Created
December 8, 2015 16:14
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Comparison of RPKM (reads per kilobase per million) and TPM (transcripts per million).
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# RPKM versus TPM | |
# | |
# RPKM and TPM are both normalized for library size and gene length. | |
# | |
# RPKM is not comparable across different samples. | |
# | |
# For more details, see: http://blog.nextgenetics.net/?e=51 | |
rpkm <- function(counts, lengths) { | |
rate <- counts / lengths | |
rate / sum(counts) * 1e6 | |
} | |
tpm <- function(counts, lengths) { | |
rate <- counts / lengths | |
rate / sum(rate) * 1e6 | |
} | |
genes <- data.frame( | |
Gene = c("A","B","C","D","E"), | |
Length = c(100, 50, 25, 5, 1) | |
) | |
counts <- data.frame( | |
S1 = c(80, 10, 6, 3, 1), | |
S2 = c(20, 20, 10, 50, 400) | |
) | |
rpkms <- apply(counts, 2, function(x) rpkm(x, genes$Length)) | |
tpms <- apply(counts, 2, function(x) tpm(x, genes$Length)) | |
genes | |
# Gene Length | |
# 1 A 100 | |
# 2 B 50 | |
# 3 C 25 | |
# 4 D 5 | |
# 5 E 1 | |
counts | |
# S1 S2 | |
# 1 80 20 | |
# 2 10 20 | |
# 3 6 10 | |
# 4 3 50 | |
# 5 1 400 | |
rpkms | |
# S1 S2 | |
# [1,] 8000 4e+02 | |
# [2,] 2000 8e+02 | |
# [3,] 2400 8e+02 | |
# [4,] 6000 2e+04 | |
# [5,] 10000 8e+05 | |
tpms | |
# S1 S2 | |
# [1,] 281690.14 486.618 | |
# [2,] 70422.54 973.236 | |
# [3,] 84507.04 973.236 | |
# [4,] 211267.61 24330.900 | |
# [5,] 352112.68 973236.010 | |
# Sample means should be equal. | |
colSums(rpkms) | |
# S1 S2 | |
# 28400 822000 | |
colSums(tpms) | |
# S1 S2 | |
# 1e+06 1e+06 | |
colMeans(rpkms) | |
# S1 S2 | |
# 5680 164400 | |
colMeans(tpms) | |
# S1 S2 | |
# 2e+05 2e+05 |
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Thank you so much!