add news

_data/carousel.yml

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 images:
+  - image: /assets/news/ice_skating_2024.jpg
   - image: /assets/news/csh_single_cell_analysis_2023-2.jpg
   - image: /assets/news/lab_retreat_fall2023.gif
   - image: /assets/news/feiyang_farewell_05192023.jpeg
@@ -10,5 +11,4 @@ images:
   - image: /assets/news/lab_event_062022.jpg
   - image: /assets/news/lab_brunch_05182022.jpeg
   - image: /assets/news/lab_hike_D3S6934.jpg
-  - image: /assets/news/lab_photo_summer2021.jpg
 

_news/2024-01-08-first-day-of-class.md

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+---
+layout: news
+title: Prof. Fan teaches Genomic Data Visualization again this semester!
+---
+
+Check out the <a href="https://jef.works/genomic-data-visualization-2024/">course website</a> for more details.
+

_news/2024-02-06-jhu-newsletter.md

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+---
+layout: news
+title: Prof. Fan is interviewed for The Johns Hopkins News-Letter.
+---
+
+Check out the [article "Navigating the cellular cosmos: A conversation with Jean Fan on spatially resolved transcriptomics"](https://www.jhunewsletter.com/article/2024/02/navigating-the-cellular-cosmos-a-conversation-with-jean-fan-on-spatially-resolved-transcriptomics) for more details. 
+
+Special thanks to JHU undergraduate student Annie Huang for her excellent interview and for writing the article. 

_news/2024-02-07-ice-skating.md

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+---
+layout: news
+title: JEFworks lab members go ice skating!
+---
+
+Special thanks to Rafael and Gohta for organizing. 
+
+<img src="/assets/news/ice_skating_2024.jpg">

_news/2024-02-09-Department-of-Medicine-Whiting-School-of-Engineering-Research-Retreat-2024.md

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+---
+layout: news
+title: JEFworks lab members present their work at the DOM/WSE Research Retreat!
+---
+
+Kalen gives a lightning talk. Lyla and Rafael present posters. 
+
+Special thanks to research retreat co-chair [Jamie Spangler](https://www.spanglerlab.org/) and the other organizers for their invitation. 

_papers/2023-12-08-stalign.md

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 ---
 layout: paper
-title: Alignment of spatial transcriptomics data using diffeomorphic metric mapping
+title: STalign - alignment of spatial transcriptomics data using diffeomorphic metric mapping
 authors: <b><u>Kalen Clifton*</u></b>, <b><u>Manjari Anant*</u></b>, <b><u>Gohta Aihara</u></b>, <b><u>Lyla Atta</u></b>, Osagie K Aimiuwu, Justus M Kebschull, Michael I Miller, Daniel Tward^, <b>Jean Fan^</b>
 image: /assets/papers/STalign_anim_small.gif
 abstract: Spatial transcriptomics (ST) technologies enable high throughput gene expression characterization within thin tissue sections. However, comparing spatial observations across sections, samples, and technologies remains challenging. To address this challenge, we developed STalign to align ST datasets in a manner that accounts for partially matched tissue sections and other local non-linear distortions using diffeomorphic metric mapping. We apply STalign to align ST datasets within and across technologies as well as to align ST datasets to a 3D common coordinate framework. We show that STalign achieves high gene expression and cell-type correspondence across matched spatial locations that is significantly improved over manual and landmark-based affine alignments. Applying STalign to align ST datasets of the mouse brain to the 3D common coordinate framework from the Allen Brain Atlas, we highlight how STalign can enable the interrogation of compositional heterogeneity across anatomical structures. 

_papers/2024-02-05-seraster.md

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+---
+layout: paper
+title: SEraster - a rasterization preprocessing framework for scalable spatial omics data analysis
+authors: <b><u>Gohta Aihara</u></b>, <b><u>Kalen Clifton</u></b>, <b><u>Mayling Chen</u></b>, <b><u>Lyla Atta</u></b>, <b><u>Brendan F. Miller</u></b>, <b>Jean Fan^</b>
+image: /assets/papers/seraster.jpg
+abstract: Spatial omics data demand computational analysis but many analysis tools have computational resource requirements that increase with the number of cells analyzed. This presents scalability challenges as researchers use spatial omics technologies to profile millions of cells. To enhance the scalability of spatial omics data analysis, we developed a rasterization preprocessing framework called SEraster that aggregates cellular information into spatial pixels. We apply SEraster to both real and simulated spatial omics data prior to spatial variable gene expression analysis to demonstrate that such preprocessing can reduce resource requirements while maintaining high performance. We further integrate SEraster with existing analysis tools to characterize cell-type spatial cooccurrence. Finally, we apply SEraster to enable analysis of a mouse pup spatial omics dataset with over a million cells to identify tissue-level and cell-type-specific spatially variable genes as well as cooccurring cell-types that recapitulate expected organ structures.
+journal: bioRxiv
+journalurl: https://www.biorxiv.org/content/10.1101/2024.02.01.578436v1
+code: https://github.com/JEFworks-Lab/SEraster
+---