Last data update: 2014.03.03

 R: Simplified interface to PLM.
basicPLMR Documentation

Simplified interface to PLM.

Description

Simplified interface to PLM.

Usage

basicPLM(pmMat, pnVec, normalize = TRUE, background = TRUE, transfo =
  log2, method = c('plm', 'plmr', 'plmrr', 'plmrc'), verbose = TRUE)

Arguments

pmMat

Matrix of intensities to be processed.

pnVec

Probeset names

normalize

Logical flag: normalize?

background

Logical flag: background adjustment?

transfo

function: function to be used for data transformation prior to summarization.

method

Name of the method to be used for normalization. 'plm' is the usual PLM model; 'plmr' is the (row and column) robust version of PLM; 'plmrr' is the row-robust version of PLM; 'plmrc' is the column-robust version of PLM.

verbose

Logical flag: verbose.

Value

A list with the following components:

Estimates

A (length(pnVec) x ncol(pmMat)) matrix with probeset summaries.

StdErrors

A (length(pnVec) x ncol(pmMat)) matrix with standard errors of 'Estimates'.

Residuals

A (nrow(pmMat) x ncol(pmMat)) matrix of residuals.

Note

Currently, only RMA-bg-correction and quantile normalization are allowed.

Author(s)

Benilton Carvalho

See Also

rcModelPLM, rcModelPLMr, rcModelPLMrr, rcModelPLMrc, basicRMA

Examples

set.seed(1)
pms <- 2^matrix(rnorm(1000), nc=20)
colnames(pms) <- paste("sample", 1:20, sep="")
pns <- rep(letters[1:10], each=5)
res <- basicPLM(pms, pns, TRUE, TRUE)
res[['Estimates']][1:4, 1:3]
res[['StdErrors']][1:4, 1:3]
res[['Residuals']][1:20, 1:3]

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(oligo)
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Loading required package: oligoClasses
Welcome to oligoClasses version 1.34.0
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: Biostrings
Loading required package: S4Vectors
Loading required package: stats4

Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: XVector
================================================================================
Welcome to oligo version 1.36.1
================================================================================
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/oligo/basicPLM.Rd_%03d_medium.png", width=480, height=480)
> ### Name: basicPLM
> ### Title: Simplified interface to PLM.
> ### Aliases: basicPLM
> ### Keywords: manip
> 
> ### ** Examples
> 
> set.seed(1)
> pms <- 2^matrix(rnorm(1000), nc=20)
> colnames(pms) <- paste("sample", 1:20, sep="")
> pns <- rep(letters[1:10], each=5)
> res <- basicPLM(pms, pns, TRUE, TRUE)
Background correcting... OK
Normalizing... OK
Summarizing... OK
> res[['Estimates']][1:4, 1:3]
        [,1]       [,2]       [,3]
a -1.6832197 -1.2218607 -1.6286597
b -1.3200136 -1.1889703  0.7343134
c -1.1530794 -0.9915304 -1.6076071
d -0.5020813 -0.6037340 -1.1606413
> res[['StdErrors']][1:4, 1:3]
       [,1]      [,2]      [,3]
a 0.6376668 0.6000350 0.6000350
b 0.6306552 0.6448365 0.6306552
c 0.5905689 0.5811092 0.5747247
d 0.5970498 0.6038205 0.5970498
> res[['Residuals']][1:20, 1:3]
             [,1]        [,2]        [,3]
 [1,] -1.11129479  0.36792088 -0.73892920
 [2,]  0.03964853 -1.57760703  0.89487715
 [3,] -1.13351235  0.39257707 -1.04042400
 [4,]  4.31007113 -1.06855573  1.85958401
 [5,]  0.30753094  1.88566481 -0.97510797
 [6,] -1.52800118  2.53777499  0.81166267
 [7,]  0.71915057 -0.55603416 -0.21972413
 [8,]  0.99900771 -1.69146111 -0.37851000
 [9,]  0.58351856  0.63639690 -1.03308835
[10,] -0.77367565 -0.37961925  0.81965981
[11,]  2.50884925  2.87057727 -0.88145700
[12,] -0.33729753 -1.18545790 -0.71273036
[13,] -1.15831588  1.12361054  3.02612950
[14,] -1.47990921  0.06161884 -0.15450957
[15,]  2.25576406 -1.70221545  0.26116984
[16,] -1.47775254 -1.07763899 -0.96898244
[17,] -0.50183206 -2.10325639  0.07391233
[18,]  1.43692947  1.78257464 -0.08014060
[19,]  1.03576652 -0.47754812  1.43889654
[20,] -0.49311139  1.64241857 -0.46368583
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>


Created & Maintained by Osamu Ogasawara (osamu.ogasawara@gmail.com) and IMSBIO