For 10X experiments, the Bioc-devel version of DropletUtils will read in the additional features as extra rows in the count matrix. This reflects how they are stored in the 10X output format. The row metadata will record the nature of the feature.
In some cases it may be desirable to keep all the features together. For starters, it seems like many of the biases are likely to be shared (w.r.t. library preparation and capture efficiency), so one could imagine using the same scaling factors for normalization of both antibody-based features and endogenous mRNAs. In addition, all of the scater visualization methods rely on SCE inputs, so if you want to overlay them with protein marker intensities, they'll need to be in the same matrix. If you really need to only use mRNAs or antibody-based features, (i) you can explicitly subset the SCE based on the rowData, or (ii) pass a subsetting vector to the various scran/scater/whatever functions to tell them to only use the specified features. Admittedly, if you're going to be doing this a lot, it would be more convenient to form a MAE containing two SCEs so that you only have to pass the SCE you want into those functions. To that end I would be willing to entertain a PR to DropletUtils to create a MAE from an SCE. I'm more reluctant to add an isSpike()-like function. The rationale behind isSpike() was that spike-ins are constant across cells (theoretically) and thus a function could use this information to improve its calculations. It's less clear what mathematically useful information can be gained from protein markers - biological info, yes, but nothing that you would use to change your algorithm. -A Steve Lianoglou wrote: > Comrades, > > Sorry if I'm out of the loop and have missed anything obvious. > > I was curious what the plans are in the single-cell bioconductor-verse > to support single cell experiments that produce counts from different > feature-spaces, such as those produced by CITE-seq / REAP-seq, for > instance. > > In these types of experiments, I'm pretty sure we want the counts > generated from those "features" (oligo-conjugated Antibodies, for > instance) to be kept in a separate space than the mRNA counts. I think > we would most naturally want to put these in something like an > `assay()` matrix with a different (rowwise) dimmension than the gene > count matrix, but that can't work since all matrices in the assay() > list need to be of the same dimensions. > > Another option might be to just add them as rows to the assay > matrices, but keep some type of feature space meta-information akin to > what `isSpike()` currently does; > > or add a new slot to SingleCellExperiment to hold counts from > different feature spaces, perhaps?; > > Or rely on something like a MultiAssayExperiment? > > Or? > > Curious to learn which way you folks are leaning ... > > Thanks! > -steve > > ps - sorry if this email came through twice, it was somehow magically > sent from an email address I don't have access to anymore. > _______________________________________________ Bioc-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/bioc-devel
