matlab矩阵自动拼接代码-awesome-single-cell:真棒单细胞

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matlab矩阵自动拼接代码真棒单细胞 用于单细胞数据分析的软件包列表(以及开发这些方法的人员),包括RNA-seq,ATAC-seq等。... 引文 软体套件 RNA序列 - [Python] - :anchor: 在数据中查找双峰,单峰和多峰特征 -[R]-ascend是一个R包,其中包含快速,简化的分析功能,这些功能经过优化,可解决单细胞RNA-seq的统计难题。 该软件包结合了新颖且已建立的方法,以提供灵活的框架来执行过滤,质量控制,归一化,降维,聚类,差异表达和广泛的绘图。 -[Python]-考虑到单细胞RNA序列实验的内在特征而开发的聚类算法。 -[R]-单细胞RNA-seq数据的贝叶斯分析。 估计特定于单元格的归一化常数。 技术变异性是根据刺入基因进行量化的。 表达计数的总可变性被分解为技术和生物学成分。 BASiCS还可以识别在两组或更多组细胞之间差异表达/过度分散的基因。 - [Python] - -[R]-BEARscc利用ERCC插入测量来对技术差异进行建模,该技术差异是基因表达的函数以及技术对低表达基因的影响。 -[matlab]-用于大规模单细胞数据的分析框架。 - [P
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内容介绍
# awesome-single-cell List of software packages (and the people developing these methods) for single-cell data analysis, including RNA-seq, ATAC-seq, etc. [Contributions welcome](https://github.com/seandavi/awesome-single-cell/blob/master/CONTRIBUTING.md)... [![Build Status](https://travis-ci.org/seandavi/awesome-single-cell.svg?branch=master)](https://travis-ci.org/seandavi/awesome-single-cell) ## Citation [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1117762.svg)](https://doi.org/10.5281/zenodo.1117762) ## Software packages ### RNA-seq - [anchor](https://github.com/yeolab/anchor) - [Python] - ⚓ Find bimodal, unimodal, and multimodal features in your data - [ascend](https://github.com/IMB-Computational-Genomics-Lab/ascend) - [R] - ascend is an R package comprised of fast, streamlined analysis functions optimized to address the statistical challenges of single cell RNA-seq. The package incorporates novel and established methods to provide a flexible framework to perform filtering, quality control, normalization, dimension reduction, clustering, differential expression and a wide-range of plotting. - [BackSPIN](https://github.com/linnarsson-lab/BackSPIN) - [Python] - Biclustering algorithm developed taking into account intrinsic features of single-cell RNA-seq experiments. - [BASiCS](https://github.com/catavallejos/BASiCS) - [R] - Bayesian Analysis of single-cell RNA-seq data. Estimates cell-specific normalization constants. Technical variability is quantified based on spike-in genes. The total variability of the expression counts is decomposed into technical and biological components. BASiCS can also identify genes with differential expression/over-dispersion between two or more groups of cells. - [BatchEffectRemoval](https://github.com/ushaham/BatchEffectRemoval) - [Python] - [Removal of Batch Effects using Distribution-Matching Residual Networks](https://doi.org/10.1093/bioinformatics/btx196) - [BEARscc](https://bitbucket.org/bsblabludwig/bearscc) - [R] - BEARscc makes use of ERCC spike-in measurements to model technical variance as a function of gene expression and technical dropout effects on lowly expressed genes. - [bigSCale](https://github.com/dfajar2/bigSCale) - [matlab] - An analytical framework for big-scale single cell data. - [bonvoyage](https://github.com/yeolab/bonvoyage) - [Python] - 📐 Transform percentage-based units into a 2d space to evaluate changes in distribution with both magnitude and direction. - [BPSC](https://github.com/nghiavtr/BPSC) - [R] - Beta-Poisson model for single-cell RNA-seq data analyses - [CALISTA](https://github.com/CABSEL/CALISTA) - [R] - CALISTA provides a user-friendly toolbox for the analysis of single cell expression data. CALISTA accomplishes three major tasks: 1) Identification of cell clusters in a cell population based on single-cell gene expression data, 2) Reconstruction of lineage progression and produce transition genes, and 3) Pseudotemporal ordering of cells along any given developmental paths in the lineage progression. - [ccRemover](https://CRAN.R-project.org/package=ccRemover) - [R] - Removes the Cell-Cycle Effect from Single-Cell RNA-Sequencing Data. [Identifying and removing the cell-cycle effect from single-cell RNA-Sequencing data](https://www.nature.com/articles/srep33892). - [celda](https://github.com/campbio/celda) - [R] - A suite of Bayesian hierarchical models and supporting functions to perform gene and cell clustering for count data generated by scRNA-seq platforms. This algorithm is an extension of the Latent Dirichlet Allocation (LDA) topic modeling framework that has been popular in text mining applications. - [Cell\_BLAST](https://github.com/gao-lab/Cell_BLAST) - [Python] - A BLAST-like toolkit for scRNA-seq data querying and automated annotation. - [CellCNN](https://github.com/eiriniar/CellCnn) - [Python] - Representation Learning for detection of phenotype-associated cell subsets - [Cellity](https://github.com/teichlab/cellity) - [R] - Classification of low quality cells in scRNA-seq data using R - [CellRanger](https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger) - [Linux Binary] - Cell Ranger is a set of analysis pipelines that process Chromium single-cell RNA-seq output to align reads, generate gene-cell matrices and perform clustering and gene expression analysis. *Software requires registration with 10xgenomics.* - [cellTree](https://www.bioconductor.org/packages/3.3/bioc/html/cellTree.html) - [R] - Cell population analysis and visualization from single cell RNA-seq data using a Latent Dirichlet Allocation model. - [clusterExperiment](https://github.com/epurdom/clusterExperiment) - [R] - Functions for running and comparing many different clusterings of single-cell sequencing data. Meant to work with SCONE and slingshot. - [Clustergrammer](https://github.com/maayanlab/clustergrammer) - [Python, JavaScript] - Interative web-based heatmap for visualizing and analyzing high dimensional biological data, including single-cell RNA-seq. Clustergrammer can be used within a Jupyter notebook as an interative widget that can be shared using GitHub and NBviewer, see [example notebook](http://nbviewer.jupyter.org/github/MaayanLab/CCLE_Clustergrammer/blob/master/notebooks/Clustergrammer_CCLE_Notebook.ipynb). - [CountClust](https://github.com/kkdey/CountClust) - [R] - Functions for fitting Grade-of-Membership models, also known as "Topic models", to RNA-seq counts. These models generalize clustering methods to allow that each cell may belong to more than one cluster/topic. - [cyclum](https://github.com/KChen-lab/cyclum) - [python] - Cyclum is a novel AutoEncoder approach that characterizes circular trajectories in the high-dimensional gene expression space. Applying Cyclum to removing cell-cycle effects leads to substantially improved delineations of cell subpopulations, which is useful for establishing various cell atlases and studying tumor heterogeneity. [bioRxiv](https://www.biorxiv.org/content/10.1101/625566v1) - [CytoGuide](https://cyteguide.cytosplore.org/) - [C++,D3] - [CyteGuide: Visual Guidance for Hierarchical Single-Cell Analysis](http://ieeexplore.ieee.org/document/8017575/) - [DECENT](https://github.com/cz-ye/DECENT) - [R] - The unique features of scRNA-seq data have led to the development of novel methods for differential expression (DE) analysis. However, few of the existing DE methods for scRNA-seq data estimate the number of molecules pre-dropout and therefore do not explicitly distinguish technical and biological zeroes. We develop DECENT, a DE method for scRNA-seq data that adjusts for the imperfect capture efficiency by estimating the number of molecules pre-dropout. - [DESCEND](https://github.com/jingshuw/descend) - [R] - DESCEND deconvolves the true gene expression distribution across cells for UMI scRNA-seq counts. It provides estimates of several distribution based statistics (five distribution measurements and the coefficients of covariates (such as batches or cell size)). - [destiny](http://bioconductor.org/packages/destiny/) - [R] - Diffusion maps are spectral method for non-linear dimension reduction introduced by Coifman et al.(2005). Diffusion maps are based on a distance metric (diffusion distance) which is conceptually relevant to how differentiating cells follow noisy diffusion-like dynamics, moving from a pluripotent state towards more differentiated states. - [DensityPath](https://doi.org/10.1101/276311) - [.] - DensityPath: a level-set algorithm to visualize and reconstruct cell developmental trajectories for large-scale single-cell RNAseq data - [DeLorean](https://cran.r-project.org/web/packages/DeLorean/index.html) - [R] - Bayesian pseudotime estimation algorithm that uses Gaussian processes to model gene expression profiles and provides a full posterior for the pseudotimes. - [DittoSeq](https://github.com/dtm2451/DittoSeq) - [R] - User Friendly Visualization tools for Single-ce
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