Usage¶

Introduction¶

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 1.

Description of RNASeq age calculator¶

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 2 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 3 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 count data for each individual tissue. To identify genes consistently differentially expressed across tissues, we adapted the binomial test discussed in de Magalhaes et al. 4 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.

Usage of RNASeq age calculator¶

To use racpy in a project:

from racpy import RNAAgeCalc

Then construct an RNAAgeCalc object (here we use “brain” as an example):

rac_obj = RNAAgeCalc(tissue = "brain")

Next we use an example of FPKM data to make prediction:

from racpy import fpkm
res = rac_obj.predict_age(fpkm)
print(res)

Here we explain the options in RNAAgeCalc.

tissue¶

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 provided or the provided tissue is not in this list, the age predictor trained on all tissues will be used to calculate RNA age.

adipose_tissue
adrenal_gland
blood
blood_vessel
brain
breast
colon
esophagus
heart
liver
lung
muscle
nerve
ovary
pancreas
pituitary
prostate
salivary_gland
skin
small_intestine
spleen
stomach
testis
thyroid
uterus
vagina

exprtype¶

exprtype is either “count” or “FPKM”. If exprtype is count, the expression data will be converted to FPKM by the internal function 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 in predict_age function (see below).

idtype¶

idtype is a string which indicates the gene id type in exprdata. Default is “symbol”. The following id types are supported.

symbol
ensembl.gene
entrezgene
refseq

stype¶

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¶

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.

DESeq2. DESeq2 signature was obtained by performing differential expression analysis on each tissue and select the top differential expressed genes.
Pearson. Pearson signature represents the genes highly correlated with chronological age by Pearson correlation.
Dev. Dev signature contains genes with large variation in expression across samples. We adapted the gene selection strategy discussed in 5, which is a gene must have at least a \(t_1\)-fold difference in expression between any two samples in the training set and at least one sample have expression level > \(t_2\) FPKM to be included in the prediction models. \(t_1\) and \(t_2\) (typically 5 or 10) are thresholds to control the degree of deviance of the genes. We used \(t_1 = t_2 = 10\) for most tissues. For some tissues with large sample size, in order to maximize the prediction accuracy while maintaining low computation cost, we increased \(t_1\) and \(t_2\) such that the number of genes retained in the model is between 2,000 and 7,000.
deMagalhaes. deMagalhaes signature contains the 73 age-related genes by 4.
GenAge. GenAge signature contains the 307 age-related genes in the Ageing Gene Database 6.
GTExAge. GTExAge signature represents the genes consistently differentially expressed across tissues discussed in our paper.
Peters. Peters signature contains the 1,497 genes differentially expressed with age discussed in 7.
all. “all” represents all the genes used when constructing the RNAAge calculator.

If the genes in exprdata do not cover all the genes in the signature, imputation will be made automatically by the KNNImputer function in missingpy.

Below are the options for the predict_age function.

exprdata¶

exprdata a pandas DataFrame 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. The index of “exprdata” should be gene ids and columns names of “exprdata” should be sample ids. Here is an example of FPKM expression data:

from racpy import fpkm
fpkm.head()

genelength¶

genelength is a pandas Series, DataFrame, numpy array, or list which contains 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 = “count”, 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¶

chronage is a pandas DataFrame 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 chronage and exprdata should be the same. If some samples’ chronological age are not available, users are expected to set the chronological age in chronage to NaN. 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.

Example¶

This example is just for illustration purpose. It does not represent any real data:

import pandas as pd
from racpy import RNAAgeCalc
from racpy import fpkm
# construct a gene expression data
fpkm_large = pd.concat([fpkm, fpkm+1, fpkm+2, fpkm+3], axis = 1)
fpkm_large = pd.concat([fpkm_large, fpkm_large, fpkm_large, fpkm_large], axis = 1)
fpkm_large.columns = ["sample"+str(item+1) for item in range(32)]
# construct the samples' chronological age
chronage2 = pd.DataFrame()
chronage2["sampleid"] = fpkm_large.columns
chronage2["age"] = range(31, 63)

rac_obj2 = RNAAgeCalc(tissue = "brain")
res2 = rac_obj2.predict_age(exprdata=fpkm_large, chronage=chronage2)
print(res2)

Visualization¶

We suggest visualizing the results by plotting RNAAge vs chronological age. This can be done by calling makeplot function and passing in the DataFrame returned by predict_age function:

import matplotlib.pyplot as plt
from racpy import makeplot
makeplot(res2)
plt.show()

References¶

1: Lonsdale, John, et al. “The genotype-tissue expression (GTEx) project.” Nature genetics 45.6 (2013): 580.
2: Collado-Torres, Leonardo, et al. “Reproducible RNA-seq analysis using recount2.” Nature biotechnology 35.4 (2017): 319-321.
3: Zou, Hui, and Trevor Hastie. “Regularization and variable selection via the elastic net.” Journal of the royal statistical society: series B (statistical methodology) 67.2 (2005): 301-320.
4(1,2): De Magalhães, João Pedro, João Curado, and George M. Church. “Meta-analysis of age-related gene expression profiles identifies common signatures of aging.” Bioinformatics 25.7 (2009): 875-881.
5: Fleischer, Jason G., et al. “Predicting age from the transcriptome of human dermal fibroblasts.” Genome biology 19.1 (2018): 221.
6: de Magalhaes, Joao Pedro, and Olivier Toussaint. “GenAge: a genomic and proteomic network map of human ageing.” FEBS letters 571.1-3 (2004): 243-247.
7: Peters, Marjolein J., et al. “The transcriptional landscape of age in human peripheral blood.” Nature communications 6.1 (2015): 1-14.