Dynamic Estimator of Microbial Communities

Multi-sample algorithm based on contigs and coverage values, to infer the relative distances of contigs from the replication origin and to accurately compare bacterial growth rates between samples. Yuan Gao and Hongzhe Li (2018) .

DEMIC

Introduction

DEMIC is an R package for comparing bacterial growth rates between metagenomic samples, based on inferred relative distances of contigs from replication origin according to their coverages. It works with a custom coverage report generated by PyCov3 that provides per-contig and per-sample coverage information for sliding windows over contigs.

Installation

DEMIC can be installed with:

install.packages("demic")

The development version can be installed from GitHub with:

# install.packages("devtools")
devtools::install_github("Ulthran/DEMIC")
library(demic)

Basic Usage

library(demic)
eptrs1 <- est_ptr(ContigCluster1)
eptrs2 <- est_ptr(ContigCluster2)

colnames(eptrs1)
# [1] "sample"      "est_ptr"     "coefficient" "pValue"      "cor"         "correctY"

eptrs1["sample"]
# 1 Sample1
# 2 Sample2
# 3 Sample3

eptrs1["est_ptr"]
# 1 1.588210
# 2 2.296994
# 3 1.840564

With a PyCov3 report:

X <- read.csv("cov3.cov3", stringsAsFactors = TRUE)
estptrs <- est_ptr(X)

Docs and Additional Help

This package is designed to be used with the COV3 output files of PyCov3. Its usage can be seen in the context of the sunbeam extension sbx_demic. Please cite Gao, Y., Li, H. Quantifying and comparing bacterial growth dynamics in multiple metagenomic samples. Nat Methods 15, 1041–1044 (2018). https://doi.org/10.1038/s41592-018-0182-0.