netSmooth
Gene network smooter
Improves single cell RNA-seq data by smoothing out noise using prior information from gene interaction networks
netSmooth: A Network smoothing based method for Single Cell RNA-seq imputation
27 stars
15 watching
6 forks
Language: HTML
last commit: 6 months ago
Linked from 1 awesome list
bioinformaticsgenomicssingle-cell
Related projects:
Repository | Description | Stars |
---|---|---|
cabsel/sincerities | Infers gene regulatory networks from time-stamped single cell transcriptional expression profiles using a statistical method | 11 |
jhu99/scbean | Analyzes single-cell multi-omics data from various modalities like RNA-seq and ATAC-seq | 16 |
brwnj/smoove-nf | An automation workflow for detecting structural variations in genomic data using the smoove toolset | 12 |
timoast/sinto | Tools for analyzing aligned single-cell data from sequencing experiments | 118 |
nbisweden/workshop-scrnaseq | An educational resource offering interactive R, Python, and Seurat labs for analyzing single-cell RNA sequencing data. | 195 |
statomics/zinbwavezinger | A software framework for integrating zingeR with ZINB-WaVE weights for RNA-seq data analysis | 23 |
aertslab/scenic | A tool for inferring Gene Regulatory Networks and cell types from single-cell RNA-seq data | 421 |
imb-computational-genomics-lab/ascend | Tools and methods for analyzing single-cell RNA sequencing data, including expression normalization and differential gene expression analysis. | 22 |
mohuangx/saver | Recover gene expression profiles from noisy single-cell RNA-seq data using regression and empirical Bayes methods. | 109 |
zjufanlab/scdeepsort | A tool for accurately annotating cell types in single-cell RNA sequencing data using deep learning | 99 |
mathewchamberlain/signacx | Classifies cellular phenotype from single-cell RNA sequencing data using neural networks trained on bulk gene expression data. | 23 |
zwang-lab/g2s3 | An imputation method that applies graph signal processing to extract gene structure from scRNA-seq data and recover true expression levels by borrowing information from adjacent genes. | 3 |
mims-harvard/ohmnet | An algorithm for learning feature representations in multi-layer networks | 79 |
catavallejos/basics | An integrated Bayesian hierarchical model to analyze single-cell sequencing data | 84 |
hmatsu1226/scode | An algorithm to infer regulatory networks from single-cell RNA sequencing data during differentiation | 42 |