DEqMS 1.19.0

- 1 Overview of DEqMS
- 2 Quick start
- 2.1 Differential protein expression analysis with DEqMS using a protein table
- 2.1.1 Download and Read the input protein table
- 2.1.2 Extract quant data columns for DEqMS
- 2.1.3 Make design table.
- 2.1.4 Make contrasts
- 2.1.5 DEqMS analysis
- 2.1.6 Visualize the fit curve - variance dependence on quantified PSM
- 2.1.7 Extract the results as a data frame and save it
- 2.1.8 Make volcanoplot

- 2.2 DEqMS analysis using MaxQuant outputs (label-free data)
- 2.3 DEqMS analysis using a PSM table (isobaric labelled data)

- 2.1 Differential protein expression analysis with DEqMS using a protein table
- 3 Comparing DEqMS to other methods

`DEqMS`

builds on top of `Limma`

, a widely-used R package for microarray data
analysis (Smyth G. et al 2004), and improves it with proteomics data specific
properties, accounting for variance dependence on the number of quantified
peptides or PSMs for statistical testing of differential protein expression.

Limma assumes a common prior variance for all proteinss, the function
`spectraCounteBayes`

in DEqMS package estimate prior variance
for proteins quantified by different number of PSMs.

A documentation of all R functions available in DEqMS is detailed in the PDF reference manual on the DEqMS Bioconductor page.

#Load the package

`library(DEqMS)`

`## Loading required package: ggplot2`

`## Loading required package: matrixStats`

`## Loading required package: limma`

As an example, we analyzed a protemoics dataset (TMT10plex labelled) in which A431 cells (human epidermoid carcinoma cell line) were treated with three different miRNA mimics (Zhou Y. Et al Oncogene 2017). The raw MS data was searched with MS-GF+ (Kim et al Nat Communications 2016) and post processed with Percolator (Kall L. et al Nat Method 2007). A tabular text output of protein table filtered at 1% protein level FDR is used.

```
url <- "https://ftp.ebi.ac.uk/pride-archive/2016/06/PXD004163/Yan_miR_Protein_table.flatprottable.txt"
download.file(url, destfile = "./miR_Proteintable.txt",method = "auto")
df.prot = read.table("miR_Proteintable.txt",stringsAsFactors = FALSE,
header = TRUE, quote = "", comment.char = "",sep = "\t")
```

```
# filter at 1% protein FDR and extract TMT quantifications
TMT_columns = seq(15,33,2)
dat = df.prot[df.prot$miR.FASP_q.value<0.01,TMT_columns]
rownames(dat) = df.prot[df.prot$miR.FASP_q.value<0.01,]$Protein.accession
# The protein dataframe is a typical protein expression matrix structure
# Samples are in columns, proteins are in rows
# use unique protein IDs for rownames
# to view the whole data frame, use the command View(dat)
```

If the protein table is relative abundance (ratios) or intensity values, Log2 transform the data. Systematic effects and variance components are usually assumed to be additive on log scale (Oberg AL. et al JPR 2008; Hill EG. et al JPR 2008).

```
dat.log = log2(dat)
#remove rows with NAs
dat.log = na.omit(dat.log)
```

Use boxplot to check if the samples have medians centered. if not, do median centering.

`boxplot(dat.log,las=2,main="TMT10plex data PXD004163")`

```
# Here the data is already median centered, we skip the following step.
# dat.log = equalMedianNormalization(dat.log)
```

A design table is used to tell how samples are arranged in different groups/classes.

```
# if there is only one factor, such as treatment. You can define a vector with
# the treatment group in the same order as samples in the protein table.
cond = as.factor(c("ctrl","miR191","miR372","miR519","ctrl",
"miR372","miR519","ctrl","miR191","miR372"))
# The function model.matrix is used to generate the design matrix
design = model.matrix(~0+cond) # 0 means no intercept for the linear model
colnames(design) = gsub("cond","",colnames(design))
```

In addition to the design, you need to define the contrast, which tells the model to compare the differences between specific groups. Start with the Limma part.

```
# you can define one or multiple contrasts here
x <- c("miR372-ctrl","miR519-ctrl","miR191-ctrl",
"miR372-miR519","miR372-miR191","miR519-miR191")
contrast = makeContrasts(contrasts=x,levels=design)
fit1 <- lmFit(dat.log, design)
fit2 <- contrasts.fit(fit1,contrasts = contrast)
fit3 <- eBayes(fit2)
```

The above shows Limma part, now we use the function `spectraCounteBayes`

in DEqMS to correct bias of variance estimate based on minimum number of
psms per protein used for quantification.We use the minimum number of PSMs
used for quantification within and across experiments to model the relation
between variance and PSM count.(See original paper)

```
# assign a extra variable `count` to fit3 object, telling how many PSMs are
# quantifed for each protein
library(matrixStats)
count_columns = seq(16,34,2)
psm.count.table = data.frame(count = rowMins(
as.matrix(df.prot[,count_columns])), row.names = df.prot$Protein.accession)
fit3$count = psm.count.table[rownames(fit3$coefficients),"count"]
fit4 = spectraCounteBayes(fit3)
```

Outputs of `spectraCounteBayes`

:

object is augmented form of “fit” object from `eBayes`

in Limma, with the
additions being:

`sca.t`

- Spectra Count Adjusted posterior t-value

`sca.p`

- Spectra Count Adjusted posterior p-value

`sca.dfprior`

- DEqMS estimated prior degrees of freedom

`sca.priorvar`

- DEqMS estimated prior variance

`sca.postvar`

- DEqMS estimated posterior variance

`model`

- fitted model

```
# n=30 limits the boxplot to show only proteins quantified by <= 30 PSMs.
VarianceBoxplot(fit4,n=30,main="TMT10plex dataset PXD004163",xlab="PSM count")
```

`VarianceScatterplot(fit4,main="TMT10plex dataset PXD004163")`