custom 244k gene expression microarray  (Agilent technologies)

Journal: OncoTargets and therapy

Article Title: WISP3 is highly expressed in a subset of colorectal carcinomas with a better prognosis

doi: 10.2147/OTT.S97025

Figure Lengend Snippet: Outlier gene analysis from TCGA dataset and three independent CRC microarray cohorts. Notes: Top 20 genes ranked in 90th and 75th outlier genes from TCGA dataset ( A ); No WISP3 positive cases defined by 90th and 75th cutoff was observed in normal colorectal tissues ( B ); WISP3 demonstrated significant outlier expression model in three independent CRC cohorts, Vilar Colorectal 2 ( C ); Vilar Colorectal ( D ); Smith Colorectal ( E ). Abbreviations: COPA, cancer outlier profile analysis; CRC, colorectal cancer; TCGA, The Cancer Genome Atlas; WISP3, WNT1-inducible-signaling pathway protein 3.

Article Snippet: TCGA mRNA expression data were produced on Agilent 244K Custom Gene Expression microarray platform (Agilent Technologies, Santa Clara, CA, USA) and Illumina RNA-Seq platform (Illumina, Inc., San Diego, CA, USA).

Techniques: Microarray, Expressing

Journal: BMC Bioinformatics

Article Title: Investigating skewness to understand gene expression heterogeneity in large patient cohorts

doi: 10.1186/s12859-019-3252-0

Figure Lengend Snippet: Skewness in the gene expression study. a . Contrasting negative and positive skewed distributions. b . The proportions of genes with negative and positive skewed expression reflect differences between microarray and RNA-Seq datasets. The dark grey bars indicate the fraction of genes with a negatively-skewed expression distribution and the light grey bars indicate the remaining fraction of genes with positively-skewed expression distribution. c . Distribution of gene expression skewness for the RNA-seq control dataset

Article Snippet: The TCGA-based datasets were profiled using Agilent 244K Custom Gene Expression Microarray (G4502A-07-3).

Techniques: Expressing, Microarray, RNA Sequencing Assay

Journal: BMC Bioinformatics

Article Title: Investigating skewness to understand gene expression heterogeneity in large patient cohorts

doi: 10.1186/s12859-019-3252-0

Figure Lengend Snippet: Pathway trends for skewness comparisons between datasets. a . Results are shown for microarray dataset comparisons. b . Results are shown for RNA-seq dataset comparisons. The bars represent the number of category-specific pathways that appear in the ten most significant pathways of the colour-specified group. “ >” refers to genes where there was an increase towards more positive skew, “ ∼” refers to genes that had negligible change in skew between the datasets, and “ <” refers to genes where there was an increase towards more negative skew. For a . in the Immune System and Metabolism plots, green refers to genes that have a greater skew in each cancer on the y-axis as compared to the control. In the AML Metabolism plots, green refers to genes that have a higher skew in AML compared to each cancer on the y-axis. Large red scores in Metabolism suggest that metabolic pathways have a lower skew in cancers compared to control. However, red scores in AML Metabolism suggest that metabolic pathways in AML have a lower skew than those in other cancers. For b . In the Translation plots, green refers to genes that have a greater skew in each cancer on the y-axis as compared to the control/other cancer on y-axis. In the LGG plots, green refers to genes that have a higher skew in LGG compared to each cancer on the y-axis. b Overview of DNA methylation and gene expression skewness analysis. This cartoon outlines the main steps for investigating the relationship between expression skewness and DNA methylation in four TCGA datasets

Article Snippet: The TCGA-based datasets were profiled using Agilent 244K Custom Gene Expression Microarray (G4502A-07-3).

Techniques: Microarray, RNA Sequencing Assay, DNA Methylation Assay, Expressing