CeDR Atlas: a knowledgebase of cellular drug response
CeDR Atlas: a knowledgebase of cellular drug response is a dataset published in Nucleic Acids Research (2021). On theSindex it has a DataRank of 1.2, placing it in the top 40.4% of the data-sharing corpus. It has been cited 42 times, with 33 citing works in its 1-hop citation network. Its calibrated FAIR score is 48/100.
Abstract
Drug response to many diseases varies dramatically due to the complex genomics and functional features and contexts. Cellular diversity of human tissues, especially tumors, is one of the major contributing factors to the different drug response in different samples. With the accumulation of single-cell RNA sequencing (scRNA-seq) data, it is now possible to study the drug response to different treatments at the single cell resolution. Here, we present CeDR Atlas (available at https://ngdc.cncb.ac.cn/cedr), a knowledgebase reporting computational inference of cellular drug response for hundreds of cell types from various tissues. We took advantage of the high-throughput profiling of drug-induced gene expression available through the Connectivity Map resource (CMap) as well as hundreds of scRNA-seq data covering cells from a wide variety of organs/tissues, diseases, and conditions. Currently, CeDR maintains the results for more than 582 single cell data objects for human, mouse and cell lines, including about 140 phenotypes and 1250 tissue-cell combination types. All the results can be explored and searched by keywords for drugs, cell types, tissues, diseases, and signature genes. Overall, CeDR fine maps drug response at cellular resolution and sheds lights on the design of combinatorial treatments, drug resistance and even drug side effects.
›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
0.538
From this paper's citation signal
Citation Network Contribution
0.675
From 25 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.
- Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profilesProceedings of the National Academy of Sciences200555,906 citationsDataRank 1.6
- The Genotype-Tissue Expression (GTEx) projectNature Genetics20139,949 citationsDataRank 18.2Top 8%
- Massively parallel digital transcriptional profiling of single cellsNature Communications20177,641 citationsDataRank 1.3
- Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophageNature Immunology20195,174 citationsDataRank 1.3
- Single-cell transcriptomics of 20 mouse organs creates a Tabula MurisNature20183,174 citationsDataRank 15.2Top 12%
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 44% comes from its base citations and 56% from the citation network (25 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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