In CHuanSite/SJD: Structured Joint Decomposition (SJD)

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Install and load SJD package

To install this package in R, run the following commands:

library(devtools)
install_github("CHuanSite/SJD")

library(SJD)

Single-Cell RNAseq Example

First, install the 'googleDrive' package

install.packages('googledrive', repos = "http://cran.us.r-project.org")

Download RNA and explaination data from Google Drive,

library(googledrive)

## Data file 
url_data <- "https://drive.google.com/file/d/1OQovDBPwRX_O2N1GSNY8fzJn-p3-fwQV/view?usp=sharing"
drive_download(url_data, overwrite = TRUE)
unzip('data.zip')

## Explaination file
url_explaination <- 'https://drive.google.com/file/d/1S3HdygRCMvPttmVd9cix4GskWj1VPJaM/view?usp=sharing'
drive_download(url_explaination, overwrite = TRUE)
unzip('data_explaination.zip')

Read data into R

library(tidyverse)

## Read in files
inVitro_bulk = read.table('1_inVitro_Bulk_Cortecon.plog2_trimNord.txt', stringsAsFactors = FALSE, header = TRUE) %>% select(-1) %>% as.matrix
inVitro_sc = read.table('2_inVitro_SingleCell_scESCdifBifurc.CelSeq_trimNord.txt', stringsAsFactors = FALSE, header = TRUE) %>% select(-1) %>% as.matrix
inVivo_bulk = read.table('3_inVivo_Bulk_BrainSpan_RNAseq_Gene_DFC_noSVA_plog2_trimNord.txt', stringsAsFactors = FALSE, header = TRUE) %>% select(-1) %>% as.matrix
inVivo_sc = read.table('4_inVivo_SingleCell_CtxDevoSC4kTopoTypoTempo_plog2_trimNord.txt', stringsAsFactors = FALSE, header = TRUE) %>% select(-1) %>% as.matrix

## legends for the 4 datasets
inVitro_bulk_exp =  read.table("1_inVitro_Bulk_Cortecon.pd.txt",stringsAsFactors = FALSE, header = T)
inVitro_sc_exp = read.table("2_inVitro_SingleCell_scESCdifBifurc.CelSeq.pd.txt", stringsAsFactors = FALSE, header = T)
inVivo_bulk_exp = read.table("3_inVivo_Bulk_BrainSpan.RNAseq.Gene.DFC.pd.txt", stringsAsFactors = FALSE, header = T)
inVivo_sc_exp = read.table("4_inVivo_SingleCell_CtxDevoSC4kTopoTypoTempo.pd.txt", stringsAsFactors = FALSE, header = T)

Conduct Two-stage linked component analysis

library(SJD)
## List of datasets and group assignment and number of components
dataset = list(inVitro_bulk, inVitro_sc, inVivo_bulk, inVivo_sc)
group = list(c(1,2,3,4), c(1,2), c(3,4), c(1,3), c(2,4), c(1), c(2), c(3), c(4))
comp_num = c(2,2,2,2,2,2,2,2,2)

## Output result
twoStageLCA_res = twoStageLCA(dataset, group, comp_num)

Visualize the result

# par(mfrow = c(2,2)) #, mai=c(0.6,0.6,0.6,0))

# common component
plot(t(twoStageLCA_res$score_list[[1]][[1]]), col = inVitro_bulk_exp$color, pch = 16, xlab = "PC1", ylab = "PC2", main = "common: inVitro_bulk", cex = 1, cex.axis = 1, cex.lab = 1, cex.main = 1)
plot(t(twoStageLCA_res$score_list[[2]][[1]]), col = inVitro_sc_exp$COLORby.DCX, pch = 16, xlab = "PC1", ylab = "PC2", main = "common: inVitro_sc", cex = 1, cex.axis = 1, cex.lab = 1, cex.main = 1)
plot(t(twoStageLCA_res$score_list[[3]][[1]]), col = inVivo_bulk_exp$color, pch = 16, xlab = "PC1", ylab = "PC2", main = "common: inVivo_bulk", cex = 1, cex.axis = 1, cex.lab = 1, cex.main = 1)
plot(t(twoStageLCA_res$score_list[[4]][[1]]), col = inVivo_sc_exp$COLORby.DCX, pch = 16, xlab = "PC1", ylab = "PC2", main = "common: inVivo_sc", cex = 1, cex.axis = 1, cex.lab = 1, cex.main = 1)


# par(mfrow = c(1,2)) #, mai=c(0.6,0.6,0.6,0))
## in vitro component
plot(t(twoStageLCA_res$score_list[[1]][[2]]), col = inVitro_bulk_exp$color, pch = 16, xlab = "PC1", ylab = "PC2", main = "common: inVitro_bulk", cex = 1, cex.axis = 1, cex.lab = 1, cex.main = 1)
plot(t(twoStageLCA_res$score_list[[2]][[2]]), col = inVitro_sc_exp$COLORby.DCX, pch = 16, xlab = "PC1", ylab = "PC2", main = "common: inVitro_sc", cex = 1, cex.axis = 1, cex.lab = 1, cex.main = 1)

## in vivo component
plot(t(twoStageLCA_res$score_list[[3]][[3]]), col = inVivo_bulk_exp$color, pch = 16, xlab = "PC1", ylab = "PC2", main = "common: inVivo_bulk", cex = 1, cex.axis = 1, cex.lab = 1, cex.main = 1)
plot(t(twoStageLCA_res$score_list[[4]][[3]]), col = inVivo_sc_exp$COLORby.DCX, pch = 16, xlab = "PC1", ylab = "PC2", main = "common: inVivo_sc", cex = 1, cex.axis = 1, cex.lab = 1, cex.main = 1)

## bulk component
plot(t(twoStageLCA_res$score_list[[1]][[4]]), col = inVitro_bulk_exp$color, pch = 16, xlab = "PC1", ylab = "PC2", main = "common: inVitro_bulk", cex = 1, cex.axis = 1, cex.lab = 1, cex.main = 1)
plot(t(twoStageLCA_res$score_list[[3]][[4]]), col = inVivo_bulk_exp$color, pch = 16, xlab = "PC1", ylab = "PC2", main = "common: inVivo_bulk", cex = 1, cex.axis = 1, cex.lab = 1, cex.main = 1)

## sc component
plot(t(twoStageLCA_res$score_list[[2]][[5]]), col = inVitro_sc_exp$COLORby.DCX, pch = 16, xlab = "PC1", ylab = "PC2", main = "common: inVitro_sc", cex = 1, cex.axis = 1, cex.lab = 1, cex.main = 1)
plot(t(twoStageLCA_res$score_list[[4]][[5]]), col = inVivo_sc_exp$COLORby.DCX, pch = 16, xlab = "PC1", ylab = "PC2", main = "common: inVivo_sc", cex = 1, cex.axis = 1, cex.lab = 1, cex.main = 1)

Reference

• Huan Chen, Brian Caffo, Genevieve Stein-O’Brien, Jinrui Liu, Ben Langmead, Carlo Colantuoni, Luo Xiao, Two-stage linked component analysis for joint decomposition of multiple biologically related data sets, Biostatistics, 2022;, kxac005, https://doi.org/10.1093/biostatistics/kxac005

CHuanSite/SJD documentation built on Nov. 29, 2024, 5:52 a.m.