How to Use ANPELA?

Contents

• Overview
• Citations
• Repo Contents
• System Requirements
• Installation Guide
• Example Data
• Usage and Examples

Overview

Single-cell proteomics (SCP) has emerged as a powerful technique that significantly advances our understanding of complex biological systems with new level of granularity. Because of the extreme difficulty in processing SCP data, ANPELA was developed for identifying the optimal workflow based on a well-designed assessment strategy.

ANPELA is a user-centric and application-oriented tool which is capable of navigating the data processing for SCP. ANPELA 3.0 has significantly improved its practicality, focusing primarily on the following points: 1. multi-scenarios deployment (versatile choices meet diverse user needs); 2. data security (local execution ensures data confidentiality); 3. open source (modular codes facilitate readers’ free editing); and 4. user-friendly interface (the visual interface enhances user application).

The local software and webserver of ANPELA are available at http://idrblab.org/anpela2024.

Citations

You can cite the ANPELA publication as follows:

ANPELA: Significantly Enhanced Quantification Tool for Cytometry-Based Single-Cell Proteomics.

Ying Zhang, Huaicheng Sun, Xichen Lian, Jing Tang, Feng Zhu*.

Advanced Science. 2023 May;10(15):e2207061.
doi: 10.1002/advs.202207061; PubMed ID: 36950745.

Repo Contents

• R: R package code.
• man: package manual for help in R session.
• vignettes: R vignettes for R session html help pages.

System Requirements

Hardware

• Minimum:
  OS: Windows® 32/64-bit, macOS®, Linux
  Processor: 2.0 GHz
  Memory: 8 GB RAM
  Storage: 2 GB available hard-disk space
  
  
• Recommended:
  OS: Windows® 32/64-bit, macOS®, Linux
  Processor: 4.0 GHz
  Memory: 32 GB RAM
  Storage: 8 GB available hard-disk space
  
  
• An internet connection is necessary to download the ANPELA desktop software or R packages (including the ANPELA R package and other prerequisite R packages).

Software

• R v4.0 or later. The installation file of R language, named ‘R-x.y.z.tar.gz’ (where x, y, and z represent the version numbers of the software release), retrieved from CRAN R-project website (https://cran.r-project.org), which is compatible with user’s operating system (the latest version of this protocol was developed and tested using ‘R 4.4.1’).

• RStudio. The RStudio installation file ‘RStudio-x.y.z.zip’ (where x, y, and z represent the version numbers of the software release) from RStudio website (https://www.rstudio.com/), which is compatible with user’s operating system.

• RTool. The RTool installation file ‘rtoolx-y-z.zip’ (where x, y, and z denote the specific version numbers of the software release) can be obtained from the official RTool website (https://cran.r-project.org/bin/windows/Rtools /);

• Prerequisite R packages. This protocol necessitates a range of R packages (as described in Installation Guide), with certain packages obtained from CRAN (https://cran.r-project.org), others installed from Bioconductor (https://bioconductor.org/), and the remaining accessible from GitHub (https://github.com/).

Installation Guide

This section provides a comprehensive guide to the installation and configuration of ANPELA R package. It outlines the necessary tools required to ensure a seamless and accurate installation of the protocol.

• Tool 1. R language, RStudio and RTool

Install the R language, RStudio and RTool using their installation files, which are compatible with user’s operating system.

• Tool 2. The required R Packages

Install a variety of R packages imported in this protocol.

Installed from CRAN (can also from other repositories): dplyr, doParallel, rstan, Rtsne, pastecs, cowplot, ggpubr, gridExtra, MLmetrics, fossil, clusterCrit, VennDiagram, stringr, bbmle, mc2d, parallel, doSNOW, foreach, igraph, mclust, pheatmap, magrittr and withr.

Installation commands:

CRAN_packages <- c("dplyr", "doParallel", "rstan", "Rtsne", "pastecs", "cowplot", 
                   "ggpubr", "gridExtra", "MLmetrics", "fossil", "clusterCrit", 
                   "VennDiagram", "stringr", "bbmle", "mc2d", "parallel", "doSNOW", 
                   "foreach", "igraph", "mclust", "pheatmap", "magrittr", "withr")
install.packages(CRAN_packages, dependencies = TRUE)

Installed from Bioconductor (can also from other repositories): flowCore, limma, SCORPIUS, slingshot, destiny, cytofkit, flowStats, flowAI, flowCut, flowClean, spillR, PeacoQC and systemPipeR.

Installation commands:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

Bioconductor_packages <- c("flowCore", "limma", "SCORPIUS", "slingshot", "destiny", 
                           "cytofkit", "flowStats", "flowAI", "flowCut", "flowClean",
                           "spillR", "PeacoQC", "systemPipeR")

BiocManager::install(Bioconductor_packages, ask = FALSE)

Installed from GitHub (can also from other repositories): Rtsne.multicore and FLOWMAPR.

Installation commands:

if (!requireNamespace("devtools", quietly = TRUE))
    install.packages("devtools")
devtools::install_github("RGLab/Rtsne.multicore")
devtools::install_github("zunderlab/FLOWMAP")

• Tool 3. ANPELA R Package

Install the ANPELA package by running the following command in RStudio:

devtools::install_github("idrblab/ANPELA")

During the installation of ANPELA, the appearance of the error message “ERROR: dependency ‘package_name’ is not available for package ‘ANPELA’” indicates that the required imported package, ‘package_name’, has not been successfully installed. Users should refer to the detailed reinstallation instructions described in Tool 2 to resolve this issue and ensure the proper installation of the missing package before proceeding with the ANPELA installation.

Example Data

You can use the example data provided in ANPELA to try it out.

FC_CSI example data: A single-cell proteomic dataset involving 23 markers of fresh thymus tissue obtained from nine patients undergoing elective thymectomy, including three myasthenia gravis (MG) patients and six healthy controls (non-MG). Download_287 MB

MC_CSI example data: A single-cell proteomic dataset consisting 35 surface markers of two antigen specific T cell lines generated by naïve CD4+ T cells stimulated with tolerogenic dendritic cells (tolDCs) or mature inflammatory myeloid dendritic cells (mDCs) pulsed with proinsulin peptide. Download_174 MB

FC_PTI example data: A single-cell proteomic temporal dataset using 10 cell surface markers in vitro hematopoietic differentiation system from human embryonic stem cells (HUES9) at 6 sequential timepoints (0, 2, 4, 6, 8, 10 day) to capture cells at different developmental stages. Download_3.70 MB

MC_PTI example data: A gated single-cell proteomic temporal dataset encoding the activation dynamics of 14 CD8+ cell intracellular markers perturbed by tetradecanoylphorbol acetate (PMA)/ionomycin at 8 sequential timepoints (0, 1, 5, 15, 30, 60, 120 and 240 min). Download_882 KB

Usage and Examples

For the usage and examples of ANPELA, users can refer to the vignette “How to Use ANPELA” built in the package.

vignette("ANPELA")

For details of each method provided by ANPELA, users can refer to the vignette “Methods_Introduction” built in the package.

vignette("Methods_Introduction")

For the functions provided by ANPELA, users can refer to other vignettes.

vignette("Getmarker")
vignette("Process")
vignette("FCprocess")
vignette("MCprocess")
vignette("Assess")
vignette("CSIassess")
vignette("PTIassess")
vignette("Ranking")
vignette("FC_CSI")
vignette("MC_CSI")
vignette("FC_PTI")
vignette("MC_PTI")