Omics Data Analyser

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.

Current version: 1.7.3. Tested R versions: 4.0.2, 3.5.1.

How to run this tool:

Step 1: Click on the V1.7.3 link above to proceed to the source code directory. Download all files in that directory.

Step 2: Copy the data template file from the downloaded directory and open it in Excel. Read the instructions in there.

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 4: Fill out the Parameters, Comparisons, Features, and Samples forms 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. 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:

sh  /path_to_the_program/run_analysis.sh  /input_path/your_data_file.xlsx  /output_path/your_result_file.xlsx  optional_BioHPC_queue_name

2. Running on your local machine. Make sure R is installed and you can run the Rscript command from a command line tool such as a Linux terminal or Windows CMD / PowerShell.

Rscript  /path_to_the_program/ODA.R  /input_path/your_data_file.xlsx  /output_path/your_result_file.xlsx

• For the first time running this program, you will need internet access so that the program can auto-download necessary R packages. If you want to install packages to a different directory, find the following line from the ODA.R and change it.

	myLibPath='~/R/4.0.2-gccmkl'	#Changed this to your own library path if necessary.

Examples: See the Examples folder for typical analysis settings and output files.

1. Raw_Data_QC: This examples shows how to compare samples with quality controls to know whether the experiment works well. For such comparisons use the raw, non-normalized data.
2. QC_Excluded_Normalized_Data_Differential_Intensities: This example shows how to do differential intensity tests between biological samples under different experimental conditions. For such comparisons you should exclude quality controls and normalize data.
3. Technical_Replicates: When there are technical replicates from the same biological subjects, the program averages them before normalization. This example shows you how to specify technical replicates.
4. Multiple_Batches: If you have data from multiple experiments and suspect batch effects, the program can help identify them by visualization. Batches can be modeled if using glm-based statistical tests. This example shows you how to specify batches.
5. Paired_or_Matched_Samples: If you have paired or matched samples, the program can do paired statistical tests. This example shows you how to specify the pairing or matching.
6. Interested_and_Excluded_Features: If you supply a list of features e.g. significant features by differential intensity tests, the program can generate plots and result sheet for selected features only. The program can also exclude features from analysis if you supply a list. This example shows you how to specify those lists.
7. List_Data: The program supports raw data in a table (features in rows and samples in columns) or list (features, samples, and values each in a column). While the above examples are all in the table format, this example shows you how to supply data in the list format.

Citation:

DeVilbiss AW, Zhao Z, Martin-Sandoval MS, Ubellacker JM, Tasdogan A, Agathocleous M, Mathews TP, Morrison SJ. Metabolomic profiling of rare cell populations isolated by flow cytometry from tissues. eLife 2021;10:e61980. PMCID: PMC7847306

Contact:

Contact Zhiyu Zhao for comments / questions / suggestions about this software.