Tag: Single Cell

SCATE – Single-cell ATAC-seq Signal Extraction and Enhancement

SCATE

:: DESCRIPTION

SCATE integrates information from co-activated CREs, similar cells, and publicly available regulome data to substantially increase the accuracy for estimating individual CRE activities in single cell and rare cell subpopulations.

::DEVELOPER

Zhicheng Ji

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows
  • R

:: DOWNLOAD

SCATE

:: MORE INFORMATION

Citation

Ji Z, Zhou W, Hou W, Ji H.
Single-cell ATAC-seq signal extraction and enhancement with SCATE.
Genome Biol. 2020 Jul 3;21(1):161. doi: 10.1186/s13059-020-02075-3. PMID: 32620137; PMCID: PMC7333383.

TSCAN 1.0 – Pseudo-time Reconstruction and Evaluation in Single-cell RNA-seq Analysis

TSCAN 1.0

:: DESCRIPTION

TSCAN (Tools for Single-Cell ANalysis) is a software tool developed to better support in silico pseudo-Time reconstruction in Single-Cell RNA-seq ANalysis.

::DEVELOPER

Zhicheng Ji

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • R

:: DOWNLOAD

 TSCAN

:: MORE INFORMATION

Citation

TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis.
Ji Z, Ji H.
Nucleic Acids Res. 2016 May 13. pii: gkw430.

ScoMAP 0.1 – Single-Cell Omics Mapping into spatial Axes using Pseudotemporall ordering

ScoMAP 0.1

:: DESCRIPTION

ScoMAP is an R package to spatially integrate single-cell omics data into virtual cells and infer enhancer-to-gene relationships.

::DEVELOPER

aertslab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/ Linux/ MacOsX
  • R

:: DOWNLOAD

ScoMAP

:: MORE INFORMATION

Citation

Bravo González-Blas C, et al.
Identification of genomic enhancers through spatial integration of single-cell transcriptomics and epigenomics.
Mol Syst Biol. 2020 May;16(5):e9438. doi: 10.15252/msb.20209438. PMID: 32431014; PMCID: PMC7237818.

cisTopic v3 – Probabilistic Modelling of cis-regulatory topics from Single Cell Epigenomics data

cisTopic v3

:: DESCRIPTION

cisTopic is an R-package to simultaneously identify cell states and cis-regulatory topics from single cell epigenomics data.

::DEVELOPER

aertslab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/ Linux/ MacOsX
  • R

:: DOWNLOAD

cisTopic

:: MORE INFORMATION

Citation

Bravo González-Blas C, Minnoye L, Papasokrati D, Aibar S, Hulselmans G, Christiaens V, Davie K, Wouters J, Aerts S.
cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data.
Nat Methods. 2019 May;16(5):397-400. doi: 10.1038/s41592-019-0367-1. Epub 2019 Apr 8. PMID: 30962623; PMCID: PMC6517279.

OEFinder 0.0.2 – Identify Ordering Effect Genes in Single Cell RNA-seq data

OEFinder 0.0.2

:: DESCRIPTION

OEFinder is a user interface to identify and visualize ordering effects in single-cell RNA-seq data.

::DEVELOPER

Ning Leng

:: SCREENSHOTS

OEFinder

:: REQUIREMENTS

  • Linux
  • R

:: DOWNLOAD

 OEFinder

:: MORE INFORMATION

Citation

OEFinder: A user interface to identify and visualize ordering effects in single-cell RNA-seq data.
Leng N, Choi J, Chu LF, Thomson JA, Kendziorski C, Stewart R.
Bioinformatics. 2016 Jan 6. pii: btw004.

GiniClust3 1.0.1 – Detecting Rare Cell Types from Single-cell Gene Expression data with Gini Index

GiniClust 3 1.0.1

:: DESCRIPTION

GiniClust is a clustering method specifically designed for rare cell type detection. It uses the Gini index to identify genes that are associated with rare cell types without prior knowledge.

::DEVELOPER

Guo-CHeng Yuan Lab

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux/Windows/MacOsX
  • Python

:: DOWNLOAD

GiniClust

:: MORE INFORMATION

Citation

Rui Dong. Guo-Cheng Yuan.
GiniClust3: a fast and memory-efficient tool for rare cell type identification.

Genome Biol, 17 (1), 144 2016 Jul 1
GiniClust: Detecting Rare Cell Types From Single-Cell Gene Expression Data With Gini Index
Lan Jiang, Huidong Chen, Luca Pinello, Guo-Cheng Yuan

Tsoucas D, Yuan GC.
GiniClust2: a cluster-aware, weighted ensemble clustering method for cell-type detection.
Genome Biology. 2018 May 10;19(1):58.

DWLS – Cell-type Deconvolution using Single-cell RNA-sequencing data

DWLS

:: DESCRIPTION

DWLS (Dampened weighted least squares) is an estimation method for gene expression deconvolution, in which the cell-type composition of a bulk RNA-seq data set is computationally inferred.

::DEVELOPER

Guo-CHeng Yuan Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Windows/MacOsX
  • R

:: DOWNLOAD

DWLS

:: MORE INFORMATION

Citation

Tsoucas D, Dong R, Chen H, Zhu Q, Guo G, Yuan GC.
Accurate estimation of cell-type composition from gene expression data.
Nature Communications. 10 (1), 2975 2019 Jul 5

RESCUE v1.0.3 – Imputing Dropouts in Single-cell RNA-sequencing data

RESCUE v1.0.3

:: DESCRIPTION

RESCUE is a computational method to mitigate the dropout problem by imputing gene expression levels using information from other cells with similar patterns.

::DEVELOPER

Guo-CHeng Yuan Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Windows/MacOsX
  • Python
  • R

:: DOWNLOAD

RESCUE

:: MORE INFORMATION

Citation

BMC Bioinformatics, 20 (1), 388 2019 Jul 12
RESCUE: Imputing Dropout Events in Single-Cell RNA-sequencing Data
Sam Tracy , Guo-Cheng Yuan , Ruben Dries

Giotto 1.0.3 – Single-cell Spatial Analysis pipeline

Giotto 1.0.3

:: DESCRIPTION

Giotto is a comprehensive pipeline for spatial transcriptomic data analysis and visualization.

::DEVELOPER

Guo-CHeng Yuan Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Windows/MacOsX
  • R
  • ImageJ library (JAR file)

:: DOWNLOAD

Giotto

:: MORE INFORMATION

Citation

Giotto, a pipeline for integrative analysis and visualization of single-cell spatial transcriptomic data
Ruben Dries, Qian Zhu, Chee-Huat Linus Eng, Arpan Sarkar, Feng Bao, Rani E George, Nico Pierson, Long Cai, Guo-Cheng Yuan
doi: https://doi.org/10.1101/701680

ECLAIR – Robust Lineage Reconstruction from Single-cell Gene Expression data

ECLAIR

:: DESCRIPTION

ECLAIR (Ensemble Clustering for Lineage Analysis, Inference and Robustness) achieves a higher level of confidence in the estimated lineages through the use of approximation algorithms for consensus clustering and by combining the information from an ensemble of minimum spanning trees so as to come up with an improved, aggregated lineage tree.

::DEVELOPER

Guo-CHeng Yuan Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Windows/MacOsX
  • Python

:: DOWNLOAD

ECLAIR

:: MORE INFORMATION

Citation

Giecold G, Marco E, Trippa L, Yuan GC.
Robust Lineage Reconstruction from High-Dimensional Single-Cell Data.
Nucleic Acids Res. 2016 May 20. pii: gkw452.