The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Dimensionality reduction by UMAP to visualize physical and genetic interactions
Open accessAbstract
Dimensionality reduction is often used to visualize complex expression profiling data. Here, we use the Uniform Manifold Approximation and Projection (UMAP) method on published transcript profiles of 1484 single gene deletions of Saccharomyces cerevisiae . Proximity in low-dimensional UMAP space identifies groups of genes that correspond to protein complexes and pathways, and finds novel protein interactions, even within well-characterized complexes. This approach is more sensitive than previous methods and should be broadly useful as additional transcriptome datasets become available for other organisms.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
UMAP: Uniform Manifold Approximation and Projection
McInnes, L., Healy, J., Saul, N., & Großberger, L. (2018) · Journal of Open Source Software · doi:10.21105/joss.00861 · Cited by 9,826
Szklarczyk, D., Morris, J. H., Cook, H., Kuhn, M., Wyder, S., Simonovic, M., Santos, A., Doncheva, N. T., Roth, A., Bork, P., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw937 · Cited by 6,980
The single-cell transcriptional landscape of mammalian organogenesis
Cao, J., Spielmann, M., Qiu, X., Huang, X., Ibrahim, D. M., Hill, A. J., Zhang, F., Mundlos, S., Christiansen, L., Steemers, F. J., et al. (2019) · Nature · doi:10.1038/s41586-019-0969-x · Cited by 4,490
Self-citationThree-dimensional intact-tissue sequencing of single-cell transcriptional states
Wang, X., Allen, W. E., Wright, M. A., Sylwestrak, E. L., Samusik, N., Vesuna, S., Evans, K., Liu, C., Ramakrishnan, C., Liu, J., et al. (2018) · Science · doi:10.1126/science.aat5691 · Cited by 1,620
A global genetic interaction network maps a wiring diagram of cellular function
Costanzo, M., VanderSluis, B., Koch, E. N., Baryshnikova, A., Pons, C., Tan, G., Wang, W., Usaj, M., Hanchard, J., Lee, S. D., et al. (2016) · Science · doi:10.1126/science.aaf1420 · Cited by 1,240
Pooled CRISPR screening with single-cell transcriptome readout.
Datlinger, P., Rendeiro, A. F., Schmidl, C., Krausgruber, T., Traxler, P., Klughammer, J., Schuster, L. C., Kuchler, A., Alpar, D., & Bock, C. (2017) · Nature Methods · doi:10.1038/nmeth.4177 · Cited by 1,096
Genome-wide CRISPR Screens in Primary Human T Cells Reveal Key Regulators of Immune Function
Shifrut, E., Carnevale, J., Tobin, V., Roth, T. L., Woo, J. M., Bui, C. T., Li, P. J., Diolaiti, M. E., Ashworth, A., & Marson, A. (2018) · Cell · doi:10.1016/j.cell.2018.10.024 · Cited by 573
Thyroid hormone regulates distinct paths to maturation in pigment cell lineages
Saunders, L. M., Mishra, A. K., Aman, A. J., Lewis, V. M., Toomey, M. B., Packer, J. S., Qiu, X., McFaline-Figueroa, J. L., Corbo, J. C., Trapnell, C., et al. (2019) · eLife · doi:10.7554/elife.45181 · Cited by 170
Self-citation
Cited by
The Specious Art of Single-Cell Genomics
Chari, T., & Pachter, L. (2021) · doi:10.1101/2021.08.25.457696 · Cited by 379
Brain mapping across 16 autism mouse models reveals a spectrum of functional connectivity subtypes
Zerbi, V., Pagani, M., Markicevic, M., Matteoli, M., Pozzi, D., Fagiolini, M., Bozzi, Y., Galbusera, A., Scattoni, M. L., Provenzano, G., et al. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01245-4 · Cited by 102
The regulatory landscape of the yeast phosphoproteome
Leutert, M., Barente, A. S., Fukuda, N. K., Rodriguez-Mias, R. A., & Villén, J. (2023) · Nature Structural & Molecular Biology · doi:10.1038/s41594-023-01115-3 · Cited by 52
(2023) · doi:10.1016/j.str.2023.04.007 · Cited by 6
Metadata
- Authors
- Dorrity, M. W., Saunders, L. M., Queitsch, C., Fields, S., & Trapnell, C.
- Venue
- Nature Communications
- Published
- 2020-03-24
- Volume
- 11
- Issue
- 1
- Pages
- 1537-1537
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W3014040695
- Cited by
- 242
- References
- 8
- FWCI
- 9.47
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Dimensionality reduction by UMAP to visualize physical and genetic interactions. https://app.cochat.ai/papers/10.1038/s41467-020-15351-4
- The paper
- Dorrity, M. W., Saunders, L. M., Queitsch, C., Fields, S., & Trapnell, C. (2020). Dimensionality reduction by UMAP to visualize physical and genetic interactions. Nature Communications. https://doi.org/10.1038/s41467-020-15351-4
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
