Displaying 1 to 19 from 19 results

ouija - Descriptive probabilistic marker gene approach to single-cell pseudotime inference

  •    R

Input is a cell-by-gene expression matrices that is non-negative and represents logged gene expression values. We recommend using log2(TPM + 1). This can either take the form of a matrix or a SingleCellExperiment (use of the SingleCellExperiment infrastructure is highly encouraged for single-cell analyses). By default the logcounts assay of a SingleCellExperiment will be used. The map_pseudotimes function extracts the maximum-a-posteriori (MAP) estimates of the pseudotimes.

switchde - Inference of switch-like differential expression along single-cell trajectories

  •    R

switchde is available on both Bioconductor and Github. switchde is an R package for detecting switch-like differential expression along single-cell RNA-seq trajectories. It assumes genes follow a sigmoidal pattern of gene expression and tests for differential expression using a likelihood ratio test. It also returns maximum likelihood estimates (MLE) for the sigmoid parameters, which allows filtering of genes for up or down regulation as well as where along the trajectory the regulation occurs.

cshl-singlecell-2017 - Single Cell Analysis course at Cold Spring Harbor Laboratory 2017

  •    Jupyter

This is one of many single cell courses/tutorials. An excellent list of all single cell package, courses, tutorials, speakers for conferences, can be found here. We'll use some additional dependencies outside of the scientific python ecosystem.

scRNA.seq.datasets - Collection of public scRNA-Seq datasets used by our group

  •    R

This repository is used to build scater objects and reports (in continuous integration manner) for various publicly available scRNA-Seq datasets used by our group. This pipeline is implemented using docker containers and cloud computing. The resulting website is available here. Below are some notes on the pipeline setup. The website is generated using MkDocs generator. Links to S3 storage and data annotations are added manually. If you are creating a pull request and adding new data please add its annotations to one of the files in the website folder.

scde - R package for analyzing single-cell RNA-seq data

  •    R

The scde package implements a set of statistical methods for analyzing single-cell RNA-seq data. scde fits individual error models for single-cell RNA-seq measurements. These models can then be used for assessment of differential expression between groups of cells, as well as other types of analysis. The scde package also contains the pagoda framework which applies pathway and gene set overdispersion analysis to identify aspects of transcriptional heterogeneity among single cells. scde is maintained by Jean Fan of the Kharchenko Lab at the Department of Biomedical Informatics at Harvard Medical School.

iSEE - R/shiny interface for interactive visualization of data in objects derived from the SummarizedExperiment class

  •    R

The iSEE package aims to provide an interactive user interface for exploring data in objects derived from the SummarizedExperiment class. Particular focus will be given to single-cell data in the SingleCellExperiment derived class. The interface is implemented with RStudio's Shiny, with a multi-panel setup for ease of navigation. ✅ scatter plots can be generated from reduced dimensionality data, or with biaxial plots of existing metadata columns.

single-cell-pseudotime - An overview of algorithms for estimating pseudotime in single-cell RNA-seq data


Single cells, many algorithms. The goal of this page is to catalog the many algorithms that estimate pseudotimes for cells based on their gene expression levels. This problem is also referred to as single-cell trajectory inference or ordering. Ultimately, it will contain method names, software links, manuscript links, and brief comments on the particular strengths of each method. Initially, it seeks simply to list as many methods as possible. Some related methods not specifically designed for RNA-seq (e.g. mass cytometry) are included as well. The list also includes methods that are specifically designed to take pseudotemporal data as input. The initial list was created by Anthony Gitter, but pull requests are very welcome. Thank you to the other contributors.

ExpressionMatrix2 - Software for exploration of gene expression data from single-cell RNA sequencing

  •    C++

This repository contains software for analysis, visualization, and clustering of gene expression data from single-cell RNA sequencing developed at Chan-Zuckerberg Initiative. It scales favorably to large numbers of cells thank to its use of Locality-Sensitive Hashing (LSH), and was successfully used, without downsampling, on a data set with over one million cells. Documentation for the latest version of this software is available online through GitHub Pages, or you can use the directions below to obtain documentation for any previous release.

fastq_utils - Validation and manipulation of FASTQ files, scRNA-seq barcode pre-processing and UMI quantification

  •    C

Set of Linux utilities to validate and manipulate fastq files. It also includes a set of programs to preprocess barcodes (namely UMIs, cells and samples), add the barcodes as tags in BAM files and count UMIs. samtools (version 0.1.19) and zlib (http://zlib.net) version 1.2.11 or latest are required to compile fastq_utils. The install_deps.sh script in the toplevel folder tries to download and compile the dependencies. The bam_annotate.sh script requires samtools (version 1.5 or higher).

BASiCS - BASiCS: Bayesian Analysis of Single-Cell Sequencing Data

  •    R

Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels within seemingly homogeneous populations of cells. However, these experiments are prone to high levels of technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the group of cells under study. For a single group of cells [1]: BASiCS provides a criterion to identify highly (and lowly) variable genes within the group.

CITE-seq-Count - Small script that allows to count TAGS from a CITE-seq experiment

  •    Python

A python package that allows to count antibody TAGS from a CITE-seq and/or cell hashing experiment.

splatter-paper - Data and analysis for the Splatter paper

  •    R

Data and analysis for the paper "Splatter: Simulation of Single-cell RNA sequencing data". Data files used in the analysis are available in the data.tar.gz file. This file should be extracted to data before attempting to run any of the analysis.

pySCENIC - pySCENIC is a lightning-fast python implementation of the SCENIC pipeline (Single-Cell rEgulatory Network Inference and Clustering) which enables biologists to infer transcription factors, gene regulatory networks and cell types from single-cell RNA-seq data

  •    Python

pySCENIC is a lightning-fast python implementation of the SCENIC pipeline (Single-Cell rEgulatory Network Inference and Clustering) which enables biologists to infer transcription factors, gene regulatory networks and cell types from single-cell RNA-seq data. The pioneering work was done in R and results were published in Nature Methods [1].

SCope - Fast visualization tool for large-scale and high dimensional single-cell data

  •    Javascript

SCope is a fast visualization tool for large-scale and high dimensional scRNA-seq datasets. Currently the data format supported by SCope is .loom. This file format for very large omics datasets is maintained by the Linnarsson Lab through the loompy Python package (https://github.com/linnarsson-lab/loompy). Visit http://scope.aertslab.org to test out SCope on several published datasets! Personal loom file files can be uploaded but will only be kept for 5 days.

zUMIs - zUMIs: A fast and flexible pipeline to process RNA sequencing data with UMIs

  •    R

zUMIs is a fast and flexible pipeline to process RNA-seq data with (or without) UMIs. The input to this pipeline is simply fastq files. In the most common cases, you will have a read containing the cDNA sequence and other read(s) containing UMI and Cell Barcode information. Furthermore, you will need a STAR index for your genome and GTF annotation file.

octopus - Bayesian haplotype-based mutation calling

  •    C++

Octopus is a mapping-based variant caller that implements several calling models within a unified haplotype-aware framework. Octopus takes inspiration from particle filtering by constructing a tree of haplotypes and dynamically pruning and extending the tree based on haplotype posterior probabilities in a sequential manner. This allows octopus to implicitly consider all possible haplotypes at a given loci in reasonable time. Octopus calls SNVs, small-medium sized indels, and small complex rearrangements in VCF 4.3.

wot - Optimal transport for time-course single cell data

  •    Python

Waddington-OT uses time-course data to infer how the probability distribution of cells in gene-expression space evolves over time, by using the mathematical approach of Optimal Transport (OT). Waddington-OT depends on Python 3.

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