Manual Single Cell Diagnostics: Methods and Protocols (Methods in Molecular Medicine)

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Methods and Protocols
  1. Cardiovascular Disease Vol 1 Genetics - Methods and Protocols (Methods in Molecular Medicine)
  2. Single Cell Diagnostics: Methods And Protocols (Methods In Molecular Medicine): RAR
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Download ZIP. Sign in Sign up. Launching GitHub Desktop Go back. Launching Xcode Launching Visual Studio Latest commit 9a Aug 22, 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.

Cardiovascular Disease Vol 1 Genetics - Methods and Protocols (Methods in Molecular Medicine)

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. 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.

Identifying and removing the cell-cycle effect from single-cell RNA-Sequencing data. This algorithm is an extension of the Latent Dirichlet Allocation LDA topic modeling framework that has been popular in text mining applications.

Single Cell Diagnostics: Methods And Protocols (Methods In Molecular Medicine): RAR

Software requires registration with 10xgenomics. 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. 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.

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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. It provides estimates of several distribution based statistics five distribution measurements and the coefficients of covariates such as batches or cell size. 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 - [. Color blindness friendly by default, and novice coder friendly as well, yet powerful enough to build publication-ready figures. Robust and scalable inference of cell lineages from gene expression data. It then fits a smooth pseudotime trajectory using principal curves.

FastProject - [Python] - Signature analysis on low-dimensional projections of single-cell expression data. GPfates - [Python] - Model transcriptional cell fates as mixtures of Gaussian Processes GPseudoClust - [Python] - Software that clusters genes for pseudotemporally ordered data and quantifies the uncertainty in cluster allocations arising from the uncertainty in the pseudotime ordering. IA-SVA provides a flexible methodology to i identify a hidden factor for unwanted heterogeneity while adjusting for all known factors; ii test the significance of the putative hidden factor for explaining the variation in the data; and iii , if significant, use the estimated factor as an additional known factor in the next iteration to uncover further hidden factors.

Nature Identify discrete, transitional and mixed-lineage states from diverse single-cell transcriptomics platforms.

Apart of the AltAnalyze toolkit along with accompanying visualization methods e. Easy-to-use graphical user and commandline interfaces.

Sequencing the Single Cell – Adventures in Genomics

Provides tools for copy-number inference from single-cell RNA-seq data. Smoothing is performed by first identifying the nearest neighbors of each cell in a step-wise fashion, based on variance-stabilized and partially smoothed expression profiles, and then aggregating their transcript counts. So far, K-Branches is intended to be used on the diffusion map representation of the data, so the user should either provide the data in diffusion map space or use the destiny package perform diffusion map dimensionality reduction.

A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data. On BioRviv and published in Cell. We show that this network captures biologically-meaningful pathways, cell-type specific modules, and connectivity patterns characteristic of complex networks. We develop a model that uses this network to discriminate biological vs. For non-biological zeros, we build a predictive model to impute the missing value using their most informative neighbors.

We demonstrate that netSmooth improves clustering results of scRNA-seq experiments from distinct cell populations, time-course experiments, and cancer genomics. NetworkInference - [Julia] - Fast implementation of single-cell network inference algorithms: Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures nimfa - [Python] - Nimfa is a Python scripting library which includes a number of published matrix factorization algorithms, initialization methods, quality and performance measures and facilitates the combination of these to produce new strategies.

The library represents a unified and efficient interface to matrix factorization algorithms and methods.

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An existing reference database of marker genes is not required, but significantly enhances performance if available. OncoNEM - [R] - OncoNEM is a probabilistic method for inferring intra-tumor evolutionarylineage trees from somatic single nucleotide variants of single cells. OncoNEM identifies homogeneous cellularsubpopulations and infers their genotypes as well as a tree describing their evolutionary relationships.

PHATE uses a novel conceptual framework for learning and visualizing the manifold inherent to biological systems in which smooth transitions mark the progressions of cells from one state to another. PhenoPath - [R] - Single-cell pseudotime with heterogeneous genetic and environmental backgrounds, including Bayesian significance testing of iteractions.

PowsimR is a flexible tool to simulate and evaluate differential expression from bulk and especially single-cell RNA-seq data making it suitable for a priori and posterior power analyses. It can be used for discovery of relevant genes, for exploration of unlabelled data, and assessment of one dataset with respect to the labels known for another dataset.

SCALE estimates kinetic parameters that characterize the transcriptional bursting process at the allelic level, while accounting for technical bias. Scanpy - [Python] - Scanpy provides computationally efficient tools that scale up to very large data sets and enables simple integraton of advanced machine learning algorithms. In addition to traditional differential expression, it can detect differences that are more complex and subtle than a mean shift. SCDE - [R] - Differential expression using error models and overdispersion-based identification of important gene sets.

Such predictive power has been experimentally validated. SCell - [matlab] - SCell is an integrated software tool for quality filtering, normalization, feature selection, iterative dimensionality reduction, clustering and the estimation of gene-expression gradients from large ensembles of single-cell RNA-seq datasets. SCell is open source, and implemented with an intuitive graphical interface. It can also be used to obtain progression-associated genes that vary along the trajectory, and genes that change their correlation structure over the trajectory; progression co-associated genes.