
## ACR_11231843 0.943549135 0.2363449 1.00719211 0.08786765 0.2448035 0.399615495
## ADAO_11159808 0.654059083 0.7365223 0.31786771 0.04099962 0.4292995 0.106744978
## AGG_11236448 0.819767597 0.6563020 0.09210857 0.09340645 0.5166284 0.008174061
## AHL_11239959 0.237510748 0.1156081 0.16424258 1.20871384 0.1750306 0.302692682
## AJGD_11119689 0.008210355 0.1508059 0.72799373 1.09878721 1.0684334 0.743965146
## AMP_11228639 0.076773597 0.1002166 0.08354674 0.23311262 0.1870228 0.979219341
## X85 X86 X87 X88 X89 X90
## ACR_11231843 1.1309995 0.3318204 0.169705850 0.75224359 0.007243292 1.47533935
## ADAO_11159808 1.2884651 1.2708608 0.607560355 0.30408343 0.195075751 0.06035056
## AGG_11236448 0.2048886 0.6327526 0.075923947 0.66283217 0.591109780 0.20640767
## AHL_11239959 0.9089198 0.6159354 0.664614272 0.08596877 0.019772305 0.34487369
## AJGD_11119689 0.1371142 0.7476956 0.006004055 1.05964147 0.214203248 0.82648047
## AMP_11228639 1.0577347 0.2396080 0.412215794 0.16925785 0.599583639 1.30160190
## X91 X92 X93 X94 X95 X96
## ACR_11231843 0.2717003 0.19744715 0.376500184 0.7331604 0.8123600 0.55460067
## ADAO_11159808 0.4421998 0.80529665 0.127916924 0.9013566 0.3082349 0.07970664
## AGG_11236448 0.2909917 0.09904979 0.008880916 0.2827778 0.9098785 0.21898269
## AHL_11239959 0.4747587 0.25214486 0.761323317 0.6338753 0.1641917 0.54500595
## AJGD_11119689 0.6238030 0.87662068 0.787996998 1.4449421 0.2580136 0.01631161
## AMP_11228639 0.8305493 0.80703554 0.187444280 0.3520511 0.1746270 0.25491150
## X97 X98 X99 X100 X101 X102
## ACR_11231843 0.3674562 0.30748514 0.2135174 1.39428230 0.06150944 1.85343796
## ADAO_11159808 0.0874975 0.06207332 0.2388713 0.90213270 0.65397939 0.08248442
## AGG_11236448 0.5276326 0.03251931 0.2964378 0.36744212 1.21882876 0.37493316
## AHL_11239959 0.1387733 0.99612598 0.1996762 0.17984257 0.01575614 0.46764285
## AJGD_11119689 0.7544742 0.11383767 0.4837966 0.08260011 0.37194273 1.07598009
## AMP_11228639 1.2246640 0.40411234 0.2545733 0.93147385 0.17092171 0.22215541
## X103 X104 X105 X106 X107
## ACR_11231843 0.27355879 0.54936063 0.28766223 0.1543180054 0.80728643
## ADAO_11159808 0.02118811 0.09541252 0.21921704 0.3441032993 0.98599398
## AGG_11236448 0.10854335 0.33075607 0.05045575 0.2544029764 0.32660503
## AHL_11239959 0.01228834 0.19107927 0.16815766 0.0003095814 0.03807210
## AJGD_11119689 0.63725342 0.20247572 0.92312830 0.1144728347 0.03756933
## AMP_11228639 0.05555407 0.32622975 0.39249326 0.1916083885 0.06715572
## X108 X109 X110 X111 X112 X113
## ACR_11231843 1.40107592 0.06638568 0.2236739 0.59903537 0.324239294 0.53898729
## ADAO_11159808 0.16939273 0.48330111 1.4479789 0.01125781 0.009723951 0.19546984
## AGG_11236448 0.46503359 0.55979697 0.2692365 0.24805002 0.055516388 0.02056208
## AHL_11239959 0.42500160 0.11036749 0.1558639 0.02372695 0.371092714 1.10110908
## AJGD_11119689 1.07585610 0.40967920 0.6457600 0.21290341 0.052172611 1.42746510
## AMP_11228639 0.04781766 0.38253691 0.2628364 0.09106762 0.022464864 1.09959443
## X114 X115 X116 X117 X118
## ACR_11231843 0.85167021 0.30037450 0.69311810 0.12071532 0.097878733
## ADAO_11159808 0.05259933 0.23561042 0.14134243 0.05918947 0.003555476
## AGG_11236448 0.38720057 0.14875104 0.08956171 0.05434231 0.108238319
## AHL_11239959 1.37162339 0.45656675 0.14975569 0.04563216 0.227583528
## AJGD_11119689 0.01117013 1.29252134 1.04205316 1.24975433 0.339867974
## AMP_11228639 0.41505390 0.06479006 0.27364225 1.18044421 0.009253524
## X119 X120 X121 X122 X123
## ACR_11231843 0.69632880 0.38465546 1.60632437 0.926084558 0.03783492
## ADAO_11159808 0.63543453 0.01538931 0.07859084 0.182927040 0.43455218
## AGG_11236448 0.37169902 0.11546236 0.10994544 0.001360211 0.15009884
## AHL_11239959 0.59154893 0.51310507 0.08314661 0.354700588 0.04269064
## AJGD_11119689 0.10183911 1.02045941 0.90823236 0.117980298 0.07275469
## AMP_11228639 0.07260914 0.49290565 0.02098191 0.119144119 1.05423784
## X124 X125 X126 X127 X128 X129
## ACR_11231843 0.96712693 0.22886155 0.02446018 0.68391509 0.30160702 0.41211177
## ADAO_11159808 0.58844254 1.39238452 0.48340198 0.21146825 0.14126654 0.08854800
## AGG_11236448 0.13036276 0.03298052 0.05311740 0.10314905 0.01745345 0.65614993
## AHL_11239959 0.18178179 0.26636228 0.29299592 0.39920830 0.24528535 0.29603486
## AJGD_11119689 0.01501314 0.22884935 0.25637437 0.01702962 0.61497235 0.51419216
## AMP_11228639 0.17674555 0.07611547 0.17197804 0.21151216 0.10692892 0.03348028
## X130 X131 X132 X133 X134 X135
## ACR_11231843 0.22601528 0.10817561 0.88873564 0.83394664 0.09470509 0.7742032
## ADAO_11159808 0.27519734 0.08511306 0.22179853 0.32298807 1.70227576 0.7517833
## AGG_11236448 0.42985650 0.14073422 0.06242467 0.03384642 0.03875185 0.1477196
## AHL_11239959 0.21161934 0.09815904 0.26500300 1.45647875 0.96996173 0.1711319
## AJGD_11119689 0.01588871 0.58894735 0.29265761 0.04344138 0.26794538 0.4661658
## AMP_11228639 0.34749656 0.37484982 0.50953142 0.03022894 0.51075298 0.8285060
## X136 X137 X138 X139 X140 X141
## ACR_11231843 0.15365836 0.1761271 0.36691405 0.006769078 0.54336175 0.0761702
## ADAO_11159808 0.53919842 0.4043973 0.04210971 0.166856180 0.27798566 0.1009634
## AGG_11236448 0.02268298 0.5032756 0.24203830 0.636589940 0.01166856 0.2631851
## AHL_11239959 0.04439656 0.8580828 0.10645356 0.428791009 0.10377986 0.2144281
## AJGD_11119689 0.19749458 0.6429315 0.91410845 0.302304765 0.12581223 0.1072460
## AMP_11228639 0.04020463 0.2948562 0.92806399 0.860242300 0.08146581 0.2682734
## X142 X143 X144 X145 X146 X147
## ACR_11231843 0.54562262 0.25612383 0.57661963 0.65028582 0.05102542 0.2657341
## ADAO_11159808 0.02377378 0.21708138 0.50406161 0.08028418 0.25018749 0.0261376
## AGG_11236448 0.84366075 0.49347313 0.14134819 0.15037216 0.19089331 0.5078440
## AHL_11239959 0.27137650 0.04545224 0.20706567 0.26137663 0.06979830 0.2141987
## AJGD_11119689 1.57444642 0.61064916 0.51239968 0.17390138 0.77048638 0.4888393
## AMP_11228639 0.14086267 0.21799109 0.08206153 0.19756147 0.24277992 1.2493877
## X148 X149 X150 X151 X152 X153
## ACR_11231843 0.2021471 0.18897725 0.04184583 0.79406666 0.75111967 0.20645655
## ADAO_11159808 0.2111108 0.37408325 0.17854977 0.46355929 0.07495632 0.56748964
## AGG_11236448 0.2713340 1.33337500 0.15996143 0.06611713 0.22701817 0.08168726
## AHL_11239959 0.5794966 0.22052322 1.38878852 0.15826284 0.16428729 0.47355487
## AJGD_11119689 0.3999856 0.03704364 0.66692902 0.69766195 0.13474959 0.08944450
## AMP_11228639 0.2384935 0.14472405 0.21185153 0.26726193 0.40201971 0.09498602
## X154 X155 X156 X157 X158
## ACR_11231843 1.272257189 0.2680478 0.455085006 0.7714997 0.002032642
## ADAO_11159808 0.385766878 0.2824129 0.002452671 0.2065068 0.168492742
## AGG_11236448 0.059499256 0.3341342 0.316293308 0.1645627 0.153132332
## AHL_11239959 0.135967462 0.4326142 0.366831995 0.1949635 0.145206553
## AJGD_11119689 0.009030549 1.1793489 0.960937831 0.7410281 1.197673689
## AMP_11228639 0.852422507 0.8033623 0.112349724 0.2625010 0.385896934
## X159 X160 X161 X162 X163
## ACR_11231843 0.68454730 0.37576645 0.046439150 0.72588598 0.23069396
## ADAO_11159808 0.04936571 0.01669247 0.286209038 0.39748827 0.08506296
## AGG_11236448 0.14976566 0.04327644 0.195309145 0.20226946 0.16639643
## AHL_11239959 0.11632699 0.13351894 0.009203534 0.18670716 0.39982501
## AJGD_11119689 0.03087216 0.76447928 0.356644220 1.18551315 1.00685466
## AMP_11228639 0.41613458 0.11351762 0.654344178 0.08287193 0.19281719
## X164 X165 X166 X167 X168 X169
## ACR_11231843 1.41904723 1.96835265 0.22453651 0.7145666 0.5754881 0.06109180
## ADAO_11159808 0.37606704 0.27326848 0.91116828 0.2053745 0.3203388 0.07877583
## AGG_11236448 0.22949380 0.37252524 0.03090648 0.3226837 0.2212241 0.05732658
## AHL_11239959 0.29136610 0.10148023 0.01836230 0.1778878 0.1194493 0.52066788
## AJGD_11119689 0.04915455 0.01039991 0.56511322 0.6238118 0.2621124 1.56149343
## AMP_11228639 0.04307867 0.41434786 0.22037879 0.5476918 0.4863969 0.11096992
## X170 X171 X172 X173 X174 X175
## ACR_11231843 0.36436476 0.003235361 1.07972811 0.6770586 0.39766494 0.6852988
## ADAO_11159808 0.05157281 0.105487130 0.20118155 0.1496182 0.26596822 0.4708543
## AGG_11236448 0.02913338 0.103799040 0.01964877 0.2850642 0.04646342 0.1799489
## AHL_11239959 0.07112259 0.212487829 0.06620376 0.0135367 0.10774194 0.1314899
## AJGD_11119689 0.04992694 0.405085116 0.68937475 0.4577023 1.20738563 0.7557966
## AMP_11228639 0.59709084 0.666445928 0.07911088 0.2300625 1.22084641 0.9760717
## X176 X177 X178 X179 X180
## ACR_11231843 0.399378144 0.75744904 0.73032169 0.381475886 0.15635475
## ADAO_11159808 0.007518295 0.35864649 0.07596763 0.009782962 0.09268504
## AGG_11236448 0.036528790 0.23682518 0.11468107 0.008754517 0.08710478
## AHL_11239959 0.012510678 0.04688495 0.10005363 0.223363918 0.01242595
## AJGD_11119689 0.533546925 0.68398788 0.25503616 0.308725614 0.77263400
## AMP_11228639 0.263221956 0.13311770 0.19214180 0.458002208 0.16912509
## X181 X182 X183 X184 X185 X186
## ACR_11231843 0.02118434 0.00362504 0.8515620 0.18691666 0.27741193 0.3226309
## ADAO_11159808 0.77691386 0.69167946 0.3304749 0.36777465 0.24049500 0.1765575
## AGG_11236448 0.11908198 0.34151672 0.3481028 0.03160759 0.06296899 0.5941278
## AHL_11239959 0.21593605 0.20575134 0.5341288 0.08508552 0.04747835 0.3499951
## AJGD_11119689 0.12541396 0.27534373 0.2557009 0.61730824 1.10237658 0.1539590
## AMP_11228639 0.16438071 0.10530443 0.3419378 0.27978744 0.08624137 0.2094389
## X187 X188 X189 X190 X191 X192
## ACR_11231843 0.78360118 0.2796341 0.71263606 0.26015615 0.2903425 0.18991084
## ADAO_11159808 0.10353408 0.7161323 0.73793571 0.52859492 0.1169515 0.06637242
## AGG_11236448 0.06184992 0.2891638 0.10697819 0.02369134 0.3179578 0.13941382
## AHL_11239959 0.45356610 0.1221347 0.03103022 0.43368225 0.1137555 0.05424454
## AJGD_11119689 0.39018633 0.2045786 0.01983635 0.40718510 0.8600180 0.06659477
## AMP_11228639 0.21237401 0.5608268 0.20180052 0.08334293 1.0187466 0.23150274
## X193 X194 X195 X196 X197
## ACR_11231843 0.107841357 0.085673550 1.29013925 0.56727183 0.79309383
## ADAO_11159808 0.152208878 0.305092497 0.12275352 0.01014060 0.61923987
## AGG_11236448 0.628984861 0.005914783 0.04133008 0.23038340 0.40341151
## AHL_11239959 0.008512754 0.119816612 0.13024374 0.83301389 0.22198743
## AJGD_11119689 1.026411329 0.282891623 2.27377845 0.15561779 0.09010344
## AMP_11228639 0.307818025 0.268524973 0.35371383 0.07082852 0.48439822
## X198 X199 X200 X201 X202 X203
## ACR_11231843 0.19390761 0.34344281 0.03432184 0.6399025 0.40583066 0.046243640
## ADAO_11159808 0.08824812 0.14570060 0.16601659 0.1344122 0.14004753 0.594973677
## AGG_11236448 0.05763741 0.06035002 0.08956124 0.2642316 0.22793260 0.004579684
## AHL_11239959 0.17443521 0.08361908 0.08550385 0.2205543 0.19990807 0.247331184
## AJGD_11119689 0.46752936 0.39686010 0.14992364 1.0010551 0.34785450 0.593884976
## AMP_11228639 0.77809400 0.11884690 0.20027426 0.1519871 0.03400995 0.652225594
## X204 X205 X206 X207 X208 X209
## ACR_11231843 0.24912337 0.197063626 0.3369069 0.5142163 0.81151948 0.38771277
## ADAO_11159808 0.32513856 0.017270835 0.7296104 0.1781492 0.16660550 0.57005590
## AGG_11236448 0.06201424 0.045591797 0.1588534 0.1387704 0.21004865 0.22915979
## AHL_11239959 0.18202277 0.057303753 0.5170445 0.2276265 0.04759935 0.39915154
## AJGD_11119689 0.54444138 0.309254513 0.8380618 0.0809182 0.16167112 1.86436510
## AMP_11228639 0.15844271 0.008974888 0.3026384 0.1444743 0.83545839 0.06487324
## X210 X211 X212 X213 X214
## ACR_11231843 0.58367634 0.06320753 0.35833146 0.07356429 0.054893668
## ADAO_11159808 0.20323158 0.18144713 0.21559618 0.44713667 0.324378430
## AGG_11236448 0.06340307 0.03910384 0.02042461 0.42853588 0.001875915
## AHL_11239959 0.65343862 0.09673763 0.31361262 0.25852532 0.660081138
## AJGD_11119689 0.42395017 0.12416557 0.21112716 0.40888314 0.296393988
## AMP_11228639 0.59726561 0.73142344 0.11367295 0.02474390 0.158201817
## X215 X216 X217 X218 X219 X220
## ACR_11231843 0.05906023 0.45943000 0.21889251 0.55039583 0.09368779 0.40440755
## ADAO_11159808 0.68268112 0.16897061 0.13647195 0.30531753 0.12324225 0.18075554
## AGG_11236448 0.07868965 0.26742244 0.27230187 0.37051399 0.20980239 0.06411624
## AHL_11239959 0.19207745 0.26527677 0.02785792 0.26546908 0.28467673 0.05832171
## AJGD_11119689 0.73419574 0.01641983 0.04197220 0.03381538 0.14985549 0.12990695
## AMP_11228639 0.10688918 0.06489051 0.05269847 0.07497943 0.83339347 0.09362927
## X221 X222 X223 X224 X225 X226
## ACR_11231843 0.19049071 0.20496341 0.06924797 0.11462558 0.09001202 0.21512435
## ADAO_11159808 0.23796873 0.61394828 0.25562839 0.01559552 0.06204188 0.24444344
## AGG_11236448 0.02491383 0.04056907 0.35305507 0.08918968 0.14050656 0.08676012
## AHL_11239959 0.07986023 0.04297700 0.13013426 0.02680425 0.14260004 0.30816736
## AJGD_11119689 0.06543978 0.12655083 1.03615233 0.31600887 0.29368371 1.42728095
## AMP_11228639 0.00207768 1.01489996 0.21241960 0.19128902 0.38143942 0.08073271
## X227 X228 X229 X230 X231 X232
## ACR_11231843 0.4291729 0.64328194 0.27754624 0.5448398 0.17907109 0.13536850
## ADAO_11159808 0.8013306 0.04548383 0.26562712 0.1658672 0.08780379 0.07796793
## AGG_11236448 0.1642674 0.09922210 0.03176376 0.4912379 0.19874848 0.55686160
## AHL_11239959 0.2726512 0.02856449 0.46101187 0.1496803 0.04981553 0.11284301
## AJGD_11119689 0.5429213 0.86127740 0.16316650 0.1875355 0.06763499 0.98974159
## AMP_11228639 0.1182412 0.65061601 0.02843593 0.2673009 0.31449020 0.01934588
## X233 X234 X235 X236 X237
## ACR_11231843 0.20294677 0.004936078 0.13143932 0.06027313 0.029578168
## ADAO_11159808 0.08788036 0.342462875 0.08504698 0.01586710 0.447754293
## AGG_11236448 0.02541435 0.249122970 0.05475308 0.23152376 0.082910928
## AHL_11239959 0.06821724 0.400504233 0.16708117 0.14068016 0.110492139
## AJGD_11119689 0.33146197 0.243530787 1.60107164 1.00831465 0.004592071
## AMP_11228639 0.59950987 0.054223012 0.04412037 0.23432068 0.254317956
## X238 X239 X240 X241 X242 X243
## ACR_11231843 0.47247486 0.88444419 0.376130855 5.848578 0.3743415 6.8715853
## ADAO_11159808 0.59863741 0.89843503 0.001112582 19.161627 11.8601407 12.6707788
## AGG_11236448 0.35590587 0.03363888 0.019906344 41.825617 7.5375310 0.5631459
## AHL_11239959 0.57524535 0.08669302 1.257231537 9.799312 8.8503584 3.5331319
## AJGD_11119689 0.01020877 0.53460878 0.076407072 15.614663 21.7300554 2.3159148
## AMP_11228639 0.11975047 0.17994234 0.071253304 74.278454 21.2659958 28.3545140
## X244 X245 X246 X247 X248 X249
## ACR_11231843 1.212893 5.4053599 7.119084 15.28118156 2.2116713 4.0278251
## ADAO_11159808 11.342341 2.4783679 1.319596 7.78966139 4.3803474 10.9436912
## AGG_11236448 3.883392 6.8843685 3.681852 2.86468612 3.5267080 12.2953089
## AHL_11239959 14.026002 8.4455885 8.689473 2.24797526 11.5702112 10.6558848
## AJGD_11119689 13.376941 2.6553456 4.381623 0.02934979 2.9018657 1.6380244
## AMP_11228639 4.785927 0.3193887 2.712271 1.07321517 0.1119197 0.2932176
## X250 X251 X252 X253 X254 X255
## ACR_11231843 4.230968 0.1883137 4.4361328 4.9348069 4.4093973 6.7331185
## ADAO_11159808 7.742754 6.0392558 2.5368184 0.3145458 4.4979165 5.0708085
## AGG_11236448 5.469311 9.8786332 0.8261306 4.0094456 0.9770059 0.7133863
## AHL_11239959 1.958953 1.4145088 8.6658881 4.0880628 1.0348649 9.4089391
## AJGD_11119689 3.544227 0.5145252 6.3450522 1.6824053 3.2126460 0.6232166
## AMP_11228639 0.157840 0.8138084 0.3929538 1.0801008 3.1056244 0.2646312
## X256 X257 X258 X259 X260 X261
## ACR_11231843 1.9762133 0.93579178 6.0100283 3.0571351 1.54232147 0.3842215
## ADAO_11159808 4.7334760 6.44443545 9.4783702 0.2898114 0.33698581 0.9009742
## AGG_11236448 4.4751416 0.34515817 0.4843672 1.0754701 0.06283354 3.8517427
## AHL_11239959 0.4852065 0.35446548 0.2574401 3.8265333 2.95356184 0.8691741
## AJGD_11119689 1.0946418 4.12262590 4.1668995 1.5698349 0.22788057 6.9034674
## AMP_11228639 0.4916785 0.07103315 0.7199646 0.4103392 1.08671137 0.2895402
## X262 X263 X264 X265 X266 X267
## ACR_11231843 2.6002511 1.10430444 0.02336846 2.3374742 0.5025686 5.0378185
## ADAO_11159808 6.7098349 2.21177265 0.71826102 2.4029995 2.2759813 3.7789734
## AGG_11236448 0.5734194 0.13949199 1.11299942 0.1314794 3.3877322 3.6114150
## AHL_11239959 1.1501982 2.88962546 5.32419393 0.3643765 1.0724044 0.2690623
## AJGD_11119689 2.5985036 1.04006215 5.43264599 1.2104396 0.5301348 0.1577908
## AMP_11228639 2.7390024 0.04809891 0.27554454 0.1999019 1.1189641 0.6687096
## X268 X269 X270 X271 X272 X273
## ACR_11231843 0.5831323 0.9783577 2.0479549 0.9238922 0.7557598 0.29081018
## ADAO_11159808 3.3253260 1.1003379 3.4222632 1.7187118 0.3116339 2.78544583
## AGG_11236448 0.9049440 3.1764513 0.3158165 0.1746159 3.9563430 0.54172820
## AHL_11239959 0.8720393 0.2882118 0.2864212 0.4383296 0.9872661 0.03147537
## AJGD_11119689 0.6199841 1.4541586 1.3212389 1.6111188 0.5216999 0.71336370
## AMP_11228639 0.3893545 0.1820798 1.8752743 0.7205132 0.9431357 3.46603021
## X274 X275 X276 X277 X278 X279
## ACR_11231843 0.89996994 0.434101445 0.17706965 1.114130034 0.818201 0.1243323
## ADAO_11159808 0.10297563 0.005523373 3.44773877 1.184151623 2.011015 3.1392105
## AGG_11236448 1.76530074 1.310928721 1.42964220 3.024306932 0.401883 0.8281645
## AHL_11239959 1.05700756 1.019235571 2.70338438 0.002951126 1.110485 1.3243382
## AJGD_11119689 1.44464965 1.022303502 0.11925587 0.404745802 2.039838 0.7185712
## AMP_11228639 0.08606386 1.224937812 0.05180415 0.168620077 1.701726 1.2528504
## X280 X281 X282 X283 X284 X285
## ACR_11231843 0.4194054 2.38229839 1.7365795 1.7978597 0.5066648 0.50289164
## ADAO_11159808 0.7270018 0.01130704 0.5212688 1.0388283 0.9493673 0.62700018
## AGG_11236448 5.9207354 0.65580562 0.8074447 3.1525108 0.8490893 0.01835407
## AHL_11239959 0.5503924 0.70264608 0.5572120 1.6333344 0.4172136 0.30559030
## AJGD_11119689 0.8071540 0.04378581 0.7365631 2.3245802 1.9796980 0.27089096
## AMP_11228639 0.4970656 0.31376147 0.1540637 0.7114498 0.3954184 0.47672667
## X286 X287 X288 X289 X290 X291
## ACR_11231843 0.73233210 0.08888497 2.26401020 0.34543451 0.18619840 0.11415485
## ADAO_11159808 0.08173826 0.14420578 0.31672438 2.47634017 2.00092175 0.62268402
## AGG_11236448 2.21523030 0.03900382 1.25560813 0.40837894 0.01399644 0.41369583
## AHL_11239959 0.33378635 0.34429828 0.06036797 0.82313582 0.02939305 0.02025657
## AJGD_11119689 0.21498381 0.40268939 0.33478576 0.01749701 0.76442488 0.44458857
## AMP_11228639 0.46731205 0.49128565 0.09666285 0.02221005 0.29742321 0.10835145
## X292 X293 X294 X295 X296 X297
## ACR_11231843 0.31159461 0.09302105 0.05915665 0.25901322 0.4771633 1.05067514
## ADAO_11159808 0.59616769 0.03502095 0.01228136 0.23902748 0.1155511 0.09665106
## AGG_11236448 0.26496712 0.66927353 0.23219247 0.80816201 1.2789929 0.64002047
## AHL_11239959 0.05367467 0.20684620 0.39182557 0.48229323 0.0372571 0.80085676
## AJGD_11119689 1.00991198 0.89924717 0.45429739 0.08590092 0.9762465 0.27207320
## AMP_11228639 0.35436989 0.21721122 0.16910957 0.01759638 0.2481277 0.31268859
## X298 X299 X300 X301 X302 X303
## ACR_11231843 0.9321941 0.648464481 1.01425475 0.7315768 2.72857701 0.07153099
## ADAO_11159808 1.8965789 0.599167360 0.35297847 2.1246968 0.00449535 0.45493334
## AGG_11236448 0.7709875 0.009326569 0.07763641 0.6863714 0.11182585 0.10970842
## AHL_11239959 0.2620627 0.239919218 0.33732714 0.2343275 0.27443549 0.05932641
## AJGD_11119689 1.1704495 1.229149415 0.77594042 0.8182855 0.10231199 0.12014327
## AMP_11228639 0.5058534 0.131831439 0.11627900 0.5801604 0.07452320 0.05325495
## X304 X305 X306 X307 X308 X309
## ACR_11231843 0.1639048 0.04842124 0.0703432 0.4060085 0.07509483 0.04447808
## ADAO_11159808 0.9414528 0.11256975 0.1694315 1.0382281 0.23265890 0.37949181
## AGG_11236448 0.3049607 0.63158206 0.2006222 0.3728191 0.17447169 0.51896106
## AHL_11239959 0.1907896 0.29974900 0.8147033 0.6298898 1.19600632 0.93066032
## AJGD_11119689 0.2716897 1.92578803 0.5990802 2.4382180 0.53577310 0.27124141
## AMP_11228639 1.2552320 0.88748663 0.1914041 0.9140920 0.62565336 0.43974751
## X310 X311 X312 X313 X314 X315
## ACR_11231843 0.46672033 0.071477462 0.2201671 1.34872679 0.51943395 0.3915925
## ADAO_11159808 0.24789428 0.270079732 1.1028110 0.37235084 0.13846721 0.7887426
## AGG_11236448 0.47299067 0.001271438 0.2545867 0.08332572 0.37890282 1.0209840
## AHL_11239959 0.03460972 0.077448599 0.3563844 0.81215793 0.02453207 0.4363844
## AJGD_11119689 0.60776532 1.779895580 1.6305871 0.19769748 0.65524263 0.4509812
## AMP_11228639 0.68536920 0.562934256 0.4314150 0.03511920 0.00295760 0.9003414
## X316 X317 X318 X319 X320
## ACR_11231843 1.20368518 0.798697223 1.52675332 0.943549135 0.2363449
## ADAO_11159808 0.09448861 0.258291569 0.09216212 0.654059083 0.7365223
## AGG_11236448 0.29150520 0.139753006 0.17857195 0.819767597 0.6563020
## AHL_11239959 0.18546077 0.008480168 0.18849292 0.237510748 0.1156081
## AJGD_11119689 0.77369087 0.334620132 0.39313970 0.008210355 0.1508059
## AMP_11228639 0.24516452 0.126962347 0.62068025 0.076773597 0.1002166
## X321 X322 X323 X324 X325 X326
## ACR_11231843 1.00719211 0.08786765 0.2448035 0.399615495 1.1309995 0.3318204
## ADAO_11159808 0.31786771 0.04099962 0.4292995 0.106744978 1.2884651 1.2708608
## AGG_11236448 0.09210857 0.09340645 0.5166284 0.008174061 0.2048886 0.6327526
## AHL_11239959 0.16424258 1.20871384 0.1750306 0.302692682 0.9089198 0.6159354
## AJGD_11119689 0.72799373 1.09878721 1.0684334 0.743965146 0.1371142 0.7476956
## AMP_11228639 0.08354674 0.23311262 0.1870228 0.979219341 1.0577347 0.2396080
## X327 X328 X329 X330 X331
## ACR_11231843 0.169705850 0.75224359 0.007243292 1.47533935 0.2717003
## ADAO_11159808 0.607560355 0.30408343 0.195075751 0.06035056 0.4421998
## AGG_11236448 0.075923947 0.66283217 0.591109780 0.20640767 0.2909917
## AHL_11239959 0.664614272 0.08596877 0.019772305 0.34487369 0.4747587
## AJGD_11119689 0.006004055 1.05964147 0.214203248 0.82648047 0.6238030
## AMP_11228639 0.412215794 0.16925785 0.599583639 1.30160190 0.8305493
## X332 X333 X334 X335 X336 X337
## ACR_11231843 0.19744715 0.376500184 0.7331604 0.8123600 0.55460067 0.3674562
## ADAO_11159808 0.80529665 0.127916924 0.9013566 0.3082349 0.07970664 0.0874975
## AGG_11236448 0.09904979 0.008880916 0.2827778 0.9098785 0.21898269 0.5276326
## AHL_11239959 0.25214486 0.761323317 0.6338753 0.1641917 0.54500595 0.1387733
## AJGD_11119689 0.87662068 0.787996998 1.4449421 0.2580136 0.01631161 0.7544742
## AMP_11228639 0.80703554 0.187444280 0.3520511 0.1746270 0.25491150 1.2246640
## X338 X339 X340 X341 X342 X343
## ACR_11231843 0.30748514 0.2135174 1.39428230 0.06150944 1.85343796 0.27355879
## ADAO_11159808 0.06207332 0.2388713 0.90213270 0.65397939 0.08248442 0.02118811
## AGG_11236448 0.03251931 0.2964378 0.36744212 1.21882876 0.37493316 0.10854335
## AHL_11239959 0.99612598 0.1996762 0.17984257 0.01575614 0.46764285 0.01228834
## AJGD_11119689 0.11383767 0.4837966 0.08260011 0.37194273 1.07598009 0.63725342
## AMP_11228639 0.40411234 0.2545733 0.93147385 0.17092171 0.22215541 0.05555407
## X344 X345 X346 X347 X348
## ACR_11231843 0.54936063 0.28766223 0.1543180054 0.80728643 1.40107592
## ADAO_11159808 0.09541252 0.21921704 0.3441032993 0.98599398 0.16939273
## AGG_11236448 0.33075607 0.05045575 0.2544029764 0.32660503 0.46503359
## AHL_11239959 0.19107927 0.16815766 0.0003095814 0.03807210 0.42500160
## AJGD_11119689 0.20247572 0.92312830 0.1144728347 0.03756933 1.07585610
## AMP_11228639 0.32622975 0.39249326 0.1916083885 0.06715572 0.04781766
## X349 X350 X351 X352 X353 X354
## ACR_11231843 0.06638568 0.2236739 0.59903537 0.324239294 0.53898729 0.85167021
## ADAO_11159808 0.48330111 1.4479789 0.01125781 0.009723951 0.19546984 0.05259933
## AGG_11236448 0.55979697 0.2692365 0.24805002 0.055516388 0.02056208 0.38720057
## AHL_11239959 0.11036749 0.1558639 0.02372695 0.371092714 1.10110908 1.37162339
## AJGD_11119689 0.40967920 0.6457600 0.21290341 0.052172611 1.42746510 0.01117013
## AMP_11228639 0.38253691 0.2628364 0.09106762 0.022464864 1.09959443 0.41505390
## X355 X356 X357 X358 X359
## ACR_11231843 0.30037450 0.69311810 0.12071532 0.097878733 0.69632880
## ADAO_11159808 0.23561042 0.14134243 0.05918947 0.003555476 0.63543453
## AGG_11236448 0.14875104 0.08956171 0.05434231 0.108238319 0.37169902
## AHL_11239959 0.45656675 0.14975569 0.04563216 0.227583528 0.59154893
## AJGD_11119689 1.29252134 1.04205316 1.24975433 0.339867974 0.10183911
## AMP_11228639 0.06479006 0.27364225 1.18044421 0.009253524 0.07260914
## X360 X361 X362 X363 X364
## ACR_11231843 0.38465546 1.60632437 0.926084558 0.03783492 0.96712693
## ADAO_11159808 0.01538931 0.07859084 0.182927040 0.43455218 0.58844254
## AGG_11236448 0.11546236 0.10994544 0.001360211 0.15009884 0.13036276
## AHL_11239959 0.51310507 0.08314661 0.354700588 0.04269064 0.18178179
## AJGD_11119689 1.02045941 0.90823236 0.117980298 0.07275469 0.01501314
## AMP_11228639 0.49290565 0.02098191 0.119144119 1.05423784 0.17674555
## X365 X366 X367 X368 X369 X370
## ACR_11231843 0.22886155 0.02446018 0.68391509 0.30160702 0.41211177 0.22601528
## ADAO_11159808 1.39238452 0.48340198 0.21146825 0.14126654 0.08854800 0.27519734
## AGG_11236448 0.03298052 0.05311740 0.10314905 0.01745345 0.65614993 0.42985650
## AHL_11239959 0.26636228 0.29299592 0.39920830 0.24528535 0.29603486 0.21161934
## AJGD_11119689 0.22884935 0.25637437 0.01702962 0.61497235 0.51419216 0.01588871
## AMP_11228639 0.07611547 0.17197804 0.21151216 0.10692892 0.03348028 0.34749656
## X371 X372 X373 X374 X375 X376
## ACR_11231843 0.10817561 0.88873564 0.83394664 0.09470509 0.7742032 0.15365836
## ADAO_11159808 0.08511306 0.22179853 0.32298807 1.70227576 0.7517833 0.53919842
## AGG_11236448 0.14073422 0.06242467 0.03384642 0.03875185 0.1477196 0.02268298
## AHL_11239959 0.09815904 0.26500300 1.45647875 0.96996173 0.1711319 0.04439656
## AJGD_11119689 0.58894735 0.29265761 0.04344138 0.26794538 0.4661658 0.19749458
## AMP_11228639 0.37484982 0.50953142 0.03022894 0.51075298 0.8285060 0.04020463
## X377 X378 X379 X380 X381 X382
## ACR_11231843 0.1761271 0.36691405 0.006769078 0.54336175 0.0761702 0.54562262
## ADAO_11159808 0.4043973 0.04210971 0.166856180 0.27798566 0.1009634 0.02377378
## AGG_11236448 0.5032756 0.24203830 0.636589940 0.01166856 0.2631851 0.84366075
## AHL_11239959 0.8580828 0.10645356 0.428791009 0.10377986 0.2144281 0.27137650
## AJGD_11119689 0.6429315 0.91410845 0.302304765 0.12581223 0.1072460 1.57444642
## AMP_11228639 0.2948562 0.92806399 0.860242300 0.08146581 0.2682734 0.14086267
## X383 X384 X385 X386 X387 X388
## ACR_11231843 0.25612383 0.57661963 0.65028582 0.05102542 0.2657341 0.2021471
## ADAO_11159808 0.21708138 0.50406161 0.08028418 0.25018749 0.0261376 0.2111108
## AGG_11236448 0.49347313 0.14134819 0.15037216 0.19089331 0.5078440 0.2713340
## AHL_11239959 0.04545224 0.20706567 0.26137663 0.06979830 0.2141987 0.5794966
## AJGD_11119689 0.61064916 0.51239968 0.17390138 0.77048638 0.4888393 0.3999856
## AMP_11228639 0.21799109 0.08206153 0.19756147 0.24277992 1.2493877 0.2384935
## X389 X390 X391 X392 X393
## ACR_11231843 0.18897725 0.04184583 0.79406666 0.75111967 0.20645655
## ADAO_11159808 0.37408325 0.17854977 0.46355929 0.07495632 0.56748964
## AGG_11236448 1.33337500 0.15996143 0.06611713 0.22701817 0.08168726
## AHL_11239959 0.22052322 1.38878852 0.15826284 0.16428729 0.47355487
## AJGD_11119689 0.03704364 0.66692902 0.69766195 0.13474959 0.08944450
## AMP_11228639 0.14472405 0.21185153 0.26726193 0.40201971 0.09498602
## X394 X395 X396 X397 X398
## ACR_11231843 1.272257189 0.2680478 0.455085006 0.7714997 0.002032642
## ADAO_11159808 0.385766878 0.2824129 0.002452671 0.2065068 0.168492742
## AGG_11236448 0.059499256 0.3341342 0.316293308 0.1645627 0.153132332
## AHL_11239959 0.135967462 0.4326142 0.366831995 0.1949635 0.145206553
## AJGD_11119689 0.009030549 1.1793489 0.960937831 0.7410281 1.197673689
## AMP_11228639 0.852422507 0.8033623 0.112349724 0.2625010 0.385896934
## X399 X400 X401 X402 X403
## ACR_11231843 0.68454730 0.37576645 0.046439150 0.72588598 0.23069396
## ADAO_11159808 0.04936571 0.01669247 0.286209038 0.39748827 0.08506296
## AGG_11236448 0.14976566 0.04327644 0.195309145 0.20226946 0.16639643
## AHL_11239959 0.11632699 0.13351894 0.009203534 0.18670716 0.39982501
## AJGD_11119689 0.03087216 0.76447928 0.356644220 1.18551315 1.00685466
## AMP_11228639 0.41613458 0.11351762 0.654344178 0.08287193 0.19281719
## X404 X405 X406 X407 X408 X409
## ACR_11231843 1.41904723 1.96835265 0.22453651 0.7145666 0.5754881 0.06109180
## ADAO_11159808 0.37606704 0.27326848 0.91116828 0.2053745 0.3203388 0.07877583
## AGG_11236448 0.22949380 0.37252524 0.03090648 0.3226837 0.2212241 0.05732658
## AHL_11239959 0.29136610 0.10148023 0.01836230 0.1778878 0.1194493 0.52066788
## AJGD_11119689 0.04915455 0.01039991 0.56511322 0.6238118 0.2621124 1.56149343
## AMP_11228639 0.04307867 0.41434786 0.22037879 0.5476918 0.4863969 0.11096992
## X410 X411 X412 X413 X414 X415
## ACR_11231843 0.36436476 0.003235361 1.07972811 0.6770586 0.39766494 0.6852988
## ADAO_11159808 0.05157281 0.105487130 0.20118155 0.1496182 0.26596822 0.4708543
## AGG_11236448 0.02913338 0.103799040 0.01964877 0.2850642 0.04646342 0.1799489
## AHL_11239959 0.07112259 0.212487829 0.06620376 0.0135367 0.10774194 0.1314899
## AJGD_11119689 0.04992694 0.405085116 0.68937475 0.4577023 1.20738563 0.7557966
## AMP_11228639 0.59709084 0.666445928 0.07911088 0.2300625 1.22084641 0.9760717
## X416 X417 X418 X419 X420
## ACR_11231843 0.399378144 0.75744904 0.73032169 0.381475886 0.15635475
## ADAO_11159808 0.007518295 0.35864649 0.07596763 0.009782962 0.09268504
## AGG_11236448 0.036528790 0.23682518 0.11468107 0.008754517 0.08710478
## AHL_11239959 0.012510678 0.04688495 0.10005363 0.223363918 0.01242595
## AJGD_11119689 0.533546925 0.68398788 0.25503616 0.308725614 0.77263400
## AMP_11228639 0.263221956 0.13311770 0.19214180 0.458002208 0.16912509
## X421 X422 X423 X424 X425 X426
## ACR_11231843 0.02118434 0.00362504 0.8515620 0.18691666 0.27741193 0.3226309
## ADAO_11159808 0.77691386 0.69167946 0.3304749 0.36777465 0.24049500 0.1765575
## AGG_11236448 0.11908198 0.34151672 0.3481028 0.03160759 0.06296899 0.5941278
## AHL_11239959 0.21593605 0.20575134 0.5341288 0.08508552 0.04747835 0.3499951
## AJGD_11119689 0.12541396 0.27534373 0.2557009 0.61730824 1.10237658 0.1539590
## AMP_11228639 0.16438071 0.10530443 0.3419378 0.27978744 0.08624137 0.2094389
## X427 X428 X429 X430 X431 X432
## ACR_11231843 0.78360118 0.2796341 0.71263606 0.26015615 0.2903425 0.18991084
## ADAO_11159808 0.10353408 0.7161323 0.73793571 0.52859492 0.1169515 0.06637242
## AGG_11236448 0.06184992 0.2891638 0.10697819 0.02369134 0.3179578 0.13941382
## AHL_11239959 0.45356610 0.1221347 0.03103022 0.43368225 0.1137555 0.05424454
## AJGD_11119689 0.39018633 0.2045786 0.01983635 0.40718510 0.8600180 0.06659477
## AMP_11228639 0.21237401 0.5608268 0.20180052 0.08334293 1.0187466 0.23150274
## X433 X434 X435 X436 X437
## ACR_11231843 0.107841357 0.085673550 1.29013925 0.56727183 0.79309383
## ADAO_11159808 0.152208878 0.305092497 0.12275352 0.01014060 0.61923987
## AGG_11236448 0.628984861 0.005914783 0.04133008 0.23038340 0.40341151
## AHL_11239959 0.008512754 0.119816612 0.13024374 0.83301389 0.22198743
## AJGD_11119689 1.026411329 0.282891623 2.27377845 0.15561779 0.09010344
## AMP_11228639 0.307818025 0.268524973 0.35371383 0.07082852 0.48439822
## X438 X439 X440 X441 X442 X443
## ACR_11231843 0.19390761 0.34344281 0.03432184 0.6399025 0.40583066 0.046243640
## ADAO_11159808 0.08824812 0.14570060 0.16601659 0.1344122 0.14004753 0.594973677
## AGG_11236448 0.05763741 0.06035002 0.08956124 0.2642316 0.22793260 0.004579684
## AHL_11239959 0.17443521 0.08361908 0.08550385 0.2205543 0.19990807 0.247331184
## AJGD_11119689 0.46752936 0.39686010 0.14992364 1.0010551 0.34785450 0.593884976
## AMP_11228639 0.77809400 0.11884690 0.20027426 0.1519871 0.03400995 0.652225594
## X444 X445 X446 X447 X448 X449
## ACR_11231843 0.24912337 0.197063626 0.3369069 0.5142163 0.81151948 0.38771277
## ADAO_11159808 0.32513856 0.017270835 0.7296104 0.1781492 0.16660550 0.57005590
## AGG_11236448 0.06201424 0.045591797 0.1588534 0.1387704 0.21004865 0.22915979
## AHL_11239959 0.18202277 0.057303753 0.5170445 0.2276265 0.04759935 0.39915154
## AJGD_11119689 0.54444138 0.309254513 0.8380618 0.0809182 0.16167112 1.86436510
## AMP_11228639 0.15844271 0.008974888 0.3026384 0.1444743 0.83545839 0.06487324
## X450 X451 X452 X453 X454
## ACR_11231843 0.58367634 0.06320753 0.35833146 0.07356429 0.054893668
## ADAO_11159808 0.20323158 0.18144713 0.21559618 0.44713667 0.324378430
## AGG_11236448 0.06340307 0.03910384 0.02042461 0.42853588 0.001875915
## AHL_11239959 0.65343862 0.09673763 0.31361262 0.25852532 0.660081138
## AJGD_11119689 0.42395017 0.12416557 0.21112716 0.40888314 0.296393988
## AMP_11228639 0.59726561 0.73142344 0.11367295 0.02474390 0.158201817
## X455 X456 X457 X458 X459 X460
## ACR_11231843 0.05906023 0.45943000 0.21889251 0.55039583 0.09368779 0.40440755
## ADAO_11159808 0.68268112 0.16897061 0.13647195 0.30531753 0.12324225 0.18075554
## AGG_11236448 0.07868965 0.26742244 0.27230187 0.37051399 0.20980239 0.06411624
## AHL_11239959 0.19207745 0.26527677 0.02785792 0.26546908 0.28467673 0.05832171
## AJGD_11119689 0.73419574 0.01641983 0.04197220 0.03381538 0.14985549 0.12990695
## AMP_11228639 0.10688918 0.06489051 0.05269847 0.07497943 0.83339347 0.09362927
## X461 X462 X463 X464 X465 X466
## ACR_11231843 0.19049071 0.20496341 0.06924797 0.11462558 0.09001202 0.21512435
## ADAO_11159808 0.23796873 0.61394828 0.25562839 0.01559552 0.06204188 0.24444344
## AGG_11236448 0.02491383 0.04056907 0.35305507 0.08918968 0.14050656 0.08676012
## AHL_11239959 0.07986023 0.04297700 0.13013426 0.02680425 0.14260004 0.30816736
## AJGD_11119689 0.06543978 0.12655083 1.03615233 0.31600887 0.29368371 1.42728095
## AMP_11228639 0.00207768 1.01489996 0.21241960 0.19128902 0.38143942 0.08073271
## X467 X468 X469 X470 X471 X472
## ACR_11231843 0.4291729 0.64328194 0.27754624 0.5448398 0.17907109 0.13536850
## ADAO_11159808 0.8013306 0.04548383 0.26562712 0.1658672 0.08780379 0.07796793
## AGG_11236448 0.1642674 0.09922210 0.03176376 0.4912379 0.19874848 0.55686160
## AHL_11239959 0.2726512 0.02856449 0.46101187 0.1496803 0.04981553 0.11284301
## AJGD_11119689 0.5429213 0.86127740 0.16316650 0.1875355 0.06763499 0.98974159
## AMP_11228639 0.1182412 0.65061601 0.02843593 0.2673009 0.31449020 0.01934588
## X473 X474 X475 X476 X477
## ACR_11231843 0.20294677 0.004936078 0.13143932 0.06027313 0.029578168
## ADAO_11159808 0.08788036 0.342462875 0.08504698 0.01586710 0.447754293
## AGG_11236448 0.02541435 0.249122970 0.05475308 0.23152376 0.082910928
## AHL_11239959 0.06821724 0.400504233 0.16708117 0.14068016 0.110492139
## AJGD_11119689 0.33146197 0.243530787 1.60107164 1.00831465 0.004592071
## AMP_11228639 0.59950987 0.054223012 0.04412037 0.23432068 0.254317956
## X478 X479 X480 DDclust_PER_FC_scaled
## ACR_11231843 0.47247486 0.88444419 0.376130855 1
## ADAO_11159808 0.59863741 0.89843503 0.001112582 1
## AGG_11236448 0.35590587 0.03363888 0.019906344 2
## AHL_11239959 0.57524535 0.08669302 1.257231537 1
## AJGD_11119689 0.01020877 0.53460878 0.076407072 1
## AMP_11228639 0.11975047 0.17994234 0.071253304 2## Mean by groups
rp_tbl_PER <- aggregate(plotting_PER, by = list(plotting_PER$DDclust_PER_FC_scaled), mean)
row.names(rp_tbl_PER) <- paste0("Group",rp_tbl_PER$DDclust_PER_FC_scaled)
rp_tbl_PER <- rp_tbl_PER %>%
select(starts_with('X'))
rp_tbl_PER <- data.frame(t(rp_tbl_PER))
head(rp_tbl_PER)## Group1 Group2
## X1 9.988256 60.812753
## X2 18.272359 30.535605
## X3 12.719218 11.138120
## X4 10.436361 9.645320
## X5 8.276730 6.683309
## X6 4.737287 5.369705# 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_FC_scaled = data.frame(matrix(ncol = 3 , nrow = 3))
colnames(rand_index_table_FC_scaled) <- c("DDclust_ACF_FC_scaled", "DDclust_EUCL_FC_scaled", "DDclust_PER_FC_scaled")
rownames(rand_index_table_FC_scaled) <- c("DDclust_ACF_FC_scaled", "DDclust_EUCL_FC_scaled", "DDclust_PER_FC_scaled")
cluster_study_FC_scaled <- list(DDclust_ACF_FC_scaled, DDclust_EUCL_FC_scaled, DDclust_PER_FC_scaled)for (i in c(1:length(cluster_study_FC_scaled))) {
for (j in c(1:length(cluster_study_FC_scaled))){
rand_index_table_FC_scaled[i,j] <- adjustedRandIndex(cluster_study_FC_scaled[[i]], cluster_study_FC_scaled[[j]])
}}head(rand_index_table_FC_scaled)## DDclust_ACF_FC_scaled DDclust_EUCL_FC_scaled
## DDclust_ACF_FC_scaled 1.0000000000 -0.0006868268
## DDclust_EUCL_FC_scaled -0.0006868268 1.0000000000
## DDclust_PER_FC_scaled 0.2176448445 0.0109808890
## DDclust_PER_FC_scaled
## DDclust_ACF_FC_scaled 0.21764484
## DDclust_EUCL_FC_scaled 0.01098089
## DDclust_PER_FC_scaled 1.00000000write.csv(cluster_study_FC_scaled, "../../data/clusters/cluster_study_FC_scaled.csv")