Stim_human_islets_RNAseq
scottzijiezhang
2020-03-09
Last updated: 2021-03-17
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Knit directory: T1D_epitranscriptome/
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| Rmd | d26010a | scottzijiezhang | 2020-03-10 | wflow_publish(c(“analysis/Stim_human_islets.Rmd”, “analysis/Stim_human_islets_RNAseq.Rmd”)) |
Stimulated patient islet
library(RADAR)
load( "~/Rohit_T1D/stim_Patient_islets/stim_patient_RADAR.RData")
geneCount <- round( geneExpression(stim_patient_RADAR) )
plotPCAfromMatrix(geneCount, variable(stim_patient_RADAR)$stim) +scale_color_discrete(name = "Treatment")
plotPCAfromMatrix(geneCount, c(rep("A",10),rep("B",12),rep("C",8)) ) + scale_color_discrete(name = "Batch")
plotPCAfromMatrix(geneCount, rep(c("F","M","M","M","M","F","M","M","M","M","F","F","M","M","M"),rep(2,15)) )+scale_color_discrete(name = "Gender")
plotPCAfromMatrix(geneCount, rep(c(62,20,24,45,61,40,52,41,21,51,57,42,57,42,62),rep(2,15)) )+scale_color_continuous(name = "Age")
Gene expression level PCA showed that samples are best separated by treatment. Gender is partially contributing to the variations. So we will do DE test with gender as covariate.
library(DESeq2)
dds <- DESeqDataSetFromMatrix( geneCount[rowMeans(geneCount)>10,],
colData = data.frame(treatment = variable(stim_patient_RADAR)$stim,
gender = rep(c("F","M","M","M","M","F","M","M","M","M","F","F","M","M","M") ),
row.names = samplenames(stim_patient_RADAR) ),
design = ~ treatment + gender
)
dds <- DESeq(dds)
res <- results( dds , contrast = c("treatment", "Stim", "Ctl") )
deseqRes <- data.frame(gene = rownames(res), log2FC = res$log2FoldChange, pvalue = res$pvalue, fdr = res$padj)
write.table( dplyr::filter(deseqRes, !is.na(pvalue) ), file = "~/Rohit_T1D/stim_Patient_islets/Stim_patient_diffExpr_DESeq2result.xls", sep = "\t", quote = F, row.names = F, col.names = T)DE genes at FDR < 5%
deseqRes <- read.table("~/Rohit_T1D/stim_Patient_islets/Stim_patient_diffExpr_DESeq2result.xls", header = T)
DT::datatable(deseqRes[deseqRes$fdr<0.05,], rownames = F)Valcano plot
library(ggplot2)
deseqRes$sig <- ifelse(deseqRes$fdr<0.05, "FDR<0.05", "Not Significant")
rownames(deseqRes) <- deseqRes$gene
DEgenes_DESeq <- deseqRes[which(abs(deseqRes$log2FC) > log2(1.5) & deseqRes$fdr < 0.05),]
DEgenes_DESeq <- DEgenes_DESeq[order(DEgenes_DESeq$pvalue, decreasing = F),]
label_gene <- as.character(DEgenes_DESeq$gene)[1:30]
p = ggplot2::ggplot(deseqRes, ggplot2::aes(log2FC, -log10(pvalue))) +
ggplot2::geom_point(ggplot2::aes(col = sig)) +
ggplot2::scale_color_manual(values = c("red", "darkgray")) +
ggplot2::ggtitle("Volcano Plot of DESeq2 analysis")+
ggplot2::scale_x_continuous(limits = c(-3.5,6))+
theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black",size = 0.75),
plot.title = element_text( size=22, hjust = 0.5),
axis.title.x=element_text(size=20, color = "black", hjust=0.5,family = "arial"),
axis.title.y=element_text(size=20, color="black", vjust=0.4, angle=90,family = "arial"),
legend.title=element_blank(),legend.text = element_text(size = 16, color = "black",family = "arial"),
axis.text.x = element_text(size = 15,color = "black",family = "arial") ,axis.text.y = element_text(size = 15,color = "black",family = "arial"),
legend.position = c(0.2,0.85) )
p + ggrepel::geom_text_repel(data=deseqRes[label_gene, ], ggplot2::aes(label=rownames(deseqRes[label_gene, ]) ),point.padding = 0.3)
| Version | Author | Date |
|---|---|---|
| fc538cf | scottzijiezhang | 2020-03-10 |
Volcano plot without gene tags.
p 
| Version | Author | Date |
|---|---|---|
| fc538cf | scottzijiezhang | 2020-03-10 |
Overlap with Lundberg et al. Overlap significantly up-regulated genes in stimulated patient islets with up-regulated genes in Lundberg et al. 2016 Diabetes.
OverlapGene <- intersect( as.character(DEgenes_DESeq$gene), c("HLA-E","IFITM1","CASP1","TLR3","IFIT3","BST2","IFIT1","MX1","CCL5","IFIT2","MNDA","TAP1","ISG15","STAT1","OAS1","GBP1","IL15","MET","CXCL10","TLR8","IL6","NOS2") )
ggplot2::ggplot(deseqRes, ggplot2::aes(log2FC, -log10(pvalue))) +
ggplot2::geom_point(ggplot2::aes(col = sig)) +
ggplot2::scale_color_manual(values = c("red", "darkgray")) +
ggplot2::ggtitle("Volcano Plot of DESeq2 analysis")+
ggplot2::scale_x_continuous(limits = c(-3.5,6))+
theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black",size = 0.75),
plot.title = element_text( size=22, hjust = 0.5),
axis.title.x=element_text(size=20, color = "black", hjust=0.5,family = "arial"),
axis.title.y=element_text(size=20, color="black", vjust=0.4, angle=90,family = "arial"),
legend.title=element_blank(),legend.text = element_text(size = 16, color = "black",family = "arial"),
axis.text.x = element_text(size = 15,color = "black",family = "arial") ,axis.text.y = element_text(size = 15,color = "black",family = "arial"),
legend.position = c(0.2,0.85) ) +
ggrepel::geom_text_repel(data=deseqRes[OverlapGene, ], ggplot2::aes(label=rownames(deseqRes[OverlapGene, ]) ),point.padding = 0.2) +
geom_point(data=deseqRes[OverlapGene, ], aes( log2FC, -log10(pvalue) ), color = "dark blue" )
KEGG pathway enrichment
library(clusterProfiler)
library(org.Hs.eg.db)
library(enrichplot)
eg.RDM <- bitr(deseqRes[deseqRes$fdr<0.05,]$gene, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db")
KEGG.RDM_res <- enrichKEGG(eg.RDM$ENTREZID,organism = "hsa",pAdjustMethod = "fdr", pvalueCutoff = 0.05,minGSSize = 3)
px <- pairwise_termsim(KEGG.RDM_res)
emapplot( px )
Stimulated EndoC
load( "~/Rohit_T1D/stim_Patient_islets/stim_EndoC_RADAR.RData")
geneCount_E <- round( geneExpression(stim_EndoC_RADAR) )
geneCount_E <- geneCount_E[rowMeans(geneCount_E)>10,]
plotPCAfromMatrix(geneCount_E, variable(stim_EndoC_RADAR)$stim) +scale_color_discrete(name = "Treatment")
dds_E <- DESeqDataSetFromMatrix( geneCount_E,
colData = data.frame(treatment = variable(stim_EndoC_RADAR)$stim,
row.names = samplenames(stim_EndoC_RADAR) ),
design = ~ treatment
)
dds_E <- DESeq(dds_E)
res_E <- results( dds_E , contrast = c("treatment", "Stim", "Ctl") )
deseqRes_E <- data.frame(gene = rownames(res_E), log2FC = res_E$log2FoldChange, pvalue = res_E$pvalue, fdr = res_E$padj)
write.table( dplyr::filter(deseqRes_E, !is.na(pvalue) ), file = "~/Rohit_T1D/stim_Patient_islets/Stim_EndoC_diffExpr_DESeq2result.xls", sep = "\t", quote = F, row.names = F, col.names = T)DE genes at FDR < 5%
deseqRes_E <- read.table("~/Rohit_T1D/stim_Patient_islets/Stim_EndoC_diffExpr_DESeq2result.xls", header = T)
DT::datatable(deseqRes_E[deseqRes_E$fdr<0.05,], rownames = F)Volcano plot
deseqRes_E$sig <- ifelse(deseqRes_E$fdr<0.05, "FDR<0.05", "Not Significant")
rownames(deseqRes_E) <- deseqRes_E$gene
DEgenes_DESeq_E <- deseqRes_E[which(abs(deseqRes_E$log2FC) > log2(1.5) & deseqRes_E$fdr < 0.05),]
DEgenes_DESeq_E <- DEgenes_DESeq_E[order(DEgenes_DESeq_E$pvalue, decreasing = F),]
label_gene <- as.character(DEgenes_DESeq_E$gene)[1:30]
pE = ggplot2::ggplot(deseqRes_E, ggplot2::aes(log2FC, -log10(pvalue))) +
ggplot2::geom_point(ggplot2::aes(col = sig)) +
ggplot2::scale_color_manual(values = c("red", "darkgray")) +
ggplot2::ggtitle("Volcano Plot of DESeq2 analysis")+
ggplot2::scale_x_continuous(limits = c(-3.5,6))+
theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black",size = 0.75),
plot.title = element_text( size=22, hjust = 0.5),
axis.title.x=element_text(size=20, color = "black", hjust=0.5,family = "arial"),
axis.title.y=element_text(size=20, color="black", vjust=0.4, angle=90,family = "arial"),
legend.title=element_blank(),legend.text = element_text(size = 16, color = "black",family = "arial"),
axis.text.x = element_text(size = 15,color = "black",family = "arial") ,axis.text.y = element_text(size = 15,color = "black",family = "arial"),
legend.position = c(0.2,0.85) )
pE 
| Version | Author | Date |
|---|---|---|
| fc538cf | scottzijiezhang | 2020-03-10 |
Overlap with Lundberg et al. Overlap significantly up-regulated genes in EndoC with up-regulated genes in Lundberg et al. 2016 Diabetes.
OverlapGene_E <- intersect( as.character(DEgenes_DESeq_E$gene), c("HLA-E","IFITM1","CASP1","TLR3","IFIT3","BST2","IFIT1","MX1","CCL5","IFIT2","MNDA","TAP1","ISG15","STAT1","OAS1","GBP1","IL15","MET","CXCL10","TLR8","IL6","NOS2") )
ggplot2::ggplot(deseqRes_E, ggplot2::aes(log2FC, -log10(pvalue))) +
ggplot2::geom_point(ggplot2::aes(col = sig)) +
ggplot2::scale_color_manual(values = c("red", "darkgray")) +
ggplot2::ggtitle("Volcano Plot of DESeq2 analysis")+
ggplot2::scale_x_continuous(limits = c(-3.5,6))+
theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black",size = 0.75),
plot.title = element_text( size=22, hjust = 0.5),
axis.title.x=element_text(size=20, color = "black", hjust=0.5,family = "arial"),
axis.title.y=element_text(size=20, color="black", vjust=0.4, angle=90,family = "arial"),
legend.title=element_blank(),legend.text = element_text(size = 16, color = "black",family = "arial"),
axis.text.x = element_text(size = 15,color = "black",family = "arial") ,axis.text.y = element_text(size = 15,color = "black",family = "arial"),
legend.position = c(0.2,0.85) ) +
ggrepel::geom_text_repel(data=deseqRes_E[OverlapGene_E, ], ggplot2::aes(label=rownames(deseqRes_E[OverlapGene_E, ]) ),point.padding = 0.2) +
geom_point(data=deseqRes_E[OverlapGene_E, ], aes( log2FC, -log10(pvalue) ), color = "dark blue" )
KEGG pathway enrichment
eg.RDM_E <- bitr(deseqRes_E[deseqRes_E$fdr < 0.05,]$gene, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db")
KEGG.RDM_res_E <- enrichKEGG(eg.RDM_E$ENTREZID,organism = "hsa",pAdjustMethod = "fdr", pvalueCutoff = 0.05,minGSSize = 3)
KEGG_x <- pairwise_termsim(KEGG.RDM_res_E)
emapplot(KEGG_x)
Compare human islets and EndoC result
Overlap of DE genes We compare the overlap of DE genes (FDR<5%) between islet and EndoC samples.
library(Vennerable)
Vcommon <- Venn(list("Stimu islets"=dplyr::filter(deseqRes,fdr <0.05)$gene,"Stim EndoC"=dplyr::filter(deseqRes_E,fdr<0.05)$gene ) )
plot(Vcommon,doWeights=TRUE)
| Version | Author | Date |
|---|---|---|
| 4879465 | scottzijiezhang | 2020-03-29 |
Common DE genes effect size correlation
library( ggpmisc )
CommonDEG <- intersect( as.character( dplyr::filter(deseqRes,fdr <0.05)$gene ), as.character( dplyr::filter(deseqRes_E,fdr<0.05)$gene ) )
CommonDEG_stat <- data.frame(gene = CommonDEG, patientFC = deseqRes[CommonDEG,"log2FC"], EndoCFC = deseqRes_E[CommonDEG,"log2FC"] )
ggplot(CommonDEG_stat, aes( x = patientFC, y = EndoCFC ) )+ geom_point()+ geom_hline(yintercept=0, linetype="dashed")+geom_vline(xintercept=0,linetype="dashed")+xlab("log2FC Stimulated islets")+ylab("log2FC Stimulated EndoC")+theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black",size = 0.7),
axis.title.x=element_text(size=22, color = "black", hjust=0.5,family = "arial"),
axis.title.y=element_text(size=22, color="black", vjust=0.4, angle=90,family = "arial"),
legend.position = c(0.85,0.95),legend.title=element_blank(),legend.text = element_text(size = 20, color = "black",family = "arial"),
axis.text.x = element_text(size = 15, color = "black",family = "arial") ,axis.text.y = element_text(size = 15,color = "black",family = "arial"))+
stat_poly_eq(aes(label = paste(..rr.label..)), label.x.npc = "left", label.y.npc = 0.95, formula = y~x, parse = TRUE, size = 8)
| Version | Author | Date |
|---|---|---|
| 4879465 | scottzijiezhang | 2020-03-29 |
Common DE genes that are directionaly consistent
#write.table( CommonDEG_stat[ CommonDEG_stat$patientFC * CommonDEG_stat$EndoCFC > 0, ] , file = "~/Rohit_T1D/stim_Patient_islets/CommonDEG.xls", sep = "\t", quote = FALSE, row.names = FALSE, col.names = TRUE)
DT::datatable( CommonDEG_stat[ CommonDEG_stat$patientFC * CommonDEG_stat$EndoCFC > 0, ] )KEGG pathway analysis of common DE genes that are directionally consistent
eg.PatDEG_EndoDEG_intersect <- bitr(CommonDEG_stat[ CommonDEG_stat$patientFC * CommonDEG_stat$EndoCFC > 0, "gene"], fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db")
KEGG_PatDEG_EndoDEG_intersect <- enrichKEGG(eg.PatDEG_EndoDEG_intersect$ENTREZID,organism = "hsa",pAdjustMethod = "fdr", pvalueCutoff = 0.05,minGSSize = 3)
dotplot(KEGG_PatDEG_EndoDEG_intersect)+theme(
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black",size = 1),
axis.title.x=element_text(size=22, color = "black", hjust=0.5,family = "arial"),
axis.title.y=element_text(size=22, color = "black", vjust=0.4, angle=90,family = "arial"),
legend.title=element_text(size=15, color = "black"),legend.text = element_text(size = 20, color = "black",family = "arial"),
axis.text.x = element_text(size = 15,color = "black",family = "arial") ,axis.text.y = element_text(size = 15,color = "black",family = "arial"))
KEGG_inter <- pairwise_termsim(KEGG_PatDEG_EndoDEG_intersect)
emapplot(KEGG_inter)
Cross DMG in T1D patient islets with DEG in stimulated islets
T1D_diffPeak <- read.table( "~/Rohit_T1D/stim_Patient_islets/T1D_patient_diffPeaks_FDR0.05.xls", sep = "\t", header = TRUE)
VcommonAll <- Venn(list("Stimu islets"=dplyr::filter(deseqRes,fdr <0.05)$gene,"Stim EndoC"=dplyr::filter(deseqRes_E,fdr<0.05)$gene, "DMG in T1D islet" = unique( T1D_diffPeak$name ) ) )
plot(VcommonAll,doWeights=TRUE)
| Version | Author | Date |
|---|---|---|
| 4879465 | scottzijiezhang | 2020-03-29 |
Common DE genes that are directionally consistent
AllThreeIntersect <- intersect( intersect( dplyr::filter(deseqRes,fdr <0.05)$gene, dplyr::filter(deseqRes_E,fdr<0.05)$gene ), unique( T1D_diffPeak$name ) )
#write.table( CommonDEG_stat[ CommonDEG_stat$gene %in% AllThreeIntersect , ] , file = "~/Rohit_T1D/stim_Patient_islets/CommonDEGinStimIsletAndStimEndoC_intersect_DMGinT1D.xls", sep = "\t", quote = FALSE, row.names = FALSE, col.names = TRUE)
DT::datatable( CommonDEG_stat[ CommonDEG_stat$gene %in% AllThreeIntersect , ], rownames = FALSE )
sessionInfo()R version 4.0.4 (2021-02-15)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.2 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=zh_CN.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=zh_CN.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=zh_CN.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=zh_CN.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 parallel stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] ggpmisc_0.3.8-1 Vennerable_3.1.0.9000 enrichplot_1.10.2
[4] org.Hs.eg.db_3.12.0 clusterProfiler_3.18.1 RADAR_0.2.4
[7] qvalue_2.22.0 RcppArmadillo_0.10.2.1.0 Rcpp_1.0.6
[10] RColorBrewer_1.1-2 gplots_3.1.1 doParallel_1.0.16
[13] iterators_1.0.13 foreach_1.5.1 ggplot2_3.3.3
[16] Rsamtools_2.6.0 Biostrings_2.58.0 XVector_0.30.0
[19] GenomicFeatures_1.42.1 AnnotationDbi_1.52.0 Biobase_2.50.0
[22] GenomicRanges_1.42.0 GenomeInfoDb_1.26.2 IRanges_2.24.1
[25] S4Vectors_0.28.1 BiocGenerics_0.36.0
loaded via a namespace (and not attached):
[1] shadowtext_0.0.7 fastmatch_1.1-0
[3] workflowr_1.6.2 BiocFileCache_1.14.0
[5] plyr_1.8.6 igraph_1.2.6
[7] splines_4.0.4 BiocParallel_1.24.1
[9] crosstalk_1.1.1 digest_0.6.27
[11] htmltools_0.5.1.1 GOSemSim_2.16.1
[13] viridis_0.5.1 GO.db_3.12.1
[15] fansi_0.4.2 magrittr_2.0.1
[17] memoise_2.0.0 annotate_1.68.0
[19] graphlayouts_0.7.1 matrixStats_0.58.0
[21] askpass_1.1 prettyunits_1.1.1
[23] colorspace_2.0-0 blob_1.2.1
[25] rappdirs_0.3.3 ggrepel_0.9.1
[27] xfun_0.21 dplyr_1.0.5
[29] crayon_1.4.1 RCurl_1.98-1.2
[31] jsonlite_1.7.2 graph_1.68.0
[33] scatterpie_0.1.5 genefilter_1.72.1
[35] survival_3.2-7 glue_1.4.2
[37] polyclip_1.10-0 gtable_0.3.0
[39] zlibbioc_1.36.0 DelayedArray_0.16.2
[41] scales_1.1.1 DOSE_3.16.0
[43] DBI_1.1.1 viridisLite_0.3.0
[45] xtable_1.8-4 progress_1.2.2
[47] bit_4.0.4 DT_0.17
[49] htmlwidgets_1.5.3 httr_1.4.2
[51] fgsea_1.16.0 ellipsis_0.3.1
[53] pkgconfig_2.0.3 XML_3.99-0.5
[55] farver_2.1.0 dbplyr_2.1.0
[57] locfit_1.5-9.4 utf8_1.1.4
[59] polynom_1.4-0 tidyselect_1.1.0
[61] labeling_0.4.2 rlang_0.4.10
[63] reshape2_1.4.4 later_1.1.0.1
[65] munsell_0.5.0 tools_4.0.4
[67] cachem_1.0.4 downloader_0.4
[69] generics_0.1.0 RSQLite_2.2.3
[71] evaluate_0.14 stringr_1.4.0
[73] fastmap_1.1.0 yaml_2.2.1
[75] knitr_1.31 bit64_4.0.5
[77] fs_1.5.0 tidygraph_1.2.0
[79] caTools_1.18.1 purrr_0.3.4
[81] ggraph_2.0.5 RBGL_1.66.0
[83] whisker_0.4 DO.db_2.9
[85] xml2_1.3.2 biomaRt_2.46.3
[87] compiler_4.0.4 curl_4.3
[89] tibble_3.1.0 tweenr_1.0.1
[91] geneplotter_1.68.0 stringi_1.5.3
[93] highr_0.8 lattice_0.20-41
[95] Matrix_1.3-2 vctrs_0.3.6
[97] pillar_1.5.1 lifecycle_1.0.0
[99] BiocManager_1.30.10 cowplot_1.1.1
[101] data.table_1.14.0 bitops_1.0-6
[103] httpuv_1.5.5 rtracklayer_1.50.0
[105] R6_2.5.0 promises_1.2.0.1
[107] KernSmooth_2.23-18 gridExtra_2.3
[109] codetools_0.2-18 MASS_7.3-53.1
[111] gtools_3.8.2 assertthat_0.2.1
[113] SummarizedExperiment_1.20.0 openssl_1.4.3
[115] DESeq2_1.30.1 rprojroot_2.0.2
[117] withr_2.4.1 GenomicAlignments_1.26.0
[119] GenomeInfoDbData_1.2.4 hms_1.0.0
[121] grid_4.0.4 tidyr_1.1.3
[123] rvcheck_0.1.8 rmarkdown_2.7
[125] MatrixGenerics_1.2.1 git2r_0.28.0
[127] ggforce_0.3.3