data_iSMNN: A list of two expression matrices for two batches. The first...

In yycunc/iSMNN: iSMNN: Batch Effect Correction for Single-cell RNA-seq data via Iterative Supervised Mutual Nearest Neighbor Refinement

A list of two expression matrices for two batches. The first batch contains 400 cells of three cell types, fibroblasts, macrophages and endothelial cells. And the second batches has 500 cells of the same three cell types.

Description

A list of two expression matrices for two batches. The first batch contains 400 cells of three cell types, fibroblasts, macrophages and endothelial cells. And the second batches has 500 cells of the same three cell types.

Usage

data("data_iSMNN")

Format

An object of class list of length 2.

Examples

# Load the example data data_SMNN
data("data_iSMNN")

# Provide the marker genes for cluster matching
markers <- c("Col1a1", "Pdgfra", "Ptprc", "Pecam1")

# Specify the cluster labels for each marker gene
cluster.info <- c("fibroblast", "fibroblast", "macrophage", "endothelial cells")

# Harmonize cluster labels across batches
library(SMNN)
batch.cluster.labels <- unifiedClusterLabelling(data_SMNN$batch1.mat, data_iSMNN$batch2.mat, features.use = markers,
                                                cluster.labels = cluster.info, min.perc = 0.3)
names(batch.cluster.labels[[1]]) <- colnames(data_iSMNN$batch1.mat)
names(batch.cluster.labels[[2]]) <- colnames(data_iSMNN$batch2.mat)

# Construct the input object for batches using Seurat
library(Seurat)
merge <- CreateSeuratObject(counts = cbind(data_iSMNN$batch1.mat, data_iSMNN$batch2.mat), min.cells = 0, min.features = 0)
batch_id <- c(rep("batch1", ncol(data_iSMNN$batch1.mat)), rep("batch2", ncol(data_iSMNN$batch2.mat)))
names(batch_id) <- colnames(merge)
merge <- AddMetaData(object = merge, metadata = batch_id, col.name = "batch_id")
merge.list <- SplitObject(merge, split.by = "batch_id")

merge.list <- lapply(X = merge.list, FUN = function(x) {
  x <- NormalizeData(x)
  x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000)
})

# Correct batch effect
corrected.results <- iSMNN(object.list = merge.list, batch.cluster.labels = batch.cluster.labels,
                           matched.clusters = c("endothelial cells", "macrophage", "fibroblast"),
                           strategy = "Short.run", iterations = 5, dims = 1:20, npcs = 30, k.filter = 30)

yycunc/iSMNN documentation built on June 11, 2022, 8:37 p.m.