IceR - Quantitative Label-Free Proteomics Workflow

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Label-free proteomics enables the unbiased quantification of thousands of proteins across large sample cohorts. Commonly used mass spectrometry-based proteomic workflows rely on data dependent acquisition (DDA). However, its stochastic selection of peptide features for fragmentation-based identification inevitably results in high rates of missing values, which prohibits the integration of larger cohorts as the number of recurrently detected peptides is a limiting factor. Peptide identity propagation (PIP) can mitigate this challenge, allowing to transfer sequencing information between samples. However, despite the promise of these approaches, current methods remain limited either in sensitivity or reliability and there is a lack of robust and widely applicable software. To address this, we here present IceR, an efficient and user-friendly quantification workflow introducing a hybrid PIP approach with superior quantification precision, accuracy, reliability and data completeness. IceR is available as an easy to-use R-package incorporating a graphical user interface and comprehensive quality control measures.

Code Snippets

install.packages("devtools")
devtools::install_github("mathiaskalxdorf/IceR")

Markdown devtools From line 27 of master/README.md

library(IceR)
runIceR()

Markdown From line 32 of master/README.md

sudo apt-get update
sudo apt-get install r-base
sudo apt install build-essential libcurl4-gnutls-dev libxml2-dev libssl-dev libgit2-dev libv8-dev
sudo apt-get install r-base-dev default-jdk

Markdown R From line 42 of master/README.md

sudo -i R

Markdown From line 51 of master/README.md

install.packages('devtools')
devtools::install_github("mathiaskalxdorf/IceR")

Markdown devtools From line 57 of master/README.md

Markdown From line 64 of master/README.md

library(IceR)
runIceR()

Markdown From line 70 of master/README.md

install.packages('devtools')
devtools::install_github("mathiaskalxdorf/IceR")

Markdown devtools From line 94 of master/README.md

library(IceR)
runIceR()

Markdown From line 103 of master/README.md

library(IceR)
runIceR()

Markdown From line 134 of master/README.md

install.packages("BiocManager")
BiocManager::install(c("PECA","limma","vsn"))

Markdown limma BiocManager From line 172 of master/README.md

177	library(IceR)

Markdown From line 177 of master/README.md

IceR <- load_Requant_data()
MaxQ <- load_MaxQ_data()

Markdown From line 183 of master/README.md

anno <- data.frame(Spike = c(3,3,9,9))
IceR <- add_annotations(IceR,anno)
MaxQ <- add_annotations(MaxQ,anno)

Markdown From line 188 of master/README.md

IceR$Protein_level$Quant_data_norm <- normalize_data(IceR$Protein_level$Quant_data,method = "median",main = "IceR - Protein-level data",norm_on_subset = which(IceR$Protein_level$Meta_data$Organism == "Homo sapiens"))
IceR$Peptide_level$Quant_data_norm <- normalize_data(IceR$Peptide_level$Quant_data,method = "median",main = "IceR - Peptide-level data",norm_on_subset = which(IceR$Peptide_level$Meta_data$Organism == "Homo sapiens"))
MaxQ$Protein_level$Quant_data_norm <- normalize_data(MaxQ$Protein_level$Quant_data,method = "median",main = "MaxQ - Protein-level data",norm_on_subset = which(MaxQ$Protein_level$Meta_data$Organism == "Homo sapiens"))
MaxQ$Peptide_level$Quant_data_norm <- normalize_data(MaxQ$Peptide_level$Quant_data,method = "median",main = "MaxQ - Peptide-level data",norm_on_subset = which(MaxQ$Peptide_level$Meta_data$Organism == "Homo sapiens"))

Markdown From line 194 of master/README.md

MaxQ <- determine_general_numbers(MaxQ)
IceR <- determine_general_numbers(IceR)

compare_general_numbers(list(MaxQ=MaxQ,IceR=IceR),colors = c("darkgrey","chocolate2"))

Markdown From line 205 of master/README.md

plot_accuracy(list(MaxQ=MaxQ,IceR=IceR),inset = c(0,0),Legendpos = "topright",colors = c("chocolate2","darkgrey"))

Markdown From line 218 of master/README.md

DE_MaxQ <- LIMMA_analysis(MaxQ$Protein_level$Quant_data_norm,assignments = MaxQ$Annotations$Spike,contrast = "9_vs_3")
DE_IceR <- PECA_analysis(IceR$Peptide_level$Quant_data_norm,ids = IceR$Peptide_level$Meta_data$Gene_name,anno = IceR$Annotations$Spike,group1_name = "9",group2_name = "3")

Markdown From line 229 of master/README.md

plot(DE_MaxQ$logFC,-log10(DE_MaxQ$P.Value),xlab="Ratio, log2",ylab="-log10 pval",main="MaxQ - 9 % vs 3 % spike-in",col=ifelse(abs(DE_MaxQ$logFC)>=1 & DE_MaxQ$adj.P.Val < 0.05,"red","black"),pch=ifelse(rownames(DE_MaxQ) != toupper(rownames(DE_MaxQ)),19,1),xlim=c(-3,3))
abline(h=-log10(0.05),lty=2)
abline(v=1,lty=2)
abline(v=-1,lty=2)
abline(v=log2(9/3),lty=3,col="red")
legend("top",legend = c("significant","not significant","Spiked","Background"),title = "Legend",col=c("red","black","black","black"),pch=c(15,15,19,1))

plot(DE_IceR$logFC,-log10(DE_IceR$P.Value),xlab="Ratio, log2",ylab="-log10 pval",main="IceR - 9 % vs 3 % spike-in",col=ifelse(abs(DE_IceR$logFC)>=1 & DE_IceR$adj.P.Val < 0.05,"red","black"),pch=ifelse(rownames(DE_IceR) != toupper(rownames(DE_IceR)),19,1),xlim=c(-4,4))
abline(h=-log10(0.05),lty=2)
abline(v=1,lty=2)
abline(v=-1,lty=2)
abline(v=log2(9/3),lty=3,col="red")
legend("top",legend = c("significant","not significant","Spiked","Background"),title = "Legend",col=c("red","black","black","black"),pch=c(15,15,19,1))

MaxQ_TP <- length(which(DE_MaxQ$logFC>=1 & DE_MaxQ$adj.P.Val < 0.05 & rownames(DE_MaxQ) != toupper(rownames(DE_MaxQ))))
MaxQ_FP <- length(which(abs(DE_MaxQ$logFC)>=1 & DE_MaxQ$adj.P.Val < 0.05 & rownames(DE_MaxQ) == toupper(rownames(DE_MaxQ))))
IceR_TP <- length(which(DE_IceR$logFC>=1 & DE_IceR$adj.P.Val < 0.05 & rownames(DE_IceR) != toupper(rownames(DE_IceR))))
IceR_FP <- length(which(abs(DE_IceR$logFC)>=1 & DE_IceR$adj.P.Val < 0.05 & rownames(DE_IceR) == toupper(rownames(DE_IceR))))
plot_Data <- data.frame(MaxQ=c(MaxQ_TP,MaxQ_FP),IceR=c(IceR_TP,IceR_FP))
p <- Barplotsstacked(plot_Data,AvgLine = F,col=c("lightblue","grey"),margins = c(4,4,4,12),Legends = c("true positives","false positives"),Legendpos = "top",inset = c(0,0),main="Comparison of DE results",ylab="Count")