Removing unwanted variation from large-scale rna sequencing data with prps

1. How do you normalize gene expression data?
2. Which RNA must be removed from a sample before performing RNA seq?
3. How do you normalize RPKM?

How do you normalize gene expression data?

Normalization is achieved by dividing expression values by the total intensity (i.e., the sum of all expression values) of the given array. Centralization¹¹ assumes that regulation is well behaved, i.e., most genes are not significantly regulated or about equal numbers of genes are up- and down-regulated.

Which RNA must be removed from a sample before performing RNA seq?

Ribosomal RNA (rRNA) is the most highly abundant component of total RNA isolated from animal or human cells and tissues, comprising the majority (>80% to 90%) of the molecules in a total RNA sample⁷. To allow efficient transcript/gene detection, highly abundant rRNAs must be removed from total RNA before sequencing.

How do you normalize RPKM?

Here's how you do it for RPKM: Count up the total reads in a sample and divide that number by 1,000,000 – this is our “per million” scaling factor. Divide the read counts by the “per million” scaling factor. This normalizes for sequencing depth, giving you reads per million (RPM)