README.md

In xiaonui/vPan: An R package to analyze and visualize PAV results from pan-genome of "map-to-pan" strategy.

vPan

Introduction

Pan-genome and PAV analysis

  Pan-genome is the totality of genes found across members of a given group, revealing the diversity and functional potential within that group. PAV(Presence-absence variation) analysis is an essential step in pan-analysis. The core genome contains genes shared by all and the distributed genome consist of genes not shared by all. The distributed genome can be further divided into genes shared in most members(soft-core genes), genes shared between some members(distributed/accessory genes), and genes present in only one member(unique/private genes).

  “Map-to-pan” is a common strategy for eukaryotic pan-genome analyses. Firstly, construct pan-genome by integrating the reference genome and non-reference sequences. Then, align reads to the pan-genome and examine gene coverage. Finally, determine gene presence/absence and analysis PAV table.

The core processing workflow of vPan

  vPan is an efficient tool to explore and visualize the complex result in PAV analysis. It provides four modules to 1) display gene coverage distribution, 2) analyze and visualize PAV table, 3) simulate pan/core genome sizes, and 4) find phenotype-associated genes.

  The workflow starts with an object of COV(module 1) or PAV(module 2-4) class. It can be produced by function get_cov_obj() and get_pav_obj(). It contains coverage/PAV matrix, arguments, phenotype information, and gene information, and will be the main input for the subsequent steps.

• Module 1 focuses on showing the coverage and the distribution of coverage. Functions: cov_heatmap() and cov_density().

• Module 2 focuses on PAV analysis, including overview, classifying genes, clustring, PCA analysis. Functions: pav_heatmap(), pav_hist(), pav_stackbar(), pav_cluster(), pav_halfviolin() and pav_pca().

• Module 3 focuses on simulating pan/core/private genome sizes and drawing growth curve. Functions : sim_stat(), sim_plot(), sim_multi_stat() and sim_multi_plot.

• Module 4 focuses on phenotype association and visualization. Functions: phen_heatmap(), phen_manhattan(), phen_block(), phen_bar() and phen_violin().

Installation

Installing R/RStudio

  If you do not already have R/RStudio installed, please do as follows:

• Install R
• Install RStudio

Check or install packages

packages <- c("snowfall", "data.table", "ggdendro", "ggplot2", "ggrepel", "ggsignif", "randomcoloR")
lapply(packages, function(x) {
    if(!require(x, character.only = TRUE)) {
        install.packages(x, dependencies = TRUE)
    }})
if (!requireNamespace("BiocManager", quietly = TRUE)){
  install.packages("BiocManager")
}
BiocManager::install("ComplexHeatmap")

Install metaFunc from github.

if (!requireNamespace("devtools", quietly = TRUE))
  install.packages("devtools")
library(devtools)
install_github("xiaonui/vPan", build_vignettes = TRUE)

User manual

  You can use vignette("vPan") in R to view the vignette of vPan. It can help you start using vPan, including the format and examples of input data, and the use of functions. You can use ?function to view the help documentation of functions, such as ?pav_heatmap.

xiaonui/vPan documentation built on Dec. 23, 2021, 6:17 p.m.