In jgygi/SPEAR: SPEAR - Sparse Supervised Bayesian Factor Model for Multi-omic Analyses

knitr::opts_chunk$set(echo = TRUE)

library(SPEAR)

Table of Contents...

Introduction

print(mtcars)

Installation

print(mtcars)

Preparing data in a MultiAssayExperiment

print(mtcars)

Creating the SPEAR model

data(su_covid19_multiomics, package = "SPEAR")
tmp <- MultiAssayExperiment::colData(su_covid19_multiomics)
tmp$WHO.status <- factor(tmp$WHO.status)
class(tmp$WHO.status)
MultiAssayExperiment::colData(su_covid19_multiomics) <- tmp

spear.model <- SPEAR::new.spear(data = su_covid19_multiomics,
                                response = "WHO.status",
                                num.factors = 12)

spear.model$train.spear()

Training the SPEAR model

print(mtcars)

Exploring the SPEAR model

print(mtcars)

Downstream interpretation example

print(mtcars)

library(SPEAR)

Load the multi-omics data from the SPEAR package:

data("su_covid19_multiomics", package = "SPEAR")

dim(su_covid19_multiomics@ExperimentList$proteomics)

sessionInfo()

jgygi/SPEAR documentation built on July 5, 2023, 5:35 p.m.