estimateDE: Estimate condition mean expression and calculate Probability of Positive Log Ratio(PPLR)

Description

Estimate condition mean expression for both experimental conditions using the expression estimates obtained by estimateExpression

Usage

estimateDE(conditions, outFile, parFile,  lambda0=NULL, samples=NULL, confidencePerc=NULL,  verbose=NULL, norm=NULL, seed=NULL, pretend=FALSE )

Arguments

conditions

List of vectors, each vector containing names of files containing the expression samples from a replicate (Can be both technical and biological replicates. However, in order to get good results biological replicates for each condition are essential).

outFile

Prefix for the output files.

parFile

File containing estimated hyperparameters.

samples

Produce samples of condition mean expression apart from PPLR and confidence.

confidencePerc

Percentage for confidence intervals. (Default is 95%)

verbose

Verbose output.

lambda0

Model parameter lambda_0.

norm

Vector of (multiplicative) normalization constants for library size normalization of expression samples. Number of constants has to match the number of expression samples files.

seed

Sets the initial random seed for repeatable experiments.

pretend

Do not execute, only print out command line calls for the C++ version of the program.

Value

.pplr: file containing the PPLR, mean log2 fold change with confidence intervals, mean condition mean expressions
.est: files containing samples of condition mean expressions for each condition - optional
.estVar: file containing samples of inferred variance of the first condition - optional

Details

This function takes as an input expression samples from biological replicates of two or more conditions and hyperparameters over precision distribution inferred by estimateHyperPar. It uses pseudo-vectors of expression samples from all replicates to infer condition mean expression for each condition.

The condition mean expression samples are used for computation of the Probability of Positive Log Ratio (PPLR) as well as $log2$ fold change of expression with confidence intervals and average condition mean expression for each transcript. Optionally the function can produce also the samples of condition mean expression for each condition.

For more than one conditions, the comparison is done pairwise between all conditions ($CP=\frac{C*(C-1)}{2}$ pairs), reporting: CPxPPLR CPx(log2FC ciLow ciHigh) CxMeanExpr.

Examples

Run this code

## Not run: 
# cond1Files = c("data-c0b0.rpkm","data-c0b1.rpkm")
# cond2Files = c("data-c1b0.rpkm","data-c1b1.rpkm")
# estimateDE(conditions=list(cond1Files, cond2Files), outFile="data.pplr",
#       parFile="data.par", norm=c(1.0, 0.999, 1.0017, 0.9998))
# ## End(Not run)