Single-cell multi-cohort dissection of the schizophrenia transcriptome
Single-cell multi-cohort dissection of the schizophrenia transcriptome is a dataset (2022). On theSindex it has a DataRank of 0.899, placing it in the top 42.8% of the data-sharing corpus. It has been cited 29 times, with 21 citing works in its 1-hop citation network. Its calibrated FAIR score is 15/100.
Abstract
Schizophrenia is a prevalent mental illness with a high societal burden, complex pathophysiology, and diverse genetic and environmental etiology. Its complexity, polygenicity, and heterogeneity have hindered mechanistic elucidation and the search for new therapeutics. We present a single-cell dissection of schizophrenia-associated transcriptomic changes in the human prefrontal cortex across two independent cohorts, one deeply profiling 48 subjects (361,996 cells), and the other broadly profiling 92 subjects (106,761 cells). We identified 25 cell types that we used to produce a high-resolution atlas of schizophrenia-altered genes and pathways. Excitatory neurons were the most affected cell group, with transcriptional changes converging on neurodevelopment and synapse-related molecular pathways. Differentially expressed gene sets implicate a coherently expressed module of trans-acting regulatory factors involved in neurodevelopment and genetically associated with schizophrenia risk. Transcriptional alterations significantly overlapped with known genetic risk factors, suggesting convergence of rare and common genomic variants on reproducible neuronal population specific alterations in schizophrenia. The severity of transcriptional pathology segregated two populations of schizophrenia subjects in a manner consistent with the expression of specific transcriptional patterns marked by genes involved in synaptic function and chromatin dynamics. Our results provide a high-resolution single cell atlas linking transcriptomic changes within specific cell populations to etiological genetic risk factors, contextualizing established knowledge within the cytoarchitecture of the human cortex and facilitating mechanistic understanding of schizophrenia pathophysiology and heterogeneity.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
- Dataset classification
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 →
DataRank Breakdown
Base Score Contribution
0.510
From this paper's citation signal
Citation Network Contribution
0.388
From 18 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- limma powers differential expression analyses for RNA-sequencing and microarray studiesNucleic Acids Research201542,254 citationsDataRank 1.6
- WGCNA: an R package for weighted correlation network analysisBMC Bioinformatics200828,621 citationsDataRank 1.5
- STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasetsNucleic Acids Research201819,062 citationsDataRank 13.8Top 14%
- Integrating single-cell transcriptomic data across different conditions, technologies, and speciesNature Biotechnology201814,465 citationsDataRank 1.4
- Biological insights from 108 schizophrenia-associated genetic lociNature20148,042 citationsDataRank 1.3
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 57% comes from its base citations and 43% from the citation network (18 citing papers contributed measurable signal).
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
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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