MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. - Wikidata - wikimedia - TriplyDB

MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data.

MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data.

http://www.wikidata.org/entity/Q31031428

about

Design and computational analysis of single-cell RNA-sequencing experiments Single-Cell Transcriptomics Bioinformatics and Computational Challenges Beta-Poisson model for single-cell RNA-seq data analyses.SCOUP: a probabilistic model based on the Ornstein-Uhlenbeck process to analyze single-cell expression data during differentiation.GiniClust: detecting rare cell types from single-cell gene expression data with Gini index.Identifying and removing the cell-cycle effect from single-cell RNA-Sequencing data.A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor.Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R DTWscore: differential expression and cell clustering analysis for time-series single-cell RNA-seq data.Combined single-cell quantitation of host and SIV genes and proteins ex vivo reveals host-pathogen interactions in individual cells Robust Inference of Cell-to-Cell Expression Variations from Single- and K-Cell Profiling.Overcoming confounding plate effects in differential expression analyses of single-cell RNA-seq data.CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data.SCnorm: robust normalization of single-cell RNA-seq data.Normalizing single-cell RNA sequencing data: challenges and opportunities Computational approaches for interpreting scRNA-seq data.ASAP: a Web-based platform for the analysis and interactive visualization of single-cell RNA-seq data.ROTS: An R package for reproducibility-optimized statistical testing Distinct activation thresholds of human conventional and innate-like memory T cells.A statistical approach for identifying differential distributions in single-cell RNA-seq experiments.Batch effects and the effective design of single-cell gene expression studies.Single-cell mRNA quantification and differential analysis with Census.Nanogrid single-nucleus RNA sequencing reveals phenotypic diversity in breast cancer.Exploiting single-cell expression to characterize co-expression replicability Dr.seq2: A quality control and analysis pipeline for parallel single cell transcriptome and epigenome data.Single-Cell RNA-Sequencing: Assessment of Differential Expression Analysis Methods.Controlled Human Malaria Infection Leads to Long-Lasting Changes in Innate and Innate-like Lymphocyte Populations.Single-cell topological RNA-seq analysis reveals insights into cellular differentiation and development.Revealing the vectors of cellular identity with single-cell genomics.Understanding development and stem cells using single cell-based analyses of gene expression.Single-Cell mRNA Sequencing in Cancer Research: Integrating the Genomic Fingerprint.Beyond comparisons of means: understanding changes in gene expression at the single-cell level.Single-Cell RNA Sequencing Reveals Expanded Clones of Islet Antigen-Reactive CD4+ T Cells in Peripheral Blood of Subjects with Type 1 Diabetes.Splatter: simulation of single-cell RNA sequencing data.The MAIT conundrum - how human MAIT cells distinguish bacterial colonization from infection in mucosal barrier tissues.Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures.Challenges and emerging directions in single-cell analysis.Controlling for Confounding Effects in Single Cell RNA Sequencing Studies Using both Control and Target Genes Accounting for technical noise in differential expression analysis of single-cell RNA sequencing data.Assessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing data.

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MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data.

http://www.wikidata.org/entity/Q31031428

description

2015 nî lūn-bûn

@nan

2015 թուականի Դեկտեմբերին հրատարակուած գիտական յօդուած

@hyw

2015 թվականի դեկտեմբերին հրատարակված գիտական հոդված

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2015年の論文

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2015年論文

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2015年論文

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2015年論文

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2015年論文

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2015年論文

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2015年论文

@wuu

name

MAST: a flexible statistical f ...... ngle-cell RNA sequencing data.

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MAST: a flexible statistical f ...... ngle-cell RNA sequencing data.

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MAST: a flexible statistical f ...... ngle-cell RNA sequencing data.

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Hannah W Miller

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Jingyuan Deng

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Masanao Yajima

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Peter S Linsley

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Raphael Gottardo

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Vivian Gersuk

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Differential expression analysis for sequence count data

Bioconductor: open software development for computational biology and bioinformatics

The Mouse Genome Database (MGD): premier model organism resource for mammalian genomics and genetics

Genetic determinants and cellular constraints in noisy gene expression

Single mammalian cells compensate for differences in cellular volume and DNA copy number through independent global transcriptional mechanisms.

Linear models and empirical bayes methods for assessing differential expression in microarray experiments

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

Stochastic gene expression in a single cell

Bystander-activated memory CD8 T cells control early pathogen load in an innate-like, NKG2D-dependent manner

Quantitative analysis of fission yeast transcriptomes and proteomes in proliferating and quiescent cells

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