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This article mainly explains "how to use TCGAbiolinks to analyze the expression profile data in TCGA". The content in the article is simple and clear, and it is easy to learn and understand. Please follow the editor's train of thought to study and learn how to use TCGAbiolinks to analyze the expression profile data in TCGA.

For transcriptome data, difference analysis and enrichment analysis is one of the core analysis contents. TCGA expression profile data download, difference analysis and enrichment analysis can be easily realized through TCGAbiolinks. Taking the gene expression profile of breast cancer as an example, the analysis process is as follows.

1. Download raw data

Because of the large number of breast cancer samples in TCGA, only some samples were selected for testing. The download process is as follows

two。 Difference analysis

The detailed steps are as follows

The data are preprocessed, and the samples with low correlation are removed according to the Spelman correlation coefficient between samples.

Normalization, calling the normalization algorithm in EDASeq

Screening genes according to the mean value of expression

Difference analysis, calling the difference algorithm in edgeR

The code is as follows

3. Enrichment analysis

The code is as follows

The visualization results are as follows

Three categories of GO plus four categories of kegg pathway data, corresponding to four bar charts, each bar chart shows the top10 items with the most significant FDR value, Abscissa I-log10 (FDR), scatter represents GeneRatio, also known as enrich factor, represents the proportion of the number of differential genes enriched to the total number of genes in this pathway.

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