Question: How to acquire anchor features for multiple dataset single-celll analysis in Seurat 3.0?
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gravatar for david.f.stein
7 months ago by
david.f.stein10 wrote:

I am attempting to analyze sequencing from mouse and chicken samples in Seurat by following the multi-dataset integration vignette on the Seurat website: https://satijalab.org/seurat/v3.1/integration.html. In their example they use FindIntegrationAnchors(object.list = reference.list, dims = 1:30) to find anchors for integrating human pancreatic samples from four different platforms. This results in many anchor features that are used in subsequent steps. However, I am attempting to recreate the analysis with mouse and chicken samples. In the FindIntegrationAnchors step, only three anchors are returned, rendering the following steps unworkable. I assume given the greater disparity between mouse and chicken expression than between different human samples, that there will be fewer anchors, but is there a way to collect more? Seurat claims this workflow can work for different species. Has anyone had success with such an analysis?

ADD COMMENTlink written 7 months ago by david.f.stein10

Are your samples comparable in terms of the cells being assayed? Are they all like lung or blood or bone marrow... or of completely different origin from these two animals? Please give some details and show the code you used.

ADD REPLYlink modified 7 months ago • written 7 months ago by ATpoint44k

They are both embryonic limb cells. My code follows the vignette. The datasets are quite large.

library(Seurat)

mouse.data <- Read10X(data.dir = "/media/david/mousecount/outs/filtered_feature_bc_matrix")
chicken.data <- Read10X(data.dir = "/media/david/chickcount/outs/filtered_feature_bc_matrix")
mouse = CreateSeuratObject(counts = mouse.data)
chicken = CreateSeuratObject(counts = chicken.data)

species.list <- c(mouse, chicken)

for (i in 1:length(species.list)) {
  species.list[[i]] <- NormalizeData(species.list[[i]], verbose = FALSE)
  species.list[[i]] <- FindVariableFeatures(species.list[[i]], selection.method = "vst", 
                                             nfeatures = 2000, verbose = FALSE)
}

SelectIntegrationFeatures(object.list = species.list, verbose = TRUE)

reference.list <- species.list
species.anchors <- FindIntegrationAnchors(object.list = reference.list, assay=c("RNA", "RNA"), dims = 1:30)
ADD REPLYlink modified 7 months ago • written 7 months ago by david.f.stein10
1

How many genes are common between the two Seurat objects?

ADD REPLYlink written 7 months ago by jared.andrews078.3k

Ahh there are only 13. I suppose that is an issue.

Is there a tool I can use to discover the orthologs between the two species and then reannotate the genes?

ADD REPLYlink modified 7 months ago • written 7 months ago by david.f.stein10
1

That it is. You will have to do some gene ID conversions to get more overlap between the two.

ADD REPLYlink modified 7 months ago • written 7 months ago by jared.andrews078.3k

Sorry just edited my response above before reading,

Is there a tool I can use to discover the orthologs between the two species and then reannotate the genes?

ADD REPLYlink written 7 months ago by david.f.stein10

biomart is probably the easiest way. There are several other questions regarding how to do this on Biostars as well if you do a little searching.

ADD REPLYlink written 7 months ago by jared.andrews078.3k
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