pandaOmics

Overview

The goal of pandaOmics is to process and analyze -Omics data easily.

Installation

pandaOmics is currently not available on [CRAN]

To install the development version hosted on this GitHub repository, use the devtools package and the following:

install.packages("devtools") #If you don't have "devtools" installed already
devtools::install_github("jamesjiadazhan/pandaOmics")

Getting Started

library(pandaOmics)

The pandaOmics package currently contains the following key functions:

LC_MS_process(): Process the raw feature table data from LC-MS and make it ready for Metabolome-wide Association Study (MWAS) analysis. Note: it is after feature extraction from the LC_MS machine.

limma_lm(): Perform Metabolome-wide Association Study (MWAS) analysis using multiple linear regression with FDR correction with the limma algorithm. Limma is intended to borrow information from the entire study population to overcome the problem of small sample sizes that small-variance features would be more likely to be statistically significant

multi_reg_2omics_fdr(): perform Multi-Regression Analysis for 2-Omics Data with FDR Correction linking the significant features from the 1st omics datga to all the 2nd omics data

Hmap_metapone(): perform a summary of significant features from metapone pathway tables and generate a heatmap for p-value less than 0.05

Examples:

LC_MS_process

LC_MS_process(raw_data, sample_id_file, metabolite_start_column=10, replicates=NULL, transformation="log2", imputation=TRUE, output_name)

limma_lm

for metabolomics

limma_lm(DATA_comp=stanford_clinical_metabolome, DATA_met=metabolomics_feature_table, output_name="stanford_clinical_metabolome", met_start_column=58, met_end_column=781, outcome="CL4.x", confounders=c("A1C", "GLU"))

for other omics (metals from ICP-MS, microbiome)

limma_lm(DATA_comp=stanford_clinical_metabolome, DATA_met=NULL, output_name="stanford_clinical_metabolome", met_start_column=58, met_end_column=781, outcome="CL4.x", confounders=c("A1C", "GLU"))

multi_reg_2omics_fdr

multi_reg_2omics_fdr(data_comp=METAL_HILIC_RAW_CLEAN,
                     sig_feature_list=ILD2_HILIC_limma_FDR02_1,
                     outcome_start_column = 2,
                     outcome_end_column = 24,
                     confounders = c("Age_at_enrollment", "Gender", "BMI_on_enrollment"),
                     suffix="HILIC")

Hmap_metapone

Hmap_metapone(path_heatmap="james/data_folder", pathway_focus="CANCER")

Additional notes

pandaOmics is licensed under the [GNU General Public License v3.0]. (https://github.com/jamesjiadazhan/dietaryindex/blob/main/CONTRIBUTING.md) for questions, feature requests and bug reports. The maintainer will review pull requests and incorporate contributions at his discretion. You may also reach out to the maintainer, James Jiada Zhan, via his email: jzha832@emory.edu. The author would like to thank Donghai Liang who is an assistant professor at Emory University for his mentorship in the metabolomics processing and analyzing. Thanks a lot for his help.