Question: Seurat integration of two datasets - GSE126783
0
gravatar for vHelmholtz
7 weeks ago by
vHelmholtz20
United States
vHelmholtz20 wrote:

Hello,

I am following the integrated analysis of the Seurat tutorial using two datasets (GSE126783: control vs retinal degeneration). Could you let me know how to make an 'object.list' for the 'FindIntegrationAnchors' function?

## SETUP THE SEURAT OBJECT
# Load the PBMC dataset
ctrl.data <- Read10X(data.dir = ".../GEO/GSE126783/GSE126783_RAW/ctrl")
LD.data <- Read10X(data.dir = ".../GEO/GSE126783/GSE126783_RAW/LD")

# Initialize the Seurat object with the raw 
ctrl <- CreateSeuratObject(counts = ctrl.data, project = "O'Koren", 
          min.cells = 3, min.features = 200)
LD <- CreateSeuratObject(counts = LD.data, project = "O'Koren", 
          min.cells = 3, min.features = 200)

## NORMALIZING THE DATA
ctrl <- NormalizeData(ctrl, normalization.method = "LogNormalize", scale.factor = 10000)
LD <- NormalizeData(LD, normalization.method = "LogNormalize", scale.factor = 10000)

## IDENTIFICATION OF HIGHLY VARIABLE FEATURES (FEATURE SELECTION)
ctrl <- FindVariableFeatures(ctrl, selection.method = "vst", nfeatures = 2000)
LD <- FindVariableFeatures(LD, selection.method = "vst", nfeatures = 2000)

## PERFORM INTEGRATION ???

Below is the code in the Seurat tutorial. I will very much appreciate it if you help me to revise the code for the analysis.

data("ifnb")
ifnb.list <- SplitObject(ifnb, split.by = "stim")

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

immune.anchors <- FindIntegrationAnchors(object.list = ifnb.list, dims = 1:20)
immune.combined <- IntegrateData(anchorset = immune.anchors, dims = 1:20)
seurat integration • 249 views
ADD COMMENTlink modified 7 weeks ago • written 7 weeks ago by vHelmholtz20
3
gravatar for Haci
7 weeks ago by
Haci340
Haci340 wrote:

With your own code, you are already preparing two objects that are normalized and for those variable genes are calculated. In that sense you just need to put your ctrl and LD objects in a list with your_list <- list(ctrl, LD). This new list can now be used for the integration as stated in the Seurat integration tutorial.

ADD COMMENTlink written 7 weeks ago by Haci340

Thank you so much, Haci.

I have performed the integration analysis by adding your code. May I ask you another question? Could you let me know how to distinguish cells in each group (cntrl or LD) in a dimension reduction plot (UMAP plot) by using "group.by" function?

https://satijalab.org/seurat/v3.1/immune_alignment.html

# Visualization
p1 <- DimPlot(immune.combined, reduction = "umap", group.by = "stim")
p2 <- DimPlot(immune.combined, reduction = "umap", label = TRUE)
plot_grid(p1, p2)
ADD REPLYlink modified 6 weeks ago • written 7 weeks ago by vHelmholtz20
1

group.by can be used the color data points according to the a column in the meta.data slot of a given Seurat object. label is tricky though, as far as I remember, the "labels" come from the "active identities" and this works fine if different identities are clustered separately, otherwise label positions do not quite make sense.

ADD REPLYlink written 6 weeks ago by Haci340
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