CellVGAE – Unsupervised scRNA-seq analysis Workflow with Graph Attention Networks

CellVGAE

:: DESCRIPTION

CellVGAE uses the connectivity between cells (such as k-nearest neighbour graphs or KNN) with gene expression values as node features to learn high-quality cell representations in a lower-dimensional space, with applications in downstream analyses like (density-based) clustering, visualisation, gene set enrichment analysis and others.

::DEVELOPER

CellVGAE team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
Python

:: DOWNLOAD

CellVGAE

:: MORE INFORMATION

Citation:

Buterez D, Bica I, Tariq I, Andrés-Terré H, Liò P.
CellVGAE: an unsupervised scRNA-seq analysis workflow with graph attention networks.
Bioinformatics. 2021 Dec 2:btab804. doi: 10.1093/bioinformatics/btab804. Epub ahead of print. PMID: 34864884.

sciCNV – Inferring CNVs from scRNA-seq

sciCNV

:: DESCRIPTION

sciCNV dissects the effects of DNA copy number variations on transcriptional programs at single-cell resolution

::DEVELOPER

Tiedemann Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows/ MacOsX
R

:: DOWNLOAD

sciCNV

:: MORE INFORMATION

Citation

Mahdipour-Shirayeh A, Erdmann N, Leung-Hagesteijn C, Tiedemann RE.
ciCNV: high-throughput paired profiling of transcriptomes and DNA copy number variations at single-cell resolution.
Brief Bioinform. 2021 Oct 15:bbab413. doi: 10.1093/bib/bbab413. Epub ahead of print. PMID: 34655292.

DrivAER – Identification of driving Transcriptional programs in single-cell RNA sequencing data.

DrivAER

:: DESCRIPTION

DrivAER is a method for identification of Driving transcriptional programs based on AutoEncoder derived Relevance scores. DrivAER infers relevance scores for transcriptional programs with respect to specified outcomes of interest in single-cell RNA sequencing data, such as psuedotemporal ordering or disease status.

::DEVELOPER

Bioinformatics and Systems Medicine Laboratory

:: SCREENSHOTS

n/a

:: REQUIREMENTS

Linux
Python

:: DOWNLOAD

DrivAER

:: MORE INFORMATION

Citation:

Simon LM, Yan F, Zhao Z.
DrivAER: Identification of driving transcriptional programs in single-cell RNA sequencing data.
Gigascience. 2020 Dec 10;9(12):giaa122. doi: 10.1093/gigascience/giaa122. PMID: 33301553; PMCID: PMC7727875.

cellassign v0.99.2 – Automated, Probabilistic Assignment of Cell Types in scRNA-seq data

cellassign v0.99.2

:: DESCRIPTION

cellassign automatically assigns single-cell RNA-seq data to known cell types across thousands of cells accounting for patient and batch specific effects. Information about a priori known markers cell types is provided as input to the model in the form of a (binary) marker gene by cell-type matrix. cellassign then probabilistically assigns each cell to a cell type, removing subjective biases from typical unsupervised clustering workflows.

::DEVELOPER

Shah Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / MacOsX / Windows
R

:: DOWNLOAD

cellassign

:: MORE INFORMATION

Citation

Zhang AW, etc.
Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling,
Nat Methods. 2019 Oct;16(10):1007-1015. doi: 10.1038/s41592-019-0529-1.

FEAST – Feature selection for scRNA-seq Clustering

FEAST

:: DESCRIPTION

FEAST is a framework designed for ranking features and selecting an optimized feature set as an input for scRNA-seq clustering.

::DEVELOPER

Hao Wu, Ph.D.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows /Linux/ MacOsX
R

:: DOWNLOAD

FEAST

:: MORE INFORMATION

Citation

Su K, Yu T, Wu H.
Accurate feature selection improves single-cell RNA-seq cell clustering.
Brief Bioinform. 2021 Feb 22:bbab034. doi: 10.1093/bib/bbab034. Epub ahead of print. PMID: 33611426.

Cell BLAST 0.5 – scRNA-Seq Querying Algorithm.

Cell BLAST 0.5

:: DESCRIPTION

Cell BLAST is a cell querying tool for single-cell transcriptomics data. For each query cell, it searches for most similar cells in the reference database. Annotations in reference cells, e.g. cell type, can then be transfered to query cells based on cell-to-cell similarities.

::DEVELOPER

Gao Lab, Peking University.

:: SCREENSHOTS

N/A

::REQUIREMENTS

Web browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Cao ZJ, Wei L, Lu S, Yang DC, Gao G.
Searching large-scale scRNA-seq databases via unbiased cell embedding with Cell BLAST.
Nat Commun. 2020 Jul 10;11(1):3458. doi: 10.1038/s41467-020-17281-7. PMID: 32651388; PMCID: PMC7351785.

RAFSIL 0.2.5 – Lerning cell-cell similarities from scRNA-seq data

RAFSIL 0.2.5

:: DESCRIPTION

RAFSIL implements a two-step procedure using feature construction and random forest based similarity learning for single cell RNA sequencing data.

::DEVELOPER

Kostka Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows / Linux / MacOsX
R

:: DOWNLOAD

RAFSIL

:: MORE INFORMATION

Citation

Bioinformatics. 2018 Jul 1;34(13):i79-i88. doi: 10.1093/bioinformatics/bty260.
Random forest based similarity learning for single cell RNA sequencing data.
Pouyan MB, Kostka D.

PROSSTT v1.4 – PRObabilistic Simulations of ScRNA-seq Tree-like Topologies

PROSSTT v1.4

:: DESCRIPTION

PROSSTT is a package with code for the simulation of scRNAseq data for dynamic processes such as cell differentiation.

::DEVELOPER

Söding Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / MacOs
Python

:: DOWNLOAD

PROSSTT

:: MORE INFORMATION

Citation:

Bioinformatics. 2019 Sep 15;35(18):3517-3519. doi: 10.1093/bioinformatics/btz078.
PROSSTT: probabilistic simulation of single-cell RNA-seq data for complex differentiation processes.
Papadopoulos N, Gonzalo PR, Söding J