R包:蛋白质组学质控评估PTXQC包

发布于:2024-07-11 ⋅ 阅读:(23) ⋅ 点赞:(0)

介绍

PTXQC包是2016年发表在J Proteome Res期刊上的R包,它主要是对MaxQuant输出结果进行提取处理从而获得评估蛋白质质量结果。

安装

从github安装,安装过程会自动构建tutorial。

devtools::install_github("cbielow/PTXQC", build_vignettes = TRUE, dependencies = TRUE)
library(PTXQC)
  • 查看帮助文档,帮助文档是以html方式展示
help(package="PTXQC")
browseVignettes(package = 'PTXQC')

输入文件

输入文件是MaxQuant结果文件的txt里面的:/combined/txt

  • parameters.txt
  • summary.txt
  • proteinGroups.txt
  • evidence.txt
  • msms.txt
  • msmsScans.txt

运行

输入包含上述输入文件的目录即可,然后使用createReport函数。

r = createReport(txt_folder)
cat(paste0("\nReport generated as '", r$report_file, "'\n\n"))

它也提供了修改报告主题或者评估步骤选择的方法,需要修改yaml_file文件,可参考如下。

require(PTXQC)
require(yaml)

## the next require() is needed to prevent a spurious error in certain R versions (might be a bug in R or a package)
## error message is:
##    Error in Scales$new : could not find function "loadMethod"
require(methods)

## specify a path to a MaxQuant txt folder
## Note: This folder can be incomplete, depending on your YAML config
if (1) {
  ## we will use an example dataset from PRIDE (dataset 2 of the PTXQC publication)
  local_zip = tempfile(fileext=".zip")
  download.file("ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2015/11/PXD003133/txt_20min.zip", destfile = local_zip)
  unzip(local_zip, exdir = tempdir()) ## extracts content
  txt_folder = file.path(tempdir(), "txt_20min")
} else {
  ## if you have local MaxQuant output, just use it
  txt_folder = "c:/Proteomics/MouseLiver/combined/txt"
}

## use a YAML config inside the target directory if present
fh_out = getReportFilenames(txt_folder)
if (file.exists(fh_out$yaml_file))
{
  cat("\nUsing YAML config already present in target directory ...\n")
  yaml_config = yaml.load_file(input = fh_out$yaml_file)
} else {
  cat("\nYAML config not found in folder '", txt_folder, "'. The first run of PTXQC will create one for you.", sep="")
  yaml_config = list()
}

r = createReport(txt_folder, mztab_file = NULL, yaml_obj = yaml_config)

cat(paste0("\nReport generated as '", r$report_file, "'\n\n"))

结果

输出的报告文件可以是html也可以是PDF格式,如图:基于PTXQC包评估以下部分

  • 样本制备(1-5);
  • 液相色谱分离肽段(6-9);
  • 质谱过程(10-18);
  • 鉴定蛋白效果(19-22)。

不同颜色代表实验过程的优劣。从图中可以看出,样本制备和质谱过程获得评分是best,而鉴定蛋白质效果这一步效果最差,这是因为我们的蛋白质样本是血液外泌体蛋白质,它含量相对血液而已本身就较少,无法达到该包给的阈值(Peptide Count > 15,000; Protein Count > 3,500),但我们可以看到Average Overall Quality是偏较好评价的(偏深绿色)。另外我们蛋白质质谱过程使用了PAIMS技术分离蛋白质,这也是我们能看到每个file有三个bar图(40;60;80电压)。

在获取图谱过程中,常会用到2018年才推出的FAIMS(High-Field Asymmetric Waveform Ion Mobility Spectrometry)技术以用于加载不同电压(肽段在ESI离子化后,进入质谱之前实现快速气相分离,提高分离的峰容量),直接使用多电压下的raw data做MaxQuant定量分析是错误的,MaxQuant软件只能识别单电压的raw data,因此需要使用FAIMS MzXML Generator 软件将raw data转换成各自电压下的MzXML文件。

List of metrics

systemic information

sessionInfo()
R version 4.0.2 (2020-06-22)
Platform: x86_64-conda_cos6-linux-gnu (64-bit)
Running under: CentOS Linux 8 (Core)

Matrix products: default
BLAS/LAPACK: /disk/share/anaconda3/lib/libopenblasp-r0.3.10.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] PTXQC_1.0.12 tibble_3.1.5 dplyr_1.0.7 

loaded via a namespace (and not attached):
 [1] tinytex_0.32       tidyselect_1.1.1   xfun_0.24          bslib_0.2.5.1      reshape2_1.4.4     purrr_0.3.4       
 [7] colorspace_2.0-2   vctrs_0.3.8        generics_0.1.0     viridisLite_0.4.0  htmltools_0.5.1.1  yaml_2.2.1        
[13] utf8_1.2.1         rlang_0.4.11       jquerylib_0.1.4    pillar_1.6.4       glue_1.4.2         DBI_1.1.1         
[19] gdtools_0.2.2      RColorBrewer_1.1-2 lifecycle_1.0.0    plyr_1.8.6         stringr_1.4.0      munsell_0.5.0     
[25] gtable_0.3.0       rvest_0.3.6        kableExtra_1.3.4   evaluate_0.14      knitr_1.33         UpSetR_1.4.0      
[31] fansi_0.5.0        Rcpp_1.0.7         scales_1.1.1       webshot_0.5.2      jsonlite_1.7.2     systemfonts_0.3.2 
[37] gridExtra_2.3      ggplot2_3.3.5      digest_0.6.27      stringi_1.4.6      ade4_1.7-18        cowplot_1.1.0     
[43] grid_4.0.2         tools_4.0.2        magrittr_2.0.1     sass_0.4.0         ggdendro_0.1.22    R6P_0.2.2         
[49] seqinr_4.2-4       crayon_1.4.1       tidyr_1.1.4        pkgconfig_2.0.3    ellipsis_0.3.2     MASS_7.3-54       
[55] data.table_1.14.0  xml2_1.3.2         assertthat_0.2.1   rmarkdown_2.9      svglite_1.2.3.2    httr_1.4.2        
[61] rstudioapi_0.13    R6_2.5.0           compiler_4.0.2  

Reference

  1. Proteomics quality control: quality control software for MaxQuant results

  2. PTXQC