semi-CAM

A semi-supervised deconvolution method for bulk transcriptomic data with partial marker gene information

Input data

1. Gene expression data of mixture samples. semi-CAM can take around 10 mixtures, if have more than 15 mixtures, dimension reduction methods such as hierarchical clustering methods need to be applied on the data first.

2. Markers for partial/all cell types. The algorithm takes markers for at least one cell type, up to markers for all number of cell types in the mixtures.

3. Number of cell types in the mixtures.

Example code

Deconvolute 9 mixtures mixed from blood and breast tissues

1. Read the gene expression data of the mixtures

data.dir="http://raw.github.com/ylidong/semi-CAM/master/Test data/"
data.mix=read.csv(paste0(data.dir, "Example_mix.csv"),row.names=1)

2. Read the marker list for blood and breast tissues

Markers list is a R list with each element contains the marker genes for one cell/tissue

RData_githubURL <- "http://raw.github.com/ylidong/semi-CAM/master/Test data/Marker.list.RData"
load(url(RData_githubURL))

print(Marker.list) ##print the markers for blood and breast

3. Source R code and download the required package

##################
##Source R codes##
##################
library(devtools)
#or install.packages("devtools")
R_githubURL <- "https://raw.github.com/ylidong/semi-CAM/master/FUNS_new/"

source_url(paste0(R_githubURL,"proportion_estimate_main.R"))
source_url(paste0(R_githubURL,"measure_conv_miss.R"))
source_url(paste0(R_githubURL,"identify_markers.R"))
source_url(paste0(R_githubURL,"small.fun.R"))
source_url(paste0(R_githubURL,"match_celltype.R"))

#########################################
##download the required CellMix package##
#########################################
# get biocLite from bioconductor
source("https://bioconductor.org/biocLite.R")
biocLite("BiocInstaller")
# or
library(BiocInstaller)

# install the required package CellMix
# On Windows or Mac OS X, this will try install the source package 
biocLite('CellMix', siteRepos=c('http://web.cbio.uct.ac.za/~renaud/CRAN'), type='both')

# on Linux this will try install the source package 
biocLite('CellMix', siteRepos=c('http://web.cbio.uct.ac.za/~renaud/CRAN'))

# if cannot open the link, can download the source codes from the folder Required_pacakages_source and install it using 
#install.packages("path_to_file/NMF_0.20.tar.gz", repos = NULL, type="source")
#install.packages("path_to_file/CellMix_1.6.2.tar.gz", repos = NULL, type="source")

library(CellMix)

4. True proportions##

P.true=cbind(c(0.67,0.33),c(0.33,0.67),c(0.33,0.67))
colnames(P.true)=paste0("MIX",1:ncol(P.true))
rownames(P.true)=c("Blood","Breast")
P.true=P.true[,rep(1:3,each=3)]

5. Use semi-CAM to estimate cell proportions

###############################
##use markers for two tissues##
###############################
set.seed(12334)
semiCAM.res=semiCAM.main(data_ssCAM=data.mix,data_est=data.mix,ncell=2,cluster_num=50,mks_in=Marker.list)
#data_ssCAM: the mixture expressions used for marker identifications
#data_est: the mixture expressions used for proportion estimates 
#ncell: the number of cell types in the mixture
#cluster_num: the number of clusters used for marker identifications
#mks_in: the available marker gene list for partial/all cell types

#output estimated proportions#
semiCAM.res$P

##############################
##only use markers for blood##
##############################
set.seed(12334)
semiCAM.res2=semiCAM.main(data_ssCAM=data.mix,data_est=data.mix,ncell=2,cluster_num=50,mks_in=Marker.list[1])

#match the tissue types using true proportions
semiCAM.match=match.cor(semiCAM.res2$P,P.true)
#output the proportions after matching
semiCAM.match$P.order