csSAM-package: csSAM package - Cell Type-Specific Statistical Analysis of...

Description Details Key functions for this package References Examples

Description

csSAM package - Cell Type-Specific Statistical Analysis of Microarray

Details

Tissues are often made up of multiple cell-types. Each with its own functional attributes and molecular signature. Yet, the proportions of any given cell-type in a sample can vary markedly. This results in a significant loss of sensitivity in gene expression studies and great difficulty in identifying the cellular source of any perturbations. Here we present a statistical methodology (cell-type specific Significance Analysis of Microarrays or csSAM) which, given microarray data from two groups of biological samples and the relative cell-type frequencies of each sample, estimates in a virtual manner the gene expression data for each cell-type at a group level, and uses these to identify differentially expressed genes at a cell-type specific level between groups.

The lower limit for the number of samples needed for deconvolving the cell-specific expression of N cell-types is N+1. For a single colour array - the result could be interpreted as the average expression level of a given gene in a cell-type of that group. Multiplied by the frequency of a given cell-type in an individual in the group, it is the amount contributed by that cell type to the overall measured expression on the array.

Key functions for this package

Additional functions exists (e.g., runSAM and fdrSAM to contrast csSAM with the tissue heterogeneity ignorant SAM).

References

Shen-Orr SS, Tibshirani R, Khatri P, Bodian DL, Staedtler F, Perry NM, Hastie T, Sarwal MM, Davis MM and Butte AJ (2010). <e2><80><9c>Cell type-specific gene expression differences in complex tissues.<e2><80><9d> Nature methods, 7(4), pp. 287-9. ISSN 1548-7105, doi: 10.1038/nmeth.1439 (URL: http://doi.org/10.1038/nmeth.1439).

Examples

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library("csSAM")
##
## Generate random dataset
##
set.seed(143)
k <- 5 # number of cell types 
ng <- 500 # number of genes
p <- 20 # number of samples
ndiff <- 100 # number of genes differentially expressed

# true cell-specific signatures
H1 <- matrix(rnorm(5*ng), ncol=ng)
H2 <- H1
# create differential expression for 3rd cell type
H2[3,1:ndiff] <- H2[3,1:ndiff] + 5 

# cell frequency matrix per sample
cc <- matrix(runif(p*k), ncol=k) 
cc <- t(scale(t(cc), center=FALSE, scale=rowSums(cc))) 
colnames(cc) <- paste('cellType', 1:ncol(cc), sep="")

# global expression matrix
G <- rbind(cc[1:10, ] %*% H1, cc[11:p, ] %*%H2 ) + matrix(rnorm(p*ng), ncol=ng)
# sample classes (2 groups) 
y <- gl(2, p/2)  

fileName = "Example File.pdf";


# Now run, either using the wrapper 
# NB: more permutations would be needed for real data
deconvResults = csSamWrapper(G, cc, y, nperms = 50, alternative = "two.sided"
								, standardize = TRUE
								, medianCenter = TRUE
								, fileName = fileName)

# Or by calling each function independently: 
# this is useful if you want to perform only cell-specific expression 
# without differential expression.

# run csSAM
fit <- csSAMfit(t(G) ~ y | cc, nperms = 200, verbose = TRUE, .rng = 123)
top <- csTopTable(fit, alternvative = 'two.sided')
head(top[[1]])

# run SAM
tt.sam <- runSAM(G, y)
falseDiscovRSAM <- fdrSAM(G, y, tt.sam, nperms=200, alternative = 'two.sided')

plotCsSAM(fit, falseDiscovRSAM, alternative='two.sided', fileName)

shenorrLab/csSAM documentation built on May 26, 2017, 8:06 a.m.