expHRD calculation

  • Query
  • Background
  • How to Use
  • Running
  • Output
  • Error

How to Use

INPUT


1. Users can currently select RNAseq normalisation via DESeq2 (Differential gene-expression analysis) normalisation.
To construct DESeq2-based RNAseq input data, users are required to first generate RNAseq expression data following an established RNA-sequencing analysis pipeline. Notably, platforms like nf-core offer well-constructed pipelines catering to RNA sequencing, guiding data transformation from Fastq format to aligned (e.g., STAR) or pseudo-alignment (e.g., Salmon) RNAseq scores. In this case, it is recommended to use aligners with STAR-RSEM pipeline.
After the construction of the expression format, users have to normalise the expression value with DESeq2 normalisation method. Elaborative instructions are well documented within the DESeq2 manual.
*Here, we used Deseq2 (v: 1.34.0).


2. The web page’s interface provides a comprehensive illustration of the RNAseq format (please refer to the example table, TCGA-OV RNAseq).
3. Users can download the sample data via the “Download deseq2 example [.csv]” button.
4. Input files must conform to the RNAseq format:
Row: GENES (ENSEMBLE ID); Columns: Sample ID (multiple samples can be analysed); Value: RNAseq expression, structured in correspondence with the DESeq2 format


5. Users have the option to command expHRD to determine the name of the result table. Users can insert the name of the output table and select the interval option and range.
6. Click the “Run” button and wait until the calculation ends. It may take a few minutes.


OUTPUT


1. Users can check the summary of expHRD results (input option, output file name, etc.).


2. The result of the HRD score plotted in the graph followed the regression function of the TCGA-OV test sample (left plot). The distribution of HRD scores is plotted as a histogram. Users can choose the background cancer type in the TCGA cohort (right plot).
3. Users can manipulate the scale of view, save, or other options of Plotly.


4. The result table illustrates the expHRD, lower HRD, predicted HRD, and higher HRD scores. Lower and higher HRD scores were calculated from the regression function, which follows users’ selected interval option and range.
5. Users can download the result table in TSV or XLS format.