GEOmirror

A package for researchers in mainland China to downlaod the GEO dataset, which is just a replacement of getGEO function from GEOquery package.

how to install

Install the development version from Github:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("GEOquery")
library(devtools)
install_github("jmzeng1314/GEOmirror")
library(GEOmirror)

If it failed, just because your bad internet connection. You can also download this project directly into your computer, and then install it locally.

Or just use source function to load the codes of geoChina function, as below:

source('http://raw.githubusercontent.com/jmzeng1314/GEOmirror/master/R/geoChina.R') 

What if all these 3 methods failed? I'm so sorry, what' a pity that there's no chance for you to use our GEOmirror !!

just one function: geoChina

use it to download GEO dataset, as below :

library(GEOquery)
library(GEOmirror)
eSet=geoChina('GSE1009') 
eSet=geoChina('GSE27533') 
eSet=geoChina('GSE95166') 

easy download GEO dataset and go through downstream analysis

Once you download the ExpressionSet of GEO dataset, you can access the expression matrix and phenotype data:

library(GEOquery)
library(GEOmirror)
## download GSE95166 data
# https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE95166
#eSet=getGEO('GSE95166', destdir=".", AnnotGPL = F, getGPL = F)[[1]]
library(GEOmirror)
eSet=geoChina('GSE95166')
eSet
eSet=eSet[[1]]


probes_expr <- exprs(eSet);dim(probes_expr)
head(probes_expr[,1:4])
boxplot(probes_expr,las=2)

## pheno info
phenoDat <- pData(eSet)
head(phenoDat[,1:4])
# https://www.ncbi.nlm.nih.gov/pubmed/31430288

groupList=factor(c(rep('npc',4),rep('normal',4)))
table(groupList)
eSet@annotation
# GPL15314	Arraystar Human LncRNA microarray V2.0 (Agilent_033010 Probe Name version)

The simple example for downstream analysis of expression profiling by array:

genes_expr=probes_expr
library("FactoMineR")
library("factoextra")
dat.pca <- PCA(t(genes_expr) , graph = FALSE)
dat.pca
fviz_pca_ind(dat.pca,
             geom.ind = "point",
             col.ind = groupList,
             addEllipses = TRUE,
             legend.title = "Groups"
)
library(limma)
design=model.matrix(~factor(groupList))
design
fit=lmFit(genes_expr,design)
fit=eBayes(fit)
DEG=topTable(fit,coef=2,n=Inf)
head(DEG)
# We observed that 2107 lncRNAs were upregulated
# while 2090 lncRNAs were downregulated by more than 2-fold,
# NKILA among these downregulated lncRNAs (Fig 1A, GSE95166).

## for volcano plot
df=DEG
attach(df)
df$v= -log10(P.Value)
df$g=ifelse(df$P.Value>0.05,'stable',
            ifelse( df$logFC >1,'up',
                    ifelse( df$logFC < -1,'down','stable') )
)
table(df$g)
df$name=rownames(df)
head(df)
library(ggpubr)
ggpubr::ggscatter(df, x = "logFC", y = "v", color = "g",size = 0.5,
          label = "name", repel = T,
          label.select =head(rownames(df)),
          palette = c("#00AFBB", "#E7B800", "#FC4E07") )
detach(df)


x=DEG$logFC
names(x)=rownames(DEG)
cg=c(names(head(sort(x),100)),
     names(tail(sort(x),100)))
cg
library(pheatmap)
n=t(scale(t(genes_expr[cg,])))
n[n>2]=2
n[n< -2]= -2
n[1:4,1:4]
ac=data.frame(groupList=groupList)
rownames(ac)=colnames(n)  
pheatmap(n,show_colnames =F,show_rownames = F,
         annotation_col=ac)

Actually, I should change the probe ID to gene names, but it's not this package should do, so I just omit it.

warnings

• 1.only the expression profiling by array datasets will be offered by our package.
• 2.it's free for all of us, so we can not make sure the internet connection will always be better than GEOquery.