pmm (version 1.4.0)

pmm: Fitting the PMM

Description

Fits the parallel mixed model.

Usage

pmm(df.data, response, weight = "None", ignore = 3, simplify =
TRUE, gene.col = "GeneID", condition.col = "condition")

Arguments

df.data

a data frame containing the variables for the model. Each row should correspond to one independent siRNA experiment. The data frame needs to have at least the following variables: GeneID, condition and a column with the measurements/readouts of the screens.

response

name of the column that contains the measurements/readouts of the screens.

weight

an optional vector of weights to be used in the fitting process of the linear mixed model. It should be a numeric vector. Default is a fit without weights.

ignore

number of minimal required sirna replicates for each gene. If a gene has less siRNA replicates it is ignored during the fitting process. Default is 3.

simplify

logical value that indicates whether the output of pmm should be simplified.

gene.col

name of the column that give a gene identifier. Default is "GeneID".

condition.col

name of the column that indicates the condition that was used for each measurement. Default is "condition".

Value

• The simplified output of pmm is a matrix that contains the c_cg effects for each condition c and gene g, as well as an estimate for the local false discovery rate. A positive estimated c_cg effect means that the response was enhanced when the corresponding gene is knocked down. A negative effect means that the response was reduced. The non-simplified output of pmm is a list of three components. The first component contains the simpilified output, i.e the matrix with the c_cg effects and fdr values, the second component contains the fit of the linear mixed model and the third component contains the a_g and b_cg values.

encoding

utf-8

Details

The Parallel Mixed Model (PMM) is composed of a linear mixed model and an assessment of the local False Discovery Rate. The linear mixed model consists of a fixed effect for condition and of two random effects for gene g and for gene g within a condition c. We fit a linear mixed model by using lmer function from lme4 R-package. To distinguish hit genes, PMM provides also an estimate of the local False Discovery Rate (FDR). pmm will only use the data of genes that have at least a certain number of siRNA replicates per condition. The number of ignored genes can be passed to pmm by the argument ignore. We recommend using at least 3 siRNA replicates per gene and condition in order to obtain a reliable fit.

Examples

data(kinome)

 ## Fitting the parallel mixed model with weights
 fit1 <- pmm(kinome,"InfectionIndex","weight_library")
 head(fit1)

 ## Fitting the parallel mixed model without weights
 fit2 <- pmm(kinome,"InfectionIndex","None")
 head(fit2)

 ## Accessing the fit of the linear mixed model
 fit3 <- pmm(kinome,"InfectionIndex","weight_library",simplify=FALSE)
 identical(fit1,fit3[[1]])
 summary(fit3[[2]])

 ## NA-Handling
 kinome$InfectionIndex[kinome$GeneID == 10000 & kinome$condition ==
 "ADENO"] <- rep(NA,12)
 fit4 <- pmm(kinome,"InfectionIndex","weight_library",3)
 head(fit4)