vizAPA v0.1.0 (released on 2023/10/12)

vizAPA: visualizing dynamics of alternative polyad-enylation from bulk and single-cell data

About

Alternative polyadenylation (APA) is a widespread post-transcriptional regulatory mechanism across all eukaryotes. With the accumulation of genome-wide APA sites, especially those with sin-gle-cell resolution, it is imperative to develop easy-to-use visualization tools to guide APA analysis. We developed vizAPA for visualizing APA dynamics from bulk and single-cell data. vizAPA imports various types of APA data and genome annotation sources through unified data structures. vizAPA also enables identification of APA markers with differential APA usage. Four unique modules are provided in vizAPA for visualizing APA dynamics across cell groups and at the single-cell level.

• The vizAPA package mainly consists of six modules.

<img src="img/schema.png" width="60%" />

(1) Data input module that imports different types of APA data through a compact data structure PACdataset.

(2) Genome annota-tion module that builds internally an annoHub data structure for adapting to different genome annotation sources from different species.

(3) Visualization module vizTracks that generates a ge-nome-browser-like plot, which utilizes tracks to display different types information related to APA, including gene models, positions and expression levels or usages of poly(A) sites, read alignments, and single-cell read counts.

(4) Visualization module vizStats that generates various charts, including violin plot, box plot, bubble plot, and dot plo, to visualize usages of any given poly(A) site(s) (e.g., proximal and distal sites) in a gene across different cell groups.

(5) Visualization module vizUMAP that learns two-dimensional embeddings for visualizing clusters of cells with simi-lar APA expression or usage profiles.

(6) Visualization module vizAPAmarkers that identifies genes with differential APA usages (called APA markers) and generate rich plots, including violin plot, heat map, bubble plot, and UMAP plot, for the visualization of selected APA markers.

Getting started

Mandatory

• R (>=4.2.2) (https://www.r-project.org/) is recommended.

Required R Packages

• Matrix, movAPA, GenomicRanges, ggplot2, ggbio, tidyr, dplyr, biomaRt, testthat, RColorBrewer, GenomicFeatures, Rsamtools, SeuratObject, bamsignals, AnnotationFilter, GenomeInfoDb, IRanges, magrittr, methods

Installation

• Install the R package using the following commands on the R console:

install.packages("devtools")
require(devtools)
install_github("BMILAB/vizAPA")
library(vizAPA)
browseVignettes('vizAPA')

##or you can download ZIP, and then unzip
devtools::install_local("your_path_of_vizAPA-master.zip", build_vignettes = TRUE)

Application examples

Vignettes can be found here. Or you can also browse the vignette using the following command on the R console:

browseVignettes('vizAPA')

Data for these vignettes can be downloaded here.

Using_vizAPA_a_minimal_tutorial

This tutorial takes a PACdataset object storing a list of poly(A) sites as input and describes how to quickly get started with vizAPA.

Using_vizAPA_vizStats_a_full_tutorial

This tutorial describes full usages of series functions related to vizStats, vizUMAP, and vizAPAmarkers in vizAPA. These functions are used for making statistics and visualization of poly(A) read counts and APA usages across cells or cell types.

Using_vizAPA_vizTracks_a_full_tutorial

This tutorial takes a PACdataset object storing a list of poly(A) sites and BAM files as input and describes full usages of series functions related to vizTracks in vizAPA. The vizTracks function generates genome-browser-like plot, which utilizes tracks to display different types information related to APA.

Read_PAC_data_from_Sierra

This documentation describes how to use Sierra to detect poly(A) sites from single-cell RNA-seq data, read the poly(A) sites data from Sierra, and then analyze it with vizAPA. We used single-cell RNA sequencing data from peripheral blood mononuclear cells (PBMCs) for demonstration.

Read_PAC_data_from_scAPAtrap

This documentation describes how to use vizAPA to visualize poly(A) sites (pAs) identified from scAPAtrap.

Note on using GFF3/GTF file as the genome annotation

## There is a unfixable bug in addAnno() for adding a GFF3/GTF file
## but we can use the following code instead
GFF='Homo_sapiens.GRCh38.88.chr6.gtf'
gff <- useGff(GFF)
annoSource=new("annoHub")
##annoSource=addAnno(annoSource, gff) ## DONOT USE THIS CODE!
annoSource@annos$gff=gff
annoSource=setDefaultAnno(annoSource, 'gff')

Citation

If you are using vizAPA, please cite: Xingyu Bi#, Wenbin Ye#, Xin Cheng, Ning Yang, and Xiaohui Wu*: vizAPA: visualizing dynamics of alternative polyadenylation from bulk and single-cell data.