Hi all,

I've got some bulk RNA-seq data (mouse brain/hippocampus) and I'm looking for tools to extract celltype composition proportions from the data. I've been looking around and most of the tools focus on using associated single-cell data, which I do not have.

Do you know of any tool which provides celltype predictions (mouse brain) by only inputting bulk RNA-seq (e.g. using internal cell reference datasets)? 

If not, is the best strategy to get some public single-cell data to use with my bulk RNA-seq (from what I've read, via MuSiC or CIBERSORTx for example)? In that case, could you recommend (if there exist) databases providing single-cell data with annotated celltypes (instead of raw, unannotated data) which I could use?

If not, what would be the best strategy to achieve this goal?

thanks for your help
>Do you know of any tool which provides celltype predictions (mouse brain) by only inputting bulk RNA-seq (e.g. using internal cell reference datasets)?

As soon as you search for tools published before 2019, the majority of deconvolution methods will probably be based on bulk RNA-seq alone.
See, for example, [this review from 2018](https://academic.oup.com/bioinformatics/article/34/11/1969/4813737).

>If not, is the best strategy to get some public single-cell data to use with my bulk RNA-seq (from what I've read, via MuSiC or CIBERSORTx for example)?

[Dorshizi et al, 2021](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8219045/) demonstrate the use of reference scRNA-seq data sets for brain-specific questions.

>In that case, could you recommend (if there exist) databases providing single-cell data with annotated celltypes (instead of raw, unannotated data) which I could use?

* The [Allen Brain Atlas](https://portal.brain-map.org/explore/transcriptome) is an incredible resource for transcriptomic (scRNA-seq) data from Mouse and Human data sets. I usually find a tad bit easier to first identify the paper with the corresponding data that I might be interested in and then track down the data on the Allen Brain Atlas servers, but I'm sure you can pick your way through.
* The [scRNAseq](http://www.bioconductor.org/packages/release/data/experiment/vignettes/scRNAseq/inst/doc/scRNAseq.html) package from bioconductor has a range of scRNA-seq data sets ready to load including cell labels
* the [SingleCell Portal[(https://singlecell.broadinstitute.org/single_cell) may be of help, too.

Apart from that, identifying publications with data sets of interest, e.g. via Pubmed or via GEO, should lead you to promising scRNAseq data set candidates; most of them should be provided with metadata (e.g. cell labels).