In GraceYoon/SPRING: Semi-Parametric Rank-based approach for INference in Graphical model (SPRING)

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SPRING

The R package SPRING (Semi-Parametric Rank-based approach for INference in Graphical model) estimates sparse microbial association networks using rank-based correlation with sparse graphical modeling techniques. The corresponding reference is

Yoon G., Gaynanova I. and Müller C.L. (2019) Microbial Networks in SPRING - Semi-parametric Rank-Based Correlation and Partial Correlation Estimation for Quantitative Microbiome Data. Frontiers in Genetics, 10:516.

The faster version of latent correlation computation part is now fully available and implemented to the R package SPRING. The corresponding reference is available on arXiv:

Yoon G., Müller C.L. and Gaynanova I. Fast computation of latent correlations. arXiv.

Installation

# install.packages("devtools")
devtools::install_github("GraceYoon/SPRING")

Example

library(SPRING)
data("QMP") # load the data available from this package, containing 106 samples and 91 OTUs.

# Apply SPRING on QMP data.
fit.spring <- SPRING(QMP, Rmethod = "approx", quantitative = TRUE, 
                     lambdaseq = "data-specific", nlambda = 50, rep.num = 50)
# With Rmethod = "original", this takes around 23 minutes.
# With Rmethod = "approx", this takes around 2.23 minutes. 
# More details on the comparison of accuracy and speed ("original" vs. "approx")
# are available on the above arXiv reference.

# StARS-selected lambda index based on the threshold (default = 0.01)
opt.K <- fit.spring$output$stars$opt.index
# Estimated adjacency matrix from sparse graphical modeling technique ("mb" method) (1 = edge, 0 = no edge)
adj.K <- as.matrix(fit.spring$fit$est$path[[opt.K]])
# Estimated partial correlation coefficient, same as negative precision matrix.
pcor.K <- as.matrix(SpiecEasi::symBeta(fit.spring$output$est$beta[[opt.K]], mode = 'maxabs'))

GraceYoon/SPRING documentation built on June 29, 2022, 4:14 p.m.