csSAM-package: Cell-specific Differential Expression (csSAM)
In csSAM: csSAM - cell-specific Significance Analysis of Microarrays

Description Details Author(s) References Examples

SAM for Cell-specific Differential Expression SAM.

Package:	csSAM
Type:	Package
Version:	1.2
Date:	2011-10-08
License:	LGPL
LazyLoad:	yes
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 singe color array - the result could be interperted as the avg. expression level of a given gene in a cell-type of that group. Multiplied by the frequecy 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:
csSamWrapper - Single wrapper function performs all functionality. csfit: For deconvolving the average cell-type specific expression for each cell-type in a given group.
csSAM: For calculating the constrast between every pair of cells being compared between the two groups.
fdrCsSAM: Estimate the false discovery rate for each cell-type specific comparison.
findSigGenes:Identifies the list of differentially expressed genes in a given cell-type at a given FDR cutoff.
plotCsSAM:Plots a fdr plot of ther results.

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

Shai Shen-Orr, Rob Tibshirani, Narasimhan Balasubramanian, David Wang

Maintainer: Shai Shen-Orr <shenorr@stanford.edu>

Shen-Orr SS, Tibshirani R, Khatri P, Bodian DL, Staedtler F, Perry NM, Hastie T, Sarwal MM, Davis MM and Butte AJ (2010). "Cell type-specific gene expression differences in complex tissues." _Nature methods_, *7*(4), pp. 287-9. ISSN 1548-7105, <URL: http://dx.doi.org/10.1038/nmeth.1439>, <URL: http://www.ncbi.nlm.nih.gov/pubmed/20208531>.

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
# (e.g. useful if you want to perform only cell-specific expression without differential expression).
## Not run: 
numset = nlevels(y)
n <- summary(y, maxsum=Inf) # number of samples in each class
numgene = ncol(G)
numcell = ncol(cc)
geneID = colnames(G)
cellID = colnames(cc)

deconv <- list()
# run analysis
for (curset in levels(y))
	deconv[[curset]]= csfit(cc[y==curset,], G[y==curset,])

rhat <- array(dim = c(numcell,numgene))
rhat[, ] <- csSAM(deconv[[1]]$ghat, deconv[[1]]$se,
                  n[1], deconv[[2]]$ghat, deconv[[2]]$se, n[2],
                  standardize=TRUE, medianCenter=TRUE, nonNeg=TRUE)

tt.sam <- runSAM(G, y)
falseDiscovR <- fdrCsSAM(G,cc,y,n,numcell,numgene, rhat,
                    nperms = 200,standardize=TRUE,alternative='two.sided',
                    medianCenter=TRUE, nonNeg=TRUE)
falseDiscovRSAM <- fdrSAM(G, y, nperms=200, alternative = 'two.sided',tt.sam)
sigGene <- findSigGene(G, cc, y, rhat, falseDiscovR)

plotCsSAM(falseDiscovR, falseDiscovRSAM,alternative='two.sided',cellID,numcell, fileName)
print (falseDiscovR$fdr.g[ , ] )

## End(Not run)

Loading required package: compiler
Warning messages:
1: In xy.coords(x, y, xlabel, ylabel, log) :
  1 x value <= 0 omitted from logarithmic plot
2: In xy.coords(x, y, xlabel, ylabel, log) :
  1 x value <= 0 omitted from logarithmic plot
3: In xy.coords(x, y, xlabel, ylabel, log) :
  1 x value <= 0 omitted from logarithmic plot
4: In xy.coords(x, y, xlabel, ylabel, log) :
  1 x value <= 0 omitted from logarithmic plot
5: In xy.coords(x, y, xlabel, ylabel, log) :
  1 x value <= 0 omitted from logarithmic plot
6: In xy.coords(x, y, xlabel, ylabel, log) :
  1 x value <= 0 omitted from logarithmic plot
png 
  2 
Warning messages:
1: In xy.coords(x, y, xlabel, ylabel, log) :
  1 x value <= 0 omitted from logarithmic plot
2: In xy.coords(x, y, xlabel, ylabel, log) :
  1 x value <= 0 omitted from logarithmic plot
3: In xy.coords(x, y, xlabel, ylabel, log) :
  1 x value <= 0 omitted from logarithmic plot
4: In xy.coords(x, y, xlabel, ylabel, log) :
  1 x value <= 0 omitted from logarithmic plot
5: In xy.coords(x, y, xlabel, ylabel, log) :
  1 x value <= 0 omitted from logarithmic plot
6: In xy.coords(x, y, xlabel, ylabel, log) :
  1 x value <= 0 omitted from logarithmic plot
          [,1]      [,2]      [,3]      [,4]      [,5]      [,6]      [,7]
[1,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[2,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[3,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.9944281
[4,] 0.9914516 0.9914516 0.9904532 0.9744428 0.9744428 0.9744428 0.9744428
[5,] 0.9243360 0.9092127 0.9016619 0.9016619 0.9016619 0.9014127 0.9014127
          [,8]      [,9]     [,10]     [,11]     [,12]     [,13]     [,14]
[1,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[2,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[3,] 0.9672000 0.9672000 0.9672000 0.9386090 0.9386090 0.9386090 0.9386090
[4,] 0.9744428 0.9743939 0.9637598 0.9500408 0.9324895 0.9324895 0.9324895
[5,] 0.9014127 0.9014127 0.9014127 0.9014127 0.9014127 0.8995532 0.8995532
         [,15]     [,16]     [,17]     [,18]     [,19]     [,20]     [,21]
[1,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[2,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.9882927 0.9778662
[3,] 0.9350233 0.9226214 0.9174490 0.9118011 0.8931844 0.8931844 0.8909434
[4,] 0.9324895 0.9324895 0.9324895 0.9184770 0.9184770 0.9184770 0.9184770
[5,] 0.8995532 0.8995532 0.8995532 0.8995532 0.8995532 0.8995532 0.8995532
         [,22]     [,23]     [,24]     [,25]     [,26]     [,27]     [,28]
[1,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[2,] 0.9778662 0.9778662 0.9778662 0.9778662 0.9494672 0.9230085 0.9199107
[3,] 0.8740461 0.8503401 0.8421071 0.8421071 0.8421071 0.8409565 0.8225909
[4,] 0.9184770 0.9176154 0.8690698 0.8690698 0.8690698 0.8690698 0.8690698
[5,] 0.8995532 0.8995532 0.8995532 0.8995532 0.8995532 0.8995532 0.8995532
         [,29]     [,30]     [,31]     [,32]     [,33]     [,34]     [,35]
[1,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[2,] 0.9141981 0.9094500 0.9008421 0.9008421 0.9008421 0.9008421 0.9008421
[3,] 0.8196602 0.7992929 0.7692187 0.7692187 0.7515698 0.7256627 0.7256627
[4,] 0.8690698 0.8392857 0.8392857 0.8392857 0.8392857 0.8392857 0.8392857
[5,] 0.8995532 0.8995532 0.8995532 0.8995532 0.8995532 0.8995532 0.8995532
         [,36]     [,37]     [,38]     [,39]     [,40]     [,41]     [,42]
[1,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[2,] 0.9008421 0.9008421 0.8865323 0.8865323 0.8700000 0.8700000 0.8700000
[3,] 0.7256627 0.7256627 0.7169048 0.7169048 0.7169048 0.7076471 0.7076471
[4,] 0.8392857 0.8392857 0.8392857 0.8392857 0.8392857 0.8392857 0.8392857
[5,] 0.8995532 0.8995532 0.8995532 0.8995532 0.8995532 0.8995532 0.8995532
         [,43]     [,44]     [,45]     [,46]     [,47]     [,48]     [,49]
[1,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[2,] 0.8471739 0.8471739 0.8471739 0.8471739 0.8471739 0.8471739 0.8463333
[3,] 0.7076471 0.7076471 0.7076471 0.7076471 0.7076471 0.7076471 0.7076471
[4,] 0.8392857 0.8392857 0.8392857 0.8392857 0.8392857 0.8392857 0.8392857
[5,] 0.8995532 0.8995532 0.8995532 0.8995532 0.8995532 0.8995532 0.8995532
         [,50]     [,51]     [,52]     [,53]     [,54]     [,55]     [,56]
[1,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[2,] 0.8463333 0.8463333 0.8450000 0.7891667 0.7573913 0.7315909 0.7085714
[3,] 0.7076471 0.7076471 0.7076471 0.7076471 0.7076471 0.7076471 0.7076471
[4,] 0.8392857 0.8392857 0.8392857 0.8392857 0.8392857 0.8392857 0.8392857
[5,] 0.8995532 0.8995532 0.8995532 0.8995532 0.8995532 0.8995532 0.8995532
         [,57]     [,58]     [,59]     [,60]     [,61]     [,62]     [,63]
[1,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[2,] 0.6514286 0.6514286 0.6514286 0.6514286 0.6514286 0.6417857 0.5946429
[3,] 0.7076471 0.7076471 0.7076471 0.6583333 0.6045833 0.5570833 0.5070833
[4,] 0.8392857 0.8392857 0.8392857 0.8392857 0.8392857 0.8392857 0.8392857
[5,] 0.8995532 0.8995532 0.8995532 0.8995532 0.8995532 0.8995532 0.8995532
         [,64]     [,65]     [,66]     [,67]     [,68]     [,69]     [,70]
[1,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[2,] 0.5946429 0.5461538 0.5026923 0.4630769 0.4562500 0.4129167 0.4129167
[3,] 0.5054545 0.5054545 0.5054545 0.4733333 0.4733333 0.4625000 0.4237500
[4,] 0.8392857 0.8392857 0.8392857 0.8392857 0.8392857 0.8392857 0.8392857
[5,] 0.8970000 0.8255000 0.8255000 0.7783333 0.7783333 0.7783333 0.7783333
         [,71]     [,72]     [,73]     [,74]     [,75]     [,76]     [,77]
[1,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[2,] 0.3777273 0.3777273 0.3777273 0.3777273 0.3777273 0.3777273 0.3777273
[3,] 0.4237500 0.4237500 0.4237500 0.4237500 0.4237500 0.4237500 0.4237500
[4,] 0.8392857 0.8392857 0.8392857 0.8392857 0.8392857 0.8392857 0.8392857
[5,] 0.7357143 0.7357143 0.7357143 0.7357143 0.7357143 0.7357143 0.7357143
         [,78]     [,79]     [,80]     [,81]     [,82]     [,83]     [,84]
[1,] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[2,] 0.3777273 0.3777273 0.3777273 0.3777273 0.3777273 0.3777273 0.3777273
[3,] 0.4087500 0.3837500 0.3437500 0.3062500 0.2787500 0.2787500 0.2787500
[4,] 0.8392857 0.8392857 0.8392857 0.8392857 0.8392857 0.8392857 0.8392857
[5,] 0.7357143 0.6812500 0.6300000 0.6300000 0.6300000 0.6300000 0.5966667
         [,85]     [,86]     [,87]     [,88]     [,89]     [,90]     [,91]
[1,] 1.0000000 1.0000000 0.9900000 0.9387500 0.9387500 0.9387500 0.9387500
[2,] 0.3683333 0.3316667 0.3316667 0.3316667 0.3316667 0.3316667 0.3175000
[3,] 0.2683333 0.2416667 0.2150000 0.2050000 0.1866667 0.1816667 0.1550000
[4,] 0.8392857 0.8392857 0.8392857 0.7850000 0.7350000 0.6800000 0.6250000
[5,] 0.5966667 0.5966667 0.5966667 0.5966667 0.5966667 0.5966667 0.5966667
         [,92]     [,93]     [,94]     [,95]     [,96]     [,97]     [,98]
[1,] 0.9387500 0.9387500 0.9387500 0.9387500 0.9387500 0.9387500 0.9387500
[2,] 0.2900000 0.2600000 0.2425000 0.2225000 0.1900000 0.1900000 0.1900000
[3,] 0.1400000 0.1266667 0.1133333 0.1133333 0.1133333 0.1133333 0.1133333
[4,] 0.5950000 0.5300000 0.4750000 0.4300000 0.3900000 0.3500000 0.3250000
[5,] 0.5966667 0.5966667 0.5966667 0.5966667 0.5966667 0.5950000 0.5400000
         [,99]    [,100]
[1,] 0.9387500 0.9387500
[2,] 0.1900000 0.1900000
[3,] 0.1133333 0.1133333
[4,] 0.3000000 0.3000000
[5,] 0.4750000 0.4750000