In their Perspective, Aviv Regev, Sarah Teichmann & colleagues discuss how cell atlases provide the missing links between genes, diseases and therapies — with benefits already seen in COVID-19, cancer and more teichlab sangerinstitute Cambridge_Uni
. Among the key computational challenges are the need for open data that reflect human diversity for training computational models, while appropriately safeguarding patient privacy; methods to decode cellular dynamics from static snapshots; algorithms and platforms for efficient querying for genes, cell states and cell types of interest; and fast iterations between lab and computation to design faithful human-derived organoids and cells for screens and therapies.
Other challenges are more fundamental. First, while analysis of expression profiles yields suggestive associations, demonstrating the causative disease role of a gene, program or cell state requires direct interventions. Using single-cell and spatial genomics with genetic screens or in human genetic cohorts and clinical trials, along with causal inference, should help advance us from correlation to causation.
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