Proteomics data analysis: mouse Tcell example with RCB designs
5 July 2019
Contents
1 Background
Duguet et al. 2017 compared the proteomes of mouse regulatory T cells (Treg) and conventional T cells (Tconv) in order to discover differentially regulated proteins between these two cell populations. For each biological repeat the proteomes were extracted for both Treg and Tconv cell pools, which were purified by flow cytometry. The data in data/quantification/mouseTcell on the pdaData repository are a subset of the data PXD004436 on PRIDE.
We will use a subset of the data with a randomized complete block (RCB) design, i.e. the dataset consists of four mice for which the proteome of both conventional and regulatory T cells are assessed.
We will analyse the data in this file as a completely randomized design, to illustrate the impact of a wrong analyssi that is not accounting for the blocking factor for mouse.
2 Data
We first import the peptides.txt file. This is the file that contains your peptide-level intensities. For a MaxQuant search [6], this peptides.txt file can be found by default in the “path_to_raw_files/combined/txt/” folder from the MaxQuant output, with “path_to_raw_files” the folder where raw files were saved. In this tutorial, we will use a MaxQuant peptides file from MaxQuant that can be found on the pdaData repository. We will use the MSnbase package to import the data.
We generate the object peptideFile with the path to the peptides.txt file. In this file we will fetch the data from the github repository linked to the pda course: https://statomics.github.io/pda. You can also replace the peptideFile with a string that points to the path of a file on your local hard drive.
With the grepEcols function we find the columns that are containing the expression data of the peptides in the peptides.txt file.
library(MSqRob)
library(MSnbase)
library(tidyverse)
library(limma)
peptidesFile <- "https://raw.githubusercontent.com/statOmics/pda/data/quantification/mouseTcell/peptidesRCB.txt"
proteinGroupsFile <-"https://raw.githubusercontent.com/statOmics/pda/data/quantification/mouseTcell/proteinGroups.txt"
ecols<-grepEcols(peptidesFile, "Intensity ", split = "\t")Next we import the peptide intensities
pepData<-readMSnSet2(peptidesFile,ecol=ecols,fnames="Sequence",sep="\t")
pepData## MSnSet (storageMode: lockedEnvironment)
## assayData: 55814 features, 8 samples
## element names: exprs
## protocolData: none
## phenoData: none
## featureData
## featureNames: AAAAAAAAAAGAAGGR AAAAAAAAAAGDSDSWDADTFSMEDPVRK ...
## YYYDKNIIHK (55814 total)
## fvarLabels: Sequence N.term.cleavage.window ... MS.MS.Count (74
## total)
## fvarMetadata: labelDescription
## experimentData: use 'experimentData(object)'
## Annotation:
## - - - Processing information - - -
## MSnbase version: 2.10.1The pepData object is an MSnSet, a container for the data, features information and experimental annotation. They can be accessed using the accessor functions ‘exprs’ (matrix of intensities, features in rows, samples in columns), ‘fData’ (properties for each feature, peptide or protein, on the rows) and ‘pData’ (properties for the samples on the columns).
We will make use from data wrangling functionalities from the tidyverse package. The %>% operator allows us to pipe the output of one function to the next function.
head(exprs(pepData))## Intensity.Tconv.M12_2 Intensity.Tconv.M12_3
## AAAAAAAAAAGAAGGR 111470000 101350000
## AAAAAAAAAAGDSDSWDADTFSMEDPVRK 13303000 10160000
## AAAAAAAGGAAIAVSTGIETATIQK 0 0
## AAAAAAGAASGIPGPVAQGIK 0 7197400
## AAAAAAGPEMVR 104440000 101950000
## AAAAAAPGGGGGEPR 11910000 12683000
## Intensity.Tconv.M5_inj1
## AAAAAAAAAAGAAGGR 965070
## AAAAAAAAAAGDSDSWDADTFSMEDPVRK 235500
## AAAAAAAGGAAIAVSTGIETATIQK 48225
## AAAAAAGAASGIPGPVAQGIK 266620
## AAAAAAGPEMVR 947110
## AAAAAAPGGGGGEPR 190550
## Intensity.Tconv.M6_inj1 Intensity.Treg.M12_2
## AAAAAAAAAAGAAGGR 0 80606000
## AAAAAAAAAAGDSDSWDADTFSMEDPVRK 6744500 0
## AAAAAAAGGAAIAVSTGIETATIQK 314120 0
## AAAAAAGAASGIPGPVAQGIK 2904800 12602000
## AAAAAAGPEMVR 12747000 44735000
## AAAAAAPGGGGGEPR 2166100 12555000
## Intensity.Treg.M12_3 Intensity.Treg.M5_inj1
## AAAAAAAAAAGAAGGR 120130000 1668800
## AAAAAAAAAAGDSDSWDADTFSMEDPVRK 14499000 437460
## AAAAAAAGGAAIAVSTGIETATIQK 0 173070
## AAAAAAGAASGIPGPVAQGIK 14162000 520550
## AAAAAAGPEMVR 70512000 1604600
## AAAAAAPGGGGGEPR 13873000 361240
## Intensity.Treg.M6_inj1
## AAAAAAAAAAGAAGGR 0
## AAAAAAAAAAGDSDSWDADTFSMEDPVRK 3881000
## AAAAAAAGGAAIAVSTGIETATIQK 317830
## AAAAAAGAASGIPGPVAQGIK 1608500
## AAAAAAGPEMVR 4689900
## AAAAAAPGGGGGEPR 1168900pepData %>% exprs %>% head## Intensity.Tconv.M12_2 Intensity.Tconv.M12_3
## AAAAAAAAAAGAAGGR 111470000 101350000
## AAAAAAAAAAGDSDSWDADTFSMEDPVRK 13303000 10160000
## AAAAAAAGGAAIAVSTGIETATIQK 0 0
## AAAAAAGAASGIPGPVAQGIK 0 7197400
## AAAAAAGPEMVR 104440000 101950000
## AAAAAAPGGGGGEPR 11910000 12683000
## Intensity.Tconv.M5_inj1
## AAAAAAAAAAGAAGGR 965070
## AAAAAAAAAAGDSDSWDADTFSMEDPVRK 235500
## AAAAAAAGGAAIAVSTGIETATIQK 48225
## AAAAAAGAASGIPGPVAQGIK 266620
## AAAAAAGPEMVR 947110
## AAAAAAPGGGGGEPR 190550
## Intensity.Tconv.M6_inj1 Intensity.Treg.M12_2
## AAAAAAAAAAGAAGGR 0 80606000
## AAAAAAAAAAGDSDSWDADTFSMEDPVRK 6744500 0
## AAAAAAAGGAAIAVSTGIETATIQK 314120 0
## AAAAAAGAASGIPGPVAQGIK 2904800 12602000
## AAAAAAGPEMVR 12747000 44735000
## AAAAAAPGGGGGEPR 2166100 12555000
## Intensity.Treg.M12_3 Intensity.Treg.M5_inj1
## AAAAAAAAAAGAAGGR 120130000 1668800
## AAAAAAAAAAGDSDSWDADTFSMEDPVRK 14499000 437460
## AAAAAAAGGAAIAVSTGIETATIQK 0 173070
## AAAAAAGAASGIPGPVAQGIK 14162000 520550
## AAAAAAGPEMVR 70512000 1604600
## AAAAAAPGGGGGEPR 13873000 361240
## Intensity.Treg.M6_inj1
## AAAAAAAAAAGAAGGR 0
## AAAAAAAAAAGDSDSWDADTFSMEDPVRK 3881000
## AAAAAAAGGAAIAVSTGIETATIQK 317830
## AAAAAAGAASGIPGPVAQGIK 1608500
## AAAAAAGPEMVR 4689900
## AAAAAAPGGGGGEPR 1168900pepData %>% sampleNames## [1] "Intensity.Tconv.M12_2" "Intensity.Tconv.M12_3"
## [3] "Intensity.Tconv.M5_inj1" "Intensity.Tconv.M6_inj1"
## [5] "Intensity.Treg.M12_2" "Intensity.Treg.M12_3"
## [7] "Intensity.Treg.M5_inj1" "Intensity.Treg.M6_inj1"The sample names are rather long and contain information on the spike-in concentration and the repeat. We will remove “Intensity.” from the string
sampleNames(pepData) <- pepData %>% sampleNames %>% str_replace(., pattern="Intensity.", replacement="")Next we will add information on the proteins to the feature data. The feature data of the object pepData contains information for each peptide in the experiment. This info on the proteins can be found in the proteingroups.txt file of maxquant. The peptides.txt file contains many data on each feature (peptide). We will only retain specified columns Protein, Sequence, Reverse and Potential.Contaminant, indicating the protein, peptide sequence, if the peptide is a decoy and if it is a contaminant, respectively.
In older and more recent versions of maxquant the names of the fields might vary. We can therefore explore the names we want to select with the shiny app that can be launched from the selectFeatureData from the MSnbase package, i.e. by running the function without an fcol argument that specifies the columns you would like to select.
The code can be found below, but is not executed by this Rmd script (note, that the flag eval=FALSE has been used as option for the R chunk).
pepData<-selectFeatureData(pepData)If we know the columns, we can specify them as a vector in the fcol slot.
pepData<-selectFeatureData(pepData,fcol=c("Proteins","Protein.names","Gene.names", "Potential.contaminant","Reverse","Sequence"))Calculate how many non zero intensities we have per peptide. This will be useful for filtering.
fData(pepData)$nNonZero<- rowSums(exprs(pepData) > 0)Peptides with zero intensities are missing peptides and should be represent with a NA value instead of 0.
exprs(pepData)[exprs(pepData)==0]<-NANext we create the data on the experimental layout. We can do this based on the samplenames. We will add an effect for cell type and also for the animal (mouse ID).
pData(pepData)$cellType <- pepData %>% sampleNames %>% substr(1,4) %>% as.factor
pData(pepData)$mouseID <- factor(paste0("m",rep(1:4,2)))We will also replace the sample names to make them shorter
sampleNames(pepData)<-pepData %>% sampleNames %>% substr(1,4) %>% paste0(.,"_m",rep(1:4,2))2.1 Data exploration
We can inspect the missingness in our data with the plotNA() function provided with MSnbase. 38% of all peptide intensities are missing and for some peptides we don’t even measure a signal in any sample. The missingness is similar across samples. Note, that we plot the peptide data, so the label protein in the plot refers to peptides.
plotNA(pepData)
When we look at density plots of the intensities, we see that most intensities are low and that there is a long tail to the right. It is more useful to explore the data upon log transformation.
plotDensities(exprs(pepData))
nrow(pepData)## [1] 55814There are 55814 peptides before preprocessing.
3 Preprocessing
We will log transform, normalize, filter and summarize the data.
3.1 Log transform the data
pepData <- log(pepData, base = 2)
plotDensities(exprs(pepData))
When we look at the densities we see small shifts in location and shape of the density distributions. So normalisation will be required. But, we will first perform filtering.
3.2 Filtering
3.2.1 Handling overlapping protein groups
In our approach a peptide can map to multiple proteins, as long as there is none of these proteins present in a smaller subgroup.
pepData<-pepData[fData(pepData)$Proteins %in% smallestUniqueGroups(fData(pepData)$Proteins),]3.2.2 Remove reverse sequences (decoys) and contaminants
We now remove the contaminants, peptides that map to decoy sequences and proteins, which were only identified by peptides with modifications.
pepData <- pepData[fData(pepData)$Reverse!="+",]
pepData <- pepData[fData(pepData)$Potential.contaminant!="+",]3.2.3 Remove peptides of proteins that were only identified with modified peptides
Proteins for which all peptides are carrying modifications (PTMs) can be considered as unreliable. We will filter out these proteins. This information is included in the Only.identified.by.site column of proteinGroups.txt file of maxquant. The variable is containing a “+” character if the protein is only identified by modified peptides and an empty string “” if this is not the case. Sometimes an NA value is also present. We will replace these by the empty character string. The information of the peptides.txt file can be linked to the proteinGroups.txt file by using the Proteins column from the peptides.txt file and the Protein.IDs column in the proteinGroups.txt file. If NAs are included in the
proteinGroups<-read.table(proteinGroupsFile, sep = "\t", header = TRUE, quote = "", comment.char = "")
only_site <- proteinGroups$Only.identified.by.site
only_site[is.na(only_site)] <- ""
proteinsOnlyIdentifiedWithPTMs <- proteinGroups$Protein.IDs[only_site=="+"]
pepData<-pepData[!(fData(pepData)$Proteins %in% proteinsOnlyIdentifiedWithPTMs),]We will now remove the proteinGroups object from the R session to free memory.
rm(proteinGroups,only_site)
gc()## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells 4158132 222.1 8603538 459.5 NA 5452430 291.2
## Vcells 8237828 62.9 24434645 186.5 16384 24430794 186.43.2.4 Drop peptides that were only identified in one sample
We want to keep peptide that were at least observed twice.
pepData<-pepData[fData(pepData)$nNonZero>=2,]
nrow(pepData)## [1] 44396We keep 44396 peptides upon filtering.
3.3 Quantile normalize the data
pepData <- normalise(pepData, "quantiles")3.4 Explore quantile normalized data
Upon normalisation the density curves for all samples coincide.
plotDensities(exprs(pepData))
We can visualize our data using a Multi Dimensional Scaling plot, eg. as provided by the limma package.
plotMDS(exprs(pepData),col=as.double(pData(pepData)$cellType))
The first axis in the plot is showing the leading log fold changes (differences on the log scale) between the samples. We notice that the leading differences (log FC) in the peptide data seems to be driven by technical variability. Indeed the samples do not seem to be clearly separated according to the spike in condition. Because there are missing values for the peptide level data we use the na.rm=TRUE argument to summarize the data based on the observed peptides for every protein.
3.5 Summarization to protein level
protData<-combineFeatures(pepData,fcol="Proteins",method="robust",na.rm=TRUE)## Your data contains missing values. Please read the relevant section
## in the combineFeatures manual page for details the effects of
## missing values on data aggregation.If you summarize using robust summarization please refer to Sticker et al. (2019) https://www.biorxiv.org/content/10.1101/668863v1
We filter proteins for which we have at least two protein expression values in both conditions:
protData<-protData[
protData %>% exprs %>% is.na %>% `!` %>% rowSums>4,]plotMDS(exprs(protData),col=as.double(pData(pepData)$cellType))
4 Data Analysis
4.1 Estimation
MSqRob is currently working with a format where we have one dataframe for each protein. This will be changed in the next release. Therefore we first have to reorganise the data.
Next the models are fitted. This is done using the fit.model function. We only have to model the data using the factor condition from the pheno data of the protein level MSnSet. The name of the factor variable is specified in the fixed argument (if multiple predictors have to be incorporated in the model, a vector of variable names has to be provided in this argument.). The argument shrinkage is used to specify if ridge regression has to be adopted. For the sake of speed we do not do this in the tutorial. The shrinkage has to be specified for each variable in the fixed effects. We also have to indicate this for the intercept (which we never shrink). So we specify it at c(0,0) to indicate that the intercept (first 0) and the parameters for the factor condition (second 0) are not penalized. We set the robust_var function equal to FALSE, this functionality will be removed from the package in the next release.
We analysis the data as a completely randomized design and only include cellType in the model.
protMSqRob <- MSnSet2protdata(protData, "Proteins",annotations=c("Protein.names","Gene.names"))
models <- fit.model(protdata=protMSqRob, response="quant_value", fixed=c("cellType"),shrinkage.fixed=c(0,0),robust_var=FALSE)4.2 Inference
Many biologists have problems with the reference coding. In MSqRob we have opted to formulate contrasts using all levels of a factor. Internally, the contrasts are than recasted according to the factor level that is the reference class.
L <- makeContrast("cellTypeTreg - cellTypeTcon", levels=c("cellTypeTreg","cellTypeTcon"))
tests <- test.contrast_adjust(models, L)
nSig <- sum(tests$signif,na.rm=TRUE)
head(tests,nSig)## Protein.names
## P24452 Macrophage-capping protein
## P21550 Beta-enolase
## P07356 Annexin A2
## Q9DC16 Endoplasmic reticulum-Golgi intermediate compartment protein 1
## Q61503 5-nucleotidase
## Q3UW53 Protein Niban
## Q80SU7 Interferon-induced very large GTPase 1
## Q60611 DNA-binding protein SATB1
## Q61205 Platelet-activating factor acetylhydrolase IB subunit gamma
## Q9QXY6 EH domain-containing protein 3
## O09131 Glutathione S-transferase omega-1
## P54071 Isocitrate dehydrogenase [NADP], mitochondrial
## Q7TPR4 Alpha-actinin-1
## Q3UN02 Lysocardiolipin acyltransferase 1
## Q04447 Creatine kinase B-type
## Q03267 DNA-binding protein Ikaros
## P13020 Gelsolin
## P09055 Integrin beta-1
## Q9CQ62 2,4-dienoyl-CoA reductase, mitochondrial
## Q99JB6 Forkhead box protein P3
## P57016 Ladinin-1
## P81183 Zinc finger protein Helios
## P08207 Protein S100-A10
## Q8BGW0 Protein THEMIS
## P00329 Alcohol dehydrogenase 1
## Q8CFB4 Guanylate-binding protein 5
## Q7TMB8 Cytoplasmic FMR1-interacting protein 1
## Q05186 Reticulocalbin-1
## Q60710 Deoxynucleoside triphosphate triphosphohydrolase SAMHD1
## P49718 DNA replication licensing factor MCM5
## P07742 Ribonucleoside-diphosphate reductase large subunit
## O70400 PDZ and LIM domain protein 1
## P70271 PDZ and LIM domain protein 4
## Q61107 Guanylate-binding protein 4
## O88508 DNA (cytosine-5)-methyltransferase 3A
## Q00417 Transcription factor 7
## Q9QYB5 Gamma-adducin
## Q8VCT3 Aminopeptidase B
## P70302 Stromal interaction molecule 1
## Q9WTK5 Nuclear factor NF-kappa-B p100 subunit;Nuclear factor NF-kappa-B p52 subunit
## P70677 Caspase-3;Caspase-3 subunit p17;Caspase-3 subunit p12
## Q8BFP9 [Pyruvate dehydrogenase (acetyl-transferring)] kinase isozyme 1, mitochondrial
## O70370 Cathepsin S
## P48758 Carbonyl reductase [NADPH] 1
## Q8VCW8 Acyl-CoA synthetase family member 2, mitochondrial
## O70293 G protein-coupled receptor kinase 6
## Q9Z0F6 Cell cycle checkpoint control protein RAD9A
## Q9CYL5 Golgi-associated plant pathogenesis-related protein 1
## Q9WUZ9 Ectonucleoside triphosphate diphosphohydrolase 5
## P54310 Hormone-sensitive lipase
## P10630 Eukaryotic initiation factor 4A-II
## P42230 Signal transducer and activator of transcription 5A
## P61028 Ras-related protein Rab-8B
## Q8K157 Aldose 1-epimerase
## O88673 Diacylglycerol kinase alpha
## Q6PHZ2 Calcium/calmodulin-dependent protein kinase type II subunit delta
## Q9R1Q7 Proteolipid protein 2
## P47856 Glutamine--fructose-6-phosphate aminotransferase [isomerizing] 1
## Q3UGR5 Haloacid dehalogenase-like hydrolase domain-containing protein 2
## P97311 DNA replication licensing factor MCM6
## P25799 Nuclear factor NF-kappa-B p105 subunit;Nuclear factor NF-kappa-B p50 subunit
## Q68FL4 Putative adenosylhomocysteinase 3
## P29416 Beta-hexosaminidase subunit alpha
## P37913 DNA ligase 1
## Q9DBX6 Cytochrome P450 2S1
## Q8C0P5 Coronin-2A
## Q9D8S4 Oligoribonuclease, mitochondrial
## Q3UDE2 Tubulin--tyrosine ligase-like protein 12
## Q99MN9 Propionyl-CoA carboxylase beta chain, mitochondrial
## Gene.names estimate se df Tval pval
## P24452 Capg 2.8361630 0.1481032 8.738754 19.149907 1.937725e-08
## P21550 Eno3 2.6045120 0.1872245 8.738754 13.911171 2.915818e-07
## P07356 Anxa2 2.2241620 0.1849286 8.738754 12.027137 9.819877e-07
## Q9DC16 Ergic1 2.1805381 0.2002555 8.738754 10.888779 2.228514e-06
## Q61503 Nt5e 2.5718806 0.2700720 8.738754 9.522944 6.617576e-06
## Q3UW53 Fam129a 3.0426788 0.3267307 8.738754 9.312497 7.917717e-06
## Q80SU7 Gvin1 1.5269534 0.1692601 8.738754 9.021343 1.020549e-05
## Q60611 Satb1 -1.8750336 0.2088208 8.738754 -8.979151 1.059379e-05
## Q61205 Pafah1b3 1.2694962 0.1422006 8.738754 8.927504 1.109148e-05
## Q9QXY6 Ehd3 -1.2786926 0.1448946 8.738754 -8.824985 1.215762e-05
## O09131 Gsto1 3.0611645 0.3214327 7.738754 9.523502 1.527546e-05
## P54071 Idh2 -1.3263895 0.1583619 8.738754 -8.375683 1.837007e-05
## Q7TPR4 Actn1 -1.6049906 0.1922279 8.738754 -8.349414 1.882911e-05
## Q3UN02 Lclat1 2.1579100 0.2554461 7.738754 8.447615 3.589713e-05
## Q04447 Ckb -1.6112296 0.2181731 8.738754 -7.385097 4.875052e-05
## Q03267 Ikzf1 -1.0968334 0.1513320 8.738754 -7.247863 5.624519e-05
## P13020 Gsn -1.6477614 0.2299394 8.738754 -7.166069 6.130837e-05
## P09055 Itgb1 1.4669577 0.1876525 7.738754 7.817418 6.180708e-05
## Q9CQ62 Decr1 1.4413715 0.2048099 8.738754 7.037607 7.029981e-05
## Q99JB6 Foxp3 2.5400776 0.2584505 5.738754 9.828103 8.371882e-05
## P57016 Lad1 2.4836822 0.3393424 7.738754 7.319105 9.742648e-05
## P81183 Ikzf2 2.5985811 0.2774924 5.738754 9.364512 1.089134e-04
## P08207 S100a10 2.6645861 0.4031672 8.738754 6.609134 1.124747e-04
## Q8BGW0 Themis -2.0237546 0.3070787 8.738754 -6.590344 1.148729e-04
## P00329 Adh1 -1.4440678 0.2214092 8.738754 -6.522166 1.240567e-04
## Q8CFB4 Gbp5 1.5306967 0.2376108 8.738754 6.442034 1.358909e-04
## Q7TMB8 Cyfip1 2.3860884 0.3087404 6.738754 7.728461 1.380601e-04
## Q05186 Rcn1 1.7849845 0.2611635 7.738754 6.834740 1.552554e-04
## Q60710 Samhd1 1.0397824 0.1660315 8.738754 6.262561 1.671310e-04
## P49718 Mcm5 0.9113382 0.1482407 8.738754 6.147691 1.912018e-04
## P07742 Rrm1 1.1504309 0.1877895 8.738754 6.126174 1.961186e-04
## O70400 Pdlim1 -1.2622753 0.2082099 8.738754 -6.062514 2.114913e-04
## P70271 Pdlim4 -3.1346604 0.4821311 7.738754 -6.501677 2.170376e-04
## Q61107 Gbp4 1.9450820 0.3220050 8.738754 6.040534 2.171007e-04
## O88508 Dnmt3a 1.1899880 0.1993696 8.738754 5.968753 2.365794e-04
## Q00417 Tcf7 -1.5831026 0.2688529 8.738754 -5.888360 2.606851e-04
## Q9QYB5 Add3 -0.9784364 0.1674141 8.738754 -5.844409 2.749858e-04
## Q8VCT3 Rnpep 0.8356827 0.1436498 8.738754 5.817499 2.841620e-04
## P70302 Stim1 -0.9272306 0.1605023 8.738754 -5.777055 2.985867e-04
## Q9WTK5 Nfkb2 1.0016477 0.1750723 8.738754 5.721338 3.197796e-04
## P70677 Casp3 1.4794873 0.2598880 8.738754 5.692789 3.312689e-04
## Q8BFP9 Pdk1 -1.5355360 0.2708411 8.738754 -5.669509 3.409705e-04
## O70370 Ctss 1.0142563 0.1804486 8.738754 5.620751 3.623059e-04
## P48758 Cbr1 -0.9470080 0.1689381 8.738754 -5.605651 3.692044e-04
## Q8VCW8 Acsf2 -1.0588995 0.1908312 8.738754 -5.548881 3.964466e-04
## O70293 Grk6 -0.8791346 0.1585372 8.738754 -5.545288 3.982427e-04
## Q9Z0F6 Rad9a -2.1001814 0.3253428 6.738754 -6.455288 4.083312e-04
## Q9CYL5 Glipr2 1.1974930 0.2210053 8.738754 5.418392 4.677162e-04
## Q9WUZ9 Entpd5 -1.1587764 0.2006132 7.738754 -5.776172 4.709142e-04
## P54310 Lipe 1.1598976 0.1847450 6.738754 6.278371 4.808805e-04
## P10630 Eif4a2 -0.9820406 0.1833182 8.738754 -5.357027 5.059427e-04
## P42230 Stat5a 0.9179775 0.1713736 8.738754 5.356588 5.062283e-04
## P61028 Rab8b 0.9839867 0.1838247 8.738754 5.352853 5.086631e-04
## Q8K157 Galm 1.3934667 0.2604875 8.738754 5.349457 5.108888e-04
## O88673 Dgka -0.8249853 0.1542696 8.738754 -5.347684 5.120542e-04
## Q6PHZ2 Camk2d 0.8123895 0.1548639 8.738754 5.245827 5.841353e-04
## Q9R1Q7 Plp2 1.6357893 0.3149087 8.738754 5.194487 6.245749e-04
## P47856 Gfpt1 0.6693069 0.1292158 8.738754 5.179759 6.367286e-04
## Q3UGR5 Hdhd2 -0.8213089 0.1585940 8.738754 -5.178689 6.376215e-04
## P97311 Mcm6 0.7436700 0.1444667 8.738754 5.147692 6.640853e-04
## P25799 Nfkb1 0.6876488 0.1348708 8.738754 5.098575 7.084850e-04
## Q68FL4 Ahcyl2 0.9590399 0.1887357 8.738754 5.081391 7.247690e-04
## P29416 Hexa -0.8075849 0.1595596 8.738754 -5.061338 7.442857e-04
## P37913 Lig1 1.4654814 0.2905106 8.738754 5.044503 7.611093e-04
## Q9DBX6 Cyp2s1 -1.8538148 0.3216508 6.738754 -5.763440 7.897876e-04
## Q8C0P5 Coro2a 3.1046432 0.5402204 6.738754 5.746994 8.028159e-04
## Q9D8S4 Rexo2 0.8160401 0.1638234 8.738754 4.981219 8.281250e-04
## Q3UDE2 Ttll12 -0.8302515 0.1675411 8.738754 -4.955509 8.571497e-04
## Q99MN9 Pccb -0.7381137 0.1496424 8.738754 -4.932518 8.840369e-04
## qval signif
## P24452 7.485433e-05 TRUE
## P21550 5.631903e-04 TRUE
## P07356 1.264473e-03 TRUE
## Q9DC16 2.152188e-03 TRUE
## Q61503 4.696490e-03 TRUE
## Q3UW53 4.696490e-03 TRUE
## Q80SU7 4.696490e-03 TRUE
## Q60611 4.696490e-03 TRUE
## Q61205 4.696490e-03 TRUE
## Q9QXY6 4.696490e-03 TRUE
## O09131 5.364465e-03 TRUE
## P54071 5.595142e-03 TRUE
## Q7TPR4 5.595142e-03 TRUE
## Q3UN02 9.905045e-03 TRUE
## Q04447 1.255489e-02 TRUE
## Q03267 1.326449e-02 TRUE
## P13020 1.326449e-02 TRUE
## P09055 1.326449e-02 TRUE
## Q9CQ62 1.429306e-02 TRUE
## Q99JB6 1.617029e-02 TRUE
## P57016 1.792183e-02 TRUE
## P81183 1.848976e-02 TRUE
## P08207 1.848976e-02 TRUE
## Q8BGW0 1.848976e-02 TRUE
## P00329 1.916924e-02 TRUE
## Q8CFB4 1.975282e-02 TRUE
## Q7TMB8 1.975282e-02 TRUE
## Q05186 2.141970e-02 TRUE
## Q60710 2.226300e-02 TRUE
## P49718 2.443891e-02 TRUE
## P07742 2.443891e-02 TRUE
## O70400 2.466647e-02 TRUE
## P70271 2.466647e-02 TRUE
## Q61107 2.466647e-02 TRUE
## O88508 2.611161e-02 TRUE
## Q00417 2.797296e-02 TRUE
## Q9QYB5 2.871000e-02 TRUE
## Q8VCT3 2.888731e-02 TRUE
## P70302 2.957540e-02 TRUE
## Q9WTK5 3.088272e-02 TRUE
## P70677 3.121200e-02 TRUE
## Q8BFP9 3.136117e-02 TRUE
## O70370 3.241447e-02 TRUE
## P48758 3.241447e-02 TRUE
## Q8VCW8 3.344373e-02 TRUE
## O70293 3.344373e-02 TRUE
## Q9Z0F6 3.356135e-02 TRUE
## Q9CYL5 3.596482e-02 TRUE
## Q9WUZ9 3.596482e-02 TRUE
## P54310 3.596482e-02 TRUE
## P10630 3.596482e-02 TRUE
## P42230 3.596482e-02 TRUE
## P61028 3.596482e-02 TRUE
## Q8K157 3.596482e-02 TRUE
## O88673 3.596482e-02 TRUE
## Q6PHZ2 4.029490e-02 TRUE
## Q9R1Q7 4.174800e-02 TRUE
## P47856 4.174800e-02 TRUE
## Q3UGR5 4.174800e-02 TRUE
## P97311 4.275603e-02 TRUE
## P25799 4.486685e-02 TRUE
## Q68FL4 4.515778e-02 TRUE
## P29416 4.563771e-02 TRUE
## P37913 4.594008e-02 TRUE
## Q9DBX6 4.693769e-02 TRUE
## Q8C0P5 4.698906e-02 TRUE
## Q9D8S4 4.774697e-02 TRUE
## Q3UDE2 4.869367e-02 TRUE
## Q99MN9 4.949325e-02 TRUEThere are 69 proteins with a significant effect at the 5% FDR level.
4.3 Plots
4.3.1 Volcano-plot
Note, that we find less proteins.
volcano<- ggplot(tests,aes(x=estimate,y=-log10(pval),color=signif)) + geom_point() + scale_color_manual(values=c("black","red"))
volcano
An interactive volcano plot can be obtained by using the plotly library. We alter the object tests to add the protein name to the data frame in ggplot, this will allow us to see the protein name in the interactive plot.
library(plotly)
volcano<- ggplot(tests %>% rownames_to_column("protein"), aes(x=estimate,y=-log10(pval),color=signif,protein=protein,annotation=Protein.names,gene=Gene.names)) + geom_point() + scale_color_manual(values=c("black","red"))
ggplotly(volcano)4.3.2 Heatmap
There are no significant proteins so we will plot a heatmap of the top 10 proteins, instead. Note, that these results are not statistically significant.
sigNames<- tests %>% rownames_to_column("protein") %>% filter(signif) %>% pull(protein)
heatmap(exprs(protData)[sigNames,])
4.3.3 Detail plot for the top 5 proteins.
We first extract the normalized protein expression values for a particular protein and we create a dataframe that contains the protein expression values and the relevant variables of the experimental design.
for (protName in rownames(tests)[1:5])
{
plotDat<-pepData[fData(pepData)$Proteins==protName] %>% exprs
plotDataStack<-data.frame(quant_value=c(plotDat),sequence=rep(rownames(plotDat),rep=ncol(plotDat)),sample=rep(colnames(plotDat),each=nrow(plotDat)),condition=rep(pData(pepData)$cellType,each=nrow(plotDat)))
plot1 <- ggplot(plotDataStack, aes(x=sample, y=quant_value,fill=condition))
print(plot1 +geom_boxplot(outlier.shape=NA) + geom_point(position=position_jitter(width=.1),aes(shape=sequence)) + scale_shape_manual(values=1:nrow(plotDat)) +labs(title = protName, x="sample", y="Peptide intensity (log2)"))
}
