aertslab

arboreto

A scalable python-based framework for gene regulatory network inference using tree-based ensemble regressors.

AUCell

AUCell: score single cells with gene regulatory networks

cisTopic

cisTopic: Probabilistic modelling of cis-regulatory topics from single cell epigenomics data

CREsted

CREsted is a Python package for training sequence-based deep learning models on scATAC-seq data, for capturing enhancer code and for designing cell type-specific sequences.

pycisTopic

pycisTopic is a Python module to simultaneously identify cell states and cis-regulatory topics from single cell epigenomics data.

pySCENIC

pySCENIC is a lightning-fast python implementation of the SCENIC pipeline (Single-Cell rEgulatory Network Inference and Clustering) which enables biologists to infer transcription factors, gene regulatory networks and cell types from single-cell RNA-seq data.

SCENIC

SCENIC is an R package to infer Gene Regulatory Networks and cell types from single-cell RNA-seq data.

scenicplus

SCENIC+ is a python package to build gene regulatory networks (GRNs) using combined or separate single-cell gene expression (scRNA-seq) and single-cell chromatin accessibility (scATAC-seq) data.

SCENICprotocol

A scalable SCENIC workflow for single-cell gene regulatory network analysis

SCope

Fast visualization tool for large-scale and high dimensional single-cell data

aertslab/pySCENIC

pySCENIC is a lightning-fast python implementation of the SCENIC pipeline (Single-Cell rEgulatory Network Inference and Clustering) which enables biologists to infer transcription factors, gene regulatory networks and cell types from single-cell RNA-seq data.

aertslab/SCENIC

SCENIC is an R package to infer Gene Regulatory Networks and cell types from single-cell RNA-seq data.

aertslab/scenicplus

SCENIC+ is a python package to build gene regulatory networks (GRNs) using combined or separate single-cell gene expression (scRNA-seq) and single-cell chromatin accessibility (scATAC-seq) data.

aertslab/AUCell

AUCell: score single cells with gene regulatory networks

aertslab/cisTopic

cisTopic: Probabilistic modelling of cis-regulatory topics from single cell epigenomics data

aertslab/pycisTopic

pycisTopic is a Python module to simultaneously identify cell states and cis-regulatory topics from single cell epigenomics data.

aertslab/arboreto

A scalable python-based framework for gene regulatory network inference using tree-based ensemble regressors.

aertslab/create_cisTarget_databases

Create cisTarget databases

aertslab/RcisTarget

RcisTarget: Transcription factor binding motif enrichment

aertslab/CREsted

CREsted is a Python package for training sequence-based deep learning models on scATAC-seq data, for capturing enhancer code and for designing cell type-specific sequences.

aertslab/single_cell_toolkit

Tools for correcting single cell barcodes for various scATAC-seq techniques and creating fragment files and spltting BAM files per cluster.

aertslab/PUMATAC

Pipeline for Universal Mapping of ATAC-seq

aertslab/nextcloud_share_url_downloader

Download files from and list content of NextCloud (password protected) share directly from the command line without needing a webbrowser.

aertslab/pycistarget

pycistarget is a python module to perform motif enrichment analysis in sets of regions with different tools and identify high confidence TF cistromes.

aertslab/scATAC-seq_benchmark

aertslab/scenicplus_analyses

SCENIC+ analyses

aertslab/Nova-ST

A repository containing the analysis scripts for Nova-ST data

aertslab/PUMATAC_tutorial

aertslab/Bravo_et_al_Liver

Bravo_et_al_Liver

aertslab/DeepBrain

DeepBrain: a collection of vertebrate sequence-based enhancer models aimed at understanding brain cell type enhancer code across and within species

aertslab/LoomXpy

Python package (compatible with SCope) to create .loom files and extend them with other data e.g.: SCENIC regulons

aertslab/scatac_fragment_tools

Tools for working with scATAC-seq fragment files

aertslab/MendelCraft

The MendelCraft mod, introducing Mendelian genetics to the chickens of Minecraft!

aertslab/ctxcore

Core functions for pycisTarget and the SCENIC tool suite

aertslab/scforest

scforest: a visual overview of single cell technology

aertslab/SpatialNF

Spatial transcriptomics NextFlow pipelines

aertslab/regulatory_regions_delineation

Create regulatory regions delineation

aertslab/spatial_fly_website

aertslab/biopython

Official git repository for Biopython (converted from CVS)

aertslab/shap

A game theoretic approach to explain the output of any machine learning model.