Contents
Why do you normalize RNA-Seq data?
The first step in the DE analysis workflow is count normalization, which is necessary to make accurate comparisons of gene expression between samples. The counts of mapped reads for each gene is proportional to the expression of RNA (“interesting”) in addition to many other factors (“uninteresting”).
What can we do with RNA-Seq data?
Some of the most popular techniques that use RNA-seq are transcriptional profiling, SNP identification, RNA editing and differential gene expression analysis3. This can give researchers vital information about the function of genes.
How long does RNA-Seq take?
The sequencing reactions can take between 1.5 and 12 d to complete, depending on the total read length of the library. Even more recently, Illumina released the MiSeq, a desktop sequencer with lower throughput but faster turnaround (generates ∼30 million paired-end reads in 24 h).
How are RPKM and TPM used in RNA Seq?
To normalize these dependencies, RPKM (reads per kilobase of transcript per million reads mapped) and TPM (transcripts per million) are used to measure gene or transcript expression levels. A common misconception is that RPKM and TPM values are already normalized, and thus should be comparable across samples or RNA-seq projects.
How to run PCA on a single cell RNA-Seq dataset?
Here are some simple examples on how to run PCA/Clustering on a single cell RNA-seq dataset. The dataset used is single-cell RNA-seq data from mouse embryonic development from Deng. et al. Science 2014, Vol. 343 no. 6167 pp. 193-196, “Single-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells”
How do you calculate the TPM of a gene?
Here’s how you calculate TPM: Divide the read counts by the length of each gene in kilobases. This gives you reads per kilobase (RPK). Count up all the RPK values in a sample and divide this number by 1,000,000.
When to use RPKM or TPM when comparing across samples?
However, RPKM and TPM represent the relative abundance of a transcript among a population of sequenced transcripts, and therefore depend on the composition of the RNA population in a sample. Quite often, it is reasonable to assume that total RNA concentration and distributions are very close across compared samples.