FPKM, RPKM, and TPM
In gene expression analysis, three critical metrics often arise: FPKM (Fragments Per Kilobase of transcript per million mapped reads), RPKM (Reads Per Kilobase of transcript per million mapped reads), and TPM (Transcripts Per Million). Understanding these concepts is essential for interpreting RNA-seq data accurately.
What are FPKM and RPKM?
FPKM and RPKM are both normalization methods used to measure gene expression levels. FPKM accounts for the sequencing depth and gene length, allowing researchers to compare expression levels across different genes within a single sample. Similarly, RPKM provides comparable data between different samples, making it a preferred choice in various studies.
The Importance of TPM
TPM has emerged as a popular alternative to FPKM and RPKM. Unlike the other two metrics, TPM normalizes the read counts by the total number of reads and adjusts for gene length simultaneously. This method allows for a more accurate comparison of gene expression levels across multiple samples. TPM also retains the relative abundance of transcripts, providing clearer insights into gene expression dynamics.
In conclusion, while FPKM, RPKM, and TPM serve similar purposes in gene expression analysis, each method has its advantages. Researchers must choose the suitable metric based on their specific analysis needs to ensure accurate data interpretation.
Table 1: Comparing FPKM, RPKM, and TPM, which are commonly used normalization methods in RNA-Seq analysis.
| Metric | Full Form | Definition | Normalization Approach | Usage | Key Difference |
|---|---|---|---|---|---|
| RPKM | Reads Per Kilobase per Million mapped reads | Normalizes for gene length and total mapped reads to compare gene expression within a single sample | Reads per kilobase of transcript per million mapped reads | Used for single-sample comparisons | RPKM is used in single-end RNA-Seq data |
| FPKM | Fragments Per Kilobase per Million mapped reads | Similar to RPKM but used for paired-end RNA-Seq, where a fragment can generate two reads | Accounts for fragments instead of reads | Preferred for paired-end RNA-Seq | Equivalent to RPKM but for paired-end reads |
| TPM | Transcripts Per Million | Normalizes for gene length and total transcript abundance across samples to compare gene expression across multiple samples | First normalizes by gene length, then scales so that the sum of TPM values in a sample equals 1 million | Best for cross-sample comparisons | TPM ensures sum of expression values is constant across samples |
Takeaways
- Use RPKM/FPKM when comparing gene expression within a sample.
- Use TPM when comparing gene expression across samples, as it is more robust to sequencing depth differences.
