8.3 Gene expression analysis using high-throughput sequencing technologies | Computational Genomics with R

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• observe here whether the samples from the case group (CASE) and samples from the control group (CTRL) can be split into two distinct clusters

• improve the quality of the input reads.

• against the null hypothesis

• activity of the gene stays the same in two different condition

• equal t

• uared coefficient of variation

• A line

• An MA plot is useful to observe if the data normalization worked wel

• x-axis denotes the average of normalized counts

• the log fold change i

• horizontal 0 line

• genes are not

• differentially expressed)

• check the biological reproducibility of the sample replicates

• inputs

• A design formula

• colData tabl

• ead count table

• a subset of the count table

• op 100 most variable genes