OmicsEV

A tool for large scale omics data tables evaluation

Installation

Install OmicsEV using Docker: sh docker pull proteomics/omicsev

If Docker Engine is not installed, please first install Docker Engine following the instruction at https://docs.docker.com/engine/install/. If Docker Engine is already installed, the OmicsEV docker can be installed using the above command line.

Use OmicsEV in docker: ```sh

change the path yourdatapath to a real path

docker run -it -v /yourdatapath/:/opt/ -u $(id -u):$(id -g) proteomics/omicsev

then lauch R

R ```

Please put all the input data files for OmicsEV under a folder (for example: /yourdatapath/, this can be any folder with write permission) and use parameter -v to map this folder to the Docker container directory "/opt/" (-v /yourdatapath/:/opt/, don't change /opt/ part) so that all the input data files can be accessed inside OmicsEV docker. After lauching R in OmicsEV docker using above code, users can then use the OmicsEV functions to perform analysis. A few examples can be found below.

It requires a basic understanding of docker to use OmicsEV inside docker: https://www.docker.com/get-started/.

Usage

Please follow the instruction in website https://bzhanglab.github.io/OmicsEV/

Examples

Example 1: evaluate RNA-Seq data tables generated using different normalization methods

The RNA-Seq data is from TCGA-BRCA project. A total of six different data tables were generated using different normalization methods. A proteomics data table is available and it was generated from the same samples. Below is the R code to run the evaluation using OmicsEV.

r library(OmicsEV) run_omics_evaluation(data_dir = "datasets/", sample_list = "sample_list.tsv", x2 = "protein.tsv", x2_label = "Protein", cpu=0, use_existing_data=TRUE, data_type="gene", class_for_ml="sample_ml.tsv") Please download the input files for above code at RNAseq6_datasets.tar.gz. It contains the following files:

shell ├── datasets │   ├── d1.tsv │   ├── d2.tsv │   ├── d3.tsv │   ├── d4.tsv │   ├── d5.tsv │   └── d6.tsv ├── protein.tsv ├── run_OmicsEV.R ├── sample_list.tsv └── sample_ml.tsv

The HTML report generated using above code is available at OmicsEV report.

This example takes about 2 hours and 40 minutes on a Linux system with 64 CPUs and 256G memory.

Example 2: evaluate proteomics data tables generated using different pipelines

The proteomics data is from CPTAC Breast project. A total of three different data tables were generated using different pipelines. An RNA-Seq data table is available and it was generated from the same samples. Below is the R code to run the evaluation using OmicsEV.

r library(OmicsEV) run_omics_evaluation(data_dir = "datasets_75/", sample_list = "sample_list_v2.tsv", x2 = "rna.tsv", cpu=0, use_existing_data=TRUE, data_type="gene", class_for_ml="sample_ml.tsv") Please download the input files for above code at proteomics3datasets.tar.gz. It contains the following files:

├── datasets_75 │   ├── CDAP.tsv │   ├── MQ_ratio.tsv │   └── paper.tsv ├── rna.tsv ├── run_OmicsEV.R ├── sample_list_v2.tsv └── sample_ml.tsv

The HTML report generated using above code is available at OmicsEV report.

This example takes about one hour on a Linux system with 64 CPUs and 256G memory.

Example 3: evaluate the data quality of single data table

The proteomics data is from CPTAC Breast project. A single data table was generated using one pipeline. An RNA-Seq data table is available and it was generated from the same samples. Below is the R code to run the evaluation using OmicsEV.

r library(OmicsEV) run_omics_evaluation(data_dir = "datasets_75/", sample_list = "sample_list_v2.tsv", x2 = "rna.tsv", cpu=0, use_existing_data=TRUE, data_type="gene", class_for_ml=c("LumA","LumB")) Please download the input files for above code at proteomics1dataset.tar.gz. It contains the following files: ├── datasets_75 │   └── paper.tsv ├── rna.tsv ├── run_OmicsEV.R ├── sample_list_v2.tsv └── sample_ml.tsv

The HTML report generated using above code is available at OmicsEV report.

This example takes about one hour on a Linux system with 64 CPUs and 256G memory.

How to cite:

Bo Wen, Eric J Jaehnig, Bing Zhang. OmicsEV: a tool for comprehensive quality evaluation of omics data tables. Bioinformatics, btac698, 2022.

Applications in publications

1. Cao, L., et al. Proteogenomic characterization of pancreatic ductal adenocarcinoma. Cell 2021;184(19):5031-5052 e5026.
2. Dou, Y., et al. Proteogenomic Characterization of Endometrial Carcinoma. Cell 2020;180(4):729-748 e726.
3. Gao, Q., et al. Integrated Proteogenomic Characterization of HBV-Related Hepatocellular Carcinoma. Cell 2019;179(2):561-577 e522.
4. Huang, C., et al. Proteogenomic insights into the biology and treatment of HPV-negative head and neck squamous cell carcinoma. Cancer Cell 2021;39(3):361-379 e316.
5. Satpathy, S., et al. Microscaled proteogenomic methods for precision oncology. Nat Commun 2020;11(1):532.
6. Anurag, M., et al. Proteogenomic markers of chemotherapy resistance and response in triple negative breast cancer. Cancer Discovery 2022; CD-22.
7. Fengchao Yu, et al. One-stop analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform. bioRxiv 2022.