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inter and intra-sample variance in 10X visium spatial transcriptome data

Hello everyone

I have 10X visium spatial transcriptome data from four samples. I am new in this field. These samples are divided into two groups : controls (2) and treated (2). Data was processed with spaceranger pipeline and followed by clusters identification based on the seurat R package.

Now I would like to identify the variance across the samples : a) across the replicates b) across the conditional groups. condition specific. I am looking for suitable methods to study the sample variance .

I would appreciate all the suggestions.

Thanks

transcriptome cell single spatial
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updated by GenoMax GenoMax • written by Omics data mining Omics data mining

You could merge the samples spots (here the spatial information will not be used ; only the expression data) with seurat merge ; then perform a PCA / tsNE / UMAP to look at the spots distribution. You can check in this github that use multiCCA to perform the merge : https://github.com/almaan/ST-mLiver and more specifically in this script https://github.com/almaan/ST-mLiver/blob/master/scripts/Liver_ST.Rmd (starts at line 354).

• 0 views • link
written by Nicolas Rosewick Nicolas Rosewick

Thx for the sharing those links. Its really helpful.

I already have merged seurat object. Based on the HVFInfo function, I got the "gmean , variance ,residual_variance" column data from merged seurat object.

head(x = HVFInfo(test.integrated))

                 gmean    variance residual_variance

AL627309.1 0.001272639 0.001832168         1.6011915
AL627309.5 0.002291916 0.003293053         1.7219783
LINC01409  0.009950325 0.018496006         0.9860352
LINC01128  0.015013608 0.021924585         0.6997041

But at this step, its giving me variance from the entire dataset. I want to calculate the variance from each sample. I might need to subset the integrated object based on the sample id and then apply the HVFInfo .

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updated by GenoMax GenoMax • written by Omics data mining Omics data mining

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