Developer

This pipeline is developed by the BMAP team.

Public Dataset

These original datasets were downloaded from: Multi-omics data

Workflw

Reference

Chen, Yi-Ju, et al. "Proteogenomics of non-smoking lung cancer in East Asia delineates molecular signatures of pathogenesis and progression." Cell 182.1 (2020): 226-244.

Introduction

The complete set of tumors in The Cancer Genome Atlas (TCGA), consist of approximately 10,000 specimens and represent 33 types of cancer. It seemed appropriate to analyze all 33 tumor types (called the PanCancer Atlas) to address the intriguing questions left unanswered: whether the inclusion of many more tumors and tumor types enhances the number of cross-tissue associations, produces additional convergent and/or divergent integrated molecular subtypes, and significantly increases the fraction of cancer patients whose classification or treatment might be affected by this new taxonomic approach.

Proposal

This PanCancer study encompassed 11,286 tumor samples from 33 cancer types, for which molecular data were available from at least one of the five assay platforms. Of these, 9,759 had complete data for 4 platforms: aneuploidy, DNA methylation, mRNA and miRNA. RPPA protein data were available for a subset of samples (7,858).

Results

Figure 1. Integrated analysis by using iCluster.

Reference

Hoadley, K. A., et al. (2018). Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer. Cell, 173: 291-304.

v2.0: Linked up with the repository system. The code and Docker image associated with this pipeline are CRS1079112374717526016 and SRS1079114140569841664, respectively.
v1.1: The pipeline was constructed using the iClusterPlus R package.
v1.0: The pipeline was constructed using the iCluster R package.

Reproducibility

The pipeline utilizes code CRS1079112374717526016 and Docker image SRS1079114140569841664, both of which are stored in the BMAP repositories.

Usage

1. Select the application 'Multi-omics integrated analysis platform'
2. Click on the 'Analysis' button
3. Click on the 'Integrated analysis'
4. Fill in the request parameters

5. Upload the compressed data in gz or rar format.

6. View the results