In satijalab/seurat-wrappers: Community-Provided Methods and Extensions for the Seurat Object

This vigettte demonstrates how to run ALRA on Seurat objects, which aims to recover missing values in scRNA-seq data through imputation. If you use ALRA, please cite:

Zero-preserving imputation of scRNA-seq data using low-rank approximation

George C. Linderman, Jun Zhao, Yuval Kluger

biorxiv, 2018.

doi: https://doi.org/10.1101/397588

GitHub: https://github.com/KlugerLab/ALRA

knitr::opts_chunk$set(
  tidy = TRUE,
  tidy.opts = list(width.cutoff = 95),
  message = FALSE,
  warning = FALSE,
  fig.height = 20,
  fig.width = 16
)

Prerequisites to install:

• Seurat
• SeuratWrappers
• SeuratData

library(Seurat)
library(SeuratData)
library(SeuratWrappers)
library(dplyr)

scRNA-seq imputation on pbmc3k

To learn more about this dataset, type ?pbmc3k

InstallData("pbmc3k")
data("pbmc3k")
# Initial processing and visualization
pbmc3k <- SCTransform(pbmc3k) %>% RunPCA() %>% RunUMAP(dims = 1:30)
# run ALRA, creates alra assay of imputed values
pbmc3k <- RunALRA(pbmc3k)

# visualize original and imputed values
pbmc3k <- NormalizeData(pbmc3k, assay = 'RNA')
features.plot <- c('CD3D', 'MS4A1', 'CD8A', 'GZMK', 'NCAM1', 'FCGR3A')
DefaultAssay(pbmc3k) <- 'RNA'
plot1 <- FeaturePlot(pbmc3k, features.plot, ncol = 2)
DefaultAssay(pbmc3k) <- 'alra'
plot2 <- FeaturePlot(pbmc3k, features.plot, ncol = 2, cols = c('lightgrey','red'))
CombinePlots(list(plot1, plot2), ncol = 1)

satijalab/seurat-wrappers documentation built on April 10, 2024, 3:25 p.m.