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How to carry out Broad GDAC data analysis of TCGA, I believe that many inexperienced people do not know what to do. Therefore, this paper summarizes the causes and solutions of the problem. Through this article, I hope you can solve this problem.

Broad GDAC collates and deeply analyzes the results of TCGA, and the relevant original data and analysis results can be viewed and downloaded through the web page.

Click Cases to view the corresponding sample information, click Data to download the corresponding result file, and click Browse to view the analysis results through FireBowse. The URL is as follows

Http://firebrowse.org/

Take Adrenocortical carcinoma as an example, select the corresponding disease in the drop-down box on the left, and then you will see the bar chart shown below on the right

Each column represents the different taxonomic data of the disease. Click on the column to download the corresponding data. On the left are the results of the detailed analysis

1. Clinical Analyses

The analysis is as follows

Aggregate AnalysisFeatures

Correlate Clinical vs CopyNumber Arm

Correlate Clinical vs CopyNumber Focal

Correlate Clinical vs Methylation

Correlate Clinical vs miRseq

Correlate Clinical vs Molecular Subtypes

Correlate Clinical vs mRNAseq

Correlate Clinical vs Mutation

Correlate Clinical vs MutationRate

Correlate Clinical vs RPPA

It provides the correlation analysis between clinical data and copy number, methylation, mRNA/miRNA expression profile, mutation information, protein expression profile and other data.

2. CopyNumber Analyses

The analysis is as follows

Aggregate AnalysisFeatures

CopyNumber Clustering CNMF

CopyNumber Clustering CNMF thresholded

CopyNumber Gistic2

Correlate Clinical vs CopyNumber Arm

Correlate Clinical vs CopyNumber Focal

Correlate CopyNumber vs mRNAseq

Correlate molecularSubtype vs CopyNumber Arm

Correlate molecularSubtype vs CopyNumber Focal

Pathway Paradigm RNASeq And Copy Number

It provides clustering based on copy number and analysis of the correlation between copy number and clinical data and mRNA expression profile.

3. Correlations Analyses

The analysis is as follows

Correlate Clinical vs CopyNumber Arm

Correlate Clinical vs CopyNumber Focal

Correlate Clinical vs Methylation

Correlate Clinical vs miRseq

Correlate Clinical vs Molecular Subtypes

Correlate Clinical vs mRNAseq

Correlate Clinical vs Mutation

Correlate Clinical vs MutationRate

Correlate Clinical vs RPPA

Correlate CopyNumber vs mRNAseq

Correlate Methylation vs mRNA

Correlate molecularSubtype vs CopyNumber Arm

Correlate molecularSubtype vs CopyNumber Focal

Correlate molecularSubtype vs Mutation

The correlation analysis between all kinds of data is provided.

4. Methylation Analyses

The analysis is as follows

Correlate Clinical vs Methylation

Correlate Methylation vs mRNA

Methylation Clustering CNMF

The clustering based on methylation data and the correlation analysis between methylation and clinical data and mRNA expression profile data are provided.

5. MiRseq Analyses

The analysis is as follows

Aggregate AnalysisFeatures

Correlate Clinical vs miRseq

MiRseq Clustering CNMF

MiRseq Clustering Consensus Plus

MiRseq FindDirectTargets

MiRseq Mature Clustering CNMF

MiRseq Mature Clustering Consensus Plus

It provides clustering based on miRNA expression profile data, prediction of miRNA target genes, and analysis of the correlation between miRNA and clinical data.

6. MRNA Analyses

The analysis is as follows

Correlate Methylation vs mRNA

Pathway GSEA mRNAseq

The correlation analysis between mRNA chip expression profile data and methylation data, as well as GSEA gene set enrichment analysis were provided.

7. MRNAseq Analyses

The analysis is as follows

Aggregate AnalysisFeatures

Correlate Clinical vs mRNAseq

Correlate CopyNumber vs mRNAseq

MiRseq FindDirectTargets

MRNAseq Clustering CNMF

MRNAseq Clustering Consensus Plus

Pathway Paradigm RNASeq

Pathway Paradigm RNASeq And Copy Number

It provides clustering based on mRNA sequencing expression profile, mRNA expression profile data and copy number, correlation analysis of clinical data, and analysis of miRNA and mRNA interaction network.

8. Mutation Analyses

The analysis is as follows

Aggregate AnalysisFeatures

Correlate Clinical vs Mutation

Correlate Clinical vs MutationRate

Correlate molecularSubtype vs Mutation

Mutation APOBEC

Mutation Assessor

Mutation CHASM

MutSig2.0

MutSig2CV

MutSigCV

Pathway Overlaps MSigDB MutSig2CV

The analysis of the correlation between mutation information and clinical data is provided.

9. Pathway Analyses

The analysis is as follows

Pathway GSEA mRNAseq

Pathway Overlaps MSigDB MutSig2CV

Pathway Paradigm RNASeq

Pathway Paradigm RNASeq And Copy Number

GSEA and other analysis contents of mRNA expression profile were provided.

10. RPPA Analyses

The analysis is as follows

Correlate Clinical vs RPPA

RPPA Clustering CNMF

RPPA Clustering Consensus Plus

The clustering based on protein chip data and the correlation analysis between protein expression profile and clinical data were provided.

Taking the correlation analysis between methylation and mRNA expression profile as an example, the results are as follows

For each analysis, it is divided into the following three parts

Overview

Results

Methods & data

The overview section provides a brief description of the results, as shown below

The results section can view the detailed analysis results, as shown below

In the methods & data section, you can view the analysis method and download the analysis results, as shown below

Through Broad GDAC, not only TCGA data can be downloaded, but also data mining can be carried out. The analysis contents and methods it provides are worth using for reference.

After reading the above, have you mastered the method of how to analyze the data of TCGA by Broad GDAC? If you want to learn more skills or want to know more about it, you are welcome to follow the industry information channel, thank you for reading!

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