PLIERsparse: sparse PLIER function (experimental)

In wgmao/PLIER: Pathway-Level Information Extractor (PLIER): a generative model for gene expression data

sparse PLIER function (experimental)

Description

sparse PLIER function (experimental)

Usage

PLIERsparse(
  data,
  priorMat,
  svdres = NULL,
  k = NULL,
  L1 = NULL,
  L2 = NULL,
  L3 = NULL,
  frac = 0.7,
  max.iter = 350,
  trace = F,
  scale = T,
  Chat = NULL,
  maxPath = 10,
  doCrossval = T,
  penalty.factor = rep(1, ncol(priorMat)),
  glm_alpha = 0.9,
  minGenes = 10,
  tol = 1e-06,
  seed = 123456,
  allGenes = F,
  rseed = NULL,
  pathwaySelection = c("complete", "fast"),
  sparseL = 0.01,
  sparseType = "SCAD"
)

Arguments

data	the data to be processed with genes in rows and samples in columns. Should be z-scored or set scale=T
priorMat	the binary prior information matrix with genes in rows and pathways/genesets in columns
svdres	Pre-computed result of the svd decomposition for data
k	The number of latent variables to return, leave as NULL to be set automatically using the num.pc "elbow" method
L1	L1 constant, leave as NULL to automatically select a value
L2	L2 constant, leave as NULL to automatically select a value
L3	L3 constant, leave as NULL to automatically select a value. Sparsity in U should be instead controlled by setting frac
frac	The fraction of LVs that should have at least 1 prior inforamtion association, used to automatically set L3
max.iter	Maximum number of iterations to perform
trace	Display progress information
scale	Z-score the data before processing
Chat	A ridge inverse of priorMat, used to select active pathways, expensive to compute so can be precomputed when running PLIER multiple times
maxPath	The maximum number of active pathways per latent variable
doCrossval	Whether or not to do real cross-validation with held-out pathway genes. Alternatively, all gene annotations are used and only pseudo-crossvalidation is done. The latter option may be preferable if some pathways of interest have few genes.
penalty.factor	A vector equal to the number of columns in priorMat. Sets relative penalties for different pathway/geneset subsets. Lower penalties will make a pathway more likely to be used. Only the relative values matter. Internally rescaled.
glm_alpha	Set the alpha for elastic-net
minGenes	The minimum number of genes a pathway must have to be considered
tol	Convergence threshold
seed	Set the seed for pathway cross-validation
allGenes	Use all genes. By default only genes in the priorMat matrix are used.
rseed	Set this option to use a random initialization, instead of SVD
pathwaySelection	Pathways to be optimized with elstic-net penalty are preselected based on ridge regression results. "Complete" uses all top pathways to fit individual LVs. "Fast" uses only the top pathways for the single LV in question.
sparseL	the lambda for sparsity on Z, default 0.02
sparseType	sparsity inducing penalty to use (SCAD or L1)

wgmao/PLIER documentation built on Sept. 1, 2024, 10:25 p.m.