Loading
Raw counts are how many sequencing reads hit each gene; TPM and FPKM rescale them for gene length and sequencing depth so genes and samples can be compared, and log2 TPM is the usual model input.
RNA-Seq analysis quantifies expression from read counts per gene (Wikipedia); because longer genes and deeper libraries collect more reads, counts are normalised to fragments per kilobase per million (FPKM) or transcripts per million (TPM), which sum to a constant per sample. Quantifiers such as Salmon estimate transcript abundance and TPM directly. Models usually take log2(TPM + 1); mixing units, or mixing TPM from one pipeline with counts from another, is a classic source of batch effects.
Shares Quantile normalisation, rank transforms and z-scores, Batch effects and harmonisation, Bulk RNA sequencing (RNA-seq) and the full transcriptome, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Quantile normalisation, rank transforms and z-scores, Batch effects and harmonisation, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Batch effects and harmonisation, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Units of measurement ontology (UO), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Bulk RNA sequencing (RNA-seq) and the full transcriptome, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares STAR and Salmon (RNA-seq alignment and quantification), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Units of measurement ontology (UO), Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Batch effects and harmonisation, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.