Software

I benefited from countless open-source materials and software until now. I’m eager to adopt the open-source methodology as a principle and share the code I’ve written to use it in my work. In addition, I value developing software for sharing methods that can be used over and over again on easily accessible platforms such as Bioconductor.

Below you will find the R package called deconvR, which I had the opportunity to develop during my internship at Max Delbrück Center for Molecular Medicine.

deconvR

I am a developer of the deconvR an R/Bioconductor package provides a collection of functions designed for analyzing deconvolution of the bulk sample(s) using an atlas of reference signature profiles and a user-selected model (non-negative least squares,quadratic programming, support vector regression, or robust linear regression).

Installation

And deconvR package can be installed from Bioconductor:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("deconvR")

You can also install the development version of deconvR directly from Github:

remotes::install_github("BIMSBbioinfo/deconvR")

How to Use deconvR

User who wish to expand their own reference atlas can use findSignatures function. atlas is the signature matrix to be extended and samples the new data to be added to the signature matrix. atlas and samples are compliant with the function requirements. After providing appropriate atlas format, users can create samples using simulateCellMix function. You can get more information about deconvR from here and also here.

methylTFR

methylTFR is an R package I develop for inferring transcription-factor activity directly from DNA methylation data. It lets researchers move from methylation profiles to interpretable, regulator-level signals that can be linked back to cell-type-specific gene regulation.

You can install the development version from GitHub:

remotes::install_github("EpigenomeInformatics/methylTFR")

ChrAccR

ChrAccR is an R package I maintain for the unified analysis of single-cell and bulk ATAC-seq data. It provides a consistent workflow for processing, quality control, and downstream analysis of chromatin accessibility across experimental designs.