- Select dataset from STAR mapping (STAR_...) in Sushi and run ‚FeatureCountApp’ with default options.
- Run CountQCApp on resulting dataset with option runGO=true
- Explore resulting ‚Static Report’:
Are the samples clustering by biological condition (dendrograms in ‚Sample Clustering’)?
Is it worth to generate more reads for underrepresented libraries?
Hint: Compare under Count Statistics ‚totalReads’ vs. ‚GenomicFeaturesWithReadsAboveThreshold’
- Run EdgeRApp on featureCounts-dataset with options
sampleGroup=Glyc_Eth
refGroup=Glucose
runGO=true
It will compare Glyc_Eth vs. Glucose.
- Explore ‚Static Report’:
How many genes are regulated with FC>2 & pValue<0.001 in comparison to all present genes?
Are more genes up- or down-regulated?
What is foldChange and pValue for YHR094C (HXT1, Glucose Transporter)?