MD-PhD Candidate @JHUBME. spatial transcriptomics at https://xitter.cf/t.co/S5zk0oAaQ1. she/her

Baltimore, MD
Joined December 2020
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
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Finally, based on our results we provide a decision tree for investigators deciding on which normalization method to use with their data.
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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!
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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/… 🧵1/n
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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.
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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.
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We hope that this work is useful to folks looking to leverage the high throughput and spatial resolution of single-cell imaging-based spatial transcriptomics' technologies to ask new biological questions that help us understand how cells behave within their spatial contexts.
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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.
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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.
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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
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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.
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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
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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
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