MapMyCells User Guide

Documentation resources

Reviewing these resources helps you use MapMyCells to its full potential:

Access & navigation

MapMyCells can be accessed in a variety of ways.

Access it via the information page linked on highlighted below. You can also access it via Atlases and Data > Brain Knowledge Platform > MapMyCells via the header menu.


Alternatively, you can access MapMyCells via the Tools section in header menu on


Lastly, go straight to MapMyCells via

Step 1

File upload

Select a file or drag it into the dotted-line area. Click the X next to the file to remove the uploaded file and start over.

More information about the file requirements and how to create a compatible h5ad file is available via the linked Learn about input file requirements, limits, and creation help page.


Step 2

Select taxonomy & algorithm

Select the mapping algorithm and reference taxonomy from the drop down menus.

Additional information about the taxonomies and algorithms is available via the Learn about available cell type references, algorithms, and output files help page.


Click the START button to initiate the mapping process.

Mapping estimate, progress, and output

We estimate the approximate runtime of the mapping, e.g. 5 minutes.

A progress indicator illustrates the different stages of the process and the overall progress.

IMPORTANT: Do NOT close the browser tab while the mapping is running. Your progress will be lost.

Once the mapping concludes, download your results via the DOWNLOAD RESULTS button.

NOTE: The download triggers in a pop-up message. Please ensure that you allow pop-ups for the MapMyCells page.

IMPORTANT: Once you close the browser tab, all files will be deleted.


Hi! When will it be available for Seurat objects?

Hi @rmc0106 , thank you for your interest in MapMyCells.

We currently don’t have a timeline for MapMyCells to accept and automatically convert Seurat objects as input files. However, I’ve documented your request for future consideration.

In the meantime, you can transform your existing data into a MapMyCells suitable format through a two-step process:

  1. Transposing your cell x gene information (MapMyCells requires rows = cells, columns = genes - Seurat provides the opposite as default)
  2. Converting your csv to an h5ad with the document available here: File Requirements and Limits -

Please let me know if you have any further questions.

Thanks for the fast reply. Do you think the conversion described here can generate the data format required by you? Thanks

HI @rmc0106

I strongly suspect that the last cell in that vignette is what you want. I, however, have no personal experience with Seurat (or really R), so I am only guessing.

I agree that that should work. If you try it and it turns out that it does NOT work, please let us know in the chat and we can update the “Creating h5ad input files in R & Python” section from the document available here: File Requirements and Limits - with appropriate steps.


I’ve been using MapMyCells, and it works great! I really appreciate the Allen Institute’s efforts to make this possible!

I was wondering if it is possible to run MapMyCells with non-default parameters and get prediction scores for the cluster labels—similar to what one would get by using the TransferData function and doing label transfer on Seurat? I’ve obtained cluster labels from MapMyCells but would like to gain more insights into my data.

Thank you!


Hi @sbhamidipati

I’m glad that you are getting good use out of MapMyCells.

I have no experience working with Seurat, so I do not know what metric it returns, but the default MapMyCells configuration should already be giving you two confidence metrics for its cell type assignments as described on this page

The CSV file will only list one of these confidence metrics, but the extended JSON file contains both the bootstrapping probability (the fraction of assignment iterations that chose the final cell type) and the average correlation (the average over all iterations of the correlation between the cell and its assigned cell type). You ought to also see runner_up_assignment, runner_up_correlation, and runner_up_probability listing the five most likely cell type assignments that weren’t chosen (assuming there are that many runners up).

Is this the kind of metric you were looking for?

If it helps to put what I said in context, the actual mapping algorithm that was run is described here

Do you still want to run MapMyCells with non-default parameters, or is this information sufficient?