Comprehensive Analysis of Tissue-wide Gene Expression and Phenotype Data Reveals Tissues Affected in Rare Genetic Disorders is a dataset published in Cell Systems (2017). On theSindex it has a DataRank of 1.4, placing it in the top 39% of the data-sharing corpus. It has been cited 22 times, with 23 citing works in its 1-hop citation network. Its calibrated FAIR score is 36/100.
Linking putatively pathogenic variants to the tissues they affect is necessary for determining the correct diagnostic workup and therapeutic regime in undiagnosed patients. Here, we explored how gene expression across healthy tissues can be used to infer this link. We integrated 6,665 tissue-wide transcriptomes with genetic disorder knowledge bases covering 3,397 diseases. Receiver-operating characteristics (ROC) analysis using expression levels in each tissue and across tissues indicated significant but modest associations between elevated expression and phenotype for most tissues (maximum area under ROC curve = 0.69). At extreme elevation, associations were marked. Upregulation of disease genes in affected tissues was pronounced for genes associated with autosomal dominant over recessive disorders. Pathways enriched for genes expressed and associated with phenotypes highlighted tissue functionality, including lipid metabolism in spleen and DNA repair in adipose tissue. These results suggest features useful for evaluating the likelihood of particular tissue manifestations in genetic disorders. The web address of an interactive platform integrating these data is provided.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
Base Score Contribution
0.470
From this paper's citation signal
Citation Network Contribution
0.896
From 18 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 34% comes from its base citations and 66% from the citation network (18 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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