scenicplus
GRN builder
Builds gene regulatory networks from single-cell gene expression and chromatin accessibility data
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.
184 stars
10 watching
29 forks
Language: Jupyter Notebook
last commit: 3 months ago
Linked from 1 awesome list
seandavi/awesome-single-cellRelated projects:
| Repository | Description | Stars |
|---|---|---|
| aertslab/scenic | A tool for inferring Gene Regulatory Networks and cell types from single-cell RNA-seq data | 420 |
 pinellolab/dictys | Reconstructs dynamic gene regulatory networks from single-cell RNA and ATAC-seq data to understand cell-type specific transcription factor regulation. | 111 |
| aertslab/cistopic | An R package for identifying cell states and cis-regulatory topics from single-cell epigenomics data | 135 |
 cabsel/sincerities | Infers gene regulatory networks from time-stamped single cell transcriptional expression profiles using a statistical method | 11 |
| aertslab/scope | A tool for fast visualization of large-scale single-cell data | 68 |
 bimsbbioinfo/netsmooth | Improves single cell RNA-seq data by smoothing out noise using prior information from gene interaction networks | 27 |
 qile0317/apackoftheclones | Software package to visualize clonal expansion in single-cell immune repertoire data | 14 |
 warrengreen/srcnn | Software for enhancing satellite images through deep learning techniques | 76 |
 broadinstitute/tangram | A Python package for aligning single-cell transcriptomic data with spatial gene expression data. | 258 |
 pistony/residualattentionnetwork | A Gluon implementation of Residual Attention Network for image classification tasks | 107 |
 bruinxiong/modified-crunet-and-residual-attention-network.mxnet | An MXNet implementation of a modified deep neural network architecture for image classification | 67 |
 bindsnet/bindsnet | A software package for simulating spiking neural networks using PyTorch. | 1,507 |
 catavallejos/basics | An integrated Bayesian hierarchical model to analyze single-cell sequencing data | 84 |
 jianhuupenn/spagcn | An algorithmic framework to integrate gene expression data with spatial location and histological information to identify distinct regions in tissue samples. | 198 |
 buenrostrolab/figr | A computational framework that integrates single-cell chromatin accessibility and gene expression data to infer transcriptional regulators of target genes. | 36 |