fdrCsSAM0: fdrCsSAM - Deprecated Version
In shenorrLab/csSAM: csSAM - cell-specific Significance Analysis of Microarrays

Description Usage Arguments Value Note Author(s) References

Estimates the false discovery rate for the identified cell-specific differences in gene expression.

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fdrCsSAM0(G, cc, y, n, numcell, numgene, rhat, nperms,
  alternative = c("two.sided", "greater", "less"), standardize = TRUE,
  medianCenter = TRUE, logRm = FALSE, logBase = 2, nonNeg = FALSE)

`G`	Matrix of gene expression, columns ordered in the same order at the cell-frequency matrix (n by p, n samples, p genes)
`cc`	Matrix of cell-frequency. (n by k, n samples, k cell-types)
`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 cell-types to consider
`numgene`	The number of genes being considered
`rhat`	The contrast in cell-type expression for each cell-type 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 cell-specific 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 cell-type 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 cell-type as determined automatically from the computed contrast.
`rhat`	A matrix object with the result of contrasting the average cell-specific expression profile of the two groups, per cell-type (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 Shen-Orr, Rob Tibshirani, Narasimhan Balasubramanian, David Wang

C++ implementation/optimisation by Renaud Gaujoux.

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, doi: 10.1038/nmeth.1439 (URL: http://doi.org/10.1038/nmeth.1439).

shenorrLab/csSAM documentation built on May 29, 2019, 9:23 p.m.