Raw data processing of the new proteomic data from timsTOF machine
Junyan Lu
2020-08-06
Last updated: 2020-08-06
Checks: 6 1
Knit directory: Proteomics/analysis/
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Data input
Read and annotate data
Read in data and annotate raw data (unnormalized)
rawTab <- read_tsv("../data/200725_cll_diaPASEF_direct_reports/20200727_104510_200725_cll_diaPASEF_direct_Report_Protein_long.tsv") %>%
select(R.Replicate, PG.ProteinAccessions, PG.ProteinNames,PG.Quantity) %>%
dplyr::rename(id = R.Replicate, name = PG.ProteinAccessions,
count = PG.Quantity, protName = PG.ProteinNames) %>%
mutate(id = paste0("A_1_",id),
protName = str_replace_all(protName,"_HUMAN","")) %>%
filter(!is.na(count))
#annotate patient ID
patAnno <- readxl::read_xlsx("../data/SampleAnnotation_cleaned.xlsx") %>%
mutate(id = paste0("A_1_",id)) %>%
select(-Institute, -Source, -diagnosis)
#annotate basic genomic feature
genAnno <- patMeta %>% select(Patient.ID, gender, IGHV.status, trisomy12)
#annotate technical variable
techTab <- readxl::read_xlsx("../data/20191025_Proteom_submitted_samples_final.xlsx") %>%
select(`Patient ID`, operator, viability, batch, `date of sample processing`, `protein conc. in ug`, `freeze-thaw cycles of peptide solution`) %>% dplyr::rename(patID = `Patient ID`, processDate = `date of sample processing`, proteinConc = `protein conc. in ug`, `freeThawCycle` = `freeze-thaw cycles of peptide solution`) %>%
mutate(batch = ifelse(batch == "test run", "0", batch))
patAnno <- left_join(patAnno, genAnno, by = c(patID = "Patient.ID")) %>%
left_join(techTab, by = "patID")
rawTab <- left_join(rawTab, patAnno, by = "id")Generate formated protein ID
idMap <- tibble(name = unique(rawTab$name)) %>%
mutate(ID = paste0("prot",seq(nrow(.))))
rawTab <- left_join(rawTab, idMap, by = "name")Create SummarizedExperiment object
protCLL <- tidyToSum(rawTab, "ID", "patID", "count",
annoCol = colnames(patAnno)[colnames(patAnno) != "patID"],
annoRow = c("name","protName"))
rowData(protCLL)$ID <- rownames(protCLL)Dimension of the original data
dim(protCLL)[1] 5196 50Initial QA
Check distribution of missing values
Pattern of missing values
plot_missval(protCLL)
Some samples show less detection rate than others.
Frequencies of missing values for each sample
proTab <- sumToTiday(protCLL, "uniprotID", "patientID")
patMiss <- group_by(proTab, patientID) %>%
summarise(freqNA = sum(is.na(count))/length(count)) %>%
arrange(desc(freqNA)) %>%
mutate(patientID = factor(patientID, levels = patientID))`summarise()` ungrouping output (override with `.groups` argument)ggplot(patMiss, aes(x = patientID, y = freqNA)) + geom_point(size=3) +
geom_segment(aes(x=patientID, xend=patientID, y=0, yend=freqNA)) +
theme(axis.text.x = element_text(angle = 90, vjust =0.5, hjust=1)) + ylab("Frenquency")
Proteins with missing values
proMiss <- group_by(proTab, uniprotID) %>%
summarise(freqNA = sum(is.na(count))/length(count)) %>%
arrange(desc(freqNA)) %>%
mutate(uniprotID = factor(uniprotID, levels = uniprotID))`summarise()` ungrouping output (override with `.groups` argument)head(proMiss)# A tibble: 6 x 2
uniprotID freqNA
<fct> <dbl>
1 prot5179 0.98
2 prot5183 0.98
3 prot5187 0.98
4 prot5191 0.98
5 prot5192 0.98
6 prot5195 0.98ggplot(proMiss, aes(x=freqNA)) + geom_histogram() +
xlab("Missing value frequency")`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Missing value cut-off versus number of remaining proteins
sumTab <- lapply(seq(0,1,by = 0.01), function(x) tibble(cut = x, freq = sum(proMiss$freqNA < x)/nrow(proMiss))) %>% bind_rows()
ggplot(sumTab, aes(x=cut, y=freq)) + geom_line() + xlab("Missing value cut-off") + ylab("Percent remaining")
Missing value frequency versus median expression
compareTab <- group_by(proTab, uniprotID) %>%
summarise(freqNA = sum(is.na(count))/length(count),
medianExpr = median(log2(count), na.rm=TRUE))
ggplot(compareTab, aes(x=freqNA, y = medianExpr)) + geom_point() + geom_smooth(method = "loess") +
ylab("Median log2 count") + xlab("Frequency of missing values")
Highly expressed proteins tend to have higher detection rate.
Remove proteins with more than 50% missing values
cut=0.5
protCLL_filt <- protCLL[rowSums(is.na(assay(protCLL)))/ncol(protCLL) <= cut,]Dimension of the filtered data
dim(protCLL_filt)[1] 4947 50Data normalization
Distribution of raw data
protTab <- sumToTiday(protCLL_filt, "id","patientID")
ggplot(protTab, aes(x=patientID, y=count)) + geom_boxplot() + scale_y_log10() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))Warning: Removed 10294 rows containing non-finite values (stat_boxplot).
Normalize data using vsn package
exprMat <- assay(protCLL_filt)
resVsn <- vsnMatrix(exprMat)
protCLL_norm <- protCLL_filt
assay(protCLL_norm) <- predict(resVsn, exprMat)Boxplot of normalized counts
protTab <- sumToTiday(protCLL_norm, "uniprotID","patientID")
ggplot(protTab, aes(x=patientID, y=count)) + geom_boxplot() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))Warning: Removed 10294 rows containing non-finite values (stat_boxplot).
Mean VS SD plot after normalization
vsn::meanSdPlot(resVsn)
Looks OK. Although lowly expressed proteins still have higher variance.
Post processing
Impute missing values
For impute missing values, first I need to see whether the data is missing at random or not.
Check missing value pattern again (random or not?)
Missing value pattern after normalization and filtering
plot_missval(protCLL_norm)
Detection rate of proteins with and without missing values
plot_detect(protCLL_norm)
Proteins with missing values have on average low intensities. Not missing at random.
Impute missing values
Impute missing values using quantile regression-based left-censored function (QRILC)
This is a method for imputing missing not at random data.
protCLL_imp <- impute(protCLL_norm, fun = "QRILC")#add QRILC imputed data
assays(protCLL_norm)[["QRILC"]] <- assay(protCLL_imp)Annotate protein information using bioMart
Prepare protein id table
idTab <- map_df(unique(rowData(protCLL)$name), ~tibble(ID = ., uniprotID = str_split(.,";")[[1]]))ensembl = useMart("ensembl",dataset="hsapiens_gene_ensembl",host="grch37.ensembl.org")
ids <- idTab$uniprotID
anno <- getBM(attributes=c('hgnc_symbol','ensembl_gene_id',
'uniprotswissprot'),
filters = 'uniprotswissprot',
values = ids,
mart = ensembl)Warning: `select_()` is deprecated as of dplyr 0.7.0.
Please use `select()` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.Warning: `filter_()` is deprecated as of dplyr 0.7.0.
Please use `filter()` instead.
See vignette('programming') for more help
This warning is displayed once every 8 hours.
Call `lifecycle::last_warnings()` to see where this warning was generated.Cache foundidTab <- left_join(idTab, anno, by = c(uniprotID="uniprotswissprot")) %>%
mutate(protName = rowData(protCLL)[match(ID, rowData(protCLL)$name),]$protName)Proteins that can not be mapped
filter(idTab, is.na(ensembl_gene_id)) %>% DT::datatable()Map those proteins using uniprotID to symbol list
idTab.miss <- filter(idTab,is.na(ensembl_gene_id))
mapSymbol <- read_tsv("../data/mapSymbol.txt")Parsed with column specification:
cols(
From = col_character(),
To = col_character()
)idTab.miss <- mutate(idTab.miss, hgnc_symbol = mapSymbol[match(uniprotID, mapSymbol$From),]$To)
#proteins that can not be mapped
unique(filter(idTab.miss, is.na(hgnc_symbol))$uniprotID) [1] "A6NFI3" "Q58FF7" "Q6DN03" "Q6DRA6" "Q6SPF0"
[6] "Q7Z739" "O60361" "P57053" "P0DOX8" "Q9BRL6"
[11] "Q9H6W3" "A0A075B6S6" "P06310" "C9J7I0" "Q6ZSR9"
[16] "A0A0B4J2H0" "P01768" "P0DP03" "P0DOX2" "Q9BZK3"
[21] "Q9NQA3" "P0DSE2" "A0A0B4J1U7" "A0A0J9YX35" "P01717"
[26] "P01893" "Q9BZD3" "P0DOX5" "P0DOX7" "P0DP91"
[31] "P0DPB6" #they can be safely removed
idTab.miss <- filter(idTab.miss, !is.na(hgnc_symbol)) %>%
dplyr::select(-ensembl_gene_id)Get ensemble IDs for those proteins using symbol
ids <- idTab.miss$hgnc_symbol
anno <- getBM(attributes=c('hgnc_symbol','ensembl_gene_id'),
filters = 'hgnc_symbol',
values = ids,
mart = ensembl)Cache foundidTab.miss <- left_join(idTab.miss, anno, by = "hgnc_symbol")Genes whose ensembl ID still can not found, use manual annotation
mapSE <- structure(c("ENSG00000180389","ENSG00000282100","ENSG00000180448",
NA,NA,NA,"ENSG00000177144","ENSG00000223614","ENSG00000160221",
"ENSG00000280071","ENSG00000278615","ENSG00000282651",
"ENSG00000275895"),
names=c("ATP5F1EP2","HSP90AB4P","ARHGAP45","IGHV3-43D","GAGE7","IGHV3-30-3","NUDT4B",
"ZNF735","GATD3A","GATD3B","C11orf98","IGHV5-10-1","U2AF1L5"))
idTab.miss <- mutate(idTab.miss, ensembl_gene_id = ifelse(is.na(ensembl_gene_id), mapSE[hgnc_symbol], ensembl_gene_id))Update annotations for proteins with missing annotation
idTab <- bind_rows(filter(idTab, !is.na(ensembl_gene_id)), idTab.miss)Retrieve chromosome information (using ensembl id)
#firstly based on id
ids <- idTab$ensembl_gene_id
anno <- getBM(attributes=c('ensembl_gene_id','chromosome_name'),
filters = 'ensembl_gene_id',
values = ids,
mart = ensembl)Cache foundidTab <- mutate(idTab,
chromosome_name = anno[match(idTab$ensembl_gene_id,anno$ensembl_gene_id),]$chromosome_name) %>%
filter(!grepl("CHR|HG",chromosome_name)) %>%
arrange(ensembl_gene_id) %>%
distinct(uniprotID, .keep_all = TRUE) #some proteins can be mapped to several genes, this is normal and mostly happens to histones. This step will remove then and only keep one. This should be fine.
filter(idTab, is.na(chromosome_name))# A tibble: 5 x 6
ID uniprotID hgnc_symbol ensembl_gene_id protName chromosome_name
<chr> <chr> <chr> <chr> <chr> <chr>
1 P0DN76;Q01081 P0DN76 U2AF1L5 ENSG00000275895 U2AF5;U2… <NA>
2 E9PRG8 E9PRG8 C11orf98 ENSG00000278615 CK098 <NA>
3 A0A0B4J2D5;P… A0A0B4J2D5 GATD3B ENSG00000280071 GAL3B;GA… <NA>
4 Q58FF6 Q58FF6 HSP90AB4P ENSG00000282100 H90B4 <NA>
5 P0DP02 P0DP02 IGHV3-30-3 <NA> HVC33 <NA> For proteins chromosome can not be annotated, manually add chromosome info
chrMap <- structure(c("15","21","11","14","14","21"),
names = c("Q58FF6","A0A0B4J2D5",
"E9PRG8","A0A0J9YXX1",
"P0DP04","P0DN76"))
idTab <- mutate(idTab, chromosome_name = ifelse(is.na(chromosome_name),
chrMap[uniprotID],chromosome_name))Annotate
rawTab <- sumToTiday(protCLL, rowID = "rowID", colID = "patID")
rawTab <- left_join(rawTab, idTab, by = c(name = "ID")) %>%
filter(!is.na(uniprotID)) %>% select(-rowID, -ID)
normTab <- sumToTiday(protCLL_norm, rowID = "rowID", colID = "patID")
normTab <- left_join(normTab, idTab, by = c(name = "ID")) %>%
filter(!is.na(uniprotID)) %>% select(-rowID, -ID)Combine and save objects
Combine table
protCLL_raw <- tidyToSum(rawTab, "uniprotID","patID", values = "count",
annoCol = colnames(patAnno)[colnames(patAnno)!="patID"],
annoRow = c("name","ensembl_gene_id","hgnc_symbol","chromosome_name"))
protCLL <- tidyToSum(normTab, "uniprotID","patID", values = c("count", "QRILC"),
annoCol = colnames(patAnno)[colnames(patAnno)!="patID"],
annoRow = c("name","ensembl_gene_id","hgnc_symbol","chromosome_name"))Annotate proteins that can not be uniquely mapped
#for all proteins
dupTab <- rowData(protCLL_raw) %>% data.frame(stringsAsFactors = FALSE) %>% rownames_to_column("id") %>%
group_by(name) %>% summarise(n=length(id)) %>% filter(n>1)`summarise()` ungrouping output (override with `.groups` argument)rowData(protCLL)$uniqueMap <- ! rowData(protCLL)$name %in% dupTab$name
rowData(protCLL_raw)$uniqueMap <- ! rowData(protCLL_raw)$name %in% dupTab$name
#a list of proteins that can not be uniquely mapped
unique(rowData(protCLL[!rowData(protCLL)$uniqueMap,])$hgnc_symbol) [1] "GATD3B" "SNRPGP15" "WASH3P" "NACA" "TIMM23"
[6] "MYL12B" "RBFOX2" "HIST1H2BK" "KRT6A" "HIST1H2AB"
[11] "PRSS1" "ZFY" "H2AFZ" "HIST1H2AK" "TRAPPC2"
[16] "TRAPPC2P1" "SULT1A3" "SULT1A4" "HSPA1A" "HSPA1B"
[21] "U2AF1L5" "IGLC2" "IGLC3" "CALM1" "CALM2"
[26] "CALM3" "GATD3A" "SNRPB" "ZFX" "MYL12A"
[31] "HIST1H2AD" "KRT6C" "UBE2D1" "ATP5E" "HIST1H2BD"
[36] "DEFA1" "DEFA3" "ARF3" "MAGOH" "SNRPG"
[41] "HIST1H2BI" "RPL39" "GNAS" "SNRPN" "HIST1H3B"
[46] "NOMO3" "ARF1" "H3F3B" "CCZ1B" "CCZ1"
[51] "U2AF1" "BNIP2" "NAB1" "GTF2H2" "NAB2"
[56] "HIST2H2AC" "LGALS9B" "ORMDL2" "RPL39P5" "ALG10"
[61] "ALG10B" "NOMO2" "TIMM23B" "ATP5EP2" "LGALS9C"
[66] "HIST2H2AA3" "MZT2B" "GTF2H2C" "MZT2A" "WASH2P"
[71] "HIST2H3D" "H2AFV" "HIST3H2A" "ATCAY" "PRSS3P2"
[76] "HIST1H2AC" "UBE2E3" "MAGOHB" "HIST1H2AH" "UBE2E2"
[81] "CLIC6" "HIST1H2BN" "HIST1H2AJ" "HIST1H2BM" "HIST1H2BL"
[86] "H2AFJ" "RBFOX1" "CLIC5" "ORMDL1" "UBE2D4" Save object
#for other projects
save(protCLL, protCLL_raw, file = "../../var/proteomic_timsTOF_new_20200806.RData")
#for this project
save(protCLL, protCLL_raw, file = "../output/proteomic_timsTOF_new_20200806.RData")
sessionInfo()R version 3.6.0 (2019-04-26)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.15.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats4 parallel stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] forcats_0.5.0 stringr_1.4.0
[3] dplyr_1.0.0 purrr_0.3.4
[5] readr_1.3.1 tidyr_1.1.0
[7] tibble_3.0.3 ggplot2_3.3.2
[9] tidyverse_1.3.0 SummarizedExperiment_1.16.1
[11] DelayedArray_0.12.3 BiocParallel_1.20.1
[13] matrixStats_0.56.0 GenomicRanges_1.38.0
[15] GenomeInfoDb_1.22.1 IRanges_2.20.2
[17] S4Vectors_0.24.4 biomaRt_2.42.1
[19] DEP_1.8.0 jyluMisc_0.1.5
[21] vsn_3.54.0 Biobase_2.46.0
[23] BiocGenerics_0.32.0 pheatmap_1.0.12
[25] cowplot_1.0.0 limma_3.42.2
loaded via a namespace (and not attached):
[1] utf8_1.1.4 shinydashboard_0.7.1 gmm_1.6-5
[4] tidyselect_1.1.0 RSQLite_2.2.0 AnnotationDbi_1.48.0
[7] htmlwidgets_1.5.1 grid_3.6.0 norm_1.0-9.5
[10] maxstat_0.7-25 munsell_0.5.0 codetools_0.2-16
[13] preprocessCore_1.48.0 DT_0.14 withr_2.2.0
[16] colorspace_1.4-1 knitr_1.29 rstudioapi_0.11
[19] ggsignif_0.6.0 mzID_1.24.0 labeling_0.3
[22] git2r_0.27.1 slam_0.1-47 GenomeInfoDbData_1.2.2
[25] KMsurv_0.1-5 farver_2.0.3 bit64_0.9-7
[28] rprojroot_1.3-2 vctrs_0.3.1 generics_0.0.2
[31] TH.data_1.0-10 xfun_0.15 BiocFileCache_1.10.2
[34] sets_1.0-18 R6_2.4.1 doParallel_1.0.15
[37] clue_0.3-57 bitops_1.0-6 fgsea_1.12.0
[40] assertthat_0.2.1 promises_1.1.1 scales_1.1.1
[43] multcomp_1.4-13 gtable_0.3.0 affy_1.64.0
[46] sandwich_2.5-1 workflowr_1.6.2 rlang_0.4.7
[49] mzR_2.20.0 GlobalOptions_0.1.2 splines_3.6.0
[52] rstatix_0.6.0 impute_1.60.0 hexbin_1.28.1
[55] broom_0.7.0 modelr_0.1.8 BiocManager_1.30.10
[58] yaml_2.2.1 abind_1.4-5 crosstalk_1.1.0.1
[61] backports_1.1.8 httpuv_1.5.4 tools_3.6.0
[64] relations_0.6-9 affyio_1.56.0 ellipsis_0.3.1
[67] gplots_3.0.4 RColorBrewer_1.1-2 MSnbase_2.12.0
[70] Rcpp_1.0.5 plyr_1.8.6 visNetwork_2.0.9
[73] progress_1.2.2 zlibbioc_1.32.0 RCurl_1.98-1.2
[76] prettyunits_1.1.1 openssl_1.4.2 ggpubr_0.4.0
[79] GetoptLong_1.0.2 zoo_1.8-8 haven_2.3.1
[82] cluster_2.1.0 exactRankTests_0.8-31 fs_1.4.2
[85] magrittr_1.5 data.table_1.12.8 openxlsx_4.1.5
[88] circlize_0.4.10 reprex_0.3.0 survminer_0.4.7
[91] pcaMethods_1.78.0 mvtnorm_1.1-1 ProtGenerics_1.18.0
[94] hms_0.5.3 shinyjs_1.1 mime_0.9
[97] evaluate_0.14 xtable_1.8-4 XML_3.98-1.20
[100] rio_0.5.16 readxl_1.3.1 gridExtra_2.3
[103] shape_1.4.4 compiler_3.6.0 KernSmooth_2.23-17
[106] ncdf4_1.17 crayon_1.3.4 htmltools_0.5.0
[109] mgcv_1.8-31 later_1.1.0.1 lubridate_1.7.9
[112] DBI_1.1.0 dbplyr_1.4.4 ComplexHeatmap_2.2.0
[115] rappdirs_0.3.1 MASS_7.3-51.6 tmvtnorm_1.4-10
[118] Matrix_1.2-18 car_3.0-8 cli_2.0.2
[121] imputeLCMD_2.0 marray_1.64.0 gdata_2.18.0
[124] igraph_1.2.5 pkgconfig_2.0.3 km.ci_0.5-2
[127] foreign_0.8-71 piano_2.2.0 xml2_1.3.2
[130] MALDIquant_1.19.3 foreach_1.5.0 XVector_0.26.0
[133] drc_3.0-1 rvest_0.3.5 digest_0.6.25
[136] rmarkdown_2.3 cellranger_1.1.0 fastmatch_1.1-0
[139] survMisc_0.5.5 curl_4.3 shiny_1.5.0
[142] gtools_3.8.2 rjson_0.2.20 nlme_3.1-148
[145] lifecycle_0.2.0 jsonlite_1.7.0 carData_3.0-4
[148] fansi_0.4.1 askpass_1.1 pillar_1.4.6
[151] lattice_0.20-41 httr_1.4.1 fastmap_1.0.1
[154] plotrix_3.7-8 survival_3.2-3 glue_1.4.1
[157] zip_2.0.4 png_0.1-7 iterators_1.0.12
[160] bit_1.1-15.2 stringi_1.4.6 blob_1.2.1
[163] caTools_1.18.0 memoise_1.1.0