Essentially a collection of single-cell related analysis tools. Including:
- Basic QC
- Brennecke analysis
- SIBER bimodal analysis
- BASiCS normalisation.
Currently works with human and mouse data only.
Install libraries
#CRAN
install.packages("ggplot2")
install.packages("pheatmap")
install.packages("devtools")
install.packages("R.utils")
install.packages("RCurl")
install.packages("statmod")
install.packages("reshape")
#Bioconductor
source("http://bioconductor.org/biocLite.R")
biocLite("edgeR")
biocLite("biomaRt")
biocLite("DESeq")
biocLite("genefilter")
biocLite("EBImage")
biocLite("topGO")
biocLite("org.Hs.eg.db")
biocLite("org.Mm.eg.db")
biocLite("Rgraphviz")
biocLite("edgeR")
#other
source("http://bioinformatics.mdanderson.org/OOMPA/oompaLite.R")
oompainstall(groupName="siber")
library(pheatmap)
library(reshape)
library(ggplot2)
library(edgeR)
library(biomaRt)
library(R.utils)
library(RCurl)
library(DESeq)
library(devtools)
library(genefilter)
library(EBImage)
library(statmod)
library(topGO)
library(org.Hs.eg.db)
library(org.Mm.eg.db)
library(Rgraphviz)
library(edgeR)
library(SIBER)
Install and load ggbiplot
install_github("vqv/ggbiplot")
library(ggbiplot)
Install and load BASiCS normalisation
install_github('catavallejos/BASiCS')
library(BASiCS)
Install and load SCRAN:
From Github:
install_github("elswob/SCRAN")
libary(SCRAN)
From Stash:
- Download using the 'Download' link
- Unzip
- Install
setwd("~/Downloads/scran_master")
install(".")
Requires counts data with symbol and length columns labelled 'Symbol' and 'Length' loaded with rownames as ensembl IDs and cell names as headers. An example data set is included:
load(system.file("data/scran_test.Rdata",package="SCRAN"))
scran_test[0:5,0:5]
Symbol Length SRR1033853 SRR1033854 SRR1033855
ENSMUSG00000000001 Gnai3 3262.00 386 24 1
ENSMUSG00000000028 Cdc45 1574.00 0 0 0
ENSMUSG00000000031 H19 1268.60 0 0 0
ENSMUSG00000000037 Scml2 3297.14 0 0 0
ENSMUSG00000000056 Narf 1785.00 0 96 0
To test with the preloaded data just call the test_run() function with a directory to place the output, e.g.
test_run("/path/to/somewhere")
On other data
a=read.delim("file.tsv",header=T,row.names=1)
sing_cols=c(3:ncol(a))
scran_run(counts=a, sing_cols=sing_cols, outDir="~/", species="mouse")
The demo data is the first 20 cells from the Treutlin et al data set http://www.nature.com/nature/journal/v509/n7500/abs/nature13173.html
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent