☰ Menu

      RNA-Seq Analysis

Home
Introduction and Lectures
Intro to the Workshop and Core
Schedule
What is Bioinformatics/Genomics?
Experimental Design and Cost Estimation
RNA Sequencing Technologies - Dr. Lutz Froenicke
Support
Zoom
Slack
Cheat Sheets
Software and Links
Scripts
Prerequisites
CLI
R
Cluster Computing
Data Reduction
Files and Filetypes
Prepare dataset
Preprocessing raw data
Indexing a Genome
Alignment with Star
Generating counts tables
Alignment/Counts with Salmon (Extra)
Data analysis
Prepare R for data analysis
Annotation from BioMart
Differential Expression Analysis
DE Quizzes R Code
Pathway Analysis
Enrichment Quizzes R Code
Comparison between STAR and Salmon
ETC
Closing thoughts
Workshop Photos
Github page
Report Errors
Biocore website

R code for selected quizzes/questions

Quiz 1

Rerun the KS test analysis using the molecular function (MF) ontology. What is the top GO term listed?

GOdata.1 <- new("topGOdata", ontology = "MF", allGenes = geneList, geneSelectionFun = function(x)x, annot = annFUN.org , mapping = "org.Mm.eg.db")
resultKS.1 <- runTest(GOdata.1, algorithm = "weight01", statistic = "ks")
tab.1 <- GenTable(GOdata.1, raw.p.value = resultKS.1, topNodes = length(resultKS.1@score), numChar = 120)
tab.1[1, "Term"]

How many genes from the top table are annotated with the term ‘actin filament binding’?

tab.1[which(tab.1[,'Term'] == 'actin filament binding'),'Annotated']

Quiz 2

How many pathways have a p-value less than 0.05?

length(which(outdat$p.value < 0.05))

Which pathway has the most genes annotated to it (excluding genes not in the top table)?

outdat$pathway.name[which.max(outdat$Annotated)]