Last updated: 2020-09-13

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Knit directory: transcriptome_cll/

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Unstaged changes:
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Hierarchical structure

Find variables that explain the data#s structure in ansupervised way using hierarchical clustering and Principal component analysis

suppressPackageStartupMessages({
  library(DESeq2)
  library(dplyr)
  library(ggplot2)
  library(tidyverse)
  library(ComplexHeatmap)
  library(ggpubr)
  library(RColorBrewer)
  library(circlize)
  library(here)
})

Data

data_dir <- here("data")
output_dir <- here("output")
figure_dir <- here("output/figures")

#dds data set. gene expression data + patmetadata
load(paste0(data_dir, "/ddsrnaCLL_150218.RData"))

#load meta data including genotyping info
load(paste0(data_dir, "/patmeta_170324.RData"))

normalize data

#Variance stabilization transformation of the raw data
ddsCLL <- estimateSizeFactors(ddsCLL)
RNAnorm <- varianceStabilizingTransformation(ddsCLL, blind=T)

Filter genes

exprMat <- assay(RNAnorm)

# filter IG genes
filtered <- as_tibble(rowData(ddsCLL)) %>% mutate(geneID = rownames(ddsCLL)) %>% filter(!grepl("IGH",symbol)) %>% filter(!grepl("IGK",symbol)) %>% filter(!grepl("IGL",symbol))  

exprMat <- exprMat[filtered$geneID,]

#top 500 most variant genes
sds <- rowSds(exprMat)
exprMat <- exprMat[order(sds, decreasing = T)[1:500],]
colnames(exprMat) <- colData(ddsCLL)$PatID
exprMat.new <- log2(exprMat)
exprMat.new <- t(scale(t(exprMat.new)))
exprMat.new[exprMat.new > 4] <- 4
exprMat.new[exprMat.new < -4] <- -4
rownames(exprMat.new) <- rowData(RNAnorm[rownames(exprMat),])$symbol

Hierarchical clustering - heatmap

#colors
colors = colorRamp2(c(-4, -1 ,0, 1, 4), c("#2166ac","#4393c3", "#f7f7f7", "#d6604d","#b2182b"))
annocol <- get_palette("jco", 10)
annocolor <- list(IGHV = c("M" = annocol[1], "U" = annocol[2]) , 
                  trisomy12 = c( "1" = annocol[8], "0" = annocol[4]), 
                  Methylation = c("IP" = annocol[5], "LP" = annocol[6], "HP" =  annocol[7]))


# Annotations
#Top annotations
ha_top = HeatmapAnnotation(df = data.frame(colData(RNAnorm)[, c("IGHV", "trisomy12", "Methylation")]), 
                           col = annocolor, annotation_width = unit(c(rep(4, 3)), "cm"), 
                           show_legend = FALSE, 
                           simple_anno_size = unit(0.9, "cm"),
                           annotation_name_gp = gpar(fontsize = 20),
                           annotation_legend_param = list(title_gp = gpar(fontsize = 70), 
                                                          labels_gp = gpar(fontsize = 55),  
                                                          grid_height = unit(3, "cm"), 
                                                          grid_width = unit(1.5, "cm"), 
                                                          gap = unit(2, "cm")))

# Annotration legend
anno_legend_list = lapply(ha_top@anno_list[c("IGHV", "trisomy12", "Methylation")], function(anno){
  color_mapping_legend(anno@color_mapping, plot = FALSE,
                       title_gp = gpar(fontsize = 20, fontface = "bold"),
                       grid_height = unit(0.7, "cm"),
                       grid_width = unit(0.3, "cm"),
                       labels_gp = gpar(fontsize = 15))
  })

#Annotate known genes from litertaure
marker_genes <- c("ADAM29", "ATM", "CLLU1", "DMD", "GLO1", "HCSL1", "KIAA0977", 
                  "LPL", "MGC9913", "PCDH9", "PEG10", "SEPT10", "TCF7", "TCL1", 
                  "TP53", "VIM", "ZAP70", "CD38")

geneIDs <- which(rownames(exprMat.new) %in% marker_genes)
labels <- rownames(exprMat.new)[geneIDs]
ha_genes <- rowAnnotation(link = row_anno_link(at = geneIDs, 
                                               labels = labels, 
                                               labels_gp = gpar(fontsize = 20)), 
                          width = unit(2.5, "cm"))

Warning: anno_link() is deprecated, please use anno_mark() instead.

h1 <- Heatmap(exprMat.new ,                                                     
              km = 3,
              gap = unit(0.5, "cm"),
              clustering_distance_columns = "euclidean",
              clustering_method_columns = "ward.D2",
              clustering_distance_rows = "pearson",
              clustering_method_rows = "ward.D2",                    
              col = colors,
              column_title_gp = gpar(fontsize = 60, fontface = "bold"), 
              column_dend_height = unit(2.5, "cm"),
              show_row_dend = FALSE, 
              show_column_names = FALSE , 
              show_row_names = FALSE, 
              row_names_gp = gpar(fontsize = 45),
              show_heatmap_legend = FALSE,
              top_annotation = ha_top)

heatmap_legend = color_mapping_legend(h1@matrix_color_mapping, plot = FALSE, 
                                      title = "Expr", title_gp = gpar(fontsize = 20, fontface = "bold"),
                                      grid_height = unit(0.7, "cm"),
                                      grid_width = unit(0.3, "cm"),
                                      labels_gp = gpar(fontsize = 15))


# arrange annotations 
pd = packLegend(anno_legend_list[[1]], anno_legend_list[[2]], anno_legend_list[[3]], heatmap_legend, max_height = unit(20, "cm"), 
    column_gap = unit(0.5, "cm"))

pdf(file=paste0(output_dir, "/cluster500exprgenes.pdf"), width=20, height=20)
draw(h1, heatmap_legend_list = pd)
dev.off()

png 
  2

p1 <- draw(h1, heatmap_legend_list = pd)

Version	Author	Date
712d2e9	aluetge	2019-11-19
6a79d5c	aluetge	2019-11-17

#save to create figure using cowplot 
saveRDS(p1, paste0(output_dir, "/figures/r_objects/heatmap_top500genes.rds"))

Principal component analysis

#Plot PCA
exprMat <- assay(RNAnorm)

#top 5000 most variant genes
sds <- rowSds(exprMat)
na_ids <- which(is.na(ddsCLL$IGHV) | is.na(ddsCLL$trisomy12) | is.na(ddsCLL$Methylation))
exprMat <- exprMat[order(sds, decreasing = T)[1:500], -na_ids]


#Calculate PCA
pcaRes <- prcomp(t(exprMat), scale =T)
varExp <- (pcaRes$sdev^2 / sum(pcaRes$sdev^2)) * 100
pcaTab <- data.frame(pcaRes$x[,c(1:10)])
names(varExp) <- colnames(pcaRes$x)

#add background information
pcaTab <- cbind(pcaTab, data.frame(colData(RNAnorm)[-na_ids, ]))

#IGHV
p <- ggscatter(pcaTab, x = "PC1", y = "PC2", color = "IGHV", palette = "jco", size = 3,
  ylab = sprintf("PC2 (%2.1f%%)",varExp[2]), xlab = sprintf("PC1 (%2.1f%%)",varExp[1]), 
  legend = "right", main = "PCA coloured by IGHV status", 
  font.legend = c(23, "plain", "black"), 
  font.tickslab = c(23, "plain", "black"), 
  font.main = 25, font.submain = 28, font.caption = 28, font.x = 28, font.y= 28) + 
  coord_fixed()

p

Version	Author	Date
6a79d5c	aluetge	2019-11-17

#Tri12
p1 <- ggscatter(pcaTab, x = "PC1", y = "PC2", color = "trisomy12", size = 3,
  ylab = sprintf("PC2 (%2.1f%%)",varExp[2]), xlab = sprintf("PC1 (%2.1f%%)",varExp[1]), 
  legend = "right", main = "PCA coloured by trisomy12", 
  font.legend = c(23, "plain", "black"), 
  font.tickslab = c(23, "plain", "black"), 
  font.main = 25, font.submain = 28, font.caption = 28, font.x = 28, font.y= 28) + 
  coord_fixed() + 
  scale_colour_manual(values = c(annocol[4], annocol[8]))

p1

Version	Author	Date
6a79d5c	aluetge	2019-11-17

#Methylation
p2 <- ggscatter(pcaTab, x = "PC1", y = "PC2", color = "Methylation", size = 3,
  ylab = sprintf("PC2 (%2.1f%%)",varExp[2]), xlab = sprintf("PC1 (%2.1f%%)",varExp[1]), 
  legend = "right", main = "PCA coloured by Methylation", 
  font.legend = c(23, "plain", "black"), 
  font.tickslab = c(23, "plain", "black"), 
  font.main = 25, font.submain = 28, font.caption = 28, font.x = 28, font.y= 28) + 
  coord_fixed() + 
  scale_colour_manual(values = c(annocol[7], annocol[5], annocol[6]))

p2

Version	Author	Date
6a79d5c	aluetge	2019-11-17

#Methylation reduced gene number
#change gene number only 300 top variant genes
#Plot PCA
exprMat <- assay(RNAnorm)

#top 5000 most variant genes
sds <- rowSds(exprMat)
na_ids <- which(is.na(ddsCLL$Methylation))
exprMat <- exprMat[order(sds, decreasing = T)[1:300], -na_ids]


#Calculate PCA
pcaRes <- prcomp(t(exprMat), scale =T)
varExp <- (pcaRes$sdev^2 / sum(pcaRes$sdev^2)) * 100
pcaTab <- data.frame(pcaRes$x[,c(1:10)])
names(varExp) <- colnames(pcaRes$x)

#add background information
pcaTab <- cbind(pcaTab, data.frame(colData(RNAnorm)[-na_ids, ]))

p3 <- ggscatter(pcaTab, x = "PC1", y = "PC2", color = "Methylation", size = 3,
  ylab = sprintf("PC2 (%2.1f%%)",varExp[2]), xlab = sprintf("PC1 (%2.1f%%)",varExp[1]), 
  legend = "right", main = "PCA Methylation - top 300 genes", 
  font.legend = c(23, "plain", "black"), 
  font.tickslab = c(23, "plain", "black"), 
  font.main = 25, font.submain = 28, font.caption = 28, font.x = 28, font.y= 28) + 
  coord_fixed() + 
  scale_colour_manual(values = c(annocol[7], annocol[5], annocol[6]))

p3

Version	Author	Date
6a79d5c	aluetge	2019-11-17

saveRDS(list("IGHV" = p, "trisomy12" = p1, "Methylation" = p2, "Methylation_red_genes" = p3), 
        file = paste0(output_dir, "/figures/r_objects/pca_top500genes.rds"))

Hierachical clustering: Tree

library(dendextend)


---------------------
Welcome to dendextend version 1.13.4
Type citation('dendextend') for how to cite the package.

Type browseVignettes(package = 'dendextend') for the package vignette.
The github page is: https://github.com/talgalili/dendextend/

Suggestions and bug-reports can be submitted at: https://github.com/talgalili/dendextend/issues
Or contact: <tal.galili@gmail.com>

    To suppress this message use:  suppressPackageStartupMessages(library(dendextend))
---------------------


Attaching package: 'dendextend'

The following object is masked from 'package:ggpubr':

    rotate

The following object is masked from 'package:stats':

    cutree

library(corrplot)

corrplot 0.84 loaded

# Compute hierarchical clustering
exprMat <- assay(RNAnorm)

#top 5000 most variant genes
sds <- rowSds(exprMat)
exprMat <- exprMat[order(sds, decreasing = T)[1:500],]

sampleDists <- dist(t(exprMat)) 
sampleDistMat <- as.matrix(sampleDists)
rownames(sampleDistMat) <- colData(RNAnorm)$PatID


res.hc <- sampleDists %>% hclust(method = "ward.D2")
res.hc$labels <- ""    

##visualize with dendextend
mycols <-c( "#969696", "#525252", "#969696" , "#000000")
dend <-  as.dendrogram(res.hc) %>%
   set("branches_lwd", 2) %>% # Branches line width
   set("branches_k_color", mycols, k = 4)


#colormatrix
colMat <- matrix(nrow = ncol(RNAnorm), ncol = 3)
colMat[,1] <- ifelse(colData(RNAnorm)$IGHV == "M", annocol[1], annocol[2])
colMat[,2] <- ifelse(colData(RNAnorm)$trisomy12 == "1", annocol[8], annocol[4])
colMat[,3] <- ifelse(colData(RNAnorm)$Methylation == "HP", annocol[7], ifelse(colData(RNAnorm)$Methylation == "IP", annocol[5], annocol[6]))
colMat[which(is.na(colMat[,3])),3] <- "grey"

colnames(colMat) <- c("IGHV", "trisomy12", "Methylation")


plot(dend)
colored_bars(colMat, dend, rowLabels = c("IGHV", "trisomy12", "Methylation"), y_scale = 60, y_shift = 10, cex.rowLabels = 1)

## Check method's correspondence
# Create multiple dendrograms by chaining
dend1 <- sampleDists %>% hclust("complete") %>% as.dendrogram
dend2 <- sampleDists %>% hclust("ward.D2") %>% as.dendrogram
dend3 <- sampleDists %>% hclust("average") %>% as.dendrogram
dend4 <- sampleDists %>% hclust("centroid") %>% as.dendrogram
# Compute correlation matrix
dend_list <- dendlist("Complete" = dend1, "Ward.D2" = dend2,
                      "Average" = dend3, "Centroid" = dend4)
cors <- cor.dendlist(dend_list)
# Print correlation matrix
round(cors, 2)

         Complete Ward.D2 Average Centroid
Complete     1.00    0.71    0.80     0.25
Ward.D2      0.71    1.00    0.83     0.24
Average      0.80    0.83    1.00     0.41
Centroid     0.25    0.24    0.41     1.00

corrplot(cors, "pie", "lower")

C1C2 group signature

#get 1000 most variable genes
exprMat <- assay(RNAnorm)
sds <- rowSds(exprMat)
var500 <- rownames(exprMat[order(sds, decreasing = T)[1:500],])
var1000 <- rownames(exprMat[order(sds, decreasing = T)[1:1000],])
var5000 <- rownames(exprMat[order(sds, decreasing = T)[1:5000],])
var10000 <- rownames(exprMat[order(sds, decreasing = T)[1:10000],])

#Diff expressed gene list from Feirrera
c1c2_genes <- read.csv(paste0(data_dir, "/c1c2/Supplemental_File2_diffExpGenesC1C2_FC2.csv"), sep = ",",row.names = 1,header = TRUE)
exprMat_c <- assay(RNAnorm)
exprMat_c <- exprMat_c[which(rownames(RNAnorm) %in% rownames(c1c2_genes)),]
dim(exprMat_c)

[1] 602 184

#overlap var1000 and c1c2 genes
overlap_c1c2 <- list("top500" = var500,
                      "top1000" = var1000,
                      "top5000" = var5000,
                      "top10000" = var10000,
                      "all" = rownames(RNAnorm),
                      "c1c2" = rownames(c1c2_genes))

upset_mat <- list_to_matrix(overlap_c1c2 )
m = make_comb_mat(overlap_c1c2 )
comb_size(m)

000010 000001 000110 000011 001110 000111 011110 001111 111110 011111 
 20111     40   4837    194   3805    163    476    195    474     24 
111111 
    26

set_name(m)

[1] "top500"   "top1000"  "top5000"  "top10000" "all"      "c1c2"

UpSet(m)

#percentage category 
cat_vec <- rep("none", length(rownames(c1c2_genes)))
cat_vec[which(rownames(c1c2_genes) %in% var10000)] <- "top10000"
cat_vec[which(rownames(c1c2_genes) %in% var5000)] <- "top5000"
cat_vec[which(rownames(c1c2_genes) %in% var1000)] <- "top1000"
cat_vec[which(rownames(c1c2_genes) %in% var500)] <- "top500"
overlap_per <- data.frame("genes" = rownames(c1c2_genes),
                          "category" = cat_vec) 

overlap_per <- overlap_per %>% group_by(category) %>% summarize("overlap" = n())
overlap_per$category <- factor(overlap_per$category, levels = c("none", "top10000", "top5000", "top1000", "top500"))

p_c1c2 <- ggbarplot(overlap_per, "category", "overlap", 
                    fill = "category", 
                    palette = "uchicago", 
                    label = TRUE, 
                    lab.pos = "in", 
                    lab.col = "white",
                    title = "Overlap c1/c2 genes and top variable genes")

p_c1c2

#Plot expr mat
colnames(exprMat) <- colData(ddsCLL)$PatID
exprMat.new <- log2(exprMat_c)
exprMat.new <- t(scale(t(exprMat.new)))
exprMat.new[exprMat.new > 4] <- 4
exprMat.new[exprMat.new < -4] <- -4

# Annotations
#Top annotations
ha_top = HeatmapAnnotation(df = data.frame(colData(RNAnorm)[, c("IGHV", "trisomy12", "Methylation")]), 
                           col = annocolor, annotation_width = unit(c(rep(4, 3)), "cm"), 
                           show_legend = FALSE, 
                           simple_anno_size = unit(0.9, "cm"),
                           annotation_name_gp = gpar(fontsize = 20),
                           annotation_legend_param = list(title_gp = gpar(fontsize = 70), 
                                                          labels_gp = gpar(fontsize = 55),  
                                                          grid_height = unit(3, "cm"), 
                                                          grid_width = unit(1.5, "cm"), 
                                                          gap = unit(2, "cm")))

# Annotration legend
anno_legend_list = lapply(ha_top@anno_list[c("IGHV", "trisomy12", "Methylation")], function(anno){
  color_mapping_legend(anno@color_mapping, plot = FALSE,
                       title_gp = gpar(fontsize = 20, fontface = "bold"),
                       grid_height = unit(0.7, "cm"),
                       grid_width = unit(0.3, "cm"),
                       labels_gp = gpar(fontsize = 15))
  })

h1 <- Heatmap(exprMat.new ,                                                     
              km = 2,
              gap = unit(0.5, "cm"),
              clustering_distance_columns = "euclidean",
              clustering_method_columns = "ward.D2",
              clustering_distance_rows = "pearson",
              clustering_method_rows = "ward.D2",                    
              col = colors,
              column_title_gp = gpar(fontsize = 60, fontface = "bold"), 
              column_dend_height = unit(2.5, "cm"),
              show_row_dend = FALSE, 
              show_column_names = FALSE , 
              show_row_names = FALSE, 
              row_names_gp = gpar(fontsize = 45),
              show_heatmap_legend = FALSE,
              top_annotation = ha_top)

heatmap_legend = color_mapping_legend(h1@matrix_color_mapping, plot = FALSE, 
                                      title = "Expr", title_gp = gpar(fontsize = 20, fontface = "bold"),
                                      grid_height = unit(0.7, "cm"),
                                      grid_width = unit(0.3, "cm"),
                                      labels_gp = gpar(fontsize = 15))


# arrange annotations 
pd = packLegend(anno_legend_list[[1]], anno_legend_list[[2]], anno_legend_list[[3]], heatmap_legend, max_height = unit(20, "cm"), 
    column_gap = unit(0.5, "cm"))

draw(h1, heatmap_legend_list = pd)

Plot mean and sd of c1/c2 group

mean_c <- rowMeans(exprMat_c)
mean_all <- rowMeans(exprMat)
mean_top <- rowMeans(exprMat[var5000,])

sd_c <- rowSds(exprMat_c)
sd_all <- rowSds(exprMat)
sd_top <- rowSds(exprMat[var5000,])

cat_vec <- rep("none", length(rownames(exprMat)))
cat_vec[which(rownames(exprMat) %in% var10000)] <- "top10000"
cat_vec[which(rownames(exprMat) %in% var5000)] <- "top5000"
cat_vec[which(rownames(exprMat) %in% var1000)] <- "top1000"
cat_vec[which(rownames(exprMat) %in% var500)] <- "top500"
cat_vec[which(rownames(exprMat) %in% rownames(c1c2_genes))] <- "c1c2"
            

tab_all_cat <- data.frame("mean_count" = mean_all,
                          "sd_count" = sd_all,
                          "gene_category" = cat_vec)
  
  
p1 <- gghistogram(tab_all_cat, x = "mean_count",
   add = "mean", bins = 12, fill = "gene_category",
  palette = "uchicago")
p1

p2 <- gghistogram(tab_all_cat, x = "sd_count",
   add = "mean", bins = 12, fill = "gene_category",
  palette = "uchicago",
  title = "Standard deviation c1c2 genes compared to top variable genes")
p2

saveRDS(list("mean" = p1, "sd" = p2, "category" = p_c1c2), 
        file = paste0(output_dir, "/figures/r_objects/mean_sd_by_c1c2.rds"))

sessionInfo()

R version 3.6.0 (2019-04-26)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.6 LTS

Matrix products: default
BLAS:   /usr/lib/libblas/libblas.so.3.6.0
LAPACK: /usr/lib/lapack/liblapack.so.3.6.0

locale:
 [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=de_DE.UTF-8        LC_COLLATE=de_DE.UTF-8    
 [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8   
 [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
 [1] grid      parallel  stats4    stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] corrplot_0.84               dendextend_1.13.4          
 [3] here_0.1                    circlize_0.4.6             
 [5] RColorBrewer_1.1-2          ggpubr_0.2                 
 [7] magrittr_1.5                ComplexHeatmap_2.0.0       
 [9] forcats_0.4.0               stringr_1.4.0              
[11] purrr_0.3.2                 readr_1.3.1                
[13] tidyr_0.8.3                 tibble_2.1.3               
[15] tidyverse_1.2.1             ggplot2_3.1.1              
[17] dplyr_0.8.1                 DESeq2_1.24.0              
[19] SummarizedExperiment_1.14.0 DelayedArray_0.10.0        
[21] BiocParallel_1.18.0         matrixStats_0.54.0         
[23] Biobase_2.44.0              GenomicRanges_1.36.0       
[25] GenomeInfoDb_1.20.0         IRanges_2.18.1             
[27] S4Vectors_0.22.0            BiocGenerics_0.30.0        

loaded via a namespace (and not attached):
 [1] colorspace_1.4-1       rjson_0.2.20           rprojroot_1.3-2       
 [4] htmlTable_1.13.1       XVector_0.24.0         GlobalOptions_0.1.0   
 [7] base64enc_0.1-3        fs_1.3.1               clue_0.3-57           
[10] rstudioapi_0.10        bit64_0.9-7            AnnotationDbi_1.46.0  
[13] lubridate_1.7.4        xml2_1.2.0             splines_3.6.0         
[16] geneplotter_1.62.0     knitr_1.23             Formula_1.2-3         
[19] jsonlite_1.6           workflowr_1.4.0        broom_0.5.2           
[22] annotate_1.62.0        cluster_2.1.0          png_0.1-7             
[25] compiler_3.6.0         httr_1.4.0             backports_1.1.4       
[28] assertthat_0.2.1       Matrix_1.2-17          lazyeval_0.2.2        
[31] cli_1.1.0              acepack_1.4.1          htmltools_0.3.6       
[34] tools_3.6.0            gtable_0.3.0           glue_1.3.1            
[37] GenomeInfoDbData_1.2.1 Rcpp_1.0.1             cellranger_1.1.0      
[40] nlme_3.1-140           xfun_0.7               rvest_0.3.4           
[43] XML_3.98-1.20          zlibbioc_1.30.0        scales_1.0.0          
[46] hms_0.4.2              yaml_2.2.0             memoise_1.1.0         
[49] gridExtra_2.3          rpart_4.1-15           latticeExtra_0.6-28   
[52] stringi_1.4.3          RSQLite_2.1.1          genefilter_1.66.0     
[55] checkmate_1.9.3        shape_1.4.4            rlang_0.3.4           
[58] pkgconfig_2.0.2        bitops_1.0-6           evaluate_0.14         
[61] lattice_0.20-38        labeling_0.3           htmlwidgets_1.3       
[64] bit_1.1-14             tidyselect_0.2.5       ggsci_2.9             
[67] plyr_1.8.4             R6_2.4.0               generics_0.0.2        
[70] Hmisc_4.2-0            DBI_1.0.0              pillar_1.4.1          
[73] haven_2.1.0            whisker_0.3-2          foreign_0.8-71        
[76] withr_2.1.2            survival_2.44-1.1      RCurl_1.95-4.12       
[79] nnet_7.3-12            modelr_0.1.4           crayon_1.3.4          
[82] rmarkdown_1.13         viridis_0.5.1          GetoptLong_0.1.7      
[85] locfit_1.5-9.1         readxl_1.3.1           data.table_1.12.2     
[88] blob_1.1.1             git2r_0.25.2           digest_0.6.19         
[91] xtable_1.8-4           munsell_0.5.0          viridisLite_0.3.0

Hierarchical structure

almut

17 November 2019

Hierarchical structure

Hierarchical clustering - heatmap

Principal component analysis

Hierachical clustering: Tree

C1C2 group signature

Plot mean and sd of c1/c2 group