PathVisio results of significant pathways found in the dataset by Sun <i>et al.</i> comparing processed data provided by ArrayExpress with the reprocessed data after quality control.

<p>Pathway analysis is based on a comparison between recurrent and non-recurrent prostate cancer. Only significant pathways with a Z-score >1.9 in at least one of the two analyses are included. Significant Z-scores are depicted in bold. A NaN value commonly occurs when none of the genes in the pathway is present in the dataset.</p

PathVisio results of significant pathways found in the dataset by Varambally <i>et al.</i> comparing processed data provided by ArrayExpress with the reprocessed data.

<p>Pathway analysis is based on a comparison between primary prostate cancer and metastatic prostate cancer. Only significant pathways with a Z-score >1.9 in at least one of the two analyses are included. Significant Z-scores are depicted in bold; matches in pathways between the analyses are in italics.</p

PathVisio results of significant pathways found in the dataset by Best <i>et al.</i> comparing processed data provided by ArrayExpress with the reprocessed data after quality control.

<p>Pathway analysis is based on a comparison between androgen-dependent and androgen-independent prostate cancer. Only significant pathways with a Z-score >1.9 in at least one of the two analyses are included. Significant Z-scores are depicted in bold; matches between the analyses are in italics.</p

Overview of the QC results of two selected datasets.

<p>Several QC results of the dataset by Varambally <i>et al.</i> comparing arrays of benign prostate tissue (maroon), primary prostate cancer (blue) and metastatic prostate cancer samples (green) are shown in panel a-d. Several QC results of the dataset by Gregg <i>et al.</i> comparing arrays of prostatic epithelial (maroon) with interstitial stromal cell samples (teal) are depicted in panel e-h. a) Boxplot of raw intensities; b) density histogram of log intensities before normalization; c) density histogram of log intensities after normalization; d) MA-plot before (upper panel) and after normalization (lower panel) of one example array of the dataset by Varambally <i>et al.</i>; e) boxplot of raw intensities; f) density histogram of log intensities before normalization; g) density histogram of log intensities after normalization; h) MA-plot before (upper panel) and after normalization (lower panel) of one example array of the dataset by Gregg <i>et al.</i></p

PathVisio results of significant pathways found in the dataset by Varambally <i>et al.</i> comparing processed data provided by ArrayExpress with the reprocessed data.

<p>Pathway analysis is based on a comparison between benign prostate tissue and primary prostate cancer. Only significant pathways with a Z-score >1.9 in at least one of the two analyses are included. Significant Z-scores are depicted in bold; matches in pathways between the analyses are in italics.</p

Characteristics of the selected datasets.

<p>The number of excluded arrays for each dataset using the standardized QC procedure is indicated in brackets. Abbreviations: NP: normal prostate, pPC: primary prostate cancer, mPC: metastatic prostate cancer, AI: androgen independent, AD: androgen dependent.</p

PathVisio results of significant pathways found in the dataset by Wallace <i>et al.</i> comparing processed data provided by ArrayExpress with the reprocessed data after quality control.

<p>Pathway analysis is based on a comparison between normal prostate tissue and prostatic adenocarcinoma. Only significant pathways with a Z-score >1.9 in at least one of the two analyses are included. Significant Z-scores are depicted in bold; matches between the analyses are in italics.</p

Standardized microarray data analysis workflow.

<p>Starting from the publicly available EMBL repository ArrayExpress: 1) Relevant prostate cancer studies were selected and downloaded; 2) Quality control and data pre-processing steps were performed in the R environment. Microarrays with insufficient sample quality, hybridization quality, signal comparability or array correlation were excluded; 3) For each included study, statistical analysis was performed and pathway analysis was run with PathVisio to identify the biological processes involved; 4) Results were then integrated and compared to literature findings.</p

From SNPs to pathways: Biological interpretation of type 2 diabetes (T2DM) genome wide association study (GWAS) results

<div><p>Genome-wide association studies (GWAS) have become a common method for discovery of gene-disease relationships, in particular for complex diseases like Type 2 Diabetes Mellitus (T2DM). The experience with GWAS analysis has revealed that the genetic risk for complex diseases involves cumulative, small effects of many genes and only some genes with a moderate effect. In order to explore the complexity of the relationships between T2DM genes and their potential function at the process level as effected by polymorphism effects, a secondary analysis of a GWAS meta-analysis is presented. Network analysis, pathway information and integration of different types of biological information such as eQTLs and gene-environment interactions are used to elucidate the biological context of the genetic variants and to perform an analysis based on data visualization. We selected a T2DM dataset from a GWAS meta-analysis, and extracted 1,971 SNPs associated with T2DM. We mapped 580 SNPs to 360 genes, and then selected 460 pathways containing these genes from the curated collection of WikiPathways. We then created and analyzed SNP-gene and SNP-gene-pathway network modules in Cytoscape. A focus on genes with robust connections to pathways permitted identification of many T2DM pertinent pathways. However, numerous genes lack literature evidence of association with T2DM. We also speculate on the genes in specific network structures obtained in the SNP-gene network, such as gene-SNP-gene modules. Finally, we selected genes relevant to T2DM from our SNP-gene-pathway network, using different sources that reveal gene-environment interactions and eQTLs. We confirmed functions relevant to T2DM for many genes and have identified some—<i>LPL</i> and <i>APOB</i>—that require further validation to clarify their involvement in T2DM.</p></div

Figure_S3 – Supplemental material for A Membranome-Centered Approach Defines Novel Biomarkers for Cellular Subtypes in the Intervertebral Disc

<p>Supplemental material, Figure_S3 for A Membranome-Centered Approach Defines Novel Biomarkers for Cellular Subtypes in the Intervertebral Disc by Guus G. H. van den Akker, Lars M. T. Eijssen, Stephen M. Richardson, Lodewijk W. van Rhijn, Judith A. Hoyland, Tim J. M. Welting and Jan Willem Voncken in CARTILAGE</p