To install and load RNAAgeCalc
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") ::install("RNAAgeCalc")BiocManager
It has been shown that both DNA methylation and RNA transcription are linked to chronological age and age related diseases. Several estimators have been developed to predict human aging from DNA level and RNA level. Most of the human transcriptional age predictor are based on microarray data and limited to only a few tissues. To date, transcriptional studies on aging using RNASeq data from different human tissues is limited. The aim of this package is to provide a tool for across-tissue and tissue-specific transcriptional age calculation based on Genotype-Tissue Expression (GTEx) RNASeq data (Lonsdale et al. 2013).
We utilized the GTEx data to construct our across-tissue and tissue-specific transcriptional age calculator. GTEx is a public available genetic database for studying tissue specific gene expression and regulation. GTEx V6 release contains gene expression data at gene, exon, and transcript level of 9,662 samples from 30 different tissues. To avoid the influence of tumor on gene expression, the 102 tumor samples from GTEx V6 release are dropped and the remaining 9,560 samples were used in the subsequent analysis. To facilitate integrated analysis and direct comparison of multiple datasets, we utilized recount2 (Collado-Torres et al. 2017) version of GTEx data, where all samples were processed with the same analytical pipeline. FPKM values were calculated for each individual sample using
getRPKM function in Bioconductor package recount.
For the tissue-specific RNASeq age calculator, elastic net (Zou and Hastie 2005) algorithm was used to train the predictors for each individual tissue. Chronological age was response variable whereas logarithm transformed FPKM of genes were predictors. The across-tissue calculator was constructed by first performing differential expression analysis on the RNASeq counts data for each individual tissue. To identify genes consistently differentially expressed across tissues, we adapted the binomial test discussed in de Magalhaes et al. (De Magalhães, Curado, and Church 2009) to find the genes with the largest number of age-related signals. A detailed explanation can be found in our paper.
The package is implemented as follows. For each tissue, signature and sample type (see below for the descriptions), we pre-trained the calculator using elastic net based on the GTEx samples. We saved the pre-trained model coefficients as internal data in the package. The package takes gene expression data as input and then match the input genes to the genes in the internal data. This matching process is automatic so that the users just need to provide gene expression data without having to pull out the internal coefficients.
The main functions to calculate RNASeq age are
predict_age_fromse. Users can use either of them.
predict_age function takes data frame as input whereas
predict_age_fromse function takes SummarizedExperiment as input. Both functions work in the same way internally. In this section, we explain the arguments in
exprdata is a matrix or data frame which contains gene expression data with each row represents a gene and each column represents a sample. Users are expected to use the argument
exprtype to specify raw count or FPKM (see below). The rownames of
exprdata should be gene ids and colnames of
exprdata should be sample ids. Here is an example of FPKM expression data:
SRR2166176 SRR2167642 AFF4 23.478648 21.7803347 ZGLP1 1.880514 2.6874455 CHDH 6.062126 5.9185735 MIR6801 0.000000 0.3882558 ZFP90 6.478456 7.1444598 KDM6B 5.849882 5.1937368
tissue is a string indicates which tissue the gene expression data is obtained from. Users are expected to provide one of the following tissues. If the tissue argument is not provide or the provided tissue is not in this list, the age predictor trained on all tissues will be used to calculate RNA age.
exprtype is either “counts” or “FPKM”. If
exprtype is counts, the expression data will be converted to FPKM by
count2FPKM automatically and the calculator will be applied on FPKM data. When calculating FPKM, by default gene length is obtained from the package’s internal database. The internal gene length information was obtained from recount2. However, users are able to provide their own gene length information by using
genelength argument (see below).
idtype is a string which indicates the gene id type in
exprdata. Default is “SYMBOL”. The following id types are supported.
stype is a string which specifies which version of pre-trained calculators to be used. Two versions are provided. If
stype="all", the calculator trained on samples from all races (American Indian/Alaska Native, Asian, Black/African American, and Caucasian) will be used. If
stype="Caucasian", the calculator trained on Caucasian samples only will be used. We found that RNA Age signatures could be different in different races (see our paper for details). Thus we provide both the universal calculator and race specific calculator. The race specific calculator for American Indian/Alaska Native, Asian, or Black/African American are not provided due to the small sample size in GTEx data.
signature is a string which indicate the age signature to use when calculating RNA age. This argument is not required.
In the case that this argument is not provided, if
tissue argument is also provided and the tissue is in the list above, the tissue specific age signature given by our DESeq2 analysis result on GTEx data will be used. Otherwise, the across tissue signature “GTExAge” will be used.
In the case that this argument is provided, it should be one of the following signatures.
If the genes in
exprdata do not cover all the genes in the signature, imputation will be made automatically by the
impute.knn function in Bioconductor package impute.
genelength is a vector of gene length in bp. The size of
genelength should be equal to the number of rows in
exprdata. This argument is optional. When using
exprtype = "FPKM",
genelength argument is ignored. When using
exprtype = "counts", the raw count will be converted to FPKM. If
genelength is provided, the function will convert raw count to FPKM based on the user-supplied gene length. Otherwise, gene length is obtained from the internal database.
chronage is a data frame which contains the chronological age of each sample. This argument is optional.
If provided, it should be a dataframe with 1st column sample id and 2nd column chronological age. The sample order in
chronage doesn’t have to be in the same order as in
exprdata. However, the samples in
exprdata should be the same. If some samples’ chronological age are not available, users are expected to set the chronological age in
chronage to NA. If
chronage contains more than 2 columns, only the first 2 columns will be considered. If more than 30 samples’ chronological age are available, age acceleration residual will be calculated. Age acceleration residual is defined as the residual of linear regression with RNASeq age as dependent variable and chronological age as independent variable.
If this argument is not provided, the age acceleration residual will not be calculated.
data.frame(sampleid = colnames(fpkm), age = c(30,50)) chronage = predict_age(exprdata = fpkm, tissue = "brain", exprtype = "FPKM", res =chronage = chronage)
signature is not provided, using DESeq2 signature automatically.
RNAAge ChronAge SRR2166176 27.32436 30 SRR2167642 49.16285 50
In the above example, we calculated the RNASeq age for 2 samples based on their gene expression data coming from brain. Since the sample size is small, age acceleration residual are not calculated.
Here is an example with sample size > 30:
# This example is just for illustration purpose. It does not represent any # real data. # construct a large gene expression data cbind(fpkm, fpkm+1, fpkm+2, fpkm+3) fpkm_large = cbind(fpkm_large, fpkm_large, fpkm_large, fpkm_large) fpkm_large =colnames(fpkm_large) = paste0("sample",1:32) # construct the samples' chronological age data.frame(sampleid = colnames(fpkm_large), age = 31:62) chronage2 = predict_age(exprdata = fpkm_large, exprtype = "FPKM", res2 =chronage = chronage2) head(res2)
RNAAge ChronAge AgeAccelResid sample1 34.84154 31 -31.254133 sample2 49.77605 32 -16.909233 sample3 65.00385 33 -2.271030 sample4 76.91602 34 9.051533 sample5 82.66541 35 14.211328 sample6 93.07443 36 24.030742
The main difference between
predict_age is that
predict_age_fromse takes SummarizedExperiment as input. The
se argument is a SummarizedExperiment object.
assays(se)should contain gene expression data. The name of
assays(se)should be either “FPKM” or “counts”. Use
exprtypeargument to specify the type of gene expression data provided.
colData(se). This is optional. If provided, the column name for chronological age in
colData(se)should be “age”. If some samples’ chronological age are not available, users are expected to set the chronological age in
colData(se)to NA. If chronological age is not provided, the age acceleration residual will not be calculated.
rowData(se). This is also optional. If using
exprtype = "FPKM", the provided gene length will be ignored. If provided, the column name for gene length in
rowData(se)should be “bp_length”. The function will convert raw count to FPKM by the user-supplied gene length. Otherwise, gene length is obtained from the internal database.
signatureare exactly the same as described in
library(SummarizedExperiment) data.frame(age = c(40, 50)) colData = SummarizedExperiment(assays=list(FPKM=fpkm), colData=colData) se = predict_age_fromse(se = se, exprtype = "FPKM") res3 =head(res3)
RNAAge ChronAge SRR2166176 34.84154 40 SRR2167642 49.77605 50
We suggest visualizing the results by plotting RNAAge vs chronological age. This can be done by calling
makeplot function and passing in the data frame returned by
R Under development (unstable) (2022-10-25 r83175) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 22.04.1 LTS Matrix products: default BLAS: /home/biocbuild/bbs-3.17-bioc/R/lib/libRblas.so LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0 locale:  LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C  LC_TIME=en_GB LC_COLLATE=C  LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8  LC_PAPER=en_US.UTF-8 LC_NAME=C  LC_ADDRESS=C LC_TELEPHONE=C  LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C attached base packages:  stats4 stats graphics grDevices utils datasets methods  base other attached packages:  SummarizedExperiment_1.29.0 Biobase_2.59.0  GenomicRanges_1.51.0 GenomeInfoDb_1.35.0  IRanges_2.33.0 S4Vectors_0.37.0  BiocGenerics_0.45.0 MatrixGenerics_1.11.0  matrixStats_0.62.0 RNAAgeCalc_1.11.0 loaded via a namespace (and not attached):  RColorBrewer_1.1-3 rstudioapi_0.14 jsonlite_1.8.3  magrittr_2.0.3 GenomicFeatures_1.51.0 farver_2.1.1  rmarkdown_2.17 BiocIO_1.9.0 zlibbioc_1.45.0  vctrs_0.5.0 memoise_2.0.1 Rsamtools_2.15.0  RCurl_1.98-1.9 base64enc_0.1-3 htmltools_0.5.3  progress_1.2.2 curl_4.3.3 Formula_1.2-4  sass_0.4.2 bslib_0.4.0 htmlwidgets_1.5.4  plyr_1.8.7 impute_1.73.0 cachem_1.0.6  GenomicAlignments_1.35.0 iterators_1.0.14 lifecycle_1.0.3  pkgconfig_2.0.3 Matrix_1.5-1 R6_2.5.1  fastmap_1.1.0 GenomeInfoDbData_1.2.9 digest_0.6.30  colorspace_2.0-3 AnnotationDbi_1.61.0 Hmisc_4.7-1  RSQLite_2.2.18 org.Hs.eg.db_3.16.0 labeling_0.4.2  filelock_1.0.2 fansi_1.0.3 mgcv_1.8-41  httr_1.4.4 compiler_4.3.0 rngtools_1.5.2  downloader_0.4 bit64_4.0.5 recount_1.25.0  backports_1.4.1 htmlTable_2.4.1 BiocParallel_1.33.0  DBI_1.1.3 highr_0.9 biomaRt_2.55.0  rappdirs_0.3.3 DelayedArray_0.25.0 rjson_0.2.21  derfinderHelper_1.33.0 tools_4.3.0 foreign_0.8-83  rentrez_1.2.3 nnet_7.3-18 glue_1.6.2  restfulr_0.0.15 nlme_3.1-160 grid_4.3.0  checkmate_2.1.0 reshape2_1.4.4 derfinder_1.33.0  cluster_2.1.4 generics_0.1.3 gtable_0.3.1  BSgenome_1.67.0 tzdb_0.3.0 tidyr_1.2.1  data.table_1.14.4 hms_1.1.2 xml2_1.3.3  utf8_1.2.2 XVector_0.39.0 foreach_1.5.2  pillar_1.8.1 stringr_1.4.1 limma_3.55.0  splines_4.3.0 dplyr_1.0.10 BiocFileCache_2.7.0  lattice_0.20-45 survival_3.4-0 rtracklayer_1.59.0  bit_4.0.4 deldir_1.0-6 GEOquery_2.67.0  tidyselect_1.2.0 locfit_1.5-9.6 Biostrings_2.67.0  knitr_1.40 gridExtra_2.3 xfun_0.34  stringi_1.7.8 yaml_2.3.6 evaluate_0.17  codetools_0.2-18 interp_1.1-3 GenomicFiles_1.35.0  qvalue_2.31.0 tibble_3.1.8 cli_3.4.1  bumphunter_1.41.0 rpart_4.1.19 munsell_0.5.0  jquerylib_0.1.4 Rcpp_1.0.9 dbplyr_2.2.1  png_0.1-7 XML_3.99-0.12 parallel_4.3.0  ellipsis_0.3.2 ggplot2_3.3.6 readr_2.1.3  assertthat_0.2.1 blob_1.2.3 prettyunits_1.1.1  doRNG_1.8.2 latticeExtra_0.6-30 jpeg_0.1-9  bitops_1.0-7 VariantAnnotation_1.45.0 scales_1.2.1  purrr_0.3.5 crayon_1.5.2 rlang_1.0.6  KEGGREST_1.39.0
Collado-Torres, Leonardo, Abhinav Nellore, Kai Kammers, Shannon E Ellis, Margaret A Taub, Kasper D Hansen, Andrew E Jaffe, Ben Langmead, and Jeffrey T Leek. 2017. “Reproducible Rna-Seq Analysis Using Recount2.” Nature Biotechnology 35 (4): 319.
De Magalhães, João Pedro, João Curado, and George M Church. 2009. “Meta-Analysis of Age-Related Gene Expression Profiles Identifies Common Signatures of Aging.” Bioinformatics 25 (7): 875–81.
Fleischer, Jason G, Roberta Schulte, Hsiao H Tsai, Swati Tyagi, Arkaitz Ibarra, Maxim N Shokhirev, Ling Huang, Martin W Hetzer, and Saket Navlakha. 2018. “Predicting Age from the Transcriptome of Human Dermal Fibroblasts.” Genome Biology 19 (1): 221.
Lonsdale, John, Jeffrey Thomas, Mike Salvatore, Rebecca Phillips, Edmund Lo, Saboor Shad, Richard Hasz, et al. 2013. “The Genotype-Tissue Expression (Gtex) Project.” Nature Genetics 45 (6): 580.
Peters, Marjolein J, Roby Joehanes, Luke C Pilling, Claudia Schurmann, Karen N Conneely, Joseph Powell, Eva Reinmaa, et al. 2015. “The Transcriptional Landscape of Age in Human Peripheral Blood.” Nature Communications 6: 8570.
Tacutu, Robi, Daniel Thornton, Emily Johnson, Arie Budovsky, Diogo Barardo, Thomas Craig, Eugene Diana, et al. 2017. “Human Ageing Genomic Resources: New and Updated Databases.” Nucleic Acids Research 46 (D1): D1083–D1090.
Zou, Hui, and Trevor Hastie. 2005. “Regularization and Variable Selection via the Elastic Net.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67 (2): 301–20.