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 Scimap is a scalable toolkit for analyzing spatial molecular data. The underlying framework is generalizable to spatial datasets mapped to XY coordinates. The package uses the [anndata](https://anndata.readthedocs.io/en/stable/anndata.AnnData.html) framework making it easy to integrate with other popular single-cell analysis toolkits. It includes preprocessing, phenotyping, visualization, clustering, spatial analysis and differential spatial testing. The Python-based implementation efficiently deals with large datasets of millions of cells.
 
-## Citing SCIMAP
+## Citing scimap
 Nirmal et al., (2024). SCIMAP: A Python Toolkit for Integrated Spatial Analysis of Multiplexed Imaging Data. Journal of Open Source Software, 9(97), 6604, [https://doi.org/10.21105/joss.06604](https://joss.theoj.org/papers/10.21105/joss.06604#)
 
 ## Installation
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 SCIMAP operates on segmented single-cell data derived from imaging data using tools such as cellpose or MCMICRO. The essential inputs for SCIMAP are: (a) a single-cell expression matrix and (b) the X and Y coordinates for each cell. Additionally, multi-stack OME-TIFF or TIFF images can be optionally provided to enable visualization of the data analysis on the original raw images.
 <br>
 
-## Citing SCIMAP
+## Citing scimap
 Nirmal et al., (2024). SCIMAP: A Python Toolkit for Integrated Spatial Analysis of Multiplexed Imaging Data. Journal of Open Source Software, 9(97), 6604, [https://doi.org/10.21105/joss.06604](https://joss.theoj.org/papers/10.21105/joss.06604#)
 
 *SCIMAP* development was led by [Ajit Johnson Nirmal](https://ajitjohnson.com/), Harvard Medical School.