Integration

Integration pipeline with SCTransform-normalized datasets

We used the subsetted Seurat objects of immune cells from each of the nineteen datasets integrated in the Human Microglia Atlas (HuMicA). The preprocessing and normalization pipelines were based on SCTransform normaliztion (see “Individual dataset processing.html” in this repository).

Load required packages

The integration and posterior analysis were performed with Seurat v5.

libs <- c("Seurat", "tidyverse")
suppressMessages(
suppressWarnings(sapply(libs, require, character.only =TRUE))
)

   Seurat tidyverse 
     TRUE      TRUE 

Seurat integration

Each dataset consists of the immune cell populations of the respective datasets.

list <- list(MG_Mathys, MG_Grubman, MG_Lau, MG_Morabito, MG_Leng, MG_Zhou, MG_Pappalardo, MG_Thrupp,
             MG_Jakel, MG_Schirmer, MG_Velmeshev, MG_Feleke, MG_Tran, MG_Franjic, MG_Yang, MG_Fullard, 
             MG_Mancuso, MG_Olah, MG_Smajic)


list <- lapply(X = list, FUN = SCTransform)

features <- SelectIntegrationFeatures(object.list = list, nfeatures = 3000)

list <- PrepSCTIntegration(object.list = list, anchor.features = features)

anchors <- FindIntegrationAnchors(object.list = list, normalization.method = "SCT",
                                  anchor.features = features, dims=1:20)

combined.sct <- IntegrateData(anchorset = anchors, normalization.method = "SCT",dims=1:20,features.to.integrate =anchors@anchor.features,
                              preserve.order = TRUE)