Author(s): Ying Ge; Yijing Chen
Date: 2025-09-22
If you use this code in your work or research, we kindly request that you cite our publication:
Xiaofan Lu, et al. (2025). FigureYa: A Standardized Visualization Framework for Enhancing Biomedical Data Interpretation and Research Efficiency. iMetaMed. https://doi.org/10.1002/imm3.70005
把RNA-seq的FPKM转换成TPM。
Convert FPKM from RNA-seq to TPM
RNA-seq基因表达量是FPKM，想转变成TPM。不仅限于TCGA数据。
FPKM跟TPM的计算都考虑了基因长度，因此相对于read count转TPM来说，从FPKM转TPM最方便快捷。
如果你需要从read count转TPM，请购买FigureYa23count2TPM；从read count转FPKM，请购买FigureYa34count2FPKM。
The RNA-seq gene expression is in FPKM and needs to be converted to TPM. This is not limited to TCGA data.
Both FPKM and TPM calculations consider gene length, making the conversion from FPKM to TPM more convenient and faster than from read count to TPM.
If you need to convert from read count to TPM, please purchase FigureYa23count2TPM; to convert from read count to FPKM, please purchase FigureYa34count2FPKM.
此处以TCGA的数据为例。
如果你自己的RNA-seq数据已经保存成easy_input.txt的格式，就跳过这步，直接进入“输入数据”
This is an example of TCGA data.
If you have saved your own RNA-seq data in
easy_input.txt format, skip this step and go directly to
“Input data”.
source("install_dependencies.R")## Starting R package installation...
## ===========================================
##
## Installing core CRAN dependencies...
## Package already installed: curl
## Package already installed: httr
## Package already installed: rvest
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## Installing core Bioconductor dependencies...
## Package already installed: AnnotationDbi
## Package already installed: BiocFileCache
## Package already installed: GenomicRanges
## Package already installed: SummarizedExperiment
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## Installing Bioconductor packages...
## Package already installed: TCGAbiolinks
## Package already installed: biomaRt
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## ===========================================
## Package installation completed!
## You can now run your R scripts in this directory.#source("https://bioconductor.org/biocLite.R")
#biocLite("TCGAbiolinks")
library(TCGAbiolinks)
expquery <- GDCquery(project = "TCGA-LIHC",
data.category = "Transcriptome Profiling",
data.type = "Gene Expression Quantification",
workflow.type = "STAR - Counts"
)
GDCdownload(expquery)
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|99.5283% ~0 s remaining |=================================================== |99.76415% ~0 s remaining |====================================================|100% ~0 s remaining |====================================================|100% Completed after 25 sexpMatrix <- TCGAanalyze_Preprocessing(expquery2)
#运行下面这行，会把FPKM保存到文件里，用来做其他的分析。
#running the following line will save the FPKM to a file to be used for additional analysis.
#write.table(expMatrix, "easy_input.txt", sep="\t", quote=F, row.names=T)
#此处为了发邮件方便，只保存前4个sample
#Here, only the first 4 samples are saved for emailing convenience
write.table(expMatrix[,1:4], "easy_input.txt", sep="\t", quote=F, row.names=T)第一列是基因ID，第一行是sample ID。
一个单元格内是一个基因在一个sample中的read count。
The first column is the gene ID and the first row is the sample ID.
Inside a cell is the read count of a gene in a sample.
expMatrix<-read.table("easy_input.txt",header = T,row.names = 1)
#查看前三个基因的FPKM值
#view FPKM values for the first three genes
expMatrix[1:3,]## TCGA.2V.A95S.01A.11R.A37K.07 TCGA.2Y.A9GS.01A.12R.A38B.07
## ENSG00000000003.15 3231 5531
## ENSG00000000005.6 0 0
## ENSG00000000419.13 1083 1597
## TCGA.2Y.A9GT.01A.11R.A38B.07 TCGA.2Y.A9GU.01A.11R.A38B.07
## ENSG00000000003.15 9031 5363
## ENSG00000000005.6 4 0
## ENSG00000000419.13 740 1334rsem作者Bo Li的文字描述： https://groups.google.com/forum/#!topic/rsem-users/uajT7gnTj-0
将FPKM/RPKMs转换为TPMs非常简单。首先将所有基因的FPKM归一化，使其总和等于1，然后将每个归一化值乘以 1e6，即可得到TPMs。
先写个函数，来源 ：https://haroldpimentel.wordpress.com/2014/05/08/what-the-fpkm-a-review-rna-seq-expression-units/
Text description by rsem author Bo Li: https://groups.google.com/forum/#!topic/rsem-users/uajT7gnTj-0
It is very easy to convert FPKM/RPKMs to TPMs. You first normalize your FPKMs from all genes so that their sum is equal to 1. Then you product 1e6 to each normalized value and you will obtain TPMs.
Write a function first , source : https://haroldpimentel.wordpress.com/2014/05/08/what-the-fpkm-a-review-rna-seq-expression-units/
fpkmToTpm <- function(fpkm)
{
exp(log(fpkm) - log(sum(fpkm)) + log(1e6))
}计算TPM值，保存到tpms里
Calculate TPM value, save to tpms
tpms <- apply(expMatrix,2,fpkmToTpm)
#查看前三个基因的TPM值
#view TPM values for the first three genes
tpms[1:3,]## TCGA.2V.A95S.01A.11R.A37K.07 TCGA.2Y.A9GS.01A.12R.A38B.07
## ENSG00000000003.15 73.71188 113.9362
## ENSG00000000005.6 0.00000 0.0000
## ENSG00000000419.13 24.70751 32.8975
## TCGA.2Y.A9GT.01A.11R.A38B.07 TCGA.2Y.A9GU.01A.11R.A38B.07
## ENSG00000000003.15 222.08919659 127.23087
## ENSG00000000005.6 0.09836749 0.00000
## ENSG00000000419.13 18.19798533 31.64758#检查一下，是不是每列的总和都是1
#check to see if the sum of each column is 1
colSums(tpms)## TCGA.2V.A95S.01A.11R.A37K.07 TCGA.2Y.A9GS.01A.12R.A38B.07
## 1e+06 1e+06
## TCGA.2Y.A9GT.01A.11R.A38B.07 TCGA.2Y.A9GU.01A.11R.A38B.07
## 1e+06 1e+06#把TPM值保存到文件
#save TPM values to file
write.table(tpms,"TCGA_FPKM2TPM.genes.txt",quote = F)sessionInfo()## R version 4.5.1 (2025-06-13)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
## [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
## [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
## [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
##
## time zone: UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] TCGAbiolinks_2.36.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 dplyr_1.1.4
## [3] farver_2.1.2 blob_1.2.4
## [5] filelock_1.0.3 Biostrings_2.76.0
## [7] S7_0.2.0 fastmap_1.2.0
## [9] BiocFileCache_2.16.2 promises_1.3.3
## [11] XML_3.99-0.19 digest_0.6.37
## [13] lifecycle_1.0.4 processx_3.8.6
## [15] KEGGREST_1.48.1 RSQLite_2.4.3
## [17] magrittr_2.0.4 compiler_4.5.1
## [19] rlang_1.1.6 sass_0.4.10
## [21] progress_1.2.3 tools_4.5.1
## [23] yaml_2.3.10 data.table_1.17.8
## [25] knitr_1.50 prettyunits_1.2.0
## [27] S4Arrays_1.8.1 bit_4.6.0
## [29] curl_7.0.0 DelayedArray_0.34.1
## [31] plyr_1.8.9 xml2_1.4.0
## [33] RColorBrewer_1.1-3 websocket_1.4.4
## [35] abind_1.4-8 withr_3.0.2
## [37] purrr_1.1.0 BiocGenerics_0.54.0
## [39] grid_4.5.1 stats4_4.5.1
## [41] ggplot2_4.0.0 scales_1.4.0
## [43] dichromat_2.0-0.1 biomaRt_2.64.0
## [45] SummarizedExperiment_1.38.1 cli_3.6.5
## [47] rmarkdown_2.29 crayon_1.5.3
## [49] generics_0.1.4 httr_1.4.7
## [51] tzdb_0.5.0 DBI_1.2.3
## [53] cachem_1.1.0 chromote_0.5.1
## [55] stringr_1.5.2 rvest_1.0.5
## [57] AnnotationDbi_1.70.0 TCGAbiolinksGUI.data_1.28.0
## [59] XVector_0.48.0 matrixStats_1.5.0
## [61] vctrs_0.6.5 Matrix_1.7-3
## [63] jsonlite_2.0.0 IRanges_2.42.0
## [65] hms_1.1.3 S4Vectors_0.46.0
## [67] bit64_4.6.0-1 tidyr_1.3.1
## [69] jquerylib_0.1.4 glue_1.8.0
## [71] ps_1.9.1 stringi_1.8.7
## [73] gtable_0.3.6 later_1.4.4
## [75] GenomeInfoDb_1.44.2 GenomicRanges_1.60.0
## [77] UCSC.utils_1.4.0 tibble_3.3.0
## [79] pillar_1.11.1 rappdirs_0.3.3
## [81] htmltools_0.5.8.1 GenomeInfoDbData_1.2.14
## [83] R6_2.6.1 dbplyr_2.5.1
## [85] httr2_1.2.1 evaluate_1.0.5
## [87] lattice_0.22-7 Biobase_2.68.0
## [89] readr_2.1.5 png_0.1-8
## [91] memoise_2.0.1 bslib_0.9.0
## [93] Rcpp_1.1.0 SparseArray_1.8.1
## [95] downloader_0.4.1 xfun_0.53
## [97] MatrixGenerics_1.20.0 pkgconfig_2.0.3