pSI (version 1.1)

specificity.index: Specificity Index Statistic

Description

specificity.index Calculates specificity index statistic (pSI) values of input expression matrix which can be used for comparative quantitative analysis to identify genes enriched in specific cell populations across a large number of profiles. This measure correctly predicts in situ hybridization patterns for many cell types. specificity.index returns a data frame of equal size as input data frame, with pSI values replacing the expression values. NOTE:Supplementary data (human & mouse expression sets, calculated pSI datasets, etc.) can be found in pSI.data package located at the following URL: http://genetics.wustl.edu/jdlab/psi_package/

Usage

specificity.index(pSI.in, pSI.in.filter, bts = 50, p_max = 0.1, e_min = 0.3, hist = FALSE, SI = FALSE)

Arguments

pSI.in

data frame with expresion values for genes in rows, and samples or cell types in columns (at this point replicate arrays have been averaged, so one column per cell type)

pSI.in.filter

matched array (same genes and samples) but with NA's for any genes that should be excluded for a particular cell type.

bts

numeric. number of distributions to average for permutation testing

p_max

numeric. maximum pvalue to be calculated

e_min

numeric. minimum expression value for a gene to be included. For microarray studies, a value of 50 has been the default value and for RNAseq studies, a value of 0.3 has been used as the default.

hist

logical. option for producing histograms of actual & permuted distributions of gene rank

SI

logical. option to output SI value instead of default pSI value

Details

$SI_{n,1}= \frac{ \sum_{k=2}^m rank( \frac{ IP_{1,n} }{ IP_{k,n} }) }{m-1}$

References

Joseph D. Dougherty, Eric F. Schmidt, Miho Nakajima, and Nathaniel Heintz Analytical approaches to RNA profiling data for the identification of genes enriched in specific cells Nucl. Acids Res. (2010)

Examples

##load sample expression matrix
data(sample.data)
##calculate specificity index on expression matrix
##(Normally for RNAseq data, and e_min of 0.3, microarrays: e_min= 50)
pSI.output <- specificity.index(pSI.in=sample.data$pSI.input, e_min=20)