R在RNAseq和基因组关注的比较多，在蛋白质组研究中虽然用的多，但是专门的博客和文章比较少，转载一个蛋白质组学数据分析的文章。
原文见https://datascienceplus.com/proteomics-data-analysis-1-3-data-acquisition-and-cleaning/

Proteomics Data Analysis (1/3): Data Acquisition and Cleaning

Published on August 9, 2018 at 7:00 am
Updated on August 26, 2018 at 11:17 pm

The analysis of DNA and RNA, the blueprint of life and its carbon copy, has become a staple in the burgeoning field of molecular biology. An emerging and exciting area of study that adds another dimension to our understanding of cellular biology is that of proteomics. The use of mass spectrometry has enabled the identification and quantification of thousands of proteins in a single sample run.

In this tutorial series, I will break down the steps to processing a high-throughput proteomics data set derived from mass spectrometry analysis as follows:

• Data acquisition and cleaning
• Data filtering and missing value imputation
• Statistical testing and data interpretation

Source of Proteomics Data

To obtain a sample data set, I combed through a proteomics data repository called PRIDE and found an interesting study on drug resistance in breast cancer cell lines. I downloaded the raw files, which are the output from mass spectrometry analysis, and processed them using a software called MaxQuant to map the spectral data to protein sequences. A total of six raw files, corresponding to three replicates of two cell lines (one resistant, one non-resistant), was used. There are numerous other ways to process mass spectrometry data (e.g. Mascot, SEQUEST, ProteinProspector) so the final data table of protein abundance measurements will vary base on the approach. The starting point for this tutorial is the MaxQuant ProteinGroups output file, which can be downloaded here.

Data Acquisition

The first step is to read the tab-separated data file into R.

raw = read.delim("proteinGroups.txt", stringsAsFactors = FALSE, colClasses = "character")

Our raw data is an enormous 1787-by-79 data frame. Proteins are arranged in rows and their information in columns. The primary columns of interest are those containing intensity measurements, which reflects protein abundance.

grep("^LFQ.intensity", names(raw), value = TRUE)
## [1] "LFQ.intensity.Parental_bR1"  "LFQ.intensity.Parental_bR2" 
## [3] "LFQ.intensity.Parental_bR3"  "LFQ.intensity.Resistant_bR1"
## [5] "LFQ.intensity.Resistant_bR2" "LFQ.intensity.Resistant_bR3"

Again, we have a total of six samples. The Parental represents intensity data from the breast cancer cell line SKBR3 while the Resistant is an drug-resistant cell line derived from culturing the parentals in the presence of an EGFR inhibitor. For more information regarding the study, please see the original publication.

Data Clean Up

Remove False Hits

The next step after data acquisition is to clean and organize our data. The first order of business is to remove the false hits, including the contaminants, reverse proteins, and proteins identified by site. These are annotated with a “+” under the columns Potential.contaminant, Reverse, and Only.identified.by.site. We filter the data frame by keeping rows without a “+” annotation in any of the three columns.

library(dplyr)
df = dplyr::filter(raw, Potential.contaminant != "+") %>%
  dplyr::filter(Reverse != "+") %>%
  dplyr::filter(Only.identified.by.site != "+")

Often, there is a column indicating the confidence of the protein identification. In our case, that is Q.value, which represents the probability that the protein is a false hit. A typical cutoff is set at 0.01. Fortunately, MaxQuant takes care of this operation and all Q values are below the threshold.

summary(as.numeric(df$Q.value))
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## 0.0000000 0.0000000 0.0000000 0.0007193 0.0012759 0.0091429

Define Protein and Gene IDs

A quick look at Protein.IDs and Fasta.headers columns tells us that the protein IDs, protein names, and gene IDs are not clearly displayed.

head(df$Protein.IDs)
## [1] "sp|A0AV96|RBM47_HUMAN" "sp|A0FGR8|ESYT2_HUMAN" "sp|A1L0T0|ILVBL_HUMAN"
## [4] "sp|A4D1S0|KLRG2_HUMAN" "sp|A5YKK6|CNOT1_HUMAN" "sp|A6NDG6|PGP_HUMAN"
head(df$Fasta.headers)
## [1] ">sp|A0AV96|RBM47_HUMAN RNA-binding protein 47 OS=Homo sapiens GN=RBM47 PE=1 SV=2"                               
## [2] ">sp|A0FGR8|ESYT2_HUMAN Extended synaptotagmin-2 OS=Homo sapiens GN=ESYT2 PE=1 SV=1"                             
## [3] ">sp|A1L0T0|ILVBL_HUMAN Acetolactate synthase-like protein OS=Homo sapiens GN=ILVBL PE=1 SV=2"                   
## [4] ">sp|A4D1S0|KLRG2_HUMAN Killer cell lectin-like receptor subfamily G member 2 OS=Homo sapiens GN=KLRG2 PE=1 SV=3"
## [5] ">sp|A5YKK6|CNOT1_HUMAN CCR4-NOT transcription complex subunit 1 OS=Homo sapiens GN=CNOT1 PE=1 SV=2"             
## [6] ">sp|A6NDG6|PGP_HUMAN Glycerol-3-phosphate phosphatase OS=Homo sapiens GN=PGP PE=1 SV=1"

We will use regular expressions to extract the protein names into a column named Protein.name, UniProt protein IDs into Protein, and gene IDs into Gene. Note that some rows may have multiple IDs separated by semicolons. In those instances, we will isolate the first one available.

# Isolate the first ID
df$Protein.IDs = sub(";.*", "", df$Protein.IDs)
df$Fasta.headers = sub(";.*", "", df$Fasta.headers)
# Extract Protein name
regex = regexpr("(?<=_HUMAN.).*(?=.OS)", df$Fasta.headers, perl = TRUE)
df$Protein.name = regmatches(df$Fasta.headers, regex)
# Extract UniProtID
regex = regexpr("(?<=\\|).*(?=\\|)", df$Protein.IDs, perl = TRUE)
df$Protein = regmatches(df$Protein.IDs, regex)
# Extract Gene ID
regex = regexpr("((?<=\\|[[:alnum:]]{6}\\|).*(?=_HUMAN)|(?<=\\|[[:alnum:]]{10}\\|).*(?=_HUMAN))",
                df$Protein.IDs, perl = TRUE)
df$Gene = regmatches(df$Protein.IDs, regex)

Transform Intensity Columns

Due to our function call for reading the data table, all columns are cast as the character data type. It will benefit our analysis to convert the intensity columns to the proper numeric data type.

intensity.names = grep("^LFQ.intensity", names(raw), value = TRUE)
df[intensity.names] = sapply(df[intensity.names], as.numeric)

Now, let us examine the distribution of protein intensities in a sample. Below is a histogram of the Parental_bR1 protein intensities.

image

The distribution is clearly skewed to the right with a few highly abundant proteins. To normalize the distribution, it is common practice to log₂ transform the intensity data.

LOG.names = sub("^LFQ.intensity", "LOG2", intensity.names)   # rename intensity columns
df[LOG.names] = log2(df[intensity.names])

Let's check out the transformed distribution on Parental_bR1 (much better!):

image

Summary

This is the first of three tutorials on proteomics data analysis. I have outlined the steps to read and clean a typical mass spectrometry-based proteomics data set.

In the next tutorial, we will examine the data in greater detail. In doing so, we will find that only a handful of proteins are quantified across all samples. In other words, proteins are often picked up in one sample but not in the others. This is known as the missing value problem. Stick around to explore techniques for filtering proteins based on the number of valid values and filling in the missing values using data imputation.