qualityplots_oligo: Make different plots for microarray quality assessment.

In machalen/QualityGraphsOligo: Make quality graphs for microarray data based on objects obtained with oligo normalization.

Description

This function can be used as a shortcut to make all quality plots included in QualityGraphsOligo package with a single call.

Usage

qualityplots_oligo(ds, picname, estimates_m = NULL, est_noctrls, labels = NULL, Pset = NULL, conditions = NULL, colors = NULL, groupn = NULL, all = TRUE, Density = TRUE, Boxplots = TRUE, Clusters = TRUE, PCA = TRUE, estimates = FALSE, noctrls = TRUE, resDir = NULL)

Arguments

ds	GeneFeatureSet object obtained with: RawData <- read.celfiles(celFiles.s, sampleNames= celnames.s)
picname	Name of output files.
estimates_m	Numeric matrix where columns are samples and rows genes. estimates_m corresponds to the expression matrix with controls.
est_noctrls	Numeric matrix where columns are samples and rows genes. est_noctrls corresponds to the expression matrix without controls.
labels	Optional. Vector with sample names. Samples are in the same order as in the ds object. Default get sample names from the ds object. It only works for boxplots and density plots
Pset	PLM object obtained from fitProbeLevelModel: Pset <- fitProbeLevelModel(RawData)
conditions	Optional, vector with sample condition in the same order as samples appear in the ds object.
colors	Optional, vector with the colous corresponding to each condition. Same order as samples in ds object in the case of boxplots, and same order as columns in the case of clusters and PCA. If the colors are set, clusters, PCA and boxplots have to be generated separately, as samples have a different order.
groupn	Optional. Number of samples to include in each density plot. If the number is lower than total number of samples, more than one plot is going to be shown. Default is groupn = all samples.
all	Optional. If TRUE, an additional plot with all density plots made with a groupn is made.
Density	Call densityplotAll_oligo function to make density plots. Default is TRUE.
Boxplots	Call boxplotAll_oligo function to make boxplots. Default is TRUE.
Clusters	Call clusterdend function to make cluster dendrograms. Default is TRUE.
PCA	Call makePCA function to make PCA plots. Default is TRUE.
estimates	If TRUE, clusters for estimates_m are made. Default is FALSE.
noctrls	If TRUE, clusters for est_noctrls are made. Default is TRUE.
resDir	Output results directory. Default is ResultsDir.

Details

This function is a shortcut to call functions: makePCA, clusterdend, boxplotAll_oligo and densityplotAll_oligo. It makes all the plots for quality assessment in microarray data. Density plots show the distribution of log2 raw intensities to check possible technical biases. Boxplots show the log2 raw intensities in boxplots to check possible technical biases. BoxplotsRMA show normalized log2 intensities with quantile normalization and Robust Multiarray average (RMA). It is used to check if normalization is made properly. Boxplot NUSE plot with normalized unscaled standard errors (NUSE). The standard error from the probe-level model are visualized as boxplots. The deviant arrays can be identified by not being centered at 1, or being more spread out than the other arrays.Boxplot RLE plot with relative log expression values (RLE). Assuming that most probesets on arrays are not changing, most of the RLE values are close 0. When examining the boxplot, the deviant arrays can be identified by not being centered at 0, or being more spread out than the other arrays.Clusters show the sample distribution using different methods. Samples are expected to group together based on their similarity. PCA show the sample distribution using a principal component analysis (PCA) in a 3D plot.

Value

Files are created in the resDir directory with the picname and .png or .pdf extensions.

Author(s)

Magdalena Arnal Segura <[e-mail]>.

machalen/QualityGraphsOligo documentation built on May 28, 2019, 2:32 a.m.