Weiyan 1/2/2019
Note: all the following data sets were generated by Lu Liu through R anlaysis.
setwd("/Users/weiyanjia/SynologyDrive/Randal\ S.\ Tibbetts/FUS\ project/Bioinfo_analysis/FUS_Replication\ timing/Replication\ timing_08312018/01_02_normalization_smoothing/plots_Jia/01022019")
library(RColorBrewer)
RT_Loess <- read.delim("all_sample_r1_r2_smooth.txt", header = TRUE, sep ="\t", stringsAsFactors = FALSE)
RT_Loess[, c(2:3)] <- sapply(RT_Loess[, c(2:3)], as.integer)
head(RT_Loess)## chr start end Clone110_R1 FUSClone110_R1 U2OS_R1 Clone110_R2
## 1 chr1 800000 820000 1.003375 1.208018 1.406038 0.9013547
## 2 chr1 820000 840000 1.022251 1.196786 1.414605 0.9081274
## 3 chr1 840000 860000 1.040475 1.186075 1.422639 0.9143926
## 4 chr1 860000 880000 1.058048 1.175888 1.430135 0.9201442
## 5 chr1 880000 900000 1.074967 1.166228 1.437088 0.9253764
## 6 chr1 900000 920000 1.091232 1.157098 1.443494 0.9300832
## FUSClone110_R2 U2OS_R2
## 1 0.9879291 0.9895395
## 2 1.0126643 1.0077907
## 3 1.0363650 1.0255111
## 4 1.0590256 1.0427003
## 5 1.0806404 1.0593578
## 6 1.1012037 1.0754831
chr_list<-c()
for (i in 1:22){
chr_list[i] <- paste0("chr",i,sep="")
print(chr_list)
}## [1] "chr1"
## [1] "chr1" "chr2"
## [1] "chr1" "chr2" "chr3"
## [1] "chr1" "chr2" "chr3" "chr4"
## [1] "chr1" "chr2" "chr3" "chr4" "chr5"
## [1] "chr1" "chr2" "chr3" "chr4" "chr5" "chr6"
## [1] "chr1" "chr2" "chr3" "chr4" "chr5" "chr6" "chr7"
## [1] "chr1" "chr2" "chr3" "chr4" "chr5" "chr6" "chr7" "chr8"
## [1] "chr1" "chr2" "chr3" "chr4" "chr5" "chr6" "chr7" "chr8" "chr9"
## [1] "chr1" "chr2" "chr3" "chr4" "chr5" "chr6" "chr7" "chr8" "chr9"
## [10] "chr10"
## [1] "chr1" "chr2" "chr3" "chr4" "chr5" "chr6" "chr7" "chr8" "chr9"
## [10] "chr10" "chr11"
## [1] "chr1" "chr2" "chr3" "chr4" "chr5" "chr6" "chr7" "chr8" "chr9"
## [10] "chr10" "chr11" "chr12"
## [1] "chr1" "chr2" "chr3" "chr4" "chr5" "chr6" "chr7" "chr8" "chr9"
## [10] "chr10" "chr11" "chr12" "chr13"
## [1] "chr1" "chr2" "chr3" "chr4" "chr5" "chr6" "chr7" "chr8" "chr9"
## [10] "chr10" "chr11" "chr12" "chr13" "chr14"
## [1] "chr1" "chr2" "chr3" "chr4" "chr5" "chr6" "chr7" "chr8" "chr9"
## [10] "chr10" "chr11" "chr12" "chr13" "chr14" "chr15"
## [1] "chr1" "chr2" "chr3" "chr4" "chr5" "chr6" "chr7" "chr8" "chr9"
## [10] "chr10" "chr11" "chr12" "chr13" "chr14" "chr15" "chr16"
## [1] "chr1" "chr2" "chr3" "chr4" "chr5" "chr6" "chr7" "chr8" "chr9"
## [10] "chr10" "chr11" "chr12" "chr13" "chr14" "chr15" "chr16" "chr17"
## [1] "chr1" "chr2" "chr3" "chr4" "chr5" "chr6" "chr7" "chr8" "chr9"
## [10] "chr10" "chr11" "chr12" "chr13" "chr14" "chr15" "chr16" "chr17" "chr18"
## [1] "chr1" "chr2" "chr3" "chr4" "chr5" "chr6" "chr7" "chr8" "chr9"
## [10] "chr10" "chr11" "chr12" "chr13" "chr14" "chr15" "chr16" "chr17" "chr18"
## [19] "chr19"
## [1] "chr1" "chr2" "chr3" "chr4" "chr5" "chr6" "chr7" "chr8" "chr9"
## [10] "chr10" "chr11" "chr12" "chr13" "chr14" "chr15" "chr16" "chr17" "chr18"
## [19] "chr19" "chr20"
## [1] "chr1" "chr2" "chr3" "chr4" "chr5" "chr6" "chr7" "chr8" "chr9"
## [10] "chr10" "chr11" "chr12" "chr13" "chr14" "chr15" "chr16" "chr17" "chr18"
## [19] "chr19" "chr20" "chr21"
## [1] "chr1" "chr2" "chr3" "chr4" "chr5" "chr6" "chr7" "chr8" "chr9"
## [10] "chr10" "chr11" "chr12" "chr13" "chr14" "chr15" "chr16" "chr17" "chr18"
## [19] "chr19" "chr20" "chr21" "chr22"
chr_list<-c(chr_list,"chrX")
RT_Loess$chr <- factor(RT_Loess$chr, levels= chr_list)
# write.table(RT_Loess,"RT_Loess_x.txt", sep="\t", row.names=FALSE, quote=FALSE, col.names = TRUE)library(reshape2)
dat.m <- melt(RT_Loess, id.vars='chr', measure.vars =c("U2OS_R1", "Clone110_R1","FUSClone110_R1","U2OS_R2","Clone110_R2","FUSClone110_R2") )
library(ggpubr)## Loading required package: ggplot2
ggboxplot(dat.m, x="variable", y="value",
ylab = "Replication Timing",
ylim=c(-4,4),
xlab ="",
legend.title ="",
x.text.angle = 45,
color = "variable", palette = "aaas"
)+
border(color ="black", size =1, linetype = NULL)
palette = "aaas"sub_chr <- subset(RT_Loess, RT_Loess$chr == "chr18")
head(sub_chr)## chr start end Clone110_R1 FUSClone110_R1 U2OS_R1 Clone110_R2
## 50006 chr18 80000 100000 -0.9158458 -0.4703737 -0.20717267 -1.3131937
## 50007 chr18 100000 120000 -0.8365058 -0.3986828 -0.14072280 -1.2315270
## 50008 chr18 120000 140000 -0.7589561 -0.3293648 -0.07661734 -1.1511733
## 50009 chr18 140000 160000 -0.6831967 -0.2624348 -0.01486341 -1.0721301
## 50010 chr18 160000 180000 -0.6092274 -0.1979082 0.04453186 -0.9943948
## 50011 chr18 180000 200000 -0.5370481 -0.1358000 0.10156135 -0.9179649
## FUSClone110_R2 U2OS_R2
## 50006 -0.8781096 -0.06621107
## 50007 -0.7956762 -0.01103857
## 50008 -0.7154465 0.04214985
## 50009 -0.6374241 0.09334109
## 50010 -0.5616127 0.14252203
## 50011 -0.4880161 0.18967957
class(sub_chr)## [1] "data.frame"
ggline(sub_chr, x="start", y=c("U2OS_R1", "Clone110_R1","FUSClone110_R1"),
merge = TRUE,
ylab = "Replication Timing",
ylim=c(-2,2),
xlim = c(2e+07,5e+07),
xlab ="Coordinate",
plot_type = "l",
palette = "aaas") +
geom_hline(yintercept =0, linetype =2) +
xscale("none", .format = TRUE)+
border(color ="black", size =1, linetype = NULL)
##3.2 RT-Chr18-R2
ggline(sub_chr, x="start", y=c("U2OS_R2", "Clone110_R2","FUSClone110_R2"),
merge = TRUE,
ylab = "Replication Timing",
ylim=c(-2,2),
xlim = c(2e+07,5e+07),
xlab ="Coordinate",
plot_type = "l",
palette = "aaas") +
geom_hline(yintercept =0, linetype =2) +
xscale("none", .format = TRUE)+
border(color ="black", size =1, linetype = NULL)
ggdensity(RT_Loess, x = c("U2OS_R1", "Clone110_R1","FUSClone110_R1"),
y="..density..",
xlim = c(-3,3),
xlab = "Replication Timing",
merge = TRUE,
add = "median", # Add median line.
palette = "aaas"
)+
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0))
ggdensity(RT_Loess, x = c("U2OS_R2", "Clone110_R2","FUSClone110_R2"),
y="..density..",
xlim = c(-3,3),
xlab = "Replication Timing",
merge = TRUE,
add = "median", # Add median line.
palette = "aaas"
)+
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0))
sub_chr <- subset(RT_Loess, RT_Loess$chr == "chr2")
ggdensity(sub_chr, x = c("U2OS_R2", "Clone110_R2","FUSClone110_R2"),
y="..density..",
xlim = c(-3,3),
xlab = "Replication Timing",
merge = TRUE,
add = "median", # Add median line.
# rug = TRUE ,
palette = "aaas"
)+
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0))
sub_chr <- subset(RT_Loess, RT_Loess$chr == "chr5")
ggdensity(sub_chr, x = c("U2OS_R2", "Clone110_R2","FUSClone110_R2"),
y="..density..",
xlim = c(-3,3),
xlab = "Replication Timing",
merge = TRUE,
add = "median", # Add median line.
palette = "aaas"
)+
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0))
sub_chr <- subset(RT_Loess, RT_Loess$chr == "chr20")
ggdensity(sub_chr, x = c("U2OS_R2", "Clone110_R2","FUSClone110_R2"),
y="..density..",
xlim = c(-3,3),
xlab = "Replication Timing",
merge = TRUE,
add = "median", # Add median line.
# rug = TRUE ,
palette = "aaas"
)+
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0))
sub_chr <- subset(RT_Loess, RT_Loess$chr == "chr2")
ggdensity(sub_chr, x = c("U2OS_R1", "Clone110_R1","FUSClone110_R1"),
y="..density..",
xlim = c(-3,3),
xlab = "Replication Timing",
merge = TRUE,
add = "median", # Add median line.
# rug = TRUE ,
palette = "aaas"
)+
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0))
sub_chr <- subset(RT_Loess, RT_Loess$chr == "chr5")
ggdensity(sub_chr, x = c("U2OS_R1", "Clone110_R1","FUSClone110_R1"),
y="..density..",
xlim = c(-3,3),
xlab = "Replication Timing",
merge = TRUE,
add = "median", # Add median line.
palette = "aaas"
)+
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0))
sub_chr <- subset(RT_Loess, RT_Loess$chr == "chr20")
ggdensity(sub_chr, x = c("U2OS_R1", "Clone110_R1","FUSClone110_R1"),
y="..density..",
xlim = c(-3,3),
xlab = "Replication Timing",
merge = TRUE,
add = "median", # Add median line.
# rug = TRUE ,
palette = "aaas"
)+
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0))
library(ComplexHeatmap)## Loading required package: grid
## ========================================
## ComplexHeatmap version 2.0.0
## 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:
## Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
## genomic data. Bioinformatics 2016.
## ========================================
library(circlize)## ========================================
## circlize version 0.4.10
## 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))
## ========================================
corr_data <- RT_Loess[4:ncol(RT_Loess)]
corr_matrix <- round(cor(corr_data, method = "pearson"), 2)
col_fun = colorRamp2(c(0.6, 0.8, 1), c("#4DAF4A", "#FFD92F", "#E41A1C"))
Heatmap(corr_matrix, name = "", col = col_fun,
row_names_side = "left",
row_order = c("U2OS_R1","U2OS_R2","FUSClone110_R1","FUSClone110_R2",
"Clone110_R1","Clone110_R2"),
column_order = c("U2OS_R1","U2OS_R2","FUSClone110_R1","FUSClone110_R2",
"Clone110_R1","Clone110_R2"),
# column_order = c("Clone110_S_R2","Clone110_S_R1","FUSClone110_S_R2","FUSClone110_S_R1",
#"U2OS_S_R2","U2OS_S_R1"),
# column_order = rev(order(colnames(corr_matrix))),
clustering_distance_rows = "pearson",
clustering_distance_columns = "pearson",
cluster_rows = FALSE,
cluster_columns = FALSE,
cell_fun = function(j, i, x, y, width, height, fill) {
grid.text(sprintf("%.2f", corr_matrix[i, j]), x, y, gp = gpar(fontsize = 10))
},
heatmap_legend_param = list(
at = c(0.6,0.7, 0.8,0.9, 1.0),
legend_height = unit(4.5, "inch"),
title_position = "topleft")
)
library(ggfortify)
library(tidyverse)## ── Attaching packages ───────────────────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ tibble 3.0.1 ✓ dplyr 1.0.0
## ✓ tidyr 1.1.0 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0
## ✓ purrr 0.3.4
## ── Conflicts ──────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(export)pca_data <- RT_Loess[4:ncol(RT_Loess)]
# head(pca_data)
pca_data2<- tibble::rowid_to_column(corr_data, "ID")
pca_data2$ID <-as.factor(pca_data2$ID)
# head(pca_data2)
Tran_pca_data <- gather(pca_data2,sample,RT,Clone110_R1:U2OS_R2)
Tran_pca_data_S <- spread(Tran_pca_data,ID, RT)
# head(Tran_pca_data_S)
pca_res <- prcomp(Tran_pca_data_S[2:ncol(Tran_pca_data_S)], scale. = FALSE)
autoplot(pca_res, data = Tran_pca_data_S, colour = 'sample')## Warning: `select_()` is deprecated as of dplyr 0.7.0.
## Please use `select()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
graph2pdf(file="/Users/weiyanjia/SynologyDrive/Randal\ S.\ Tibbetts/FUS\ project/Bioinfo_analysis/FUS_Replication\ timing/Replication\ timing_08312018/01_02_normalization_smoothing/plots_Jia/01022019/PCA_RT.pdf", width=8, aspectr=sqrt(2),font = "Arial",bg = "transparent")## Exported graph as /Users/weiyanjia/SynologyDrive/Randal S. Tibbetts/FUS project/Bioinfo_analysis/FUS_Replication timing/Replication timing_08312018/01_02_normalization_smoothing/plots_Jia/01022019/PCA_RT.pdf
Tran_pca_data_S_T<- Tran_pca_data_S%>%
column_to_rownames("sample")
pca_res <- prcomp(Tran_pca_data_S_T, scale. = FALSE)
var_explained <- pca_res$sdev^2/sum(pca_res$sdev^2)
var_explained[1:6]## [1] 8.009698e-01 7.954648e-02 6.457223e-02 3.359288e-02 2.131863e-02
## [6] 2.133411e-30
names(pca_res)## [1] "sdev" "rotation" "center" "scale" "x"
pca_res$x %>%
as.data.frame %>%
rownames_to_column("sample") %>%
ggplot(aes(x=PC1,y=PC2)) + geom_point(aes(color=sample),size=4) +
theme_bw(base_size=20) +
labs(x=paste0("PC1: ",round(var_explained[1]*100,1),"%"),
y=paste0("PC2: ",round(var_explained[2]*100,1),"%")) +
theme(legend.position="top")
graph2pdf(file="/Users/weiyanjia/SynologyDrive/Randal\ S.\ Tibbetts/FUS\ project/Bioinfo_analysis/FUS_Replication\ timing/Replication\ timing_08312018/01_02_normalization_smoothing/plots_Jia/01022019/PCA_RT2.pdf", width=8, aspectr=sqrt(2),font = "Arial",bg = "transparent")## Exported graph as /Users/weiyanjia/SynologyDrive/Randal S. Tibbetts/FUS project/Bioinfo_analysis/FUS_Replication timing/Replication timing_08312018/01_02_normalization_smoothing/plots_Jia/01022019/PCA_RT2.pdf
pca_res$x %>%
as.data.frame %>%
rownames_to_column("sample") %>%
ggplot(aes(x=PC1,y=PC2, label=sample, color=sample)) +
geom_label(aes(fill = sample), colour = "white", fontface = "bold")+
theme_bw(base_size=20) +
labs(x=paste0("PC1: ",round(var_explained[1]*100,1),"%"),
y=paste0("PC2: ",round(var_explained[2]*100,1),"%"))+
theme(legend.position="top")
graph2pdf(file="/Users/weiyanjia/SynologyDrive/Randal\ S.\ Tibbetts/FUS\ project/Bioinfo_analysis/FUS_Replication\ timing/Replication\ timing_08312018/01_02_normalization_smoothing/plots_Jia/01022019/PCA_RT3.pdf", width=8, aspectr=sqrt(2),font = "Arial",bg = "transparent")## Exported graph as /Users/weiyanjia/SynologyDrive/Randal S. Tibbetts/FUS project/Bioinfo_analysis/FUS_Replication timing/Replication timing_08312018/01_02_normalization_smoothing/plots_Jia/01022019/PCA_RT3.pdf
pca_res_scale <- prcomp(Tran_pca_data_S[2:ncol(Tran_pca_data_S)], scale. = TRUE)
# pca_res$x
autoplot(pca_res_scale, data = Tran_pca_data_S, colour = 'sample')
library(M3C)
# pca_data
# s <- colnames(pca_data[1,])
#
# celltypes<-c("Clone110_R1","FUSClone110_R1","U2OS_R1","Clone110_R2","FUSClone110_R2","U2OS_R2")
# tsne(pca_data,labels=as.factor(celltypes))
pca(pca_data,legendtextsize = 10,axistextsize = 10,dotsize=2)
pca(pca_data,labels=as.factor(colnames(pca_data[1,])),legendtextsize = 10,axistextsize = 10,dotsize=2)
RT_Loess$diff_U2OS_Clone110_R1 <- RT_Loess$Clone110_R1 - RT_Loess$U2OS_R1
RT_Loess$diff_U2OS_FUSClone110_R1 <- RT_Loess$FUSClone110_R1 - RT_Loess$U2OS_R1
RT_Loess$diff_U2OS_Clone110_R2 <- RT_Loess$Clone110_R2 - RT_Loess$U2OS_R2
RT_Loess$diff_U2OS_FUSClone110_R2 <- RT_Loess$FUSClone110_R2 - RT_Loess$U2OS_R2
head(RT_Loess)## chr start end Clone110_R1 FUSClone110_R1 U2OS_R1 Clone110_R2
## 1 chr1 800000 820000 1.003375 1.208018 1.406038 0.9013547
## 2 chr1 820000 840000 1.022251 1.196786 1.414605 0.9081274
## 3 chr1 840000 860000 1.040475 1.186075 1.422639 0.9143926
## 4 chr1 860000 880000 1.058048 1.175888 1.430135 0.9201442
## 5 chr1 880000 900000 1.074967 1.166228 1.437088 0.9253764
## 6 chr1 900000 920000 1.091232 1.157098 1.443494 0.9300832
## FUSClone110_R2 U2OS_R2 diff_U2OS_Clone110_R1 diff_U2OS_FUSClone110_R1
## 1 0.9879291 0.9895395 -0.4026630 -0.1980204
## 2 1.0126643 1.0077907 -0.3923539 -0.2178189
## 3 1.0363650 1.0255111 -0.3821632 -0.2365636
## 4 1.0590256 1.0427003 -0.3720869 -0.2542465
## 5 1.0806404 1.0593578 -0.3621212 -0.2708598
## 6 1.1012037 1.0754831 -0.3522625 -0.2863955
## diff_U2OS_Clone110_R2 diff_U2OS_FUSClone110_R2
## 1 -0.08818479 -0.001610420
## 2 -0.09966328 0.004873531
## 3 -0.11111856 0.010853844
## 4 -0.12255610 0.016325284
## 5 -0.13398139 0.021282613
## 6 -0.14539989 0.025720594
gghistogram(RT_Loess, x = c("diff_U2OS_FUSClone110_R1", "diff_U2OS_Clone110_R1"),
y="..count..",
position = "dodge",
xlim = c(-3,3),
bins = 50,
xlab = "Replication Timing",
color = ".x.",
fill = ".x.",
merge = TRUE,
add = "median", # Add median line.
palette = "aaas"
)+
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0))
## 6.1
R2
gghistogram(RT_Loess, x = c("diff_U2OS_FUSClone110_R2","diff_U2OS_Clone110_R2"),
y="..count..",
position = "dodge",
xlim = c(-3,3),
bins = 100,
xlab = "Replication Timing",
color = ".x.", fill = ".x.",
merge = TRUE,
add = "median", # Add median line.
palette = "aaas"
)+
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) 
p <- ggline(RT_Loess, x="start", y=c("U2OS_R2", "Clone110_R2","FUSClone110_R2"),
merge = TRUE,
size= 0.5,
ylab = "Replication Timing",
ylim=c(-3,3),
xlab ="Coordinate",
plot_type = "l",
palette = "aaas") +
geom_hline(yintercept =0, linetype =2) +
xscale("none", .format = TRUE)+
border(color ="black", size =0.5, linetype = NULL)+
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0), breaks = c(-2,-1, 0, 1, 2))
p + facet_grid(rows = vars(chr), scales = "free", space = "free", margins = FALSE, shrink = TRUE)+
theme(panel.spacing = unit(0.1, "lines"),
panel.border = element_rect(linetype ="solid", fill = NA)) 
library(ggsci)
## palette from package ggsci
palette1 <- pal_aaas("default")(10)
# palette1
palette2 <- pal_npg("nrc")(10)
# palette2
palette3 <- pal_lancet("lanonc")(9)
# palette3
big_palette <- c(palette1,palette2,palette3)
# big_palette
big_palette_clean <- big_palette[!duplicated(big_palette)]
# big_palette_clean
p <- ggline(RT_Loess, x="start", y="U2OS_R2",
merge = TRUE,
size = 0.5,
ylab = "Replication Timing",
ylim=c(-3,3),
#xlim = c(0.0e+00,2.6e+08),
xlab ="Chromosome",
plot_type = "l",
color = "chr",
palette = big_palette_clean) +
geom_hline(yintercept =0, linetype =2) +
xscale("none", .format = TRUE)+
border(color ="black", size =0.5, linetype = NULL)+
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0), breaks = c(-2,-1, 0, 1, 2)) +
theme (
axis.text.x = element_blank(),
axis.ticks.x = element_blank()
)
p + facet_grid(cols = vars(chr), scales = "free", space = "free", drop = TRUE, margins = FALSE, shrink = TRUE, switch = "x") +
theme(panel.spacing = unit(0, "lines")) 
sessionInfo()## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] parallel grid stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] ggsci_2.9 M3C_1.6.0 Biobase_2.44.0
## [4] BiocGenerics_0.30.0 export_0.2.2.9001 forcats_0.5.0
## [7] stringr_1.4.0 dplyr_1.0.0 purrr_0.3.4
## [10] readr_1.3.1 tidyr_1.1.0 tibble_3.0.1
## [13] tidyverse_1.3.0 ggfortify_0.4.10 circlize_0.4.10
## [16] ComplexHeatmap_2.0.0 ggpubr_0.4.0 ggplot2_3.3.2
## [19] reshape2_1.4.4 RColorBrewer_1.1-2
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 uuid_0.1-4 snow_0.4-3
## [4] backports_1.1.8 systemfonts_0.2.3 NMF_0.22.0
## [7] plyr_1.8.6 splines_3.6.3 sigclust_1.1.0
## [10] crosstalk_1.1.0.1 gridBase_0.4-7 digest_0.6.25
## [13] foreach_1.5.0 htmltools_0.5.0 matrixcalc_1.0-3
## [16] viridis_0.5.1 fansi_0.4.1 magrittr_1.5
## [19] cluster_2.1.0 doParallel_1.0.15 openxlsx_4.1.5
## [22] modelr_0.1.8 officer_0.3.12 colorspace_1.4-1
## [25] blob_1.2.1 rvest_0.3.5 haven_2.3.1
## [28] xfun_0.15 crayon_1.3.4 jsonlite_1.7.0
## [31] survival_3.2-3 iterators_1.0.12 glue_1.4.1
## [34] registry_0.5-1 rvg_0.2.5 gtable_0.3.0
## [37] webshot_0.5.2 GetoptLong_1.0.2 car_3.0-8
## [40] shape_1.4.4 abind_1.4-5 scales_1.1.1
## [43] DBI_1.1.0 rngtools_1.5 bibtex_0.4.2.2
## [46] rstatix_0.6.0 miniUI_0.1.1.1 Rcpp_1.0.5
## [49] viridisLite_0.3.0 xtable_1.8-4 clue_0.3-57
## [52] foreign_0.8-75 htmlwidgets_1.5.1 httr_1.4.1
## [55] ellipsis_0.3.1 pkgconfig_2.0.3 farver_2.0.3
## [58] dbplyr_1.4.4 tidyselect_1.1.0 labeling_0.3
## [61] rlang_0.4.6 manipulateWidget_0.10.1 later_1.1.0.1
## [64] munsell_0.5.0 cellranger_1.1.0 tools_3.6.3
## [67] cli_2.0.2 generics_0.0.2 broom_0.5.6
## [70] evaluate_0.14 fastmap_1.0.1 yaml_2.2.1
## [73] knitr_1.29 fs_1.4.2 zip_2.0.4
## [76] rgl_0.100.54 dendextend_1.13.4 nlme_3.1-148
## [79] mime_0.9 xml2_1.3.2 compiler_3.6.3
## [82] rstudioapi_0.11 curl_4.3 png_0.1-7
## [85] ggsignif_0.6.0 reprex_0.3.0 stringi_1.4.6
## [88] gdtools_0.2.2 stargazer_5.2.2 lattice_0.20-41
## [91] Matrix_1.2-18 vctrs_0.3.1 pillar_1.4.4
## [94] lifecycle_0.2.0 GlobalOptions_0.1.2 corpcor_1.6.9
## [97] data.table_1.12.8 flextable_0.5.10 httpuv_1.5.4
## [100] R6_2.4.1 promises_1.1.1 gridExtra_2.3
## [103] rio_0.5.16 codetools_0.2-16 assertthat_0.2.1
## [106] pkgmaker_0.31.1 rjson_0.2.20 withr_2.2.0
## [109] hms_0.5.3 doSNOW_1.0.18 rmarkdown_2.3
## [112] carData_3.0-4 Rtsne_0.15 shiny_1.5.0
## [115] lubridate_1.7.9 base64enc_0.1-3