小编注：近年来RNA-Seq被广泛应用，衡量基因表达水平的方法也开始多样化，例如RPK, RPKM, FPKM, TPM等。那他们之间有什么样的区别、以及怎样换算呢。之前PLoB上分享了一篇关于这方面介绍的文章《RNA-Seq基因表达水平衡量方法》，主要侧重于公式计算，今天这里再与大家分享一篇，另外一篇文章。
It used to be when you did RNA-seq, you reported your results in RPKM (Reads Per Kilobase Million) or FPKM (Fragments Per Kilobase Million). However, TPM (Transcripts Per Kilobase Million) is now becoming quite popular. Since there seems to be a lot of confusion about these terms, I thought I’d use a StatQuest to clear everything up.
These three metrics attempt to normalize for sequencing depth and gene length. 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)
- Divide the RPM values by the length of the gene, in kilobases. This gives you RPKM.
FPKM is very similar to RPKM. RPKM was made for single-end RNA-seq, where every read corresponded to a single fragment that was sequenced. FPKM was made for paired-end RNA-seq. With paired-end RNA-seq, two reads can correspond to a single fragment, or, if one read in the pair did not map, one read can correspond to a single fragment. The only difference between RPKM and FPKM is that FPKM takes into account that two reads can map to one fragment (and so it doesn’t count this fragment twice).
TPM is very similar to RPKM and FPKM. The only difference is the order of operations. 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. This is your “per million” scaling factor.
- Divide the RPK values by the “per million” scaling factor. This gives you TPM.
So you see, when calculating TPM, the only difference is that you normalize for gene length first, and then normalize for sequencing depth second. However, the effects of this difference are quite profound.
When you use TPM, the sum of all TPMs in each sample are the same. This makes it easier to compare the proportion of reads that mapped to a gene in each sample. In contrast, with RPKM and FPKM, the sum of the normalized reads in each sample may be different, and this makes it harder to compare samples directly.
Here’s an example. If the TPM for gene A in Sample 1 is 3.33 and the TPM in sample B is 3.33, then I know that the exact same proportion of total reads mapped to gene A in both samples. This is because the sum of the TPMs in both samples always add up to the same number (so the denominator required to calculate the proportions is the same, regardless of what sample you are looking at.)
With RPKM or FPKM, the sum of normalized reads in each sample can be different. Thus, if the RPKM for gene A in Sample 1 is 3.33 and the RPKM in Sample 2 is 3.33, I would not know if the same proportion of reads in Sample 1 mapped to gene A as in Sample 2. This is because the denominator required to calculate the proportion could be different for the two samples.