The Signature Regulatory Clustering (SiRCle)
method has first been released as a biorxiv preprint and since then we
have enhanced the method further as part of our revision in genome
medicine.
The SiRCle method was first developed in 2022 to integrate DNA
methylation, RNA-seq and proteomics data at the gene level to
deconvolute the association between dysregulation within and across
possible regulatory layers (DNA methylation, transcription and/or
translation). The results of the study were published on biorxiv in a
shared effort by Mora & Schmidt et.
al.. As
part of the revisions in Genome Medicine, we have updated some parts of
the SiRCle clustering method and extended our methods section in the
manuscript. We also added new data and a PanCan analysis. All of this
can be found in Mora&Schmidt et
al.
[@Mora_Schmidt2024]. You can find the detailed code for both
manuscripts on our
website
or in our SiRCleManuscript GitHub
repository.
SiRCle uses logical regulatory rules, sircleRCM to group genes
in SiRCle clusters based on the data layer (DNA methylation,
transcription and/or translation) where dys-regulation first occurs.
Using the output of sircleRCM, the SiRCle clusters, one can find the
primary biological processes altered by applying Over Representation
Analysis (ORA) and the drivers behind it using Transcription Factor (TF)
analysis.
Lastly, to compare patient’s subsets (e.g. based on stage), we found
that integrating across the data layers prior to performing differential
analysis and biological enrichment better captures the biological
signal. Hence we use a variational autoencoder (VAE) to learn gene-wise
relationships across the three data layers to obtain an integrated value
for each gene (sircleVAE). Using the integrated value we next
perform a Mann-Whitney U test to identify genes with a significant
integrated difference between the patient’s groups.
We have generated several vignettes showcasing the the usage of SiRCleR
using the publicly available datasets as in the manuscript Mora&Schmidt
et
al.,
which are included as example data within SiRCleR. You can find those
tutorial on the top under the “Tutorials” button, where you can follow a
specific user case example and learn more about how to choose the
settings including the Background Method, input data thresholds and
Regulation Grouping. You can also follow the links below:
*
ChooseSettings
*
DataAnalysisExample
If you are seeking to also apply the variational autoencoder on the SiRCle clustering output, please visit the python package or use reticulate in R. You can find the python package on the Python SiRCle.
SiRCleR is an R package. 1. Install Rtools if you haven’t done
this yet, using the appropriate version
(e.g.windows or
macOS). 2. Install the
latest development version from GitHub with: SiRCleR package
direcly in R:
devtools::install_github("https://github.com/ArianeMora/SiRCleR") library(SiRCleR)
While we have done our best to ensure all the dependencies are documented, if they aren’t please let us know and we will try to resolve them.
Note if you are running Windows you might have an issue with long
paths, which you can resolve in the registry on Windows 10:
Computer Configuration > Administrative Templates > System > Filesystem > Enable Win32 long paths
(If you have a different version of Windows, just google “Long paths
fix” and your Windows version)
Please post questions and issues related to SiRCleR functions on
the Issues section of this GitHub repository.
If you want to reproduce the results of of our publication, please use the python package version found here: https://doi.org/10.1101/2022.07.02.498058
If you use this please cite our manuscript Mora&Schmidt et al.. Ariane Mora & Christina Schmidt, Brad Balderson, Christian Frezza & Mikael Bodén. 2024. “SiRCle (Signature Regulatory Clustering) Model Integration Reveals Mechanisms of Phenotype Regulation in Renal Cancer.” Genome Medicine 16 (1): 144. https://doi.org/10.1186/s13073-024-01415-3.