specificity.index: Specificity Index Statistic

In pSI: 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{ ∑_{k=2}^m rank( \frac{ IP_{1,n} }{ IP_{k,n} }) }{m-1}

Author(s)

Xiaoxiao Xu, Alan B. Wells, David OBrien, Arye Nehorai, Joseph D. Dougherty

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)

pSI documentation built on May 1, 2019, 10:28 p.m.