biSoftK: NIPALS algorithm with soft thresholding operator on rows and...

In mengchen18/mogsa: Multiple omics data integrative clustering and gene set analysis

Description

An internal function called by mbpca.

Usage

biSoftK(
  x,
  maxiter,
  kp,
  kt,
  weight.p,
  weight.t,
  pos = FALSE,
  unit.pb = TRUE,
  unit.tb = FALSE
)

Arguments

x	The input matrix, rows are observations, columns are variables
maxiter	Number of maximum interation the algorithm can run
kp	The number (>=1) or proportion (<1) of variables want to keep. It could be a single value or a vector has the same length as x so the sparsity of individual matrix could be different.
kt	The number (>=1) or proportion (<1) of non-zero scores for obvservations.
weight.p	The weight of variables. It could be 1) a vector has the same length as x, one value for each table/block; 2) one number, all variables share the same weight or 3) a list of vectors, the length of each vector should be the same with the columns numbers of the corresponding table/block, so every variables has a unique weight.
weight.t	The weight for observation. For accepted values or formats, see weight.p.
pos	Logical value, if only non-negaitve values in the loading and score vectors.
unit.pb	Logical value, whether the length of table/block loading should be unit length.
unit.tb	Logical value, whether the length of table/block score should be unit length.

Details

This function also use the NIPALS algorithm, but it generalized nipalsSoftK from several aspects: 1. Allowing sparsity on both columns and rows of matrices 2. Allowing weights for columns and rows 3. Allowing loading and/or score vectors of blocks to be unit length 4. Allowing only positive number in loading and score vectors

Value

an list object contains the following elements:

tb - the block scores

pb - the block loadings

t - the global scores

w - the wegihts of block scores to construct the global score.

Author(s)

Chen Meng

See Also

msvd # examples data("NCI60_4arrays") d <- lapply(NCI60_4arrays[2:4], function(x) scale(t(x))) # x1 <- biSoftK(d, maxiter = 1000, kp = Inf, kt = Inf, weight.p = rep(1, length(d)), weight.t = rep(1, length(d)), pos = FALSE, unit.pb = TRUE, unit.tb = TRUE)

x1s <- biSoftK(d, maxiter = 1000, kp = Inf, kt = Inf, weight.p = 1, weight.t = 1, pos = FALSE, unit.pb = TRUE, unit.tb = TRUE)

identical(x1, x1s) barplot(c(x1$t), col= as.factor(substr(colnames(NCI60_4arrays$agilent), 1, 2)))

x2 <- biSoftK(d, maxiter = 1000, kp = 30, kt = Inf, weight.p = rep(1, length(d)), weight.t = rep(1, length(d)), pos = FALSE, unit.pb = TRUE, unit.tb = TRUE) barplot(c(x2$t), col= as.factor(substr(colnames(NCI60_4arrays$agilent), 1, 2)))

x3 <- biSoftK(d, maxiter = 1000, kp = 40, kt = Inf, weight.p = rep(1, length(d)), weight.t = rep(1, length(d)), pos = TRUE, unit.pb = TRUE, unit.tb = TRUE) barplot(c(x3$t), col= as.factor(substr(colnames(NCI60_4arrays$agilent), 1, 2)))

x4 <- biSoftK(d, maxiter = 1000, kp = 30, kt = 6, weight.p = rep(1, length(d)), weight.t = rep(1, length(d)), pos = FALSE, unit.pb = TRUE, unit.tb = TRUE) barplot(c(x4$t), col= as.factor(substr(colnames(NCI60_4arrays$agilent), 1, 2)), names.arg = colnames(NCI60_4arrays$agilent), las = 2)

plot(x4$t, x4$tb$hgu133) plot(x4$t, x4$tb$hgu133p2) plot(x4$t, x4$tb$hgu95) heatmap(t(d$hgu133[, which(x4$pb$hgu133 != 0) ])) barplot(t(d$hgu133[, which.max(x4$pb$hgu133) ]), las = 2) barplot(t(d$hgu133[, which.max(x4$pb$hgu133) ]), las = 2)

mengchen18/mogsa documentation built on June 7, 2020, 6:05 p.m.