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ARXIV:2604.02511 · GENOMICS & TRANSCRIPTOMICS · SUBMITTED 06 APR · 20:16 UTC · FRESHNESS UNKNOWN
ARXIV:2604.02511GENOMICS & TRANSCRIPTOMICSSUBMITTED 06 APR · 20:16 UTCFRESHNESS UNKNOWNArka Jain · Umesh Sharma · arXiv
A reproducible pipeline to recover valuable transcription factor insights from incomplete single-cell perturbation data, enabling deeper biological discovery.
Opportunity summary
Pain A reproducible pipeline to recover valuable transcription factor insights from incomplete single-cell perturbation data, enabling deeper biological discovery.
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A reproducible pipeline to recover valuable transcription factor insights from incomplete single-cell perturbation data, enabling deeper biological discovery. Here we re-analyze the human TF Atlas dataset (GSE216481), a MORF-based pooled overexpression screen spanning 3,550…
Public pooled single-cell perturbation atlases are valuable resources for studying transcription factor (TF) function, but downstream re-analysis can be limited by incomplete deposited metadata and missing internal controls. Here we re-analyze the human TF…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Together, these results show that the deposited TF Atlas data can support validated TF-specific transcriptional and pathway analyses when paired with principled external controls,…
Genomics & Transcriptomics moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A reproducible pipeline to recover valuable transcription factor insights from incomplete single-cell perturbation data, enabling deeper biological discovery.
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10.48550/arXiv.2604.02511A reproducible pipeline to recover valuable transcription factor insights from incomplete single-cell perturbation data, enabling deeper biological discovery.
Abstract
Public pooled single-cell perturbation atlases are valuable resources for studying transcription factor (TF) function, but downstream re-analysis can be limited by incomplete deposited metadata and missing internal controls. Here we re-analyze the human TF Atlas dataset (GSE216481), a MORF-based pooled overexpression screen spanning 3,550 TF open reading frames and 254,519 cells, with a reproducible pipeline for quality control, MORF barcode demultiplexing, per-TF differential expression, and functional enrichment. From 77,018 cells in the pooled screen, we assign 60,997 (79.2\%) to 87 TF identities. Because the deposited barcode mapping lacks the GFP and mCherry negative controls present in the original library, we use embryoid body (EB) cells as an external baseline and remove shared batch/transduction artifacts by background subtraction. This strategy recovers TF-specific signatures for 59 of 61 testable TFs, compared with 27 detected by one-vs-rest alone, showing that robust TF-level signal can be rescued despite missing intra-pool controls. HOPX, MAZ, PAX6, FOS, and FEZF2 emerge as the strongest transcriptional remodelers, while per-TF enrichment links FEZF2 to regulation of differentiation, EGR1 to Hippo and cardiac programs, FOS to focal adhesion, and NFIC to collagen biosynthesis. Condition-level analyses reveal convergent Wnt, neurogenic, EMT, and Hippo signatures, and Harmony indicates minimal confounding batch effects across pooled replicates. Our per-TF effect sizes significantly agree with Joung et al.'s published rankings (Spearman $ρ= -0.316$, $p = 0.013$; negative because lower rank indicates stronger effect). Together, these results show that the deposited TF Atlas data can support validated TF-specific transcriptional and pathway analyses when paired with principled external controls, artifact removal, and reproducible computation.
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PROBLEM
A reproducible pipeline to recover valuable transcription factor insights from incomplete single-cell perturbation data, enabling deeper biological discovery. Here we re-analyze the human TF Atlas dataset (GSE216481), a MORF-based pooled overexpression screen spanning 3,550 TF o...
METHOD
Public pooled single-cell perturbation atlases are valuable resources for studying transcription factor (TF) function, but downstream re-analysis can be limited by incomplete deposited metadata and missing internal controls. Here we re-analyze the human TF Atlas dataset (GSE2164...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Together, these results show that the deposited TF Atlas data can support validated TF-specific transcriptional and pathway analyses when paired with principled external controls, artifact removal, and re...
WHY NOW
Genomics & Transcriptomics moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A reproducible pipeline to recover valuable transcription factor insights from incomplete single-cell perturbation data, enabling deeper biological discovery. Here we re-analyze the human TF Atlas dataset (GSE216481), a MORF-based pooled overexpression screen spanning 3,550 TF open reading frames and 254,519 cells, with a reproducible pipeline for quality control, MORF barcode demultiplexing, per-TF differential expression, and functional enrichment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Public pooled single-cell perturbation atlases are valuable resources for studying transcription factor (TF) function, but downstream re-analysis can be limited by incomplete deposited metadata and missing internal controls. Here we re-analyze the human TF Atlas dataset (GSE216481), a MORF-based pooled overexpression screen spanning 3,550 TF open reading frames and 254,519 cells, with a reproducible pipeline for quality control, MORF barcode demultiplexing, per-TF differential expression, and functional enrichment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Together, these results show that the deposited TF Atlas data can support validated TF-specific transcriptional and pathway analyses when paired with principled external controls, artifact removal, and reproducible computation. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Genomics & Transcriptomics moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A reproducible pipeline to recover valuable transcription factor insights from incomplete single-cell perturbation data, enabling deeper biological discovery.
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