README.md

In broadinstitute/massPattern: What the Package Does (One Line, Title Case)

massPattern User Manual

MassPattern is tool for quality control, statistical analysis, and visualization of metabolomic data. massPattern is currently under development and test and we will regularly release the documentation and tutorials

MassPattern is part of netome framework and its manuscript has been submitted!

Citation:

Ali Rahnavard, Daniel Hitchcock, Julian Avila Pacheco, Amy Deik, Courtney Dennis, Sarah Jeanfavre, Kerry Pierce, Kevin Bullock, Zach Costliow, Clary B. Clish, netome: a computational framework for metabolite profiling and omics network analysis. bioRxiv 443903; doi: https://doi.org/10.1101/443903

For installation and a quick demo, read the massPattern User Manual.

Installation

Install massPattern in RStudio

1. Install devtools :
   • > install.packages('devtools')
   • >library(devtools)
2. Install massPattern (and also all dependencies from CRAN):
   • install.packages(c('dplyr','pbapply', 'lme4', 'lmerTest', 'car', 'cplm', 'pscl', 'logging', 'ggrepel', 'gridExtra', 'future', 'cowplot'), repos='http://cran.r-project.org')
   • > devtools::install_github('netome/massPattern', force = TRUE)

Input files (netome format)

massPattern demo

Comming soon

Load the library

library(massPattern)

R script neteome_process.R to use and call the common functions

# load netome format data
loaded_data <- massPattern::load_data()
# check the wiki for detailed parameters

# explanatory visualization
pcoa_plots <- massPattern::ordplots(data = loaded_data$data, metadata = loaded_data$sample_metadata, output = output_path, outputname = 'pcoa', method = 'pcoa')

Functions

massPattern support statistical analyses with visualizations. Here we dicuss several common functions between all studies and for more details on these functions and other functions check out our Wiki pages.

Load the file of your metabolite profiles

# load the library
library(massPattern)

# call the load_data function
loaded_data <- massPattern:::load_data(input=/path-to-file/filename.xlsx, type='known', sheet = 1, name = 'Metabolite')

data <- loaded_data$data

# ensure all data are stored as numeric
data <- massPattern:::numeric_dataframe(data)

# sample info
sample_info <- loaded_data$sample_metadata

# feature info ( e.g. m/z and RT)
features_info <- loaded_data$feature_metadata

parameters:

• input: is a excel fil in netome format

• type: can be known for charterized metabolites with a name or all for all measured features including known and uncharxterized metabolites

• sheet: defualt is 1, to read first tab of the excel sheet of the excel files but user can use different tab number.

• ID: is a word to use identifier for features (metabolites), the options are Meatbolite, HMDB_ID, and Compound_ID.

Output files

Comming soon

User Manual! (in the wiki)

broadinstitute/massPattern documentation built on Oct. 9, 2019, 10:56 p.m.