Evidence map›Paper›PMID 38390437›Full record

ArticleJournal of bioinformatics and systems biology : Open access2023

NMF Clustering: Accessible NMF-based Clustering Utilizing GPU Acceleration.

Ted Liefeld, Edwin Huang, Alexander T Wenzel, Kenneth Yoshimoto, Ashwyn K Sharma, Jason K Sicklick, Jill P Mesirov, Michael Reich

Abstract read
In one paragraph

Article in Journal of bioinformatics and systems biology : Open access, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Ted LiefeldUniversity of California San Diego, Department of Medicine, School of Medicine, La Jolla, CA, 92093, USA.
Edwin HuangUniversity of California San Diego, Department of Medicine, School of Medicine, La Jolla, CA, 92093, USA.
Alexander T WenzelUniversity of California San Diego, Department of Medicine, School of Medicine, La Jolla, CA, 92093, USA.
Kenneth YoshimotoUniversity of California San Diego, San Diego Supercomputer Center, La Jolla, CA, 92093, USA.
Ashwyn K SharmaUniversity of California San Diego, Moores Cancer Center, La Jolla, CA, 92093, USA.
Jason K SicklickUniversity of California San Diego, Departments of Surgery and Pharmacology, Moores Cancer Center,La Jolla, CA, 92093, USA.
Jill P MesirovUniversity of California San Diego, Department of Medicine, School of Medicine, La Jolla, CA, 92093, USA.
Michael ReichUniversity of California San Diego, Department of Medicine, School of Medicine, La Jolla, CA, 92093, USA.

Funding

UCSD Cancer Center Training Program in Drug DevelopmentT32CA121938 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Michael Bouvet, Dwayne G. Stupack · 2006 to 2026
$10.3M
GenePattern and GenePattern Notebook: Integrative 'Omic Analysis for Cancer ResearchU24CA248457 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI MESIROV, JILL P., TAMAYO, PABLO · 2020 to 2024
$4.6M
GenePattern Notebooks for Cancer ResearchU24CA194107 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI MESIROV, JILL P. · 2015 to 2019
$2.8M
Targeting Drug-Resistant Cancer Stem Cell Niches of Gastrointestinal Stromal TumorR01CA226803 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SICKLICK, JASON KEITH · 2019 to 2023
$2.1M
Platform and portal development for accessible, reproducible cancer genomics researchR50CA243876 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI LIEFELD, JOHN THEODORE · 2019 to 2023
$1.1M
Methods for improved detection of activated molecular pathways in cancerF31CA257344 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI WENZEL, ALEXANDER THOMAS · 2021 to 2023
$122k
NCI NIH HHS F31 CA257344NCI NIH HHS R01 CA226803NCI NIH HHS R50 CA243876NCI NIH HHS T32 CA121938NCI NIH HHS U24 CA194107NCI NIH HHS U24 CA248457
6 · The paper itself

Abstract

Non-negative Matrix Factorization (NMF) is an algorithm that can reduce high dimensional datasets of tens of thousands of genes to a handful of metagenes which are biologically easier to interpret. Application of NMF on gene expression data has been limited by its computationally intensive nature, which hinders its use on large datasets such as single-cell RNA sequencing (scRNA-seq) count matrices. We have implemented NMF based clustering to run on high performance GPU compute nodes using CuPy, a GPU backed python library, and the Message Passing Interface (MPI). This reduces the computation time by up to three orders of magnitude and makes the NMF Clustering analysis of large RNA-Seq and scRNA-seq datasets practical. We have made the method freely available through the GenePattern gateway, which provides free public access to hundreds of tools for the analysis and visualization of multiple 'omic data types. Its web-based interface gives easy access to these tools and allows the creation of multi-step analysis pipelines on high performance computing (HPC) clusters that enable reproducible

Indexed as

GeneNMFscRNA-seq

Identifiers

PMID38390437
PMCPMC10883375

What OpenQuestion holds

Textmetadata
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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.