SortData


A matrix of counts where each row specifies a gene and each column specifies a replicate.


counts


A vector specifying the treatment group for each column of the counts matrix. Only two treatment groups of either paired or unpaired data are allowed.


treatment


A vector specifying the replicate for each column of the counts matrix when there is paired data; optional if data is unpaired.


replic


Character vector specifying one of "unpaired" or "paired", depending on the structure of the data.<p></p><p></p>

sort.method


An optional positive numeric vector of multiplicative normalization factors for each column of the counts matrix. 


norm.factors


A function called in SimData used to trim and sort the matrix of counts provided.


RNA sequencing analysis methods are often derived by relying on hypothetical parametric models for read counts that are not likely to be precisely satisfied in practice. Methods are often tested by analyzing data that have been simulated according to the assumed model. This testing strategy can result in an overly optimistic view of the performance of an RNA-seq analysis method. We develop a data-based simulation algorithm for RNA-seq data. The vector of read counts simulated for a given experimental unit has a joint distribution that closely matches the distribution of a source RNA-seq dataset provided by the user. Users control the proportion of genes simulated to be differentially expressed (DE) and can provide a vector of weights to control the distribution of effect sizes. The algorithm requires a matrix of RNA-seq read counts with large sample sizes in at least two treatment groups. Many datasets are available that fit this standard.

SortData: SortData

Description

Usage

Arguments

Value