Package 'DEqMS'

Title: a tool to perform statistical analysis of differential protein expression for quantitative proteomics data.
Description: DEqMS is developped on top of Limma. However, Limma assumes same prior variance for all genes. In proteomics, the accuracy of protein abundance estimates varies by the number of peptides/PSMs quantified in both label-free and labelled data. Proteins quantification by multiple peptides or PSMs are more accurate. DEqMS package is able to estimate different prior variances for proteins quantified by different number of PSMs/peptides, therefore acchieving better accuracy. The package can be applied to analyze both label-free and labelled proteomics data.
Authors: Yafeng Zhu
Maintainer: Yafeng Zhu <[email protected]>
License: LGPL
Version: 1.23.4
Built: 2024-09-22 05:33:30 UTC
Source: https://github.com/bioc/DEqMS

Help Index

normalize to have equal medians in all samples

Description

This function is to normaliza out the differences of protein medians in different samples

Usage

equalMedianNormalization(dat)

Arguments

dat

an numeric data frame or matrix containing protein relative abundance in log2 scale

Value

a data frame or matrix with normalized protein relative abundance

Author(s)

Yafeng Zhu

Examples

library(ExperimentHub)
eh = ExperimentHub(localHub=TRUE)
query(eh, "DEqMS")
dat.psm = eh[["EH1663"]]

dat.psm.log = dat.psm
dat.psm.log[,3:12] =  log2(dat.psm[,3:12])
# use the 3 ctrl samples as reference channels to calculate log2 ratio
dat.gene = medianSummary(dat.psm.log,group_col = 2,ref_col =c(3,7,10))
dat.gene.nm = equalMedianNormalization(dat.gene)

summarize peptide/PSM intensity into protein level relative abundance estimate by taking the median

Description

This function is to calculate proteins'relative abundance by median method

Usage

medianSummary(dat,group_col=2,ref_col)

Arguments

dat

an data frame with peptide/psm intensities in log2 scale
group_col

the column by which peptides/psm intensity are grouped. Usually the gene/protein id column. Default is 2
ref_col

an integer vector indicating the column(s) used as denominator to calcualte relative petide ratio.

Value

a data frame containing protein relative abundance estimate in log2 scale

Author(s)

Yafeng Zhu

Examples

library(ExperimentHub)
eh = ExperimentHub(localHub=TRUE)
query(eh, "DEqMS")
dat.psm = eh[["EH1663"]]

dat.psm.log = dat.psm
dat.psm.log[,3:12] =  log2(dat.psm[,3:12])
# use the 3 ctrl samples as reference channels to calculate log2 ratio
dat.gene = medianSummary(dat.psm.log,group_col = 2,ref_col =c(3,7,10))

summarize peptide/PSM intensity into protein level relative abundance estimate by median sweeping method

Description

This function is to calculate proteins'relative abundance by median sweeping method

Usage

medianSweeping(dat,group_col=2)

Arguments

dat

an data frame with peptide/PSM intensities in log2 scale
group_col

the column by which peptides/PSM intensity are grouped. Usually the gene/protein id column. Default is 2

Value

a data frame with protein relative abundance estimate in log2 scale

Author(s)

Yafeng Zhu

Examples

library(ExperimentHub)
eh = ExperimentHub(localHub=TRUE)
query(eh, "DEqMS")
dat.psm = eh[["EH1663"]]

dat.psm.log = dat.psm
dat.psm.log[,3:12] =  log2(dat.psm[,3:12])

dat.gene.nm = medianSweeping(dat.psm.log,group_col = 2)

summarize peptide/PSM intensity into protein level relative abundance estimate by Turkey median polish procedure

Description

This function is to calculate proteins'relative abundance by Turkey median polish

Usage

medpolishSummary(dat,group_col=2)

Arguments

dat

an data frame containing peptide/psm intensities in log2 scale
group_col

the column by which peptides/psm intensity are grouped. Usually the gene/protein column. Default is 2

Value

a data frame containing protein relative abundance estimate in log2 scale

Author(s)

Yafeng Zhu

Examples

library(ExperimentHub)
eh = ExperimentHub(localHub=TRUE)
query(eh, "DEqMS")
dat.psm = eh[["EH1663"]]

dat.psm.log = dat.psm
dat.psm.log[,3:12] =  log2(dat.psm[,3:12])

dat.gene = medpolishSummary(dat.psm.log,group_col=2)

output the DEqMS analysis results in a data frame

Description

This function is to generate DEqMS outputs in a data frame.

Usage

outputResult(fit, coef_col=1)

Arguments

fit

an list object produced by spectraCounteBayes function
coef_col

is an integer indicating the column of fit$coefficients for which corresponding t-statistics and p-values are extracted in the output

Value

a data frame object with the last three columns being: sca.t - Peptide or Spectra Count Adjusted posterior t-value sca.P.Value - Adjusted posterior p-value sca.adj - sca.P.Value adjusted by BH method

Author(s)

Yafeng Zhu

Examples

library(ExperimentHub)
eh = ExperimentHub(localHub=TRUE)
query(eh, "DEqMS")
dat.psm = eh[["EH1663"]]

dat.psm.log = dat.psm
dat.psm.log[,3:12] =  log2(dat.psm[,3:12])

dat.gene.nm = medianSweeping(dat.psm.log,group_col = 2)
    
psm.count.table = as.data.frame(table(dat.psm$gene)) # generate PSM count table
rownames(psm.count.table)=psm.count.table$Var1
    
cond = c("ctrl","miR191","miR372","miR519","ctrl",
"miR372","miR519","ctrl","miR191","miR372")

sampleTable <- data.frame(
row.names = colnames(dat.psm)[3:12],
cond = as.factor(cond)
)
    
gene.matrix = as.matrix(dat.gene.nm)
design = model.matrix(~cond,sampleTable)

fit1 <- eBayes(lmFit(gene.matrix,design))
# add PSM count for each gene
fit1$count <- psm.count.table[rownames(fit1$coefficients),2]  

fit2 = spectraCounteBayes(fit1)
    
DEqMS.results = outputResult(fit2, coef_col=3)

plot log2 intensities of all peptides for one gene in different samples

Description

This function is to plot log2 intensities of all peptides for one gene in different samples.

Usage

peptideProfilePlot(dat, col=2, gene)

Arguments

dat

a data frame with peptide/psm log2 intensities
col

an integer indicates the column number where the gene protein id is. default is 2, asumming the gene/protein is in the second column
gene

an character indicates the gene name/id to be plotted

Value

return a ggplot2 object

Author(s)

Yafeng Zhu

Examples

library(ExperimentHub)
eh = ExperimentHub(localHub=TRUE)
query(eh, "DEqMS")
dat.psm = eh[["EH1663"]]

dat.psm.log = dat.psm
dat.psm.log[,3:12] =  log2(dat.psm[,3:12])
    
peptideProfilePlot(dat.psm.log,col=2,gene="TGFBR2")

plot the residuals against the number of quantified peptides/PSMs.

Description

This function is to plot the residuals of fit model on the vertical axis and the peptide or PSM count on the horizontal axis.

Usage

Residualplot(fit, xlab="log2(count)",
ylab="Variance(fitted - observed)", main="")

Arguments

fit

an object returned from spectraCounteBayes function
xlab

the title for x axis
ylab

the title for y axis
main

the title for the figure

Value

return a plot graphic

Author(s)

Yafeng Zhu

Examples

library(ExperimentHub)
eh = ExperimentHub(localHub=TRUE)
query(eh, "DEqMS")
dat.psm = eh[["EH1663"]]

dat.psm.log = dat.psm
dat.psm.log[,3:12] =  log2(dat.psm[,3:12])

dat.gene.nm = medianSweeping(dat.psm.log,group_col = 2)
    
psm.count.table = as.data.frame(table(dat.psm$gene)) # generate PSM count table
rownames(psm.count.table)=psm.count.table$Var1
    
cond = c("ctrl","miR191","miR372","miR519","ctrl",
"miR372","miR519","ctrl","miR191","miR372")

sampleTable <- data.frame(
row.names = colnames(dat.psm)[3:12],
cond = as.factor(cond)
)
    
gene.matrix = as.matrix(dat.gene.nm)
design = model.matrix(~cond,sampleTable)

fit1 <- eBayes(lmFit(gene.matrix,design))
# add PSM count for each gene
fit1$count <- psm.count.table[rownames(fit1$coefficients),2]  

fit2 = spectraCounteBayes(fit1)
    
Residualplot(fit2,xlab="log2(PSM count)",main="TMT data PXD004163")

Peptide/Spectra Count Based Empirical Bayes Statistics for Differential Expression

Description

This function is to calculate peptide/PSM count adjusted t-statistics, p-values.

Usage

spectraCounteBayes(fit, fit.method="loess", coef_col)

Arguments

fit

an list object produced by Limma eBayes function, it should have one additional attribute $count, which stored the peptide or PSM count quantified for the gene in label-free or isobaric labelled data.
fit.method

the method used to fit variance against the number of peptides/PSM count quantified. Two available methods: "loess","nls" and "spline". default "loess"."loess" uses loess and span = 0.75, "nls"" uses a explicit formula y~a+b/x. "spline" uses smooth.spline and "generalized cross-validation" for smoothing parameter computation. For "nls", independent variable x is peptide/PSM count, response y is pooled variance (fit$sigma^2). For "loess" and "spline" method, both x and y are log transformed before applying the two methods. In most of time, "loess" is sufficient. To quickly assess the fit model, use VarianceScatterplot and Residualplot functions.
coef_col

an integer vector indicating the column(s) of fit$coefficients for which the function is to be performed. if not specified, all coefficients are used.

Details

This function adjusts the T-statistics and p-values for quantitative MS proteomics experiment according to the number of peptides/PSMs used for quantification. The method is similar in nature to intensity-based Bayes method (Maureen A. Sartor et al BMC Bioinformatics 2006).

Value

a list object with the following components

count

Peptide or PSM count used for quantification
sca.t

Spectra Count Adjusted posterior t-value
sca.p

Spectra Count Adjusted posterior p-value
sca.dfprior

Spectra Count Adjusted prior degrees of freedom
sca.priorvar

Spectra Count Adjusted prior variance
sca.postvar

Spectra Count Adjusted posterior variance
model

fitted model
fit.method

The method used to fit the model

Author(s)

Yafeng Zhu

Examples

library(ExperimentHub)
eh = ExperimentHub(localHub=TRUE)
query(eh, "DEqMS")
dat.psm = eh[["EH1663"]]

dat.psm.log = dat.psm
dat.psm.log[,3:12] =  log2(dat.psm[,3:12])

dat.gene.nm = medianSweeping(dat.psm.log,group_col = 2)
    
psm.count.table = as.data.frame(table(dat.psm$gene)) # generate PSM count table
rownames(psm.count.table)=psm.count.table$Var1
    
cond = c("ctrl","miR191","miR372","miR519","ctrl",
"miR372","miR519","ctrl","miR191","miR372")

sampleTable <- data.frame(
row.names = colnames(dat.psm)[3:12],
cond = as.factor(cond)
)
    
gene.matrix = as.matrix(dat.gene.nm)
design = model.matrix(~cond,sampleTable)

fit1 <- eBayes(lmFit(gene.matrix,design))
# add PSM count for each gene
fit1$count <- psm.count.table[rownames(fit1$coefficients),2]  

fit2 = spectraCounteBayes(fit1)

generate a boxplot of the variance

Description

This function is to draw a boxplot of the variance of genes quantified by different number of peptides/PSMs. Red curve indicate DEqMS prior variance.

Usage

VarianceBoxplot(fit,n=20, xlab="count", ylab = "log(Variance)", main="")

Arguments

fit

an object returned from spectraCounteBayes function
n

set a number to plot only the genes with count value smaller or equal to n
xlab

the title for x axis
ylab

the title for y axis
main

the title for the figure

Value

return a plot graphic

Author(s)

Yafeng Zhu

Examples

library(ExperimentHub)
eh = ExperimentHub(localHub=TRUE)
query(eh, "DEqMS")
dat.psm = eh[["EH1663"]]

dat.psm.log = dat.psm
dat.psm.log[,3:12] =  log2(dat.psm[,3:12])

dat.gene.nm = medianSweeping(dat.psm.log,group_col = 2)
    
psm.count.table = as.data.frame(table(dat.psm$gene)) # generate PSM count table
rownames(psm.count.table)=psm.count.table$Var1
    
cond = c("ctrl","miR191","miR372","miR519","ctrl",
"miR372","miR519","ctrl","miR191","miR372")

sampleTable <- data.frame(
row.names = colnames(dat.psm)[3:12],
cond = as.factor(cond)
)
    
gene.matrix = as.matrix(dat.gene.nm)
design = model.matrix(~cond,sampleTable)

fit1 <- eBayes(lmFit(gene.matrix,design))
# add PSM count for each gene
fit1$count <- psm.count.table[rownames(fit1$coefficients),2]  

fit2 = spectraCounteBayes(fit1)
    
VarianceBoxplot(fit2,xlab="PSM count",main="TMT data PXD004163")

generate a scatter plot of the variance

Description

This function is to draw a scatter plot of the variance against the number of quantified peptides/PSMs.Red curve indicate DEqMS prior variance.

Usage

VarianceScatterplot(fit, xlab="log2(count)",
ylab = "log(Variance)", main="")

Arguments

fit

an object returned from spectraCounteBayes function
xlab

the title for x axis
ylab

the title for y axis
main

the title for the figure

Value

return a plot graphic

Author(s)

Yafeng Zhu

Examples

library(ExperimentHub)
eh = ExperimentHub(localHub=TRUE)
query(eh, "DEqMS")
dat.psm = eh[["EH1663"]]

dat.psm.log = dat.psm
dat.psm.log[,3:12] =  log2(dat.psm[,3:12])

dat.gene.nm = medianSweeping(dat.psm.log,group_col = 2)
    
psm.count.table = as.data.frame(table(dat.psm$gene)) # generate PSM count table
rownames(psm.count.table)=psm.count.table$Var1
    
cond = c("ctrl","miR191","miR372","miR519","ctrl",
"miR372","miR519","ctrl","miR191","miR372")

sampleTable <- data.frame(
row.names = colnames(dat.psm)[3:12],
cond = as.factor(cond)
)
    
gene.matrix = as.matrix(dat.gene.nm)
design = model.matrix(~cond,sampleTable)

fit1 <- eBayes(lmFit(gene.matrix,design))
# add PSM count for each gene
fit1$count <- psm.count.table[rownames(fit1$coefficients),2]  

fit2 = spectraCounteBayes(fit1)
    
VarianceScatterplot(fit2,xlab="log2(PSM count)",main="TMT data PXD004163")