
MD-PhD Candidate @JHUBME. spatial transcriptomics at https://xitter.cf/t.co/S5zk0oAaQ1. she/her
Baltimore, MD
Joined December 2020
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Check out our now published investigation into how experimental design and analysis choices can affect downstream results when analyzing imaging-based spatial transcriptomics data. rdcu.be/dKFby
Finally, based on our results we provide a decision tree for investigators deciding on which normalization method to use with their data.
Thanks to @kalen_clifton, @manjari_017, @Gohta_Aihara, who contributed to this project, to @JEFworks for guidance and mentorship, and to the reviewers for providing thoughtful feedback that greatly improved the work!
Excited to share some updates and recommendations on normalization approaches for #singlecell imaging-based targeted #spatialtranscriptomics data. See more in our pre-print: bit.ly/jefworks-imSRT-norm
We've updated our #bioRxiv preprint investigating the potential impact of #geneexpression normalization methods with different gene panels in analysis + interpretation of #singlecell imaging-based targeted #spatialtranscriptomics data. Check it out: biorxiv.org/content/10.1101/…
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Lyla Atta retweeted
Check out our new #bioinformatics method to characterize cell-type spatial relationships across length scales in #singlecell #spatialomics data
Preprint: biorxiv.org/content/10.1101/…
Software: github.com/JEFworks-Lab/CRAW…
Led by @rdsantospeixoto + @bf_miller_ w/ @BruskoMaigan +team
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This results in region- and cell type-specific false +ve/-ve differential gene expression tests and different sign log fold changes. We see these effects across single-cell imaging-based spatial transcriptomics technologies.
These issues can be mitigated with larger gene panels or gene panels that are more representative of the gene expression of the tissue's component cell types.
We find that when using count based normalization methods such as library size normalization, downstream differential gene expression and fold change analyses give different results with different gene panels.
Given advancing imaging-based spatial transcriptomics tech and many new datasets to interrogate spatial biology, we (@kalen_clifton, @manjari_017, @JEFworks, and I) wanted to explore how experimental design choices and gene expression normalization can affect downstream analyses.
Happy to share some of what I've been working on recently!
See 🧵 below for a summary.
And this 🧵for more details: xitter.cf/JEFworks/status/169904…
And check out the preprint for all the details: biorxiv.org/content/10.1101/…
#spatialtranscriptomics #singlecell #scRNAseq
How should we normalize #singlecell #geneexpression counts from imaging-based #spatialtranscriptomics? We kept getting asked this so we wrote a paper about it!
#bioRxiv preprint: biorxiv.org/content/10.1101/…
Led by @lylaatta w/ @kalen_clifton + @manjari_017 #AcademicTwitter
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.@JohnsHopkins says that termination, discrimination, sexual assault, and health and safety don't deserve protections in a contract. We demand better and are willing to fight for our workplace rights. #NotSafeatJHU #MyRightsMyContract #JohnsHopkins
JHU closed on-site employee COVID-19 testing due to "reduced demand". I was repeatedly denied testing after a known exposure. @JohnsHopkinsSPH claims to be the #1 school of public health, but JHU refuses to provide access to public health protections during an ongoing pandemic.
Lyla Atta retweeted
Replying to @JohnsHopkins
@JohnsHopkins says that termination, discrimination, sexual assault, and health and safety don't deserve protections in a contract. We demand better and are willing to fight for our workplace rights. #NotSafeatJHU #MyRightsMyContract #JohnsHopkins
Lyla Atta retweeted
Thrilled to share our #bioinformatics tool for aligning #spatialtranscriptomics data using #LDDMM across technologies, partial matched tissues, to 3D atlas
📰 biorxiv.org/content/10.1101/…
🖥️ github.com/JEFworks-Lab/STal… #Python
Congrats @kalen_clifton @manjari_017 w/ collab @UCLA @JHUBME








