qfa.epi: Finds genetic interaction strengths and p-values

Description Usage Arguments Value

View source: R/qfa.R

Description

Fits a genetic independence model between control strains and double mutant strains, either using rjags and a Bayesian linear regression model, or lm and maximum likelihood. For each ORF, the probability that it is a false discovery of a suppressor or enhancer is calculated. These probabilities are then fdr corrected and returned along with genetic interaction scores.

Usage

1
2
qfa.epi(double,control,qthresh=0.05,orfdict="ORF2GENE.txt",GISthresh=0.0,plot=TRUE,
modcheck=FALSE,wctest=TRUE,bootstrap=NULL,Nboot=5000,subSamp=Inf,reg="lmreg",fdef="fit")

Arguments

double

Either a qfa.posterior or the results of qfa.fit for the double mutants

control

Either a qfa.posterior or the results of qfa.fit for the control strains

qthresh

The FDR corrected cut off

orfdict

Location of file giving a column of ORFs first and a column of corresponding gene names second - so gene names can be plotted

GISthresh

When returning interaction hitlists, this variable determines the cutoff for strength of genetic interaction.

plot

If TRUE, then a 2-way fitness plot is made.

modcheck

If TRUE then diagnostic residual plots are output to “ModelCheck.pdf”

wctest

If TRUE, then use the Wilcoxon test for differences in medians as a measure of statistical significance of genetic interaction. This is the default. If FALSE, then use a t-test for difference in mean fitnesses instead.

bootstrap

If TRUE, then use bootstrapping procedure to check if genetic interactions are significant. If false, then use linear regression and t-test or wilcoxon test.

Nboot

Number of bootstrap samples to generate if using bootstrapping procedure

subSamp

Number of subsamples of available replicates to sample when bootstrapping (default, Inf, uses all available replicates, i.e. each summary (each bootstrap sample) is based on sampling subSamp from N with replacement. If subSamp==Inf, then subSamp is set equal to N.

reg

String specifying what type of regression to use. Default is least squares regression as implemented in lm function: "lmreg". Alternatives include "quantreg", "splitreg" and "perpreg". See lm.epi function help for further details.

fdef

String specifying what fitness definition to use. Must be the name of a column common to double and control. Typical options include: "nAUC", "r", "MDRMDP". The default "fit" is included for backwards compatibility with earlier versions of this function which relied on users manually creating a "fit" column that includes their required fitness definition values. This was usually achieved by copying an existing column (e.g. "MDRMDP").

Value

Returns an R list containing three data frames: Results, Enhancers and Suppressors. Each data frame has the following columns:


qfa documentation built on Feb. 22, 2020, 3:01 a.m.

Related to qfa.epi in qfa...