Proteomics Data Analysis in R/Bioconductor
Welcome!
1
Isobaric Quantification: Proteomics
1.1
Prepare MS/MS Identifications
1.1.1
Read MS-GF+ Data
1.1.2
Correct Isotope Selection Error
1.1.3
Remove Contaminants
1.1.4
MS/MS ID Filter: Peptide Level
1.1.5
MS/MS ID Filter: Protein Level
1.1.6
Inference of Parsimonious Protein Set
1.1.7
Remove Decoy PSMs
1.2
Prepare Reporter Ion Intensities
1.2.1
Read MASIC Output
1.2.2
Filter MASIC Data
1.3
Create Study Design Tables
1.3.1
Fractions
1.3.2
Samples
1.3.3
References
1.4
Create Quantitative Cross-tab
1.5
Create MSnSet
2
Isobaric Quantification: Phosphoproteomics
2.1
Prepare MS/MS Identifications
2.1.1
Read MS-GF+ Data
2.1.2
Correct Isotope Selection Error
2.1.3
Remove Unmodified Peptides
2.1.4
Remove Contaminants
2.1.5
Improve Phosphosite Localization
2.1.6
MS/MS ID Filter: Peptide Level
2.1.7
MS/MS ID Filter: Protein Level
2.1.8
Inference of Parsimonious Protein Set
2.1.9
Map Sites to Protein Sequences
2.1.10
Remove Decoy PSMs
2.2
Prepare Reporter Ion Intensities
2.2.1
Read MASIC Output
2.2.2
Filter MASIC Data
2.3
Create Study Design Tables
2.4
Create Quantitative Cross-tab
2.5
Create MSnSet
3
Spectral Counting
4
Feature ID Conversion
4.1
Conversion with
biomaRt
4.2
Conversion with
AnnotationHub
4.3
Conversion Using FASTA Headers
5
Exploratory Data Analysis
5.1
Count Features in Samples
5.2
Sample Boxplots
5.3
Estimate Blood Contamination
5.4
PCA
6
Heatmaps
6.1
Expression Heatmaps
6.2
Correlation Heatmaps
6.2.1
Sample Correlation
6.2.2
Feature Correlation
6.3
Heatmap Annotation
6.3.1
Modifying Default Colors
6.4
Modifications
6.4.1
Row and column labels
6.4.2
Label colors
6.4.3
Label specific features
6.4.4
Heatmap body color
6.4.5
Horizontal heatmaps
6.4.6
Legends
7
Differential Analysis
7.1
Linear Regression
7.2
Two-Sample t-tests
7.2.1
One comparison
7.2.2
Multiple comparisons
7.3
One-Way ANOVA
7.4
p-value Histograms
7.5
Volcano Plots
7.5.1
Base plot
7.5.2
Label top features
7.5.3
Label specific features
7.5.4
Modify point colors
7.5.5
Multiple volcano plots
7.6
UpSet Plots
8
Pathway Analysis
8.1
Annotation Databases
8.1.1
Gene Ontology
8.1.1.1
Semantic Similarity
8.1.1.2
GO Subsets/Slims
8.1.2
Reactome
8.1.3
KEGG
8.1.4
MSigDB
8.2
Over-Representation Analysis
8.2.1
Overview
8.2.2
Mathematical Details
8.2.3
Drawbacks
8.2.4
Examples
8.2.4.1
ORA with
fgsea
8.2.4.2
ORA with
clusterProfiler
/
ReactomePA
8.2.4.3
ORA with
GOstats
8.3
Gene Set Enrichment Analysis
8.3.1
Overview
8.3.2
Examples
8.3.2.1
GSEA with
fgsea
8.3.2.2
GSEA with
clusterProfiler
/
ReactomePA
References
Proteomics Data Analysis in R/Bioconductor
5.2
Sample Boxplots
boxplot
(
exprs
(oca.set))
