Plotting Answer 4

Section 1

Please load the dateset SubsetRlog_7D.csv from "data/SubsetRlog_7D.csv"

hint: use read.csv() function to load SubsetRlog_7D.csv. If you failed to import SubsetRlog_7D.csv to R, please check whether you set up your work directory properly by using getwd() and setwd()

# This is the work directory I am using and yours will be different
# setwd("/Volumes/bioinfomatics$/yfwang/BioinformaticsCore/GitHub/LMS_PlottingInR/course/")

exp_Data <- read.csv("data/SubsetRlog_7D.csv",row.names = 1)

head(exp_Data)

##                     DOX24H_1  DOX24H_2  DOX24H_3   DOX7D_1   DOX7D_2   DOX7D_3
## ENSMUSG00000050711 12.109594 12.155740 12.089761 13.891323 13.856755 14.095029
## ENSMUSG00000026463 12.288243 12.069297 11.934777 11.971843 11.908514 11.748345
## ENSMUSG00000051079  7.177476  7.282678  7.415341  8.097972  8.558204  8.653715
## ENSMUSG00000037995  9.178713  9.370469  9.047744  7.834046  7.667787  7.540055
## ENSMUSG00000039910  9.024315  8.790573  8.878026  9.497840  9.751352  9.731984
## ENSMUSG00000019880  8.308681  8.701553  8.403844  9.026944  9.351475  9.373127
##                     VEH24H_1  VEH24H_2  VEH24H_3   VEH7D_1   VEH7D_2   VEH7D_3
## ENSMUSG00000050711 12.361107 12.560950 12.404746 13.640879 13.552177 13.578203
## ENSMUSG00000026463 12.543844 12.334998 12.195093 12.357483 12.181816 12.315383
## ENSMUSG00000051079  6.761927  6.861818  6.899967  7.445987  7.440482  7.938914
## ENSMUSG00000037995  9.222387  9.152175  9.299462  8.064490  7.994608  8.025586
## ENSMUSG00000039910  9.257527  9.246675  9.442477  9.346837  9.238912  9.323775
## ENSMUSG00000019880  8.305506  8.296453  8.429132  8.762056  8.791535  8.877247

class(exp_Data)

## [1] "data.frame"

use scale() function to perform the z-score transform on each genes

hint: use ?scale to see how to use this function properly and you might need t() to perform this analysis properly

?scale
?t

scaled_max <- t(scale(t(exp_Data)))

use Heatmap() function from R bioconductor packages ComplexHeatmap and RColorBrewer to create the heatmap below:

note: TimePoint colour codes are from brewer.pal(10, "Paired")[c(9:10)] and Treatment colour codes are from brewer.pal(6, "Dark2")[c(1,2)]

library("ComplexHeatmap")
library("RColorBrewer")
# check the order of the columns and show Veh.24hr, Dox.24hr, Veh.7D and Dox.7D samples
head(scaled_max)

##                      DOX24H_1    DOX24H_2   DOX24H_3    DOX7D_1   DOX7D_2
## ENSMUSG00000050711 -1.1422782 -1.08467574 -1.1670354  1.0817870  1.038637
## ENSMUSG00000026463  0.5810754 -0.36760259 -0.9504706 -0.7898684 -1.064266
## ENSMUSG00000051079 -0.5768369 -0.41151302 -0.2030349  0.8697113  1.592960
## ENSMUSG00000037995  0.8875988  1.15123888  0.7075324 -0.9611463 -1.189731
## ENSMUSG00000039910 -0.9069069 -1.69238691 -1.3985060  0.6843528  1.536270
## ENSMUSG00000019880 -1.0572857 -0.04486377 -0.8120520  0.7936580  1.629967
##                      DOX7D_3   VEH24H_1   VEH24H_2   VEH24H_3    VEH7D_1
## ENSMUSG00000050711  1.336066 -0.8283242 -0.5788677 -0.7738506  0.7691668
## ENSMUSG00000026463 -1.758269  1.6885784  0.7836612  0.1774642  0.8810887
## ENSMUSG00000051079  1.743055 -1.2298673 -1.0728892 -1.0129387 -0.1548750
## ENSMUSG00000037995 -1.365347  0.9476440  0.8511121  1.0536132 -0.6443163
## ENSMUSG00000039910  1.471184 -0.1232086 -0.1596752  0.4983090  0.1769150
## ENSMUSG00000019880  1.685764 -1.0654662 -1.0887967 -0.7468858  0.1110505
##                       VEH7D_2     VEH7D_3
## ENSMUSG00000050711  0.6584434  0.69093158
## ENSMUSG00000026463  0.1199358  0.69867306
## ENSMUSG00000051079 -0.1635261  0.61975449
## ENSMUSG00000037995 -0.7403945 -0.69780421
## ENSMUSG00000039910 -0.1857634  0.09941564
## ENSMUSG00000019880  0.1870174  0.40789275

Sam_Veh24H <- grep("VEH24H",colnames(scaled_max))
Sam_Veh24H

## [1] 7 8 9

Sam_Dox24H <- grep("DOX24H",colnames(scaled_max))
Sam_Dox24H

## [1] 1 2 3

Sam_Veh7D <- grep("VEH7D",colnames(scaled_max))
Sam_Veh7D

## [1] 10 11 12

Sam_Dox7D <- grep("DOX7D",colnames(scaled_max))
Sam_Dox7D

## [1] 4 5 6

scaled_max_ok <- scaled_max[,c(Sam_Veh24H,Sam_Dox24H,
                               Sam_Veh7D,Sam_Dox7D)]
head(scaled_max_ok)

##                      VEH24H_1   VEH24H_2   VEH24H_3   DOX24H_1    DOX24H_2
## ENSMUSG00000050711 -0.8283242 -0.5788677 -0.7738506 -1.1422782 -1.08467574
## ENSMUSG00000026463  1.6885784  0.7836612  0.1774642  0.5810754 -0.36760259
## ENSMUSG00000051079 -1.2298673 -1.0728892 -1.0129387 -0.5768369 -0.41151302
## ENSMUSG00000037995  0.9476440  0.8511121  1.0536132  0.8875988  1.15123888
## ENSMUSG00000039910 -0.1232086 -0.1596752  0.4983090 -0.9069069 -1.69238691
## ENSMUSG00000019880 -1.0654662 -1.0887967 -0.7468858 -1.0572857 -0.04486377
##                      DOX24H_3    VEH7D_1    VEH7D_2     VEH7D_3    DOX7D_1
## ENSMUSG00000050711 -1.1670354  0.7691668  0.6584434  0.69093158  1.0817870
## ENSMUSG00000026463 -0.9504706  0.8810887  0.1199358  0.69867306 -0.7898684
## ENSMUSG00000051079 -0.2030349 -0.1548750 -0.1635261  0.61975449  0.8697113
## ENSMUSG00000037995  0.7075324 -0.6443163 -0.7403945 -0.69780421 -0.9611463
## ENSMUSG00000039910 -1.3985060  0.1769150 -0.1857634  0.09941564  0.6843528
## ENSMUSG00000019880 -0.8120520  0.1110505  0.1870174  0.40789275  0.7936580
##                      DOX7D_2   DOX7D_3
## ENSMUSG00000050711  1.038637  1.336066
## ENSMUSG00000026463 -1.064266 -1.758269
## ENSMUSG00000051079  1.592960  1.743055
## ENSMUSG00000037995 -1.189731 -1.365347
## ENSMUSG00000039910  1.536270  1.471184
## ENSMUSG00000019880  1.629967  1.685764

ht_tp <- Heatmap(scaled_max_ok,
                 cluster_columns = F,show_row_names = F,
                 col = rev(colorRampPalette(brewer.pal(8, "RdBu"))(25)))
draw(ht_tp)

# add top annotation
# create top annotation
anno_alltp <- data.frame(SampleNames=colnames(scaled_max_ok))
anno_alltp$TimePoint <- sub("(.+)([2|7].+)(_)(.+)","\\2",anno_alltp$SampleNames)
anno_alltp$TimePoint <- as.factor(anno_alltp$TimePoint)
anno_alltp$Treatment <- sub("(.+)([2|7].+)(_)(.+)","\\1",anno_alltp$SampleNames)
anno_alltp$Treatment <- as.factor(anno_alltp$Treatment)

alltime_col <- brewer.pal(10, "Paired")[c(9:10)]
names(alltime_col) <- levels(anno_alltp$TimePoint)

trt_col <- brewer.pal(6, "Dark2")[c(1,2)]
names(trt_col) <- levels(anno_alltp$Treatment)

allcol_ha = HeatmapAnnotation(TimePoint = anno_alltp$TimePoint,
                           Treatment = anno_alltp$Treatment,
                           col = list(TimePoint = alltime_col,
                                      Treatment = trt_col))
ht_tp2 <- Heatmap(scaled_max_ok,
                 cluster_columns = F,show_row_names = F,
                 show_column_names = F,
                 col = rev(colorRampPalette(brewer.pal(8, "RdBu"))(25)),
                 top_annotation = allcol_ha)
draw(ht_tp2)

save this heatmap as pdf file and png file.

pdf_name <- "heatmap_exercise.pdf"
png_name <- "heatmap_exercise.png"

pdf(pdf_name, width=4.5, height=5)
  draw(ht_tp2)
dev.off()

## quartz_off_screen 
##                 2

png(png_name)
  draw(ht_tp2)
dev.off()

## quartz_off_screen 
##                 2

Plotting Answer 4 - heatmap

MRC LMS

April 2022

Section 1