RAPToR (Real Age Prediction from Transcriptome staging on Reference) is a tool to accurately predict the developmental age of individual samples from their gene expression profiles.
We stage samples on high-resolution references built from existing developmental profiling time-series. Inferred age can then be used in multiple ways to precisely estimate perturbations effects on developmental timing, increase power in differential expression analyses, estimate differential expression due to uncontrolled development and most importantly, to recover perturbation specific effects on gene expression even in the extreme scenario when the perturbation is completely confounded by development.
Please cite our paper if you use RAPToR in your research:
- Bulteau, R., Francesconi, M. Real age prediction from the transcriptome with RAPToR. Nat Methods (2022). (https://doi.org/10.1038/s41592-022-01540-0)
To install the latest version of RAPToR, run the following in your R console :
if (!requireNamespace("remotes", quietly = TRUE))
install.packages("remotes")
remotes::install_github("LBMC/RAPToR", build_vignettes = T)When dependencies are met, installation should take under 20 seconds.
Users can choose to install the RAPToR package dependencies manually from an R console:
# CRAN packages
install.packages(c("ica", "mgcv", "parallel", "data.table", "pryr", "beeswarm", "Rdpack", "R.rsp"))
# Bioconductor packages
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("limma")We also recommend to install the following packages used in RAPToR vignettes to download demo data:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(c("GEOquery", "biomaRt"))We have verified RAPToR works with R v3.6.3, v4.1.1, and v4.1.2 on Unix (Ubuntu 18.04/20.04/22.04 LTS), Windows 10, and macOS (10.14) systems.
Standard datasets can easily run with 4Gb of RAM and 2 CPU cores.
For reference, the GSE80157 (dsaeschimann2017) dataset used for demo in the main vignette (43 samples by ~19500 genes)
can be both downloaded and staged with RAPToR in under 30 seconds, using less than 2Gb of RAM.
You can access the package's main vignette from your R console with
library(RAPToR)
vignette("RAPToR")
# or
vignette("RAPToR-pdf")RAPToR is a 2-step process:
- A reference gene expression time-series is interpolated to build a near-continuous, high-temporal-resolution reference (a number of which are included in associated data-packages, see below).
- A correlation profile of each of your samples against this reference is dressed, and the timing of the correlation peak is the estimated age. Bootstrapping on genes then gives a confidence interval of the estimates.
The RAPToR package allows you to estimate the developmental age of individual samples from their gene expression profiles.
This means that any method outputting information on gene expression on a large scale is appropriate : RNA-seq (preferably TPM), MicroArray...
Data must not be gene-centered, as this destroys the relationship between gene levels within a sample.
We recommend you get our data-packages with pre-built references of common organisms for quick & easy usage.
wormRefNematode references (C. elegans embryo, larval and young-adult development)drosoRefDrosophila references (D. melanogaster embryo development)zebraRefZebrafish references (D. rerio embryo and larval development)mouseRefMouse references (M. musculus embryo development)
Please also update reference data-packages to their latest version to work with this version of RAPToR
- Introduced a reference (
ref) object and correspondingmake_ref()and print functions.refobjects to- make reference-building more direct, from a
geimobject, - simplify the age estimation call (now simply
ae(samp, ref)), - store reference metadata (such as the reference time unit) for use in subsequent plotting/printing.
- make reference-building more direct, from a
- Updated
aeobject printing to include reference metadata when available. - Optimized ae bootstrap correlation (>2x faster)
- Rewrote
aeplotting function to - display bootstrap estimates by default.
- include reference time units when available.
- fix
aeplotting graphics bug (larger first plot with overlayed and missing elements) when displaying multiple plots side by side. - add sample label control (truncate, adjust margin space)
- cover more parameter edge cases
- Updated
geimprinting to include reference metadata when available. - Added functions to compare log-fold-changes between sample groups to a reference and quantify the impact of development on differential expression analysis.
ref_compare()gets matching reference time points to the samples and compares logFCs between given sample groups, and between matching reference time points (giving an estimate of development logFCs between groups).get_logFC()extracts sample and reference logFCs between specified groups from the output ofref_compare().- Renamed
plot_cor.ae()toplot_cor(). - Removed deprecated plotting function for
df_CV - Updated vignette documentation:
- Added a vignette on correcting DE analysis for development
vignette("RAPToR-DEcorrection") - Updated vignette sections related to reference-building with the new objects.
- Added a reference-building section and example for aging references
- Re-formatted vignettes with Bioconductor style
- Added PDF versions of all vignettes (index entries with
-pdf, e.g.vignette("RAPToR-pdf"))
- Revised the main vignette.
- Updated README info.
- Included biocViews in
DESCRIPTIONto automatically install bioconductor dependencies (thanks @helenmiller16). - Removed deprecated pls-dependent functions.
- Fixed edge cases for 1-component reference building.
- Added software, hardware, and runtime info to README.
- Added installation instructions for dependencies.
- Added preprint citation to README and documentation.
- Updated showcase vignettes relevant to the paper analyses
Prior updates can be found in the NEWS file.