README.md

In LeiLi-Uchicago/HTOreader: An R package for cell hashing demultiplexing

HTOreader

An R toolkit for cell hashing (HTO) demultiplexing

How to install

make sure you installed these two dependencies: install.packages("flexmix") install.packages("hash") then: if (!require("devtools")) {install.packages("devtools")} devtools::install_github("WilsonImmunologyLab/HTOreader")

Hnads on Example of Hybrid demultiplexing using real-world data

Please click the following link for a detailed example

Hybrid demultiplexing using real-world data

Please refer to /docs folder of this repository. We have provided the HTML version and the R markdown file. Data for this sample under /data folder.

Quick start

The pbmc.hashtag in the following example is a Seurat object. Please load your "HTO" assay using the Seurat package (More details in our example).

There are two main functions, HTOClassification for Hashtag demultiplexing, PlotHTO for plotting distributions of hashtags pbmc.hashtag <- HTOClassification(pbmc.hashtag, assay = 'HTO', method = 'CLR') VlnPlot(pbmc.hashtag,features = rownames(pbmc.hashtag@assays[["HTO"]]@data), group.by = 'HTOid')

Users can use PlotHTO function to check the quality of the cutoff of each HTO plots1 <- PlotHTO(pbmc.hashtag, assay = 'HTO', method = 'CLR') this function returns a list of ggplot objects, use plot_grid function to plot them: plot_grid(plotlist = plots1)

Users can also specify the cutoff manually: pbmc.hashtag <- HTOClassification(pbmc.hashtag, assay = 'HTO', method = 'CLR', specify_cutoff = c(2.5,2,2,2,2,2,2,2)) or use a different normalization method (CLR or log): pbmc.hashtag <- HTOClassification(pbmc.hashtag, assay = 'HTO', method = 'log') users also can specify the cutoff manually for PlotHTO function to find out the best cutoff: plots1 <- PlotHTO(pbmc.hashtag, assay = 'HTO', method = 'CLR', specify_cutoff = c(2.5,2,2,2,2,2,2,2))

LeiLi-Uchicago/HTOreader documentation built on Aug. 14, 2024, 8:38 p.m.