Oct 02, 2020

Public workspaceFR-Match: cell type matching for scRNAseq data

DOI
dx.doi.org/10.17504/protocols.io.bmyfk7tn
  • 1J. Craig Venter Institute
  • Human Cell Atlas Method Development Community
Yun Renee Zhang
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DOI: dx.doi.org/10.17504/protocols.io.bmyfk7tn
External link: https://github.com/JCVenterInstitute/FRmatch
Protocol Citation: Yun Renee Zhang, Brian Aevermann, Richard Scheuermann 2020. FR-Match: cell type matching for scRNAseq data. protocols.io https://dx.doi.org/10.17504/protocols.io.bmyfk7tn
Manuscript citation:
https://doi.org/10.1101/2020.05.01.073445
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License,  which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: October 01, 2020
Last Modified: October 02, 2020
Protocol Integer ID: 42727
Keywords: single cell RNA sequencing, cell types, data integration,
Abstract
FR-Match is a supervised cell phenotype matching strategy for cluster-to-cluster cell transcriptome integration across scRNAseq experiments.

An R package and Shiny application are provided at https://github.com/JCVenterInstitute/FRmatch.
Before start

Software
R programming language
NAME
The R Foundation
DEVELOPER
Comprehensive R Archive Network
SOURCE LINK
Require R Shiny package.
Launch Shiny app
Launch Shiny app
Interactively explore and match scRNAseq cell type clusters with the seamless Shiny app. The Shiny app may serve as a quick start demo with pre-loaded datasets.
Command
runShiny()
Optional
Data preparation and exploration
Data preparation and exploration
Use the built-in data preparation function to create data objects with required and optional input data elements. Create data objects for experiment 1 (E1) and experiment 2 (E2)
Command
dat_E1 <- make_data_object()
dat_E2 <- make_data_object()

View comparative cell cluster sizes.
Command
plot_clusterSize(dat_E1, dat_E2)

View "barcode" plot for cluster of interest.
Command
plot_cluster_by_markers(dat_E1, cluster.name = "cluster_of_interest")

Run main algorithm
Run main algorithm
10m
10m
Use wrapper function to perform bi-directional matching.
Computational step
Critical
Map E1 to E2.
Command
rst12 <- FRmatch(sce.query = dat_E1, sce.ref = dat_E2)

Map E2 to E1.
Command
rst21 <- FRmatch(sce.query = dat_E2, sce.ref = dat_E1)

Combine and plot matching results
Combine and plot matching results
Combine the bi-directional matching results and plot.
Command
plot_bi_FRmatch(rst12, rst21)

Additional plots
Additional plots
Some optional plotting functions to help studying the matching results.
Optional
Plot one-directional matching results.
Command
plot_FRmatch(rst12)
Plot one-directional matching p-values.
Command
plot_FRmatch(rst12, type = "padj")

Minimum spanning tree (MST) plot. MST can be plotted by turning on the plot option in the test function.
Command
FR.test(samp1, samp2, plot.MST = TRUE)