Cibersort Small Intestine

This dataset contains nine tissues (heart, hippocampus, hypothalamus, kidney, liver, prefrontal cortex, skeletal liver, small intestine, and spleen) from C57BL/6J mice that were fed 2-deoxyglucose (6g/L) through their drinking water for 96hrs or 4wks. 96hr mice were given their 2DG treatment 2 weeks after the other cohort started the 4 week treatment. The organs from the mice were harvested and processed for metabolomics and transcriptomics. The data in this document pertains to the transcriptomics data only. The counts that were used were FPKM normalized before being log transformed. It was determined that sample A113 had low RNAseq quality and through further analyses with PCA, MA plots, and clustering was an outlier and will be removed for the rest of the analyses performed. This document will deconvolve the green4 module, as well as all genes identified in the small intestine due to the predominant immunological signature that was identified when looking at overrepresented pathways for the green4 module.

tdata.FPKM.sample.info <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_phenotypes.RData"))
tdata.FPKM.sample.info.pheno <- tdata.FPKM.sample.info[,c(27238:27240)] %>% rownames_to_column()

cibersort <- read.csv(here("Data","Small Intestine","CIBERSORTx_Job16_Results.csv"))
cibersort <- cibersort[,-c(27:29)]
cibersort.phenotypes <- left_join(cibersort, tdata.FPKM.sample.info.pheno, by = c("Mixture" = "rowname"))
cibersort.phenotypes <- cibersort.phenotypes %>% column_to_rownames(var = "Mixture")
cibersort.phenotypes <- cibersort.phenotypes %>% mutate("Module" = rep("Green4",16))

cibersort.all <- read.csv(here("Data","Small Intestine","CIBERSORTx_Job29_Results.csv"))
cibersort.all <- cibersort.all[,-c(27:29)]
cibersort.all.phenotypes <- left_join(cibersort.all, tdata.FPKM.sample.info.pheno, by = c("Mixture" = "rowname"))
cibersort.all.phenotypes <- cibersort.all.phenotypes %>% column_to_rownames(var = "Mixture")
cibersort.all.phenotypes <- cibersort.all.phenotypes %>% mutate("Module" = rep("All",16))

cibersort.compare <- rbind(cibersort.phenotypes,cibersort.all.phenotypes)

Check Distribution

Raw Percentages

for(i in 1: length(cibersort[-1])){
  cat("\n####", names(cibersort[i+1]), "\n")
  hist(cibersort[,i+1],xlab = "Percentage of cells", main = names(cibersort[i+1]))
  cat("\n \n")
}

Mast.Cells

Neutrophil.Cells

Eosinophil.Cells

B.Cells.Memory

B.Cells.Naive

Plasma.Cells

T.Cells.CD8.Actived

T.Cells.CD8.Naive

T.Cells.CD8.Memory

M0.Macrophage

M1.Macrophage

M2.Macrophage

Treg.Cells

T.Cells.CD4.Memory

T.Cells.CD4.Naive

T.Cells.CD4.Follicular

Th1.Cells

Th17.Cells

Th2.Cells

Monocyte

GammaDelta.T.Cells

NK.Resting

NK.Actived

DC.Actived

DC.Immature

Logged Percentages

log.cibersort <- log(cibersort[,2:26])

for(i in 1: length(log.cibersort[-1])){
    cat("\n####", names(log.cibersort[i+1]), "\n")
  hist(log.cibersort[,i+1], xlab = "Percentage of cells",main = names(log.cibersort[i+1]))
  cat("\n \n")
}

Neutrophil.Cells

Eosinophil.Cells

B.Cells.Memory

B.Cells.Naive

Plasma.Cells

T.Cells.CD8.Actived

T.Cells.CD8.Naive

T.Cells.CD8.Memory

M0.Macrophage

M1.Macrophage

M2.Macrophage

Treg.Cells

T.Cells.CD4.Memory

T.Cells.CD4.Naive

T.Cells.CD4.Follicular

Th1.Cells

Th17.Cells

Th2.Cells

Monocyte

GammaDelta.T.Cells

NK.Resting

NK.Actived

DC.Actived

DC.Immature

Density Plots

Green4 module

process <- preProcess(as.data.frame(cibersort), method=c("range"))

norm_scale <- predict(process, as.data.frame(cibersort))

for(i in 1: length(norm_scale[-1])){
  cat("\n####", names(norm_scale[i+1]), "\n")
  plot(density(norm_scale[,i+1]), main = names(norm_scale[,i+1]))
  cat("\n \n")
}

Mast.Cells

Neutrophil.Cells

Eosinophil.Cells

B.Cells.Memory

B.Cells.Naive

Plasma.Cells

T.Cells.CD8.Actived

T.Cells.CD8.Naive

T.Cells.CD8.Memory

M0.Macrophage

M1.Macrophage

M2.Macrophage

Treg.Cells

T.Cells.CD4.Memory

T.Cells.CD4.Naive

T.Cells.CD4.Follicular

Th1.Cells

Th17.Cells

Th2.Cells

Monocyte

GammaDelta.T.Cells

NK.Resting

NK.Actived

DC.Actived

DC.Immature

norm_scale_phenotypes <- left_join(norm_scale, tdata.FPKM.sample.info.pheno, by = c("Mixture" = "rowname"))
norm_scale_phenotypes <- norm_scale_phenotypes %>% column_to_rownames(var = "Mixture")

norm_scale_phenotypes$Time <- as.factor(norm_scale_phenotypes$Time)
norm_scale_phenotypes$Treatment <- as.factor(norm_scale_phenotypes$Treatment)

model <- list()
adjust <- list()

for(i in 1:length(norm_scale[-1])) {
    name <- colnames(norm_scale_phenotypes[,i])
    
    #m <- art(data = cibersort.all.phenotypes, cibersort.all.phenotypes[,i] ~ Time*Treatment)
    m <- lm(data = norm_scale_phenotypes, norm_scale_phenotypes[,i] ~ Time*Treatment)
             model[[i]] <- anova(m)
             adjust[[i]] <- p.adjust(model[[i]]$`Pr(>F)`, method = "BH")
}
names(model) <- colnames(norm_scale[-1])
names(adjust) <- colnames(norm_scale[-1])

All genes in Small Intestine

process <- preProcess(as.data.frame(cibersort.all), method=c("range"))

norm_scale_all <- predict(process, as.data.frame(cibersort))

for(i in 1: length(norm_scale_all[-1])){
    cat("\n####", names(norm_scale_all[i+1]), "\n")
  d <- density(norm_scale_all[,i+1])
  plot(d)
  cat("\n \n")
}

Mast.Cells

Neutrophil.Cells

Eosinophil.Cells

B.Cells.Memory

B.Cells.Naive

Plasma.Cells

T.Cells.CD8.Actived

T.Cells.CD8.Naive

T.Cells.CD8.Memory

M0.Macrophage

M1.Macrophage

M2.Macrophage

Treg.Cells

T.Cells.CD4.Memory

T.Cells.CD4.Naive

T.Cells.CD4.Follicular

Th1.Cells

Th17.Cells

Th2.Cells

Monocyte

GammaDelta.T.Cells

NK.Resting

NK.Actived

DC.Actived

DC.Immature

norm_scale_all_phenotypes <- left_join(norm_scale_all, tdata.FPKM.sample.info.pheno, by = c("Mixture" = "rowname"))
norm_scale_all_phenotypes <- norm_scale_all_phenotypes %>% column_to_rownames(var = "Mixture")

norm_scale_all_phenotypes$Time <- as.factor(norm_scale_all_phenotypes$Time)
norm_scale_all_phenotypes$Treatment <- as.factor(norm_scale_all_phenotypes$Treatment)

model <- list()
adjust <- list()

for(i in 1:length(norm_scale_all[-1])) {
   print(i)
    name <- colnames(norm_scale_all_phenotypes[,i])
    #m <- art(data = cibersort.all.phenotypes, cibersort.all.phenotypes[,i] ~ Time*Treatment)
    m <- lm(data = norm_scale_all_phenotypes, norm_scale_all_phenotypes[,i] ~ Time*Treatment)
             model[[i]] <- anova(m)
             adjust[[i]] <- p.adjust(model[[i]]$`Pr(>F)`, method = "BH")
}

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names(model) <- colnames(norm_scale_all[-1])
names(adjust) <- colnames(norm_scale_all[-1])

ANOVA Non-parametric

To perform analysis of variance Aligned Rank Transformation was performed, which is designed to handle non-parametric multi-factorial experiments. P-value was adjusted using Benjamini-hochberg procedure.

Green4 Module

cibersort.phenotypes$Time <- as.factor(cibersort.phenotypes$Time)
cibersort.phenotypes$Treatment <- as.factor(cibersort.phenotypes$Treatment)

model <- list()
adjust <- list()

for(i in 1:length(cibersort[-1])) {
    name <- colnames(cibersort.phenotypes[,i])
    m <- art(data = cibersort.phenotypes, cibersort.phenotypes[,i] ~ Time*Treatment)
             model[[i]] <- anova(m)
             adjust[[i]] <- p.adjust(model[[i]]$`Pr(>F)`, method = "BH")
}
names(model) <- colnames(cibersort[-1])
names(adjust) <- colnames(cibersort[-1])

cibersort.matrix <- matrix(nrow = 25, ncol = 4)
for(i in 1:length(adjust)){
  
  cibersort.matrix[i,] <- cbind(colnames(cibersort.phenotypes)[i],adjust[[i]][1],adjust[[i]][2],adjust[[i]][3])
}
colnames(cibersort.matrix) <- c("Immune Cells","Time","Treatment","Time by Treatment")
cibersort.matrix <- as.data.frame(cibersort.matrix)

DT::datatable(cibersort.matrix, extensions = 'Buttons', rownames = FALSE,
              filter="top", options = list(dom = 'Blfrtip', buttons = c('copy', 'csv', 'excel'), lengthMenu = list(c(10,25,50,-1), c(10,25,50,"All")), 
                                           scrollX= TRUE), class = "display")

All Small Intestine Genes

cibersort.all.phenotypes$Time <- as.factor(cibersort.all.phenotypes$Time)
cibersort.all.phenotypes$Treatment <- as.factor(cibersort.all.phenotypes$Treatment)

model <- list()
adjust <- list()

for(i in 1:length(cibersort.all[-1])) {
    name <- colnames(cibersort.all.phenotypes[,i])
    #m <- art(data = cibersort.all.phenotypes, cibersort.all.phenotypes[,i] ~ Time*Treatment)
    m <- lm(data = cibersort.all.phenotypes, cibersort.all.phenotypes[,i] ~ Time*Treatment)
             model[[i]] <- anova(m)
             adjust[[i]] <- p.adjust(model[[i]]$`Pr(>F)`, method = "BH")
}
names(model) <- colnames(cibersort.all[-1])
names(adjust) <- colnames(cibersort.all[-1])

cibersort.all.matrix <- matrix(nrow = 25, ncol = 4)
for(i in 1:length(adjust)){
  
  cibersort.all.matrix[i,] <- cbind(colnames(cibersort.all.phenotypes)[i],adjust[[i]][1],adjust[[i]][2],adjust[[i]][3])
}
colnames(cibersort.all.matrix) <- c("Immune Cells","Time","Treatment","Time by Treatment")
cibersort.all.matrix <- as.data.frame(cibersort.all.matrix)

DT::datatable(cibersort.all.matrix, extensions = 'Buttons', rownames = FALSE,
              filter="top", options = list(dom = 'Blfrtip', buttons = c('copy', 'csv', 'excel'), lengthMenu = list(c(10,25,50,-1), c(10,25,50,"All")), 
                                           scrollX= TRUE), class = "display")

Compare module to all genes in small intestine

cibersort.compare$Time <- as.factor(cibersort.compare$Time)
cibersort.compare$Treatment <- as.factor(cibersort.compare$Treatment)

model <- list()
adjust <- list()

for(i in 1:length(cibersort.all[-1])) {
    name <- colnames(cibersort.compare[,i])
    #m <- art(data = cibersort.compare, cibersort.compare[,i] ~ Time*Treatment)
    m <- lm(data = cibersort.compare, cibersort.compare[,i] ~ Module*Time*Treatment)
             model[[i]] <- anova(m)
             adjust[[i]] <- p.adjust(model[[i]]$`Pr(>F)`, method = "BH")
}
names(model) <- colnames(cibersort.all[-1])
names(adjust) <- colnames(cibersort.all[-1])

cibersort.all.matrix <- matrix(nrow = 25, ncol = 8)
for(i in 1:length(adjust)){
  
  cibersort.all.matrix[i,] <- cbind(colnames(cibersort.compare)[i],adjust[[i]][1],adjust[[i]][2],adjust[[i]][3],adjust[[i]][4],adjust[[i]][5],adjust[[i]][6],adjust[[i]][7])
}
colnames(cibersort.all.matrix) <- c("Immune Cells","Module","Time","Treatment","Module:Time","Module:Treatment","Time:Treatment","Module:Time:Treatment")
cibersort.all.matrix <- as.data.frame(cibersort.all.matrix)

DT::datatable(cibersort.all.matrix, extensions = 'Buttons', rownames = FALSE,
              filter="top", options = list(dom = 'Blfrtip', buttons = c('copy', 'csv', 'excel'), lengthMenu = list(c(10,25,50,-1), c(10,25,50,"All")), 
                                           scrollX= TRUE), class = "display")

Plot to compare Green4 module to all genes in small intestine

meltcibersort.all <- melt(cibersort.compare)
p <- ggplot(meltcibersort.all, aes(variable,value, fill = c(Treatment),alpha = Module))
p <- p + geom_bar(stat = "summary", position = "dodge") + basic_theme + theme(axis.text.x = element_text(angle = 90)) + xlab("Immune Cells") + ylab("Percentage") + labs(fill = c("Treatment")) + scale_alpha_discrete(range = c(.4,1))
p

tdata.FPKM.sample.info <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_phenotypes.RData"))

tdata.FPKM <- readRDS(here("Data","20190406_RNAseq_B6_4wk_2DG_counts_numeric.RData"))

log.tdata.FPKM <- log(tdata.FPKM + 1)
log.tdata.FPKM <- as.data.frame(log.tdata.FPKM)

log.tdata.FPKM.sample.info <- cbind(log.tdata.FPKM, tdata.FPKM.sample.info[,27238:27240])

log.tdata.FPKM.sample.info <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(rowname != "A113") %>% column_to_rownames()

log.tdata.FPKM.subset <- log.tdata.FPKM[,colMeans(log.tdata.FPKM != 0) > 0.5] 

log.tdata.FPKM.subset <- log.tdata.FPKM.subset %>% rownames_to_column() %>% filter(rowname != "A113") %>% column_to_rownames()

log.tdata.FPKM.sample.info.subset.small.intestine <- log.tdata.FPKM.sample.info %>% rownames_to_column() %>% filter(Tissue == "Small Intestine") %>% column_to_rownames()

log.tdata.FPKM.sample.info.subset.small.intestine <- log.tdata.FPKM.sample.info.subset.small.intestine[,colMeans(log.tdata.FPKM.sample.info.subset.small.intestine != 0) > 0.5]

cibersort.all.genes <- log.tdata.FPKM.sample.info.subset.small.intestine[,-c(17912:17914)] %>% t() 

cibersort.all.genes <- as.data.frame(cibersort.all.genes) %>% rownames_to_column()

ensembl.location <- readRDS(here("Data","Ensembl_gene_id_and_location.RData"))

cibersort.all.genes <- left_join(cibersort.all.genes,ensembl.location, by = c("rowname" = "ensembl_gene_id"))

cibersort.all.genes <- as.data.frame(cbind(cibersort.all.genes$external_gene_name, cibersort.all.genes[,-c(1,18:22)])) 

cibersort.all.genes <- cibersort.all.genes[!duplicated(cibersort.all.genes$`cibersort.all.genes$external_gene_name`),] 

rownames(cibersort.all.genes) <- cibersort.all.genes$`cibersort.all.genes$external_gene_name`

cibersort.all.genes <- cibersort.all.genes[,-1]

write.table(cibersort.all.genes, here("Data","Small Intestine","B6_2DG_Small_Intestine_all_gene_expression_for_cibersort.txt"), col.names = T, row.names = T)

Analysis performed by Ann Wells

The Carter Lab The Jackson Laboratory 2023

ann.wells@jax.org

Cibersort Small Intestine

Ann Wells

April 23, 2023

Check Distribution

Raw Percentages

Mast.Cells

Neutrophil.Cells

Eosinophil.Cells

B.Cells.Memory

B.Cells.Naive

Plasma.Cells

T.Cells.CD8.Actived

T.Cells.CD8.Naive

T.Cells.CD8.Memory

M0.Macrophage

M1.Macrophage

M2.Macrophage

Treg.Cells

T.Cells.CD4.Memory

T.Cells.CD4.Naive

T.Cells.CD4.Follicular

Th1.Cells

Th17.Cells

Th2.Cells

Monocyte

GammaDelta.T.Cells

NK.Resting

NK.Actived

DC.Actived

DC.Immature

Logged Percentages

Neutrophil.Cells

Eosinophil.Cells

B.Cells.Memory

B.Cells.Naive

Plasma.Cells

T.Cells.CD8.Actived

T.Cells.CD8.Naive

T.Cells.CD8.Memory

M0.Macrophage

M1.Macrophage

M2.Macrophage

Treg.Cells

T.Cells.CD4.Memory

T.Cells.CD4.Naive

T.Cells.CD4.Follicular

Th1.Cells

Th17.Cells

Th2.Cells

Monocyte

GammaDelta.T.Cells

NK.Resting

NK.Actived

DC.Actived

DC.Immature

Density Plots

Green4 module

Mast.Cells

Neutrophil.Cells

Eosinophil.Cells

B.Cells.Memory

B.Cells.Naive

Plasma.Cells

T.Cells.CD8.Actived

T.Cells.CD8.Naive

T.Cells.CD8.Memory

M0.Macrophage

M1.Macrophage

M2.Macrophage

Treg.Cells

T.Cells.CD4.Memory

T.Cells.CD4.Naive

T.Cells.CD4.Follicular

Th1.Cells

Th17.Cells

Th2.Cells

Monocyte

GammaDelta.T.Cells

NK.Resting

NK.Actived