docs/conos.md

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

Integration of datasets using Conos

Compiled: July 15, 2019

• • Systematic comparative analysis of human PBMC
  • Interferon-stimulated and control PBMC
  • Eight human pancreatic islet datasets

This vignette demonstrates the use of the Conos package in Seurat. Commands and parameters are based off of the Conos tutorial. If you use Conos in your work, please cite:

Joint analysis of heterogeneous single-cell RNA-seq dataset collections

Nikolas Barkas, Viktor Petukhov, Daria Nikolaeva, Yaroslav Lozinsky, Samuel Demharter, Konstantin Khodosevich, Peter V. Kharchenko

Nature Methods, 2019.

doi: 10.1038/s41592-019-0466-z

GitHub: https://github.com/hms-dbmi/conos

Prerequisites to install:

• Seurat
• Conos
• SeuratWrappers
• SeuratData

library(conos)
library(Seurat)
library(SeuratData)
library(SeuratWrappers)

Systematic comparative analysis of human PBMC

To learn more about this dataset, type ?pbmcsca

InstallData("pbmcsca")
data("pbmcsca")
pbmcsca.panel <- SplitObject(pbmcsca, split.by = "Method")
for (i in 1:length(pbmcsca.panel)) {
    pbmcsca.panel[[i]] <- NormalizeData(pbmcsca.panel[[i]]) %>% FindVariableFeatures() %>% ScaleData() %>% 
        RunPCA(verbose = FALSE)
}
pbmcsca.con <- Conos$new(pbmcsca.panel)
pbmcsca.con$buildGraph(k = 15, k.self = 5, space = "PCA", ncomps = 30, n.odgenes = 2000, matching.method = "mNN", 
    metric = "angular", score.component.variance = TRUE, verbose = TRUE)
pbmcsca.con$findCommunities()
pbmcsca.con$embedGraph()
pbmcsca <- as.Seurat(pbmcsca.con)
DimPlot(pbmcsca, reduction = "largeVis", group.by = c("Method", "ident", "CellType"), ncol = 3)

Interferon-stimulated and control PBMC

To learn more about this dataset, type ?ifnb

InstallData("ifnb")
data("ifnb")
ifnb.panel <- SplitObject(ifnb, split.by = "stim")
for (i in 1:length(ifnb.panel)) {
    ifnb.panel[[i]] <- NormalizeData(ifnb.panel[[i]]) %>% FindVariableFeatures() %>% ScaleData() %>% 
        RunPCA(verbose = FALSE)
}
ifnb.con <- Conos$new(ifnb.panel)
ifnb.con$buildGraph(k = 15, k.self = 5, space = "PCA", ncomps = 30, n.odgenes = 2000, matching.method = "mNN", 
    metric = "angular", score.component.variance = TRUE, verbose = TRUE)
ifnb.con$findCommunities()
ifnb.con$embedGraph()
ifnb <- as.Seurat(ifnb.con)
DimPlot(ifnb, reduction = "largeVis", group.by = c("stim", "ident", "seurat_annotations"), ncol = 3)

Eight human pancreatic islet datasets

To learn more about this dataset, type ?panc8

InstallData("panc8")
data("panc8")
panc8.panel <- SplitObject(panc8, split.by = "replicate")
for (i in 1:length(panc8.panel)) {
    panc8.panel[[i]] <- NormalizeData(panc8.panel[[i]]) %>% FindVariableFeatures() %>% ScaleData() %>% 
        RunPCA(verbose = FALSE)
}
panc8.con <- Conos$new(panc8.panel)
panc8.con$buildGraph(k = 15, k.self = 5, space = "PCA", ncomps = 30, n.odgenes = 2000, matching.method = "mNN", 
    metric = "angular", score.component.variance = TRUE, verbose = TRUE)
panc8.con$findCommunities()
panc8.con$embedGraph()
panc8 <- as.Seurat(panc8.con)
DimPlot(panc8, reduction = "largeVis", group.by = c("replicate", "ident", "celltype"), ncol = 3)

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