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 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

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

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 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

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

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

Table_1_Beyond Pathway Analysis: Identification of Active Subnetworks in Rett Syndrome.DOCX

Pathway and network approaches are valuable tools in analysis and interpretation of large complex omics data. Even in the field of rare diseases, like Rett syndrome, omics data are available, and the maximum use of such data requires sophisticated tools for comprehensive analysis and visualization of the results. Pathway analysis with differential gene expression data has proven to be extremely successful in identifying affected processes in disease conditions. In this type of analysis, pathways from different databases like WikiPathways and Reactome are used as separate, independent entities. Here, we show for the first time how these pathway models can be used and integrated into one large network using the WikiPathways RDF containing all human WikiPathways and Reactome pathways, to perform network analysis on transcriptomics data. This network was imported into the network analysis tool Cytoscape to perform active submodule analysis. Using a publicly available Rett syndrome gene expression dataset from frontal and temporal cortex, classical enrichment analysis, including pathway and Gene Ontology analysis, revealed mainly immune response, neuron specific and extracellular matrix processes. Our active module analysis provided a valuable extension of the analysis prominently showing the regulatory mechanism of MECP2, especially on DNA maintenance, cell cycle, transcription, and translation. In conclusion, using pathway models for classical enrichment and more advanced network analysis enables a more comprehensive analysis of gene expression data and provides novel results.</p

Data_Sheet_1_Beyond Pathway Analysis: Identification of Active Subnetworks in Rett Syndrome.ZIP

Pathway and network approaches are valuable tools in analysis and interpretation of large complex omics data. Even in the field of rare diseases, like Rett syndrome, omics data are available, and the maximum use of such data requires sophisticated tools for comprehensive analysis and visualization of the results. Pathway analysis with differential gene expression data has proven to be extremely successful in identifying affected processes in disease conditions. In this type of analysis, pathways from different databases like WikiPathways and Reactome are used as separate, independent entities. Here, we show for the first time how these pathway models can be used and integrated into one large network using the WikiPathways RDF containing all human WikiPathways and Reactome pathways, to perform network analysis on transcriptomics data. This network was imported into the network analysis tool Cytoscape to perform active submodule analysis. Using a publicly available Rett syndrome gene expression dataset from frontal and temporal cortex, classical enrichment analysis, including pathway and Gene Ontology analysis, revealed mainly immune response, neuron specific and extracellular matrix processes. Our active module analysis provided a valuable extension of the analysis prominently showing the regulatory mechanism of MECP2, especially on DNA maintenance, cell cycle, transcription, and translation. In conclusion, using pathway models for classical enrichment and more advanced network analysis enables a more comprehensive analysis of gene expression data and provides novel results.</p