Literature DB >> 34978688

Analysis of Microarray Data from Medulloblastoma Tissue Samples.

Debojyoti Dhar1, Gopala Kallapura2.   

Abstract

As a laboratory tool, microarray is used to detect the expression of thousands of genes at the same time. Typically, microscope slides have DNA microarrays that are printed with thousands of tiny spots in specified positions. Each spot contains a known DNA sequence or gene. These slides are commonly referred to as gene chips or DNA chips. The DNA molecules printed to each slide serve as probes to detect gene expression, which is also known as the transcriptome or the set of messenger RNA (mRNA) transcripts expressed by a group of genes. The goal of this chapter is to discuss the steps involved computational analysis of data after the completion of a typical microarray experiment.
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Affymatrix arrays [more specific can be—GeneChip® Mouse Gene 2.0 ST Array (Affymetrix)]; Gene Set Enrichment Analysis (GSEA); KEGG (Kyoto Encyclopedia of Genes and Genomes); LIMMA (Linear Models for Microarray); Medulloblastoma; Microarray; STRING (Search Tool for Recurring Instances of Neighbouring Genes)

Mesh:

Year:  2022        PMID: 34978688     DOI: 10.1007/978-1-0716-1952-0_6

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  18 in total

1.  Use of within-array replicate spots for assessing differential expression in microarray experiments.

Authors:  Gordon K Smyth; Joëlle Michaud; Hamish S Scott
Journal:  Bioinformatics       Date:  2005-01-18       Impact factor: 6.937

2.  A comparison of background correction methods for two-colour microarrays.

Authors:  Matthew E Ritchie; Jeremy Silver; Alicia Oshlack; Melissa Holmes; Dileepa Diyagama; Andrew Holloway; Gordon K Smyth
Journal:  Bioinformatics       Date:  2007-08-25       Impact factor: 6.937

3.  ROBUST HYPERPARAMETER ESTIMATION PROTECTS AGAINST HYPERVARIABLE GENES AND IMPROVES POWER TO DETECT DIFFERENTIAL EXPRESSION.

Authors:  Belinda Phipson; Stanley Lee; Ian J Majewski; Warren S Alexander; Gordon K Smyth
Journal:  Ann Appl Stat       Date:  2016-07-22       Impact factor: 2.083

4.  limma powers differential expression analyses for RNA-sequencing and microarray studies.

Authors:  Matthew E Ritchie; Belinda Phipson; Di Wu; Yifang Hu; Charity W Law; Wei Shi; Gordon K Smyth
Journal:  Nucleic Acids Res       Date:  2015-01-20       Impact factor: 16.971

5.  The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored.

Authors:  Damian Szklarczyk; Andrea Franceschini; Michael Kuhn; Milan Simonovic; Alexander Roth; Pablo Minguez; Tobias Doerks; Manuel Stark; Jean Muller; Peer Bork; Lars J Jensen; Christian von Mering
Journal:  Nucleic Acids Res       Date:  2010-11-02       Impact factor: 16.971

6.  STRING v10: protein-protein interaction networks, integrated over the tree of life.

Authors:  Damian Szklarczyk; Andrea Franceschini; Stefan Wyder; Kristoffer Forslund; Davide Heller; Jaime Huerta-Cepas; Milan Simonovic; Alexander Roth; Alberto Santos; Kalliopi P Tsafou; Michael Kuhn; Peer Bork; Lars J Jensen; Christian von Mering
Journal:  Nucleic Acids Res       Date:  2014-10-28       Impact factor: 16.971

7.  The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible.

Authors:  Damian Szklarczyk; John H Morris; Helen Cook; Michael Kuhn; Stefan Wyder; Milan Simonovic; Alberto Santos; Nadezhda T Doncheva; Alexander Roth; Peer Bork; Lars J Jensen; Christian von Mering
Journal:  Nucleic Acids Res       Date:  2016-10-18       Impact factor: 16.971

8.  STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.

Authors:  Damian Szklarczyk; Annika L Gable; David Lyon; Alexander Junge; Stefan Wyder; Jaime Huerta-Cepas; Milan Simonovic; Nadezhda T Doncheva; John H Morris; Peer Bork; Lars J Jensen; Christian von Mering
Journal:  Nucleic Acids Res       Date:  2019-01-08       Impact factor: 16.971

9.  STRING v9.1: protein-protein interaction networks, with increased coverage and integration.

Authors:  Andrea Franceschini; Damian Szklarczyk; Sune Frankild; Michael Kuhn; Milan Simonovic; Alexander Roth; Jianyi Lin; Pablo Minguez; Peer Bork; Christian von Mering; Lars J Jensen
Journal:  Nucleic Acids Res       Date:  2012-11-29       Impact factor: 16.971

10.  SVD-phy: improved prediction of protein functional associations through singular value decomposition of phylogenetic profiles.

Authors:  Andrea Franceschini; Jianyi Lin; Christian von Mering; Lars Juhl Jensen
Journal:  Bioinformatics       Date:  2015-11-26       Impact factor: 6.937

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