Editorial & Scientific Analysis

Critical Perspectives in Computational Omics

Rigorous analysis on spatial transcriptomics failure modes, single-cell foundation model stress tests, and statistical pitfalls in modern genomics.

Systems Biology & Cell Fate Transitions
2026-09-182 min read

When does a virtual cell predict a biological decision?

A systems biology perspective on the Arc Institute State paper in Cell, examining whether large scale perturbation models capture authentic cell fate bifurcations.

A systems biology perspective on the Arc Institute State paper in Cell, examining whether large scale perturbation models capture authentic cell fate bifurcations.

#virtual cell#cell fate decisions#epigenetic competence#perturbation response#Arc Institute#State#systems biology
Industry Data Strategy
2026-08-262 min read

One hundred million cells. Three hundred seventy-nine molecules.

Tahoe-100M's 95.6 million cells cover 379 unique compounds; scale narratives that blur arms with molecules overstate what any model can learn.

Tahoe-100M's 95.6 million cells cover 379 unique compounds; scale narratives that blur arms with molecules overstate what any model can learn.

#Tahoe-100M#perturbation atlas#chemical coverage#extrapolation#data strategy
Industry R&D Strategy
2026-08-242 min read

Phase I is AI's home turf. Phase II is the honest scorecard.

AI discovered molecules clear Phase I at 80 to 90% against a 40 to 65% historical rate, but Phase II shows no improvement, around 40% versus 37%; AI is strong at molecule properties and has not yet shown it picks the right biology.

AI discovered molecules clear Phase I at 80 to 90% against a 40 to 65% historical rate, but Phase II shows no improvement, around 40% versus 37%; AI is strong at molecule properties and has not yet shown it picks the right biology.

#AI drug discovery#Phase I Phase II success rates#clinical translation#value allocation
Spatial Transcriptomics
2026-08-192 min read

2M Single Cells vs. 18,000 Genes — The Spatial Deconvolution Reality

Evaluating the trade-offs between 2 million single cells and whole-transcriptome spot deconvolution across 128 kidney spatial samples.

Evaluating the trade-offs between 2 million single cells and whole-transcriptome spot deconvolution across 128 kidney spatial samples.

#single-cell#spatial transcriptomics#deconvolution#RCTD#Tangram#Visium#Xenium#kidney
Spatial Transcriptomics
2026-08-182 min read

Unifying 128 Kidney Spatial Transcriptomics Samples Across 5 Platforms

A systematic re-analysis of 128 publicly available kidney spatial-transcriptomics datasets across five technologies, harmonized and open-sourced via SpatialRenal.

A systematic re-analysis of 128 publicly available kidney spatial-transcriptomics datasets across five technologies, harmonized and open-sourced via SpatialRenal.

#spatial transcriptomics#kidney#Xenium#CosMx#Visium#GeoMx#SpatialRenal#benchmark
Epigenomic Profiling
2026-08-152 min read

Why Zero Background in CUT&Tag Misleads Peak Callers

Why the low background of CUT&Tag causes ChIP-seq peak callers to misidentify Tn5 accessibility hotspots as genuine transcription factor binding sites.

Why the low background of CUT&Tag causes ChIP-seq peak callers to misidentify Tn5 accessibility hotspots as genuine transcription factor binding sites.

#CUT&Tag#ChIP-seq#SEACR#MACS2#peak calling#pA-Tn5#chromatin
Vulnerability & Cell State
2026-08-082 min read

Closing a Transposable Element Forces Leukemic Stem Cells to Differentiate

Grillo et al. (Nature Genetics 2026): CRISPRi repression of LTR12C transposable elements forces leukemic stem cells to differentiate — a targetable chromatin state that encodes a cell-fate decision.

Grillo et al. (Nature Genetics 2026): CRISPRi repression of LTR12C transposable elements forces leukemic stem cells to differentiate — a targetable chromatin state that encodes a cell-fate decision.

#transposable elements#leukemia#stemness#ATAC-seq#CRISPRi#vulnerability#cell state#AML
industry-benchmark
2026-08-012 min read

The benchmark that mattered: seven foundation models, one linear baseline

Ahlmann-Eltze et al. found seven deep learning models fail to beat a simple additive baseline at perturbation prediction; the discipline this creates is worth more than any model.

Ahlmann-Eltze et al. found seven deep learning models fail to beat a simple additive baseline at perturbation prediction; the discipline this creates is worth more than any model.

#perturbation prediction#foundation models#additive baseline#benchmark#risk reduction