G2S3

Gene imputation method

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.

G2S3 (Sparse and Smooth Signal of Gene Graph-based imputation)

GitHub

3 stars
3 watching
0 forks
Language: MATLAB
last commit: over 3 years ago
Linked from 1 awesome list

graphsignalprocessingscrna-imputation-methodsscrna-seq

Backlinks from these awesome lists:

Related projects:

Repository Description Stars
chenmengjie/viper A software package implementing a method to impute missing values in gene expression data using a penalized regression model 18
h3abionet/chipimputation An imputation workflow tool for genomics data analysis 20
bimsbbioinfo/netsmooth Improves single cell RNA-seq data by smoothing out noise using prior information from gene interaction networks 27
lingfeiwang/normalisr A software framework for analyzing single-cell RNA sequencing data 18
insilicodb/snp-imputation-nf A portable and scalable pipeline for imputing missing single nucleotide polymorphism (SNP) data in genetic studies. 1
su-informatics-lab/dstg Software implementation of graph-based AI method for decomposing spatial transcriptomics data 34
zqfang/gseapy A software package for performing gene set enrichment analysis in various types of biological data. 566
jamesjcai/scgeatoolbox A toolset for analyzing single-cell gene expression data from RNA sequencing experiments. 24
qsong-github/sclm A tool to automatically identify co-expressed genes across multiple single-cell RNA-seq datasets 2
zjufanlab/spatalk An R package that infers cell-cell communication and ligand-receptor-target networks from spatially resolved transcriptomic data 62
pachterlab/cwgflhgcchap_2021 Analyzing multiplexed single-cell RNA-seq data from a marine organism, providing tools for preprocessing, clustering, and analysis of gene expression across different cell types. 2
broadinstitute/tangram A Python package for aligning single-cell transcriptomic data with spatial gene expression data. 258
mohuangx/saver Recover gene expression profiles from noisy single-cell RNA-seq data using regression and empirical Bayes methods. 109
krishnaswamylab/magic An algorithm for denoising high-dimensional biological data by learning the manifold structure of the data using graph imputation 345
cellgeni/scrna.seq.course An educational resource teaching computational analysis of single-cell RNA-seq data using R and Bioconductor tools 123