# R version 4.3.3 (2024-02-29 ucrt)
# Platform: x86_64-w64-mingw32/x64 (64-bit)
# Running under: Windows 10 x64 (build 19045)
#
# Matrix products: default
#
#
# locale:
# [1] LC_COLLATE=Arabic_World.utf8 LC_CTYPE=Arabic_World.utf8
# [3] LC_MONETARY=Arabic_World.utf8 LC_NUMERIC=C
# [5] LC_TIME=Arabic_World.utf8
# if (!require("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#
# BiocManager::install("ComplexHeatmap")
#install.packages("viridis")
#install.packages("hrbrthemes")
library(readr)
library(ggfortify)
## Loading required package: ggplot2
library(rgl)
library("scatterplot3d")
library(matrixStats)
library(devtools)
## Loading required package: usethis
library(ComplexHeatmap)
## Loading required package: grid
## ========================================
## ComplexHeatmap version 2.18.0
## Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
## Github page: https://github.com/jokergoo/ComplexHeatmap
## Documentation: http://jokergoo.github.io/ComplexHeatmap-reference
##
## If you use it in published research, please cite either one:
## - Gu, Z. Complex Heatmap Visualization. iMeta 2022.
## - Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
## genomic data. Bioinformatics 2016.
##
##
## The new InteractiveComplexHeatmap package can directly export static
## complex heatmaps into an interactive Shiny app with zero effort. Have a try!
##
## This message can be suppressed by:
## suppressPackageStartupMessages(library(ComplexHeatmap))
## ========================================
library(circlize)
## ========================================
## circlize version 0.4.16
## CRAN page: https://cran.r-project.org/package=circlize
## Github page: https://github.com/jokergoo/circlize
## Documentation: https://jokergoo.github.io/circlize_book/book/
##
## If you use it in published research, please cite:
## Gu, Z. circlize implements and enhances circular visualization
## in R. Bioinformatics 2014.
##
## This message can be suppressed by:
## suppressPackageStartupMessages(library(circlize))
## ========================================
library(cluster)
library(viridis)
## Loading required package: viridisLite
library(hrbrthemes)
library(ggplot2)
setwd("D:/bioinform/BCB/MODA")
data <- read.delim("D:/bioinform/BCB/MODA/Parkinson_exp.txt", row.names=1)
metadata <- read.delim("D:/bioinform/BCB/MODA/Parkinson_phenotable.txt", row.names=1)
summary (data)
## C_F_69_1156 C_F_72_1152 C_F_74_1157 C_M_68_1151
## Min. : 6.554 Min. : 6.465 Min. : 6.527 Min. : 6.549
## 1st Qu.: 6.953 1st Qu.: 6.966 1st Qu.: 6.940 1st Qu.: 6.966
## Median : 7.113 Median : 7.152 Median : 7.093 Median : 7.120
## Mean : 7.236 Mean : 7.256 Mean : 7.225 Mean : 7.245
## 3rd Qu.: 7.328 3rd Qu.: 7.389 3rd Qu.: 7.305 3rd Qu.: 7.336
## Max. :14.033 Max. :14.012 Max. :14.035 Max. :14.004
## C_M_73_1020 C_M_75_1150 C_M_78_1147 C_M_79_1024
## Min. : 6.528 Min. : 6.557 Min. : 6.558 Min. : 6.564
## 1st Qu.: 6.941 1st Qu.: 6.971 1st Qu.: 6.959 1st Qu.: 6.977
## Median : 7.088 Median : 7.135 Median : 7.106 Median : 7.122
## Mean : 7.227 Mean : 7.286 Mean : 7.242 Mean : 7.248
## 3rd Qu.: 7.295 3rd Qu.: 7.388 3rd Qu.: 7.315 3rd Qu.: 7.333
## Max. :14.013 Max. :11.700 Max. :13.504 Max. :13.965
## C_M_89_1022 PD_F_81_1144 PD_F_81_1158 PD_F_84_1154
## Min. : 6.544 Min. : 6.550 Min. : 6.590 Min. : 6.598
## 1st Qu.: 6.966 1st Qu.: 6.976 1st Qu.: 6.966 1st Qu.: 6.970
## Median : 7.119 Median : 7.141 Median : 7.130 Median : 7.121
## Mean : 7.248 Mean : 7.260 Mean : 7.246 Mean : 7.245
## 3rd Qu.: 7.340 3rd Qu.: 7.374 3rd Qu.: 7.368 3rd Qu.: 7.338
## Max. :13.946 Max. :13.897 Max. :14.062 Max. :14.022
## PD_M_71_1155 PD_M_72_1146 PD_M_73_1148 PD_M_77_1143
## Min. : 6.603 Min. : 6.571 Min. : 6.575 Min. : 6.592
## 1st Qu.: 6.976 1st Qu.: 6.960 1st Qu.: 6.970 1st Qu.: 6.978
## Median : 7.132 Median : 7.129 Median : 7.132 Median : 7.130
## Mean : 7.267 Mean : 7.245 Mean : 7.249 Mean : 7.258
## 3rd Qu.: 7.360 3rd Qu.: 7.367 3rd Qu.: 7.353 3rd Qu.: 7.353
## Max. :13.433 Max. :14.064 Max. :13.998 Max. :13.767
## PD_M_77_1153 PD_M_79_1145 PD_M_83_1149
## Min. : 6.566 Min. : 6.601 Min. : 6.583
## 1st Qu.: 6.982 1st Qu.: 6.971 1st Qu.: 6.962
## Median : 7.135 Median : 7.128 Median : 7.133
## Mean : 7.260 Mean : 7.253 Mean : 7.246
## 3rd Qu.: 7.363 3rd Qu.: 7.350 3rd Qu.: 7.375
## Max. :13.859 Max. :13.965 Max. :14.061
boxplot(data) #boxplot shows if there is an abnormal sample that should be excluded (i.e above or below the others)
dat <- stack(as.data.frame(data[,1:dim(data)[2]])) #converting the columns into two columns (value and index) for plotting.
#plotting the converted data in density plot. Density plot shows the general distribution of the data
ggplot(dat, aes(x=values, fill=ind)) + geom_density(alpha=0.3)
# base_size <- 11.5
# base_family = "Arial Narrow"
# subtitle_family = base_family
# theme_ipsum(
# base_family = "Arial Narrow",
# base_size = 11.5,
# plot_title_family = base_family,
# plot_title_size = 18,
# plot_title_face = "bold",
# plot_title_margin = 10,
# subtitle_family = base_family,
# subtitle_size = 12,
# subtitle_face = "plain",
# subtitle_margin = 15,
# strip_text_family = base_family,
# strip_text_size = 12,
# strip_text_face = "plain",
# caption_family = base_family,
# caption_size = 9,
# caption_face = "italic",
# caption_margin = 10,
# axis_text_size = base_size,
# axis_title_family = subtitle_family,
# axis_title_size = 9,
# axis_title_face = "plain",
# axis_title_just = "rt",
# plot_margin = margin(30, 30, 30, 30),
# grid_col = "#cccccc",
# grid = TRUE,
# axis_col = "#cccccc",
# axis = FALSE,
# ticks = FALSE
# )
# # Code coming from @drob: https://gist.github.com/dgrtwo/eb7750e74997891d7c20#file-geom_flat_violin-r
# "%||%" <- function(a, b) {
# if (!is.null(a)) a else b
# }
#
# geom_flat_violin <- function(mapping = NULL, data = NULL, stat = "ydensity",
# position = "dodge", trim = TRUE, scale = "area",
# show.legend = NA, inherit.aes = TRUE, ...) {
# layer(
# data = dat,
# mapping = mapping,
# stat = stat,
# geom = GeomFlatViolin,
# position = position,
# show.legend = show.legend,
# inherit.aes = inherit.aes,
# params = list(
# trim = trim,
# scale = scale,
# ...
# )
# )
# }
#
# #' @rdname ggplot2-ggproto
# #' @format NULL
# #' @usage NULL
# #' @export
# GeomFlatViolin <-
# ggproto("GeomFlatViolin", Geom,
# setup_data = function(data, params) {
# data$width <- data$width %||%
# params$width %||% (resolution(data$x, FALSE) * 0.9)
#
# # ymin, ymax, xmin, and xmax define the bounding rectangle for each group
# data %>%
# group_by(group) %>%
# mutate(ymin = min(y),
# ymax = max(y),
# xmin = x,
# xmax = x + width / 2)
#
# },
#
# draw_group = function(data, panel_scales, coord) {
# # Find the points for the line to go all the way around
# data <- transform(data, xminv = x,
# xmaxv = x + violinwidth * (xmax - x))
#
# # Make sure it's sorted properly to draw the outline
# newdata <- rbind(plyr::arrange(transform(data, x = xminv), y),
# plyr::arrange(transform(data, x = xmaxv), -y))
#
# # Close the polygon: set first and last point the same
# # Needed for coord_polar and such
# newdata <- rbind(newdata, newdata[1,])
#
# ggplot2:::ggname("geom_flat_violin", GeomPolygon$draw_panel(newdata, panel_scales, coord))
# },
#
# draw_key = draw_key_polygon,
#
# default_aes = aes(weight = 1, colour = "grey20", fill = "white", size = 0.5,
# alpha = NA, linetype = "solid"),
#
# required_aes = c("x", "y")
# )
# # Final plot inspired from @jbburant: https://gist.github.com/jbburant/b3bd4961f3f5b03aeb542ed33a8fe062
# data %>%
# #sample_frac(0.4) %>%
# ggplot(aes(x = ind, y = values, fill = values)) +
# geom_flat_violin(scale = "count", trim = FALSE, width=2) +
# scale_fill_viridis(discrete = TRUE) +
# stat_summary(fun.data = mean_sdl, fun.args = list(mult = 1), geom = "pointrange", position = position_nudge(4.9)) +
# geom_dotplot(binaxis = "y", dotsize = 0.8, stackdir = "down", binwidth = 0.3, position = position_nudge(-0.025)) +
# theme_ipsum() +
# theme(
# legend.position = "none"
# ) +
# ylab("value")
data.pca=prcomp(t(data),scale=T, cor=TRUE, scores=TRUE)
## Warning: In prcomp.default(t(data), scale = T, cor = TRUE, scores = TRUE) :
## extra arguments 'cor', 'scores' will be disregarded
autoplot(data.pca, data = metadata, colour = 'sample.type',frame = F)
autoplot(data.pca, data = metadata, colour = 'sample.type',frame = T, frame.type="norm")
autoplot(data.pca, data = metadata, colour = 'gender',frame = F)
autoplot(data.pca, data = metadata, colour = 'sample.type',frame = F,shape="gender", label = TRUE, label.size = 2)
summary(data.pca)
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6
## Standard deviation 75.2909 61.7205 51.2346 43.19331 37.46963 31.53900
## Proportion of Variance 0.2544 0.1710 0.1178 0.08373 0.06301 0.04464
## Cumulative Proportion 0.2544 0.4254 0.5432 0.62688 0.68989 0.73453
## PC7 PC8 PC9 PC10 PC11 PC12
## Standard deviation 28.95386 25.52281 24.89589 23.3183 22.67543 21.53917
## Proportion of Variance 0.03762 0.02923 0.02782 0.0244 0.02307 0.02082
## Cumulative Proportion 0.77215 0.80138 0.82920 0.8536 0.87667 0.89749
## PC13 PC14 PC15 PC16 PC17 PC18
## Standard deviation 21.10347 20.4699 20.23369 19.29639 18.2830 17.42787
## Proportion of Variance 0.01999 0.0188 0.01837 0.01671 0.0150 0.01363
## Cumulative Proportion 0.91748 0.9363 0.95466 0.97137 0.9864 1.00000
## PC19
## Standard deviation 4.239e-13
## Proportion of Variance 0.000e+00
## Cumulative Proportion 1.000e+00
data.pca$x
## PC1 PC2 PC3 PC4 PC5
## C_F_69_1156 -75.60301 65.4525786 6.626959 -19.251293 16.7641294
## C_F_72_1152 -29.70676 -125.6147546 140.229593 18.748439 66.8317738
## C_F_74_1157 -39.60072 103.3914674 15.305508 -3.469030 31.5898507
## C_M_68_1151 41.97571 20.4779798 -10.746490 20.045924 18.7294434
## C_M_73_1020 -39.78672 131.1151617 34.062105 -10.792591 24.2655930
## C_M_75_1150 124.04257 -30.2992513 6.432806 -154.167137 0.4303546
## C_M_78_1147 62.10851 86.1120402 30.776401 9.341532 -2.1668798
## C_M_79_1024 52.57128 -10.4874854 -42.835953 51.365796 -12.2813567
## C_M_89_1022 58.45209 12.1090208 -12.955981 24.659891 14.3576900
## PD_F_81_1144 57.89452 -59.6690635 -21.984996 9.947777 30.2602050
## PD_F_81_1158 -117.13408 -45.3586267 -62.633500 -13.312952 -18.3831826
## PD_F_84_1154 33.10378 2.5984270 -33.388611 34.939744 -1.4010779
## PD_M_71_1155 -28.26782 -0.3621644 94.886828 -3.345118 -125.8648207
## PD_M_72_1146 -120.74156 -22.8274868 -49.127090 -30.561152 4.2194102
## PD_M_73_1148 -65.15931 -33.8366024 30.342498 29.457382 -10.6225824
## PD_M_77_1143 56.02363 -9.0566221 -19.259920 25.703535 -24.9298201
## PD_M_77_1153 84.14781 -47.6488852 -41.679882 21.269892 -18.7078687
## PD_M_79_1145 61.20012 7.6576831 -9.419044 14.064027 4.1886404
## PD_M_83_1149 -115.52004 -43.7534164 -54.631228 -24.644667 2.7204985
## PC6 PC7 PC8 PC9 PC10 PC11
## C_F_69_1156 11.275991 14.357228 -28.9569836 25.426501 5.203210 30.920648
## C_F_72_1152 -18.964608 17.249856 -10.3300356 -10.817400 -5.839618 -5.574849
## C_F_74_1157 -35.625940 23.480075 37.5453232 4.229006 30.557561 -44.654634
## C_M_68_1151 15.175489 -20.709962 28.7423745 -15.805913 1.498140 22.921077
## C_M_73_1020 -14.403079 3.552480 -12.8163298 30.796617 -22.939354 10.636700
## C_M_75_1150 -18.398802 -26.249104 1.1585762 5.550726 3.785788 -3.809884
## C_M_78_1147 37.145582 -14.967701 -35.1934678 -56.478684 -35.056579 -13.415294
## C_M_79_1024 -88.090160 -24.905385 -48.4165189 -9.528058 24.023000 16.053598
## C_M_89_1022 6.162941 2.315195 24.4708504 -26.181010 23.289298 -8.609989
## PD_F_81_1144 34.694558 19.964699 6.4456262 8.666935 20.567865 25.564918
## PD_F_81_1158 -10.537155 -9.908557 -0.3829595 -30.376013 -14.477635 -36.259340
## PD_F_84_1154 -22.909029 -14.410260 49.6229444 5.584899 -23.764512 23.409446
## PD_M_71_1155 -6.844159 22.955465 17.2117908 -7.818022 10.389795 16.501658
## PD_M_72_1146 15.689394 11.951830 -14.7827099 -12.594863 6.206323 18.538862
## PD_M_73_1148 24.950906 -89.250265 7.2240346 39.782232 -5.355376 -15.071586
## PD_M_77_1143 46.610129 12.398881 -31.8337006 29.185058 38.868302 -33.389519
## PD_M_77_1153 -13.907670 47.124363 -3.7566212 36.881758 -54.765528 -21.692175
## PD_M_79_1145 32.267689 8.877905 7.1601831 -8.666937 1.753731 10.286204
## PD_M_83_1149 5.707923 16.173258 6.8876235 -7.836832 -3.944411 7.644160
## PC12 PC13 PC14 PC15 PC16
## C_F_69_1156 -12.7561564 5.8975486 -20.580111 28.788264 -47.7806764
## C_F_72_1152 11.8905757 -1.0406967 -6.000162 10.239965 4.1191848
## C_F_74_1157 -10.8349181 -21.8356479 8.865740 -12.326571 -10.0832010
## C_M_68_1151 -5.7928179 35.7375437 -13.988448 7.543272 23.2152689
## C_M_73_1020 18.5945321 27.8883744 18.109273 -10.487225 32.9945241
## C_M_75_1150 3.2029534 -1.8123075 -3.107898 2.790906 -0.4572260
## C_M_78_1147 2.9471397 -19.8204990 -6.505372 -22.134823 -12.8551255
## C_M_79_1024 -6.4268076 -6.1162545 8.242624 -3.907687 2.9157409
## C_M_89_1022 -31.1964907 20.7972290 -32.921103 8.284762 3.8150701
## PD_F_81_1144 0.9551349 16.6257550 31.609245 -49.320030 -24.5297927
## PD_F_81_1158 18.3348895 42.4459390 22.557810 15.143248 -19.2242195
## PD_F_84_1154 50.6105504 -23.7806832 -17.044130 6.092894 -16.5739780
## PD_M_71_1155 -3.7879688 5.3366903 3.552004 -6.640781 -0.3719524
## PD_M_72_1146 -10.7338753 -17.4945976 -16.043028 -5.727409 26.4102447
## PD_M_73_1148 -24.8437832 -10.5628697 3.465478 -8.576397 -2.6512935
## PD_M_77_1143 42.0995045 -0.5366841 -17.987202 8.354000 12.6363485
## PD_M_77_1153 -30.7550068 2.7321555 -9.673932 -2.139208 1.7056945
## PD_M_79_1145 -12.3424281 -26.1594670 52.477841 48.891181 10.6668429
## PD_M_83_1149 0.8349726 -28.3015283 -5.028627 -14.868361 16.0485458
## PC17 PC18 PC19
## C_F_69_1156 8.2612665 5.2719559 4.646262e-13
## C_F_72_1152 0.9992159 -1.0787535 3.642529e-13
## C_F_74_1157 16.1975156 -12.3384387 4.145765e-13
## C_M_68_1151 50.1450606 -7.1050316 4.030527e-13
## C_M_73_1020 -21.4745068 9.4937487 4.282880e-13
## C_M_75_1150 -0.5034387 0.4094081 2.716707e-13
## C_M_78_1147 4.4646952 -0.2028761 3.097691e-13
## C_M_79_1024 4.1903415 2.4047843 1.329041e-13
## C_M_89_1022 -43.5636866 18.4735541 3.463894e-13
## PD_F_81_1144 -5.8211145 -3.9637117 4.095157e-13
## PD_F_81_1158 -3.1391344 -3.2753201 3.815529e-13
## PD_F_84_1154 -13.5024044 -7.9651164 3.615411e-13
## PD_M_71_1155 0.6287463 -0.4316228 4.113992e-13
## PD_M_72_1146 -14.6838948 -46.1579599 4.459723e-13
## PD_M_73_1148 -3.7808614 -0.2962268 4.052378e-13
## PD_M_77_1143 5.6648858 1.4488836 3.627655e-13
## PD_M_77_1153 6.6284064 -6.3736657 1.851748e-13
## PD_M_79_1145 -5.3172207 1.3594613 3.499144e-13
## PD_M_83_1149 14.6061285 50.3269275 5.220312e-13
mycolors= rep("lightgreen",dim(metadata)[1])
mycolors[which(metadata$sample.type=="PD")]="navy"
plot3d(data.pca$x[,1:3], pch=30 ,col=mycolors , size = 9)
snapshot3d()
## Warning in snapshot3d(): webshot = TRUE requires the webshot2 package and
## Chrome browser; using rgl.snapshot() instead
s3d <- scatterplot3d(data.pca$x[,1:3], pch=".",col.axis="blue", col.grid="lightblue")
s3d$points3d(data.pca$x[1:9,1:3], pch="o",col="lightgreen", cex=1)
s3d$points3d(data.pca$x[10:19,1:3], pch="o",col="navy", cex=1)
data.pca1 <- princomp(data,cor=T,scores = TRUE,)
names(data.pca1)
## [1] "sdev" "loadings" "center" "scale" "n.obs" "scores" "call"
plot(data.pca1$loadings[,1],data.pca1$loadings[,2] )
View(data.pca1$loadings[,])
data=data.matrix(data, rownames.force = NA)
sds=rowSds(data)
data.sds=as.data.frame(cbind(data,sds))
data.sds.sorted=data.sds[order(data.sds$sds, decreasing = T),]
top100genes=rownames(data.sds.sorted)[1:100]
top100genes
## [1] "204141_at TUBB2A" "203282_at GBE1" "200799_at NA"
## [4] "202581_at NA" "200633_at UBB" "202482_x_at RANBP1"
## [7] "200863_s_at RAB11A" "209118_s_at TUBA1A" "208609_s_at NA"
## [10] "208845_at VDAC3" "207985_at NA" "217963_s_at NGFRAP1"
## [13] "33767_at NEFH" "208641_s_at RAC1" "212971_at CARS"
## [16] "214925_s_at SPTAN1" "211458_s_at NA" "200655_s_at NA"
## [19] "201553_s_at LAMP1" "209409_at GRB10" "216333_x_at NA"
## [22] "200006_at PARK7" "216290_x_at NA" "201035_s_at HADH"
## [25] "218353_at RGS5" "206093_x_at NA" "203378_at PCF11"
## [28] "214290_s_at NA" "200602_at APP" "207198_s_at NA"
## [31] "203999_at SYT1" "213451_x_at NA" "210601_at CDH6"
## [34] "203933_at RAB11FIP3" "214247_s_at DKK3" "212098_at MGAT5"
## [37] "203057_s_at PRDM2" "208503_s_at GATAD1" "203182_s_at SRPK2"
## [40] "216038_x_at DAXX" "201090_x_at TUBA1B" "204038_s_at LPAR1"
## [43] "200607_s_at RAD21" "214687_x_at ALDOA" "201429_s_at RPL37A"
## [46] "203627_at IGF1R" "213646_x_at TUBA1B" "218332_at BEX1"
## [49] "214211_at FTH1" "203485_at RTN1" "206169_x_at ZC3H7B"
## [52] "200966_x_at ALDOA" "201242_s_at ATP1B1" "205192_at MAP3K14"
## [55] "211600_at PTPRO" "209556_at NCDN" "215131_at IQCK"
## [58] "202483_s_at RANBP1" "205113_at NEFM" "210794_s_at MEG3"
## [61] "221829_s_at TNPO1" "217714_x_at NA" "202967_at GSTA4"
## [64] "218566_s_at CHORDC1" "210825_s_at PEBP1" "221619_s_at MTCH1"
## [67] "217446_x_at NA" "216955_at TAF1" "209638_x_at RGS12"
## [70] "214629_x_at RTN4" "205202_at PCMT1" "211076_x_at ATN1"
## [73] "41220_at SEPT9" "214023_x_at TUBB2B" "202228_s_at NPTN"
## [76] "200021_at CFL1" "209469_at GPM6A" "200884_at CKB"
## [79] "222315_at LOC100996756" "205347_s_at NA" "204106_at TESK1"
## [82] "205628_at PRIM2" "202252_at RAB13" "209167_at GPM6B"
## [85] "212987_at FBXO9" "203277_at DFFA" "210466_s_at SERBP1"
## [88] "203130_s_at KIF5C" "200912_s_at NA" "200603_at PRKAR1A"
## [91] "212639_x_at TUBA1B" "211802_x_at CACNA1G" "204515_at HSD3B1"
## [94] "206998_x_at PRB3" "211774_s_at MMACHC" "204743_at TAGLN3"
## [97] "205353_s_at PEBP1" "201319_at MYL12A" "207556_s_at DGKZ"
## [100] "219657_s_at KLF3"
top.100=data[top100genes,]
part1=abs(top.100[1:10, ])
part2=abs(top.100[11:20, ])
part3=abs(top.100[21:30, ])
part4=abs(top.100[31:40, ])
part5=abs(top.100[41:50, ])
part6=abs(top.100[51:60, ])
part7=abs(top.100[61:70, ])
part8=abs(top.100[71:80, ])
part9=abs(top.100[81:90, ])
part10=abs(top.100[91:100, ])
col_fun = colorRamp2(c(min(top.100, na.rm=T), mean(top.100, na.rm=T), max(top.100, na.rm=T) ), c("blue", "white", "red"))
column_ha = HeatmapAnnotation(Sample.type= metadata$sample.type, Age = metadata$age, Sex= metadata$gender,annotation_name_gp = gpar(fontsize = 9, fontface = "bold"))
Heatmap(part1,name = "Part 1_top.100 genes", column_title = "samples", row_title = "genes", col=col_fun, cluster_rows = diana,cluster_columns = agnes,top_annotation = column_ha, row_names_gp = gpar(fontsize = 7), column_names_gp = gpar(fontsize = 7) )
Heatmap(part2,name = "Part 2_top.100 genes", column_title = "samples", row_title = "genes", col=col_fun , cluster_rows = diana,cluster_columns = agnes,top_annotation = column_ha, row_names_gp = gpar(fontsize = 7), column_names_gp = gpar(fontsize = 7))
Heatmap(part3,name = "Part 3_top.100 genes", column_title = "samples", row_title = "genes", col=col_fun, cluster_rows = diana,cluster_columns = agnes,top_annotation = column_ha, row_names_gp = gpar(fontsize = 7), column_names_gp = gpar(fontsize = 7))
Heatmap(part4,name = "Part 4_top.100 genes", column_title = "samples", row_title = "genes", col=col_fun, cluster_rows = diana,cluster_columns = agnes,top_annotation = column_ha, row_names_gp = gpar(fontsize = 7), column_names_gp = gpar(fontsize = 7))
Heatmap(part5,name = "Part 5_top.100 genes", column_title = "samples", row_title = "genes", col=col_fun, cluster_rows = diana,cluster_columns = agnes,top_annotation = column_ha, row_names_gp = gpar(fontsize = 7), column_names_gp = gpar(fontsize = 7))
Heatmap(part6,name = "Part 6_top.100 genes", column_title = "samples", row_title = "genes", col=col_fun, cluster_rows = diana,cluster_columns = agnes,top_annotation = column_ha, row_names_gp = gpar(fontsize = 7), column_names_gp = gpar(fontsize = 7))
Heatmap(part7,name = "Part 7_top.100 genes", column_title = "samples", row_title = "genes", col=col_fun, cluster_rows = diana,cluster_columns = agnes,top_annotation = column_ha, row_names_gp = gpar(fontsize = 7), column_names_gp = gpar(fontsize = 7))
Heatmap(part8,name = "Part 8_top.100 genes", column_title = "samples", row_title = "genes", col=col_fun, cluster_rows = diana,cluster_columns = agnes,top_annotation = column_ha, row_names_gp = gpar(fontsize = 7), column_names_gp = gpar(fontsize = 7))
Heatmap(part9,name = "Part 9_top.100 genes", column_title = "samples", row_title = "genes", col=col_fun, cluster_rows = diana,cluster_columns = agnes,top_annotation = column_ha, row_names_gp = gpar(fontsize = 7), column_names_gp = gpar(fontsize = 7))
Heatmap(part10,name = "Part 10_top.100 genes", column_title = "samples", row_title = "genes", col=col_fun, cluster_rows = diana,cluster_columns = agnes,top_annotation = column_ha, row_names_gp = gpar(fontsize = 7), column_names_gp = gpar(fontsize = 7))
