MSstatsBig is designed to overcome challenges when
analyzing very large mass spectrometry (MS)-based proteomics
experiments. These experiments are generally (but not always) acquired
with DIA and include a large number of MS runs. MSstatsBig
leverages software that can work on datasets without loading them into
memory. This avoids a major problem where a dataset cannot be loaded
into a standard computers RAM.
MSstatsBig includes functions which are designed to
replace the converters included in the MSstats package. The
goal of these converters is to perform filtering on the PSM files of
identified and quantified data to remove data that is not required for
differential analysis. Once this data is filtered down it should be able
to be loaded into your computer’s memory. After the converters are run,
the standard MSstats workflow can be followed.
MSstatsBig currently includes converters for Spectronaut
and FragPipe. Beyond these converters, users can manually use the
underlying functions by putting their data into MSstats
format and running the underlying MSstatsPreprocessBig
function. This way, either by using native export format of signal
processing tools or by converting raw data chunk by chunk (for example
with the readr::read_delim_chunked function), MSstatsBig
can be used with other popular tools such as DIA-NN.
The dataset included in this package is a small subset of a work by
Clark et al. [1]. It is a large DIA dataset that includes over 100 runs.
The experimental data was identified and quantified by FragPipe and the
included dataset is the msstats.csv output for
FragPipe.
## ProteinName PeptideSequence PrecursorCharge FragmentIon
## 1 Q86U42 (UniMod:1)AAAAAAAAAAGAAGGR 1 b4
## 2 Q86U42 (UniMod:1)AAAAAAAAAAGAAGGR 1 y6
## 3 Q86U42 (UniMod:1)AAAAAAAAAAGAAGGR 1 y7
## 4 Q86U42 (UniMod:1)AAAAAAAAAAGAAGGR 1 b5
## 5 Q86U42 (UniMod:1)AAAAAAAAAAGAAGGR 1 y8
## 6 Q86U42 (UniMod:1)AAAAAAAAAAGAAGGR 1 y9
## ProductCharge IsotopeLabelType Condition BioReplicate
## 1 1 L T 1522
## 2 1 L T 1522
## 3 1 L T 1522
## 4 1 L T 1522
## 5 1 L T 1522
## 6 1 L T 1522
## Run Intensity
## 1 CPTAC_CCRCC_W_JHU_20190112_LUMOS_C3N-01522_T 3747562.00
## 2 CPTAC_CCRCC_W_JHU_20190112_LUMOS_C3N-01522_T 770585.44
## 3 CPTAC_CCRCC_W_JHU_20190112_LUMOS_C3N-01522_T 1379359.12
## 4 CPTAC_CCRCC_W_JHU_20190112_LUMOS_C3N-01522_T 3706759.50
## 5 CPTAC_CCRCC_W_JHU_20190112_LUMOS_C3N-01522_T 77361.97
## 6 CPTAC_CCRCC_W_JHU_20190112_LUMOS_C3N-01522_T 338863.41First we run the MSstatsBig converter. The converter
will save the dataset to a place on your computer, and will return an
arrow object. Once then, you can read the data from text file or load
the arrow data.frame into memory by using the
dplyr::collect function. The “collected” data can then be
treated as a standard R data.frame.
setwd(tempdir())
converted_data = bigFragPipetoMSstatsFormat(
system.file("extdata", "fgexample.csv", package = "MSstatsBig"),
"output_file.csv",
backend="arrow",
max_feature_count = 20)
# The returned arrow object needs to be collected for the remaining workflow
converted_data = as.data.frame(dplyr::collect(converted_data))Once the converter is run the standard MSstats workflow
can be followed:
dataProcess function summarizes peptide-level data into
protein-level estimates.groupComparison function fits a linear model to
protein-level data and performs statistical inference for comparisons of
selected groups.Details of the MSstats workflow can be found in [2].
##
## Attaching package: 'MSstats'## The following object is masked from 'package:grDevices':
##
## savePlot# converted_data = read.csv("output_file.csv")
summarized_data = dataProcess(converted_data,
use_log_file = FALSE)## INFO [2024-11-29 06:25:54] ** Features with one or two measurements across runs are removed.
## INFO [2024-11-29 06:25:54] ** Fractionation handled.
## INFO [2024-11-29 06:25:54] ** Updated quantification data to make balanced design. Missing values are marked by NA
## INFO [2024-11-29 06:25:54] ** Log2 intensities under cutoff = 12.59 were considered as censored missing values.
## INFO [2024-11-29 06:25:54] ** Log2 intensities = NA were considered as censored missing values.
## INFO [2024-11-29 06:25:54] ** Use all features that the dataset originally has.
## INFO [2024-11-29 06:25:54]
## # proteins: 1
## # peptides per protein: 1-1
## # features per peptide: 11-11
## INFO [2024-11-29 06:25:54]
## NAT T
## # runs 56 50
## # bioreplicates 56 50
## # tech. replicates 1 1
## INFO [2024-11-29 06:25:54] == Start the summarization per subplot...
## | | | 0% | |======================================================================| 100%
## INFO [2024-11-29 06:25:54] == Summarization is done.# Build contrast matrix
comparison = matrix(c(-1, 1),
nrow=1, byrow=TRUE)
row.names(comparison) <- c("T-NAT")
colnames(comparison) <- c("NAT", "T")
model_results = groupComparison(contrast.matrix = comparison,
data = summarized_data,
use_log_file = FALSE)## INFO [2024-11-29 06:25:54] == Start to test and get inference in whole plot ...
## | | | 0% | |======================================================================| 100%
## INFO [2024-11-29 06:25:54] == Comparisons for all proteins are done.## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] MSstats_4.15.0 MSstatsBig_1.5.0 rmarkdown_2.29
##
## loaded via a namespace (and not attached):
## [1] gtable_0.3.6 xfun_0.49 bslib_0.8.0
## [4] ggplot2_3.5.1 htmlwidgets_1.6.4 caTools_1.18.3
## [7] ggrepel_0.9.6 lattice_0.22-6 vctrs_0.6.5
## [10] tools_4.4.2 bitops_1.0-9 generics_0.1.3
## [13] parallel_4.4.2 tibble_3.2.1 fansi_1.0.6
## [16] pkgconfig_2.0.3 Matrix_1.7-1 KernSmooth_2.23-24
## [19] data.table_1.16.2 checkmate_2.3.2 assertthat_0.2.1
## [22] lifecycle_1.0.4 compiler_4.4.2 gplots_3.2.0
## [25] statmod_1.5.0 munsell_0.5.1 htmltools_0.5.8.1
## [28] sys_3.4.3 buildtools_1.0.0 sass_0.4.9
## [31] yaml_2.3.10 lazyeval_0.2.2 preprocessCore_1.69.0
## [34] marray_1.85.0 plotly_4.10.4 pillar_1.9.0
## [37] nloptr_2.1.1 jquerylib_0.1.4 tidyr_1.3.1
## [40] MASS_7.3-61 cachem_1.1.0 limma_3.63.2
## [43] boot_1.3-31 nlme_3.1-166 gtools_3.9.5
## [46] tidyselect_1.2.1 digest_0.6.37 dplyr_1.1.4
## [49] purrr_1.0.2 arrow_17.0.0.1 maketools_1.3.1
## [52] splines_4.4.2 fastmap_1.2.0 grid_4.4.2
## [55] colorspace_2.1-1 cli_3.6.3 magrittr_2.0.3
## [58] survival_3.7-0 utf8_1.2.4 withr_3.0.2
## [61] scales_1.3.0 backports_1.5.0 bit64_4.5.2
## [64] httr_1.4.7 bit_4.5.0 lme4_1.1-35.5
## [67] evaluate_1.0.1 knitr_1.49 log4r_0.4.4
## [70] MSstatsConvert_1.17.0 viridisLite_0.4.2 rlang_1.1.4
## [73] Rcpp_1.0.13-1 glue_1.8.0 minqa_1.2.8
## [76] jsonlite_1.8.9 R6_2.5.1D. J. Clark, S. M. Dhanasekaran, F. Petralia, P. Wang and H. Zhang, “Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma,” Cell, vol. 179, pp. 964-983, 2019.
D. Kohler et al., “MSstats Version 4.0: Statistical Analyses of Quantitative Mass Spectrometry-Based Proteomic Experiments with Chromatography-Based Quantification at Scale”, J. Proteome Res. 22, 5, pp. 1466–1482, J. Proteome Res. 2023, 22, 5, 1466–1482