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The "identities" can be accessed with the Idents() function in Seurat v3. You should change cell.num <- table(shox2crepos_mtmg@ident) to cell.num <- table(Idents(shox2crepos_mtmg))

I assume you are referring to VlnPlot() of Seurat. The reason that you are getting such a plot is because your distribution is highly skewed, most of your points are zeros, not surprising for scRNA-se …

I would use permutation: i) Get the difference that you are interested in, i.e. number/proportion of monocytes in one group vs the other. ii) Get the relevant columns (condition, cell label, ...) from …

I believe it is mostly a cost issue as spike-ins are "expensive" to use given the large number of cells that are/can be sequenced via droplet based methods (as compared to plate-based ones). The 10x l …

Seurat is expecting individual datasets to be normalized separately prior to data integration. In that respect lists of objects corresponding to different datasets are handy to manipulate each object/ …

You can use the group.by argument of the DimPlot() function. group.by accepts column names present in the meta.data column of the Seurat objects.

That is because SCTransform (mostly) outputs discrete values, you can check the @slot of your Seurat object.

It is absolutely necessary to remove low quality cells: In the case of CCA (and this applies to other "integration" or "data alignment" methods as well), one would need to use "anchors", basically sam …

I believe when you say alignment, you mean aligning reads to a genome (sometimes to transcriptome) and count these to get count matrices. In the aforementioned paper, however, what is meant is "bringi …

This can be a good starting point: table([email protected]$the_column_that_has_the_labels_of_interest) Basically you will be counting the number of rows (each single cell is a row) in the m …

You should have fed "1h" to your function call but you fed 1h instead. The latter is looking for an object with that name and as the error shows, there is no such object.

As was suggested by @benn, you will need to add the TdTomato sequence to the end of your fasta formatted genome file. If your TdTomato sequence is in fasta format already, cat will do the trick. If no …

The first two plots are used in order to estimate the number of PCs to be used in later stages, for example clustering (this was split in to two functions in Seurat v3). The number of PCs selected wou …

your_new_object <- subset(your_object, subset = mapping.score > 0.5)

Read10X() works on mtx files, that is not the root of your problem. The problem is that you are pushing non-10x-data to look and work like 10x-data. You can just use CreateSeuratObject() and feed you …