L1000 Connectivity Map perturbational profiles from Broad Institute LINCS Center for Transcriptomics LINCS Pilot PHASE I (n=1,319,138; updated March 03, 2017)
The Library of Integrated Cellular Signatures (LINCS) is an NIH program which funds the generation of perturbational profiles across multiple cell and perturbation types, as well as read-outs, at a massive scale. The LINCS Center for Transcriptomics at the Broad Institute uses the L1000 high-throughput gene-expression assay to build a Connectivity Map which seeks to enable the discovery of functional connections between drugs, genes and diseases through analysis of patterns induced by common gen...
Provenance — who produced it, who reused it
Linked to 87 papers in the literature. Roles are inferred factual signals (who deposited the data vs who reused it), with counts — never a judgement about any author.
- Proteogenomic Characterization Reveals Therapeutic Vulnerabiliti... 2020 · 820 cites
- Proteogenomic Landscape of Breast Cancer Tumorigenesis and Targe... 2020 · 553 cites
- De novo generation of hit-like molecules from gene expression si... 2020 · 416 cites
- A proteogenomic portrait of lung squamous cell carcinoma 2021 · 385 cites
- Aneuploidy renders cancer cells vulnerable to mitotic checkpoint... 2021 · 302 cites
- Cas9 activates the p53 pathway and selects for p53-inactivating... 2020 · 285 cites
- Multiplexed single-cell transcriptional response profiling to de... 2020 · 183 cites
- EuRBPDB: a comprehensive resource for annotation, functional and... 2019 · 132 cites
- Extending the small-molecule similarity principle to all levels... 2020 · 128 cites
- Connecting omics signatures and revealing biological mechanisms... 2022 · 115 cites
- SigCom LINCS: data and metadata search engine for a million gene... 2022 · 113 cites
- Stable gene expression for normalisation and single-sample scori... 2020 · 108 cites
- The COVID-19 Drug and Gene Set Library 2020 · 85 cites
- Deep learning of pharmacogenomics resources: moving towards prec... 2019 · 77 cites
- Cheminformatics Tools for Analyzing and Designing Optimized Smal... 2019 · 76 cites
64 further papers cite this accession but reuse could not be confirmed.
Deep data QC
metadata only · no data-level QC for this typeStandardized, field-standard QC computed by touching the data — every metric states how it was obtained
No quantitative QC rubric exists for this data type yet, so it is deliberately left unscored — this is an honest "not applicable", not a poor rating.
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0 · provisional — verify independently