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Sarah Ancheta

@Sarah_E_Ancheta

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Associate Computational Biologist @czbiohub SF, incoming PhD student @UCSF #CZBiohubSF #RNAvelocity #zebrahub

San Francisco, CA
Joined March 2023
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@Sarah_E_Ancheta
Sarah Ancheta
9 months
RT @loicaroyer: 🚨Big news! Five years in the making, our Zebrahub paper is now published in #Cell 🎉. We’ve built a timecourse atlas of zebr….
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@Sarah_E_Ancheta
Sarah Ancheta
1 year
8/8 Read the full preprint to dive deeper into our methods and results. Your feedback and questions are welcome! Many thanks for the amazing mentorship from @Merlin_Lange, @ale_agranados, @sculptorofdance and @loicaroyer #AcademicTwitter #RNAvelocity #ScienceTwitter.
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@Sarah_E_Ancheta
Sarah Ancheta
1 year
7/8 The choice of RNA velocity method can significantly impact the insights gained from scRNA-seq data. We hope our benchmark helps researchers choose the best tool for their data. Many thanks to #CZBiohubSF for their support! @czbiohub #OpenScience #Bioinformatics.
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@Sarah_E_Ancheta
Sarah Ancheta
1 year
6/8 Our benchmark highlights the robustness of RNA velocity methods to sequencing depth. Velocyto emerged as the most robust method. We evaluated the robustness of each method's directionality predictions by comparing the subset reads with 100% of the data.
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@Sarah_E_Ancheta
Sarah Ancheta
1 year
5/8 Only one gene, Pdx1, was consistently identified as a top driver across all five RNA velocity methods for the pancreas beta lineage. This highlights the variability in driver gene predictions and the importance of multi-method approaches. #Genomics #pancreas #CellRank
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@Sarah_E_Ancheta
Sarah Ancheta
1 year
4/8 We observed significant discrepancies in RNA velocity predictions among different methods. We explore the agreement (or lack thereof) between methods for the zebrafish neuromesodermal progenitor dataset. #Bioinformatics #zebrahub
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@Sarah_E_Ancheta
Sarah Ancheta
1 year
3/8 Local neighborhood consistency in RNA velocity methods varies by cell type. High consistency is often seen as a positive indicator, but low consistency may indicate cellular heterogeneity.
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@Sarah_E_Ancheta
Sarah Ancheta
1 year
2/8 We analyze the performance of five RNA velocity methods across three developmental contexts. RNA velocity methods vary significantly in their predictions. Understanding these differences is crucial for trajectory inference. #single-cell #devbio #zebrafish #pancreas
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@Sarah_E_Ancheta
Sarah Ancheta
1 year
1/8 Excited to share our preprint on RNA velocity! 🚀🧬 In this study, we compare 5 RNA velocity methods across 3 datasets. Check out our findings! #scRNAseq #RNAvelocity 📄
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@Sarah_E_Ancheta
Sarah Ancheta
2 years
RT @loicaroyer: We present #Zebrahub: a timecourse atlas of zebrafish embryonic development, combining #scRNAseq time-course data with #lig….
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