DESCRIPTION

Package: voomDDA

Title: Voom Based Diagonal Discriminant Analysis for RNA-Seq Data Classification

Version: 1.0.0

Author: Gokmen Zararsiz, Dincer Goksuluk, Selcuk Korkmaz

Description: Some functions for sample classification in RNA-Seq data

Maintainer: Gokmen Zararsiz <gokmenzararsiz@erciyes.edu.tr>

Depends: R (>= 3.1.0), pamr, limma

Suggests: MLSeq

License: GPL-2

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weighted.stats{voomDDA}

Calculation of Weighted Statistics

Description

This function calculates several weighted statistics that is necessary for voomDDA and voomNSC classifiers.

Usage

weighted.stats(x = x, w = w, conditions = conditions)

Arguments

x: a data matrix with n columns and p rows, whose weighted statistics is to be computed. Rows indicate genes, where columns indicate samples.

w: a weight matrix with n columns and p rows. 

conditions: a numeric or factor vector containing the outcome for each sample representing experimental conditions.

Details

voom function in limma package takes RNA-Seq read counts as input, applies voom transformation and produces both expression values and weights in a pxn data matrix. weighted.stats calculates a number of statistics that is necessary for other functions in this package. These functions can be used to classify RNA-Seq data using voom precision weights.

Value

n: number of samples

p: number of genes

nclass: number of class

se.scale: scale factors for the within class standard errors defined as sqrt(1/n.class-1/n)

weightedMean: overall weighted means for each gene

weightedMean.C: weighted means calculated for each gene in each class

WeightedSD.C: weighted standard deviations calculated for each gene in each class

weightedSD.pooled: overall pooled and weighted standard deviations for each gene

delta: a matrix containing the relative differences, also called as t scores, in gene expression for each group

Authors

Gokmen Zararsiz (gokmenzararsiz@erciyes.edu.tr), Dincer Goksuluk (dincer.goksuluk@hacettepe.edu.tr), Selcuk Korkmaz (selcuk.korkmaz@hacettepe.edu.tr)

References

Law CW, Chen Y, Shi W, et al. (2014). voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology, doi:10.1186/gb-2014-15-2-r29

Tibshirani R, Hastie T, Narasimhan B, et al. (2002). Diagnosis of multiple cancer types by shrunken centroids of gene expression PNAS 99: 6567-72.

Examples

# weighted statistics will be calculated for Fisher's iris data

x = as.matrix(iris[,1:4])

# generate some data for weight matrix

w = matrix(rnorm(150*4) + 1, ncol=4)

# iris data outcome

conditions = iris[,5]

# calculate weighted statistics

weighted.stats(t(x), t(w), conditions)

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voomDDA.train{voomDDA}

Train a Voom Based Diagonal Discriminant Analysis for RNA-Seq Classification

Description

A function that applies voom based diagonal discriminant analysis to train and classify RNA-Seq data

Usage

voomDDA.train(counts, conditions, normalization = "TMM", pooled.var = TRUE)

Arguments

counts: numeric pxn matrix or data frame of read counts. Rows correspond to p genes (transcripts, exons, etc.), while columns correspont to biological samples.

conditions: factor or numeric vector for class labels representing experimental conditions

normalization: normalization of count data to adjust sample spesific differences before classification. tmm: Trimmed mean of M values. quantile: quantile normalization. none: Normalization is not applied. 

pooled.var: logical flag. If true (by default), the covariance matrices are assumed to be constant across classes and voomDLDA linear classifier is used. Otherwise (pool= FALSE), the covariance matrices may vary across classes and voomDQDA quadratic classifier is used.

Details

voomDDA is an RNA-Seq classifier which takes read counts as input, applies voom transformation and incorporates voom precision weights and log-cpm values in an extension of diagonal discriminant analysis for prediction.

Value

classNames: names of each experimental condition

nclass: number of class

normalization: used normalization model in the training process

PooledVar: TRUE - voom based diagonal linear discriminant analysis (voomDLDA). FALSE - voom based diagonal quadratic discriminant analysis (voomDQDA)

weightedStats: returns the same as weightedStats()

Authors

Gokmen Zararsiz (gokmenzararsiz@erciyes.edu.tr), Dincer Goksuluk (dincer.goksuluk@hacettepe.edu.tr), Selcuk Korkmaz (selcuk.korkmaz@hacettepe.edu.tr)

References

Dudoit S, Fridlyand J, Speed TP (2000). Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data. (Statistics, UC Berkeley, June 2000, Tech Report \#576)

Law CW, Chen Y, Shi W, et al. (2014). voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology, doi:10.1186/gb-2014-15-2-r29

Examples

#use cervical data in MLSeq package

library(MLSeq)

data(cervical)

#create cervical conditions, train and test sets

set.seed(12345)

ratio=0.7

conditions = factor(rep(c("N","T"), c(29,29)))

ind = sample(58, ceiling(58*ratio), FALSE)

train = cervical[,ind]

test = cervical[,-ind]

tr.cond = conditions[ind]

ts.cond = conditions[-ind]

#train a voomDLDA classifier using quantile normalization

fit = voomDDA.train(counts = train, conditions = tr.cond, normalization = "quan", TRUE)

#train a voomDQDA classifier using TMM normalization

fit2 = voomDDA.train(counts = train, conditions = tr.cond, normalization = "TMM", FALSE)

####

predict.voomDDA{voomDDA}

Extract Predictions From voomDDA.train() Objects

Description

This function predicts the class labels of test data for a given voomDDA model

Usage

predict.voomDDA(object, newdata)

Arguments

object: a fitted training model object after voomDDA.train()

newdata: new test read count data to be predicted

Details

predict.voomDDA() function predicts the class labels of a test data based on the voomDDA training model.

Value

a vector of predicted classes of test data

Authors

Gokmen Zararsiz (gokmenzararsiz@erciyes.edu.tr), Dincer Goksuluk (dincer.goksuluk@hacettepe.edu.tr), Selcuk Korkmaz (selcuk.korkmaz@hacettepe.edu.tr)

References

Dudoit S, Fridlyand J, Speed TP (2000). Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data. (Statistics, UC Berkeley, June 2000, Tech Report \#576)

Law CW, Chen Y, Shi W, et al. (2014). voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology, doi:10.1186/gb-2014-15-2-r29

Examples

#use cervical data in MLSeq package

library(MLSeq)

data(cervical)

#create cervical conditions, train and test sets

set.seed(12345)

ratio=0.7

conditions = factor(rep(c("N","T"), c(29,29)))

ind = sample(58, ceiling(58*ratio), FALSE)

train = cervical[,ind]

test = cervical[,-ind]

tr.cond = conditions[ind]

ts.cond = conditions[-ind]

#train a voomDLDA classifier using quantile normalization

fit = voomDDA.train(counts = train, conditions = tr.cond, normalization = "quan", TRUE)

#train a voomDQDA classifier using TMM normalization

fit2 = voomDDA.train(counts = train, conditions = tr.cond, normalization = "TMM", FALSE)

#predict the labels of test data classes and create a confusion matrix

pred = predict.voomDDA(fit, test)

table(ts.cond, pred)

pred2 = predict.voomDDA(fit2, test)

table(ts.cond, pred2)

####

voomNSC.train{voomDDA}

Train a Voom Based Nearest Shrunken Centroids Classifier for RNA-Seq Classification

Description

A function that applies voom based nearest shrunken centroids classifier to train and classify RNA-Seq data

Usage

voomNSC.train(counts, conditions, n.threshold = 30, offset.percent = 50, remove.zeros = TRUE, normalization = "TMM")

Arguments

counts: numeric pxn matrix or data frame of read counts. Rows correspond to p genes (transcripts, exons, etc.), while columns correspont to biological samples.

conditions: factor or numeric vector for class labels representing experimental conditions

n.threshold : number of threshold values desired (default 30)

offset.percent: Fudge factor added to the denominator of each t-statistic, expressed as a percentile of the gene standard deviation values. This is a small positive quantity to penalize genes with expression values near zero, which can result in very large ratios. This factor is expecially impotant for Affy data. Default is the median of the standard 

deviations of each gene

remove.zeros: remove threshold values yielding zero genes? Default TRUE

normalization: normalization of count data to adjust sample spesific differences before classification. tmm: Trimmed mean of M values. quantile: quantile normalization. none: Normalization is not applied. 

Details

voomNSC is an RNA-Seq classifier which takes read counts as input, applies voom transformation and incorporates voom precision weights and log-cpm values in an extension of nearest shrunken centroids classifers for prediction.

Value

call: the calling sequence used

weightedMean: a vector containing the overall weighted unshrunken centroids

weightedMean.C: a matrix containing the weighted unshrunken centroids for each class

delta: a matrix containing the relative differences, also called as t scores, in gene expression for each group

errors: number of training errors for each threshold value

nonzero: number of genes that survived the thresholding for each threshold value

normalization: normalization of count data to adjust sample spesific differences before classification. tmm: Trimmed mean of M values. quantile: quantile normalization. none: Normalization is not applied.

offset: offset.percent used in the training process

opt.threshold: optimal threshold value that gives the minimum training error with the lowest number of genes

prior: prior probabilities used in the training process (proportions of the class frequencies)

prob: an array of predicted class probabilities. of dimension n by nclass by n.threshold. n is the number samples, nclass is the number of classes, n.threshold is the number of thresholds tried

weightedSD.pooled: a vector of pooled and weighted standard deviations for each gene

se.scale: scale factors for the within class standard errors defined as sqrt(1/n.class-1/n)

SelectedGenes: names of genes that survived the thresholding for each threshold value

SelectedGenesIndex: indexes of genes that survived the thresholding for each threshold value

threshold: a vector of the threshold tried in the shrinkage

conditions: conditions

pred.conditions: a matrix containing the predicted class labels for each threshold value 

Authors

Trevor Hastie,Robert Tibshirani, Balasubramanian Narasimhan, and Gilbert Chu originally wrote pamr.train() in CRAN package pamr which was modified for RNA-Seq classification by Gokmen Zararsiz (gokmenzararsiz@erciyes.edu.tr), Dincer Goksuluk (dincer.goksuluk@hacettepe.edu.tr), Selcuk Korkmaz (selcuk.korkmaz@hacettepe.edu.tr)

References

Tibshirani R, Hastie T, Narasimhan B, et al. (2002). Diagnosis of multiple cancer types by shrunken centroids of gene expression PNAS 99: 6567-72.

Dudoit S, Fridlyand J, Speed TP (2000). Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data. (Statistics, UC Berkeley, June 2000, Tech Report \#576)

Law CW, Chen Y, Shi W, et al. (2014). voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology, doi:10.1186/gb-2014-15-2-r29

Examples

#use cervical data in MLSeq package

library(MLSeq)

data(cervical)

#create cervical conditions, train and test sets

set.seed(12345)

ratio=0.7

conditions = factor(rep(c("N","T"), c(29,29)))

ind = sample(58, ceiling(58*ratio), FALSE)

train = cervical[,ind]

test = cervical[,-ind]

tr.cond = conditions[ind]

ts.cond = conditions[-ind]

#train a voomNSC classifier using TMM normalization

fit = voomNSC.train(counts = train, conditions = tr.cond, normalization = "TMM")

####

predict.voomNSC{voomDDA}

Extract Predictions From voomNSC.train() Objects

Description

This function predicts the class labels of test data for a given voomNSC model

Usage

predict.voomNSC(fit, newdata, threshold = fit$opt.threshold, prior = fit$prior)

Arguments

fit: a fitted training model object after voomNSC.train()

newdata: new test read count data to be predicted

threshold: threshold value which will be used in the prediction process. default is fit$opt.threshold, the value that gives the minimum training error with the lowest number of genes

prior: prior probabilities which will be used in the prediction process. default is fit$prior.

Details

predict.voomNSC() function predicts the class labels of a test data based on the voomNSC training model.

Value

a vector of predicted classes of test data

Authors

Trevor Hastie,Robert Tibshirani, Balasubramanian Narasimhan, and Gilbert Chu originally wrote pamr.train() in CRAN package pamr which was modified for RNA-Seq classification by Gokmen Zararsiz (gokmenzararsiz@erciyes.edu.tr), Dincer Goksuluk (dincer.goksuluk@hacettepe.edu.tr), Selcuk Korkmaz (selcuk.korkmaz@hacettepe.edu.tr)

References

Tibshirani R, Hastie T, Narasimhan B, et al. (2002). Diagnosis of multiple cancer types by shrunken centroids of gene expression PNAS 99: 6567-72.

Dudoit S, Fridlyand J, Speed TP (2000). Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data. (Statistics, UC Berkeley, June 2000, Tech Report \#576)

Law CW, Chen Y, Shi W, et al. (2014). voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology, doi:10.1186/gb-2014-15-2-r29

Examples

#use cervical data in MLSeq package

library(MLSeq)

data(cervical)

#create cervical conditions, train and test sets

set.seed(12345)

ratio=0.7

conditions = factor(rep(c("N","T"), c(29,29)))

ind = sample(58, ceiling(58*ratio), FALSE)

train = cervical[,ind]

test = cervical[,-ind]

tr.cond = conditions[ind]

ts.cond = conditions[-ind]

#apply a voomNSC with TMM normalization

fit = voomNSC.train(counts = train, conditions = tr.cond, normalization = "TMM")

#predict the labels of test data classes with the optimum threshold value and create a confusion matrix

pred = predict.voomNSC(fit, test)

table(ts.cond, pred)

#use another threshold value

pred2 = predict.voomNSC(fit, test, 1.34)

table(ts.cond, pred2)

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