iProFun.reg: Linear regression on all outcome data types

In songxiaoyu/iProFun: An integrative analysis tool to screen for Proteogenomic Functional traits perturbed by DNA alterations

Linear regression on all outcome data types

Description

Linear regression on all outcome data types with all types of DNA alterations (results not formatted for iProFun input)

Usage

iProFun.reg(
  yList,
  xList,
  covariates,
  permutation.col = 0,
  var.ID = c("Gene_ID"),
  Y.rescale = F,
  var.ID.additional = NULL,
  seed = NULL
)

Arguments

yList	yList is a list of data matrix for outcomes.
xList	xList is a list of data matrix for predictors.
covariates	covariates is a list of data matrix for covariate.
permutation.col	permutation.col provides the index of the data types that should be permuated. permutation.col = 0 (default): no permuatation and analysis is on original data. 0 < permutate <= length of yList: permuate the label of the corresponding data type in yList. For example, permutate =2, permute the y label of second data matrix.
var.ID	var.ID gives the variable name (e.g. gene/protein name) to match different data types. If IDs are not specified, the first columns will be considered as ID variable.
Y.rescale	Y.rescale (default = False) gives whether each outcome variable should be standardized to mean 0 and sd 1 before regression.
var.ID.additional	var.ID.additional allows to output additional variables from the input. Often helpful if multiple rows (e.g. probes) are considered per gene to allow clear index of results.
seed	seed allows users to externally assign seed to replicate results.

Value

list with the same length as xlist. Nested within each list, it contains

reg.out.list:

reg.out.list returns the regression summary for each outcome data types as a list. Within each list, see output of iProFun.reg.1y for details.

songxiaoyu/iProFun documentation built on Dec. 8, 2022, 3:54 p.m.