<![CDATA[BOL: Robert M Willioms's pages]]> https://bioinformaticsonline.com/pages/owner/robert? https://bioinformaticsonline.com/pages/view/34465/rnaseq-data-analysis-links Mon, 27 Nov 2017 16:28:11 -0600 https://bioinformaticsonline.com/pages/view/34465/rnaseq-data-analysis-links <![CDATA[RNAseq data analysis links !]]> RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping.

A survey of best practices for RNA-seq data analysis

RNA-seq workflow: gene-level exploratory analysis and DE

RNAseq analysis notes from Tommy Tang

Analysis of RNA ‐ Seq Data

RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR

Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks.

EBI RNA-Seq exercise

An open RNA-Seq data analysis pipeline tutorial with an example

RNA-Seq Analysis Workflow

Transcript-level expression analysis of RNA-seq experiments

]]> Robert M Willioms https://bioinformaticsonline.com/pages/view/34463/single-cell-rnaseq-data-analysis-tutorial Mon, 27 Nov 2017 16:24:29 -0600 https://bioinformaticsonline.com/pages/view/34463/single-cell-rnaseq-data-analysis-tutorial <![CDATA[Single Cell RNAseq data analysis tutorial !!]]>
  • A major breakthrough (replaced microarrays) in the late 00’s and has been widely used since
  • Measures the average expression level for each gene across a large population of input cells
  • Useful for comparative transcriptomics, e.g. samples of the same tissue from different species
  • Useful for quantifying expression signatures from ensembles, e.g. in disease studies
  • Insufficient for studying heterogeneous systems, e.g. early development studies, complex tissues (brain)
  • Does not provide insights into the stochastic nature of gene expression

    • Following are the useful links:

    Single Cell RNAseq data analysis Tutorial

    A step-by-step workflow for low-level analysis of single-cell RNA-seq data

    A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

    SCell: single-cell RNA-seq analysis software

    https://github.com/diazlab/SCell

    Beta-Poisson model for single-cell RNA-seq data analyses

    https://github.com/nghiavtr/BPSC

    Sincera: A Computational Pipeline for Single Cell RNA-Seq Profiling Analysis

    https://research.cchmc.org/pbge/sincera.html

    SC3 – consensus clustering of single-cell RNA-Seq data

    http://biorxiv.org/content/early/2016/09/02/036558

    Citrus: A toolkit for single cell sequencing analysis

    http://biorxiv.org/content/early/2016/09/14/045070

    Single-Cell Resolution of Temporal Gene Expression during Heart Development

    http://www.cell.com/developmental-cell/fulltext/S1534-5807(16)30682-7

    Scalable latent-factor models applied to single-cell RNA-seq data separate biological drivers from confounding effects

    http://biorxiv.org/content/early/2016/11/15/087775

    Single cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes

    http://genome.cshlp.org/content/early/2016/11/18/gr.212720.116.abstract

    SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation

    http://biorxiv.org/content/early/2016/11/21/088856

    SCOUP is a probabilistic model to analyze single-cell expression data during differentiation

    https://github.com/hmatsu1226/SCOUP

    scLVM is a modelling framework for single-cell RNA-seq data

    https://github.com/PMBio/scLVM

    Selective Locally linear Inference of Cellular Expression Relationships (SLICER) algorithm for inferring cell trajectories

    https://github.com/jw156605/SLICER

    SinQC: A Method and Tool to Control Single-cell RNA-seq Data Quality

    http://www.morgridge.net/SinQC.html

    TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis

    https://github.com/zji90/TSCAN

    Visualization and cellular hierarchy inference of single-cell data using SPADE

    http://www.nature.com/nprot/journal/v11/n7/full/nprot.2016.066.html

    OEFinder: Identify ordering effect genes in single cell RNA-seq data

    https://github.com/lengning/OEFinder

    ]]>     Robert M Willioms