In qingnanl/SCAVG: Single-Cell Averaging Tool

knitr::opts_chunk$set(echo = TRUE)
library(knitr)
opts_chunk$set(tidy.opts = list(width.cutoff = 60), tidy = T)

Introduction

Here is a demonstration of how to use the package SRAVG to create a Seurat object with cells grouped and averaged.

Load libraries

library(Signac)
library(Seurat)
library(EnsDb.Hsapiens.v86)
library(BSgenome.Hsapiens.UCSC.hg38)
library(SRAVG)

Use the pbmc3k-multiome dataset from the 10x website. This data is preprocessed using default Signac parameters

data <- readRDS("C:/Users/liang/work/34_gene_correlation_simpson/LinkPeak/pbmc3k.rds")
print(data@assays$RNA)

print(data@assays$peaks)

print(data@reductions)

"RNA" and "peaks" are the two assays we want to average; "pca" will be used as the low-dimensional coordinates for clustering cells to meta-cells.

Visualize the clustering at single-cell level:

DimPlot(data, group.by = 'seurat_clusters', label = TRUE) + NoLegend()

Run SRAVG

Here we use 'pca' as the dimension reduction to evaluate the distances between cells, and top 10 PCs are used (dr_dims). We try to form meta-cells by averaging 10 cells to 1. The group will be within each individual 'seurat_clusters'. We also average two other columns in the meta.data and transfer to the output seurat, which are 'nCount_RNA', and 'nFeature_RNA'. At this time we only support numerical columns for 'extra_meta'.

start_time <- Sys.time()

data_avg <- sravg(object = data, dr_key = 'pca', dr_dims = 1:10, group_size = 10,
                             group_within = 'seurat_clusters',peak_assay = "peaks", peak_slot = "data",
                             extra_meta = c('nCount_RNA', 'nFeature_RNA', 'nCount_ATAC', 'nFeature_ATAC'))
end_time <- Sys.time()
end_time - start_time

The output is a Seurat object

print(data_avg@assays$RNA)

print(data_avg@assays$peaks)

We can still run UMAP on the data_avg object. The 'pca' in this averaged object is calculated with averaging the original 'pca' coordinates (for each meta-cell).

data_avg <- RunUMAP(data_avg, dims = 1:10, verbose = FALSE)
DimPlot(data_avg, group.by = 'seurat_clusters', label = T)

Compute the sparsity of matrix (proportion of zeros) before and after averaging

sparsity <- function(matrix){
  sparsity <- sum(matrix == 0)/(dim(matrix)[1] * dim(matrix)[2])
  return(sparsity)
}

print(sparsity(data@assays$RNA@counts))

print(sparsity(data_avg@assays$RNA@counts))

print(sparsity(data@assays$peaks@counts))

print(sparsity(data_avg@assays$peaks@counts))

qingnanl/SCAVG documentation built on June 25, 2022, 6:26 a.m.