This is an R program to visualize and analyze -omics data such as those from microarray, metabolomics and proteomics experiments. Next-generation sequencing such as RNA-Seq and single-cell RNA-Seq analyses are to be supported in the future. The input of this program is intensity or count data supplied in a list or table along with parameter settings, all in an Excel file. The output is an Excel file with figures and analysis result sheets.
This is an R program to visualize and analyze -omics data such as those from metabolomics, lipidomics, proteomics, microarray, and Bulk RNA-Seq experiments. The input of this program is an intensity, count, or ratio data table along with parameter settings, all as spreadsheets in an Excel file. The output includes spreadsheets in an Excel file, figure folders, and an .rdata file for interactive visualization (under development).
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## How to run this tool:
## How to run this tool:
**Step 1:** Click on the Download button at the top right corner of this page to download the source code and unzip on your computer.
**Step 1:** Click on the download link https://cloud.biohpc.swmed.edu/index.php/s/nnq28yDTPwKFTNM (password: ODA@CRI_UTSW) to download a copy of the ODA tool.
**Step 2:**Copy the data template file from the unzipped directory and open it in Excel. Read the instructions in there.
**Step 2:**Make a copy of data template file from your ODA directory and open it in Excel. Read the instructions in there. This is your ***input file***.
**Step 3:** Copy your data to the ***RawData*** sheet of the template. Data can be a list or table with samples, features, feature descriptions and values. See data template for details.
**Step 3:** Copy your data table to the ***RawData*** sheet of your ***input file***. Data should be a table with samples, features, optional feature descriptions, and values. See data template for details.
**Step 4:** Fill in the ***Parameters***, ***Comparisons***, ***Features***, and ***Samples*** sheets as necessary.
**Step 4:** Fill in the ***Parameters***, ***Comparisons***, ***Features***, and ***Samples*** sheets as necessary.
**Step 5:** Run the program with your data and save results in an Excel file. If visualization is enabled, a Figures folder will be created to save the plots in the Portable Network Graphics (.png) and postscript (.ps) formats. If enrichment analysis is enabled, an EnrichmentAnalysis folder will be created to save the results. See below for ways of running the program.
**Step 5:** Run the program with your ***input file*** and save results in an ***output folder***. If visualization is enabled, a Figures folder will be created to save the plots. If enrichment analysis is enabled, an EnrichmentAnalysis folder will be created to save the results. See below for ways of running the program.
1. Running on the BioHPC @ UTSW. Log on the BioHPC ***Portal***, launch a ***Web Visualization*** node, open a terminal from there, and run the following:
1. Running on the BioHPC @ UTSW. Log on the BioHPC ***Portal***, launch a ***Web Visualization*** node, open a terminal from there, and run the following:
```
```
sh /path_to_the_program/oda_analysis.sh /input_path/your_data_file.xlsx /output_path/ optional_BioHPC_queue_name
sh /path_to_the_program/oda_analysis.sh /input_path/your_data_file.xlsx /output_path/ optional_BioHPC_queue_name
```
```
2. Running on your local machine with a Singularity / Docker / Podman container. Make sure Singularity / Docker / Podman is installed and you can run it from a command line tool such as a Linux terminal or Windows CMD / PowerShell. You should request a copy of the corresponding container from me.
2. Running on your local machine with a Singularity / Docker / Podman container. Make sure Singularity / Docker / Podman is installed and you can run it from a command line tool such as a Linux terminal or Windows CMD / PowerShell. If you do not use Singularity, you should request a copy of the corresponding Docker / Podman container from me.
3. Running on your local machine. Make sure R and required packages are installed and you can run the Rscript command from a command line tool such as a Linux terminal or Windows CMD / PowerShell.
3. Running on your local machine. Make sure R and required packages are installed and you can run the Rscript command from a command line tool such as a Linux terminal or Windows CMD / PowerShell.
