vineetbio / dge-analysis

DGE-Analysis

Index

• Install
• DGE
• plots
• Golub

Install

Install the required packages and link to the correct github repository.

library(remotes) 
remotes::install_github(repo="afitz-gmu/DGE-Analysis", build_opts = c("--no-resave-data", "--no-manual"))
library(GenomicVisualization)

DGE

Basic function to perfrom Differential Gene Expression by EdgeR, DESeq2 and Voom.

data("count.table")
design <- data.frame("trt" = colnames(count.table))
rownames(design) <- design$trt
design$trt <- as.integer(grepl("T",design[,1]))
DGE.list <- DGE(count.table = count.table , design.matrix = design , method=c("EdgeR","voom") )
VennDig(DGE.list)

Plots

Prepare the data.

data("DEG")
DE.list<-list("edger" =dge_edger, "edgerql" = dge_edgerql, "deseq2" = dge_deseq2, "voom" = dge_voom )

Volcano plots

multiVolcano(DE.list, FoldChange = 1.4, DE.Only = FALSE , show.genes = TRUE )

For user defined gene list

genes.list<-c("Gene7673", "Gene28034", "Gene38639")
multiVolcano(DE.list, FoldChange = 1.4, DE.Only = FALSE , genes = genes.list , show.genes = TRUE)

For showing Differentially expressed genes. This command will show only a few of the outlier genes)

multiVolcano(DE.list, show.genes = FALSE)

For showing Differentially expressed genes (marking show.genes as false for more asthetic plot)

multiVolcano(DE.list, FoldChange = 1.4 , show.genes = FALSE)

Bar plot

TabulateStats(DE=DE.list , FoldChange=0 , cutoff =0.01 )

Tabular Result

	edger	edgerql	voom
DOWN	1210	794	302
No	33947	34858	34287
UP	821	326	1389

Venn Diagram

VennDig(DE=DE.list , FoldChange=1.5 , cutoff =0.01 , type.sig ="FDR")

Upset plot

UpSetPlot(DE=DE.list , FoldChange=0 , cutoff =0.01 , type.sig = "p")

Prapotional Venn Diagram

EulerrPlot(DE=DE.list , FoldChange=0 , cutoff =0.05 , type.sig ="p")

CI interval plot and data frame

DGE.CI(DE=DE.list , FoldChange=1.2 , cutoff =0.05 , type.sig ="p")

	edger FoldChange	edgerql FoldChange	voom FoldChange	Min	Max	edger pvalue	edgerql pvalue	voom pvalue	Min	Max
Gene11876	0.253268010646449	0.25665960045484	0.333519105983995	0.253268010646449	0.333519105983995	0.00966910768271969	0.0138679244791296	0.0390346542589136	0.00966910768271969	0.0390346542589136
Gene15604	0.27180343736292	0.271787631013647	0.302097146830169	0.271787631013647	0.302097146830169	0.000348901761561591	0.00103259777034587	3.97342702281636e-09	3.97342702281636e-09	0.00103259777034587
Gene15888	0.30094351744471	0.300302928429722	0.332617475249204	0.300302928429722	0.332617475249204	0.000405266030230859	2.99397057448929e-06	1.16326308848636e-09	1.16326308848636e-09	0.000405266030230859
Gene22288	0.263474804206977	0.263400660850057	0.2916799978793	0.263400660850057	0.2916799978793	0.0005861510066759	4.17234374228856e-05	1.5530006115196e-10	1.5530006115196e-10	0.0005861510066759
Gene29009	0.294253032199853	0.293993599013282	0.330000812105133	0.293993599013282	0.330000812105133	2.62505647540443e-05	2.81215059098373e-06	5.0579118548581e-10	5.0579118548581e-10	2.62505647540443e-05
Gene30812	3.0011502354894	2.82286115292464	3.54654652335402	2.82286115292464	3.54654652335402	0.0443053766662665	0.0192060783610423	0.00229564569695183	0.00229564569695183	0.0443053766662665
Gene32136	0.253951391140971	0.253847733944933	0.276660037325252	0.253847733944933	0.276660037325252	0.000311904095164778	8.10948071015179e-07	8.89107509409887e-12	8.89107509409887e-12	0.000311904095164778
Gene3219	0.26132542426818	0.260852100787794	0.292821559694857	0.260852100787794	0.292821559694857	3.9108014179455e-08	7.25440228196772e-08	4.04635632813972e-11	4.04635632813972e-11	7.25440228196772e-08
Gene3636	0.276773935358202	0.276658009021903	0.30742464752486	0.276658009021903	0.30742464752486	0.000320786232115754	3.12890638531982e-05	2.80586403681253e-10	2.80586403681253e-10	0.000320786232115754
Gene36720	0.282886374057711	0.283071871234626	0.310527439322474	0.282886374057711	0.310527439322474	0.000489633506875775	2.9117905222642e-06	1.90836815806636e-10	1.90836815806636e-10	0.000489633506875775
Gene37809	0.274775812479113	0.274781151367946	0.297749013272049	0.274775812479113	0.297749013272049	0.00409971202637718	0.0403344717227362	5.83264594328474e-07	5.83264594328474e-07	0.0403344717227362
Gene38199	0.295370543964787	0.296061444016233	0.328422674982562	0.295370543964787	0.328422674982562	8.2482427048141e-05	8.10948071015179e-07	1.52215838845913e-09	1.52215838845913e-09	8.2482427048141e-05
Gene38639	6.7835229404579	6.58741660386054	9.6638046407115	6.58741660386054	9.6638046407115	0.0573934369490823	0.136024039700254	0.00415995911576501	0.00415995911576501	0.136024039700254
Gene38937	0.283452115852866	0.27553856211236	0.315105821098576	0.27553856211236	0.315105821098576	0.00825053138204574	0.195476227936559	0.181147657675811	0.00825053138204574	0.195476227936559
Gene40770	0.289698705765732	0.289780204987156	0.325317239975445	0.289698705765732	0.325317239975445	3.4623122963608e-05	3.08747716025276e-05	5.52436063074383e-10	5.52436063074383e-10	3.4623122963608e-05
Gene6678	0.251819899040539	0.246647238865664	0.301947985426532	0.246647238865664	0.301947985426532	0.00010352524415427	1.56612785739337e-07	4.16666249706773e-10	4.16666249706773e-10	0.00010352524415427
Gene6710	0.271459164291596	0.27145692542583	0.301120050678203	0.27145692542583	0.301120050678203	0.000430650590640714	0.0118339982149324	9.04425211253826e-08	9.04425211253826e-08	0.0118339982149324
Gene720	0.273484715998214	0.273434784275442	0.302884849886368	0.273434784275442	0.302884849886368	0.00122751324077398	0.00102621047894686	4.53220294233197e-09	4.53220294233197e-09	0.00122751324077398
Gene7339	0.284743280003845	0.275612814902474	0.32548462536702	0.275612814902474	0.32548462536702	1.22959638267496e-07	0.00588064870290072	0.0108920996649206	1.22959638267496e-07	0.0108920996649206
Gene7673	0.21499523132271	0.216980068912695	0.244685085090055	0.21499523132271	0.244685085090055	0.0218327855690298	3.60045900816876e-06	4.35821145336272e-12	4.35821145336272e-12	0.0218327855690298
Gene9230	0.282854723987658	0.282620482988113	0.310124743179966	0.282620482988113	0.310124743179966	0.000351248577561045	8.10948071015179e-07	3.29044695217997e-10	3.29044695217997e-10	0.000351248577561045

Golub

# Install the required packages

BiocManager::install("multtest")
library(multtest)

# load the golub dataset

data(golub)
ds<-data.frame("trt" = golub.cl)
colnames(golub) <-paste0("Name",1:length(golub.cl))
row.names(ds) <- colnames(golub)
ds$trt <- paste0("C",ds$trt)
xx<-as.data.frame(round(exp(golub)*1000))
DGE.list <- DGE(count.table = xx , design.matrix = ds )
VennDig(DGE.list)

DGE.CI(DE=DE.list , FoldChange=1.2 , cutoff =0.05 , type.sig ="p")