Tutorial 5: Statistical analysis of DIA data

Transcription

1 Tutrial 5: Statistical analysis f DIA data 1. Intrductin One f the mst frequently asked questins is: Hw t get a prtein matrix t answer a particular bilgical questin? Nwadays there are several nice sftware tls available that can help t reach this gal. Tw f them are quite advanced: a) MSstats which can als be used fr DDA and SRM analysis b) mapdia In this Tutrial, yu will learn hw t prcess yur utput f the OpenSWATH pyprphet TRIC alignment and hw t extract bilgical infrmatin. We will fcus n MSstats in R. In principle MSstats can als be used within Skyline as a plugin. MSstats is cmpsed f 4 individual parts: a) Data prcessing and Quality cntrl b) Grup cmparisn c) Prtein quantificatin d) Design sample size Tip! Fr mre detailed infrmatin abut functinality and ptins, visit msstats.rg In rder t feed ur data int MSstats we need t mdify and refrmat the utput f the TRIC alignment. This can easily be dne with the package SWATH2stats. SWATH2stats prvides functins t anntate the data with infrmatin abut bilgical replicate and cnditin and easily cnvert the data int the crrect frmat fr varius dwnstream sftwares as MSstats r mapdia. Additinally, it ffers multiple ways t filter the data, fr example t btain a specific FDR. Tip! Please find mre detailed infrmatin abut SWATH2stats at 1

2 2. Prepare data fr MSstats Start by pening Rstudi. G t File New File R Script. Nte! In the previus Tutrials we have used the Terminal in the lwer left crner. Make sure yu are using the R cnsle nw. R script list f variables plts/help/library R cnsle First define the wrking directry. This will avid lng paths when lading and saving data. G t Sessin Set wrking directry Chse directry and search fr yur flder Tutrial5_Statistics. Nte! Alternatively, yu can als set the directry directly in the cmmand line by cpying this cmmand in yur script and then run it: setwd('c:/dia_curse/tutrial5_statistics/') Nw lad in the required packages. Nte! Alternatively, yu can als search the respective packages in the lwer bx n the right side f R studi. library(swath2stats) library(msstats) Lad in the OpenSWATH utput. data <- read.delim2( 'C:/DIA_Curse/Tutrial4_OpenSWATH/aligned.tsv', dec='.', sep='\t', header=true, stringsasfactrs = FALSE) 2

3 Nte! If yu wrk n a UNIX envirnment yu can use fread() frm the package data.table which reads in the data similarly t read.delim2, but faster. The class f the data will be data.table instead f a data.frame. Therefre, yu need t cnvert it int the crrect class. data <- as.data.frame(data) sep defines the separatr between clumns, and dec the decimal separatr. The first line in the OpenSWATH utput cntains clumn names, therefre header=true. read.delim2 has the disadvantage t transfrm clumns int factrs, that are hard t wrk with. Therefre, we set stringsasfactrs = FALSE t avid this. Check the help f read.delim2() fr mre infrmatin by typing?read.delim2 Tip! Just start typing the beginning f the file name and then press the tab key and the name will be cmpleted autmatically. This will help t avid typs. Due t the new frmat f ur data, we need t adjust sme clumn names in rder t be recgnized in the fllwing prcedure. names(data)[names(data) == "FullUniMdPeptideName"] <- "FullPeptideName" names(data)[names(data) == "aggr_fragment_anntatin"] <- "aggr_fragment_anntatin" names(data)[names(data) == "aggr_peak_area"] <- "aggr_peak_area" Let s check the dimensins f ur data. dim(data) Nte! R always prints number f rws first and then number f clumns. Tip! Click n the variable data in the tp-right windw f R studi, t have a glance in the data. A windw will appear abve yur R cnsle. Explre the data structure and make yurself familiar with the numerus clumns. As yu can see n the R cnsle n the bttm left, the text View(data) appeared. This means that yu can als type View(data) t pen the viewing panel. Nte! Many clumns cntain variables frm the mdel built in OpenSWATH. We d nt need mst f them in ur further statistical analysis. Befre we apply this filter n the actual data, we reduce the number f clumns t the nes we really need fr the further analysis. data.reduced <- reduce_openswath_utput(data) View(data.reduced) Tip! Check hw the data lks nw by typing View(data.reduced) r by clicking n data.reduced in the tp right Envirnment panel. We als want t get rid f irt peptides that are still in the data, we nly needed them fr RT calibratin (as yu hpefully remember ). Additinally we will filter ut all nn-prtetypic peptides. 3

4 data.reduced <- data.reduced[grep( "irt", data.reduced$prteinname, invert = TRUE),] data.reduced <- data.reduced[grep( ";", data.reduced$prteinname, invert = TRUE),] Nte! The grep cmmand searches the expressin irt in the clumn data.reduced$prteinname and invert=true means that the hits are remved frm the table. We als need t anntate the data with additinal infrmatin. In yur flder Tutrial5_Statistics yu can find a file called DIA_curse_anntatin.txt Open this with Excel and investigate what infrmatin yu can find there. Then lad in the anntatin file int R studi. Nte! Remember t change the filename if yu have used the QE data. anntatin.file <- read.delim( file = 'DIA_Curse_antatin_TTOF.txt', sep='\t', header=true) Tip! Check if the data was crrectly laded by clicking n anntatin.file in the data verview again. Nw anntate the data: data.anntated <- sample_anntatin(data.reduced, anntatin.file) In SWATH2stats we can als have a quick lk fr the number f prteins and peptides in each sample. cunt_analytes(data.anntated) We can see that there are slight differences between the samples, but nthing wrrying. If yu wuld see here a sample that is shwing far less prteins than ther samples, yu might cnsider nt using this sample fr yur analysis. Cautin! The screenshts yu see are representing the results using the utput that was generated with the DDA library (Tutrial 1). If yu used the DIA-UMPIRE library yu will have different numbers. We can nw filter ur data set. We want t make sure that we just use cmplete bservatins. 4

5 data.filtered <- filter_mscre_cnditin( data.anntated, mscre=0.01, n.replica=3) Nte! This functin filters the data based n cmpleteness f the measured transitin grups amng samples. We use all 6 samples (1 = 100%) here that need t pass the mscre f The mscre crrespnds t the q-value, which means that filtering fr 0.01 results in a table with 1% FDR at the transitin level. R tells yu here again, that it is keeping 92 % f the data. In ttal it deletes 2180 transitin grups. Again we shuld check hw the number f peptides/prteins changed after filtering. cunt_analytes(data.filtered) T feed the data int MSstats (r mapdia) we need t split the transitin grups int single transitins. data.transitin <- disaggregate(data.filtered) In the last step the clumns are renamed t match the requirements fr MSstats. MSstats.input <- cnvert4msstats(data.transitin) Nte! Dn t wrry abut the warning messages at this stage. Save the prduced MSstats input and the intermediate data. save(data.anntated, data.reduced, data.filtered, data.transitin, file="swath2stats_wrkflw_ttof.rdata") save(msstats.input, file="msstats_input_ttof.rdata") 5

6 3. MSstats analysis 3.1. Data prcessing and Quality cntrl First run the prcessing functin t lg2-transfrm. We d nt nrmalize the data because we expect varying abundances fr the prteins, peptides and transitins f E.cli and yeast. We will later check fr equal intensity distributins and whether anther type f nrmalizatin is necessary in the QCplts. data.prcessed <- dataprcess(msstats.input, nrmalizatin = FALSE, summarymethd = "TMP", censredint = "NA", cutffcensred = "minfeature", MBimpute = FALSE) The summary methd "TMP" stands fr Tukey s median plish which is a rbust parameter estimatin methd with median acrss rws and clumns. censredint = "NA" assumes that missing values are censred, meaning belw the limit f detectin. Because MBimpute is false, missing values will get the minimum value fr each feature. Nte! The result f this functin will be a large list with several variables frm which data.prcessed$prcesseddata cntains ur prcessed data set. Nw we want t check fr ptential quality issues. Therefre, we will make a quality cntrl plt. dataprcessplts( data.prcessed, type = "QCPlt", legend.size = 4, which.prtein = "sp P06733 ENOA_HUMAN", address = FALSE) Nte! As we have data fr thusands f prteins, pltting all f them wuld exceed the time we have in this tutrial. Therefre, the ptin which.prtein defines which prteins shuld be pltted with either prtein number r prtein name. In this and the fllwing plts we fcus making the plt fr 1 randm example prtein. With the ptin address = FALSE, plts will be shwn in the graphical panel, but nt saved in a file. 6

7 As yu can see the distributins f the lg2 prtein intensities in the different samples are very similar. Fr the example human prtein this is als the case. We can g ahead with ur analysis. Questin: What d yu expect this plt t lk like fr a yeast r E.cli prtein? Nte! In RStudi yu can see the plts in the lwer right crner. Using the arrws at the tp left yu can navigate back and frth t lk at the generated plts. If yu want t save a plt, click n the drp-dwn Exprt buttn. Additinally, we can als plt the peptide prfiles. dataprcessplts( data.prcessed, type = "PrfilePlt", featurename = "Peptide", legend.size = 4, riginalplt= TRUE, summaryplt = TRUE, which.prtein = "sp P06733 ENOA_HUMAN", address = FALSE) In the first prfile plt yu can see the transitins f several peptides (black, red and green) f the human example prtein (yu will need t click the back arrw in the pltting windw t see this). The figure n the right shws the prfile plt where the dtted red line indicates the prtein summarizatin. The last plt summarizes mean f lg2-intensities with cnfidence interval fr each cnditin per prtein dataprcessplts( data.prcessed, type = "Cnditinplt", which.prtein = "sp P06733 ENOA_HUMAN", address = FALSE) 7

8 Here yu can see the cnditin plt f ur human example prtein. Des this lk as yu wuld expect? Select ther prteins r a few randm numbers and check hw the different plts yu generated abve lk fr different prteins frm different rganisms. Fr this, yu have t exchange which.prtein = "sp P06733 ENOA_HUMAN" by any ther name e.g. which.prtein = "tr C8ZBP1 C8ZBP1_YEAS8" r which.prtein = "sp P29745 PEPT_ECOLI" fr TTOF r "sp P25738 MSYB_ECOLI" fr QE Questins: Can yu already identify trends? D yu see what yu wuld expect frm ur sample? As usual, save the prcessed data: save(data.prcessed, file="msstats_prcessed_ttof.rdata") 3.2. Grup Cmparisn T perfrm a grup cmparisn, we first need t define what shuld cmpared. We wuld like t cmpare Mix A with Mix B. Fr each f these grups we have 3 samples. In rder t define the cmparisn matrix in the crrect rder we will check the rder f the grup levels, which R autmatically assigns alphanumerically. levels(data.prcessed$prcesseddata$group_original) Grups are A and B and we wuld like t calculate the changes in B vs. A Cautin! If yu have mre than 2 grups yu will need t design the cmparisn matrix differently. Fr this check the manual at msstats.rg cmparisn <- matrix(c(-1, 1), nrw=1) rwnames(cmparisn) <- "B vs. A" 8

9 Perfrm the grup cmparisn. result.grupcmparisn <- grupcmparisn( cntrast.matrix = cmparisn, data = data.prcessed) The result is a list cnsisting f several variables. T check the grup cmparisn utcme, we need result.grupcmparisn$cmparisnresult Nw we plt ur grup cmparisn result. MSstats als ffers an easy slutin fr this. grupcmparisnplts(result.grupcmparisn$cmparisnresult, type = "VlcanPlt", sig = 0.05, FCcutff = 2, PrteinName = FALSE, address = FALSE) Nte! With sig we define the significance level and with FCcutff the desired fld change. Questin! Can yu distinguish the different species? Des the significance level and fld change cutff make sense in ur case? 9

10 T make the bilgy behind ur data clearer we will make anther Vlcan plt with prteins clred by species. with( result.grupcmparisn$cmparisnresult, plt( lg2fc, -lg10(adj.pvalue), pch=20, main="mix B vs. Mix A", xlim=c(-3, 3), ylim=c(0,3))) with( subset(result.grupcmparisn$cmparisnresult, grepl("human", Prtein)), pints(lg2fc, -lg10(adj.pvalue), pch=20, cl="aquamarine4")) with( subset(result.grupcmparisn$cmparisnresult, grepl("yeas8", Prtein)), pints(lg2fc, -lg10(adj.pvalue), pch=20, cl="chclate3")) with( subset(result.grupcmparisn$cmparisnresult, grepl("ecoli", Prtein)), pints(lg2fc, -lg10(adj.pvalue), pch=20, cl="slateblue2")) legend("tpright", legend = c("human 1:1", "Yeast 2:1", "E.cli 1:4"), fill = c("aquamarine4", "chclate3", "slateblue2"), cl = NULL) Nte! Dn t wrry if yu dn t fully understand hw this secnd vlcan plt was created this is nly t make yu see the nice difference between the rganisms and is specific t ur exemplary dataset 10

11 Yu can always g back t the plts we prduced earlier, by clicking the little arrw abve the plt in the Plts tab. If yu want t save a plt, click Exprt in the Plts tab and save the plt in yur favrite frmat. If yu wuld like t further wrk n the grupcmparsin result utside f R, yu can als exprt the table: write.table(result.grupcmparisn$cmparisnresult, file = "grupcmparisn_result_ttof.tsv", qute = FALSE, rw.names = FALSE, sep = "\t") Nte! qute = FALSE will avid that all entries will be written in qutatin marks and rw.names = FALSE avids having the rwname-numbers being written in ur file Quantificatin In rder t perfrm ther dwnstream analysis, it is useful t have a matrix f prtein intensities fr each sample. Perfrm a prtein quantificatin. quantificatin.result <- quantificatin(data.prcessed, type = "Sample", frmat = "matrix") Again we can write the table int a file t use it utside the R envirnment. write.table(quantificatin.result, file = "quantificatin_result_ttof.tsv", qute = FALSE, rw.names = FALSE, sep = "\t") With this data, yu can nw perfrm ther dwnstream analyses, like classificatin r regressin analyses. In ur tutrial, this wuld exceed the time we have and wuld neither fit the specific needs yu will have with yur wn data Design sample size Fr future experiments it might be useful t estimate the required samples r t predict the pwer a specific analysis will have if yu are restricted in the number f replicates yu use. First perfrm an anlysis f the required sample size if yu wish fr a specific pwer and fld change f yur grup cmparisn. result.sample <- designsamplesize(result.grupcmparisn$fittedmdel, numsamples = TRUE, desiredfc = c(1.25, 3), FDR = 0.05, pwer = 0.8) 11

12 Nw visualize the result in a plt: designsamplesizeplts(result.sample) Nw d the same analysis the ther way arund and ask hw much pwer yur analysis will have fr a given sample size: result.pwer <- designsamplesize(result.grupcmparisn$fittedmdel, numsamples = 3, desiredfc = c(1.25, 3), FDR = 0.05, pwer = TRUE) Check again the result in a plt: designsamplesizeplts(result.pwer) 12

13 We are nw finished with the statistical data analysis f ur 6 SWATH runs. As yu culd see, we were able t very well separate the prteins cming frm the different species. Well dne! Yu managed t get thrugh all f the tutrials. We hpe that yu learned hw t build a library based n DDA and DIA data, perfrm an OpenSWATH analyses, fllwed by pyprphet scring and TRIC alignment and finally hw t use this utput t extract bilgical infrmatin. We als hpe yu can apply the new knwledge nw n yur wn data and wish yu a lt f fun with this. We wuld like t thank SystemsX fr supprting the Zurich. 13