Description Usage Arguments Value Note Author(s) References
Estimates the false discovery rate for the identified cellspecific differences in gene expression.
1 2 3 
G 
Matrix of gene expression, columns ordered in the same order at the cellfrequency matrix (n by p, n samples, p genes) 
cc 
Matrix of cellfrequency. (n by k, n samples, k celltypes) 
y 
A numeric vector of group association of each sample. Either 1 or 2. 
n 
A nuermic vector describing the number of samples in a group 
numcell 
The number of celltypes to consider 
numgene 
The number of genes being considered 
rhat 
The contrast in celltype expression for each celltype as observed between the two groups being compared. 
nperms 
The number of permutations to perform. 
alternative 
Type of test to conduct  choose between 'two.sided','greater',or 'less' 
standardize 
Standardize sample or not. Default is TRUE 
medianCenter 
Median center rhat distributions. Default is TRUE. 
logRm 
Exponentiate data for deconvolution stage. Default is FALSE 
logBase 
Base of logaritm used to determine exponentiation factor. Default is 2 
nonNeg 
For single channel arrays. Set any cellspecific expression estimated as negative, to a ceiling of 0. It is conservative in its study of differential expression. Default is FALSE. 
A list.
fdr.g 
A matirx false dicovery rates for csSAM comparison for each celltype at different thresholds. A set of 100 theresholds is determined automatically from the data (k by 100, where k is number of cells). 
avrhatperm 

rhatperm 
A matrix sized pXkXg which stores the contrast of a given gene g in cell type k in permutation p of the data. 
cutp.g 
A matrix k by 100, where k is the number of cell tpes. Lists the 100 cutoff thresholds for each celltype as determined automatically from the computed contrast. 
rhat 
A matrix object with the result of contrasting the average cellspecific expression profile of the two groups, per celltype (Size k by g where k is the number of cells and g is the number of genes). 
ncall.g 
Number of genes called significant at the given cutoff threshold with a FDR matching that indicated in fdr.g 
From version 1.3, the FDR computation is implemented in C++, using Rcpp.
Shai ShenOrr, Rob Tibshirani, Narasimhan Balasubramanian, David Wang
C++ implementation/optimisation by Renaud Gaujoux.
ShenOrr SS, Tibshirani R, Khatri P, Bodian DL, Staedtler F, Perry NM, Hastie T, Sarwal MM, Davis MM and Butte AJ (2010). “Cell typespecific gene expression differences in complex tissues.” Nature methods, 7(4), pp. 2879. ISSN 15487105, doi: 10.1038/nmeth.1439 (URL: http://doi.org/10.1038/nmeth.1439).
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