Evidence map›Paper›PMID 34778889›Full record

ArticleACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine2021

Fast and memory-efficient scRNA-seq

Daniel N Baker, Nathan Dyjack, Vladimir Braverman, Stephanie C Hicks, Ben Langmead

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. A scalable and unbiased discordance metric with H.Biostatistics (Oxford, England) · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Daniel N BakerDepartment of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
Nathan DyjackDepartment of Biostatistics, Johns Hopkins University, Bloomberg, School of Public Health, Baltimore, MD, USA.
Vladimir BravermanDepartment of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
Stephanie C HicksDepartment of Biostatistics, Johns Hopkins University, Bloomberg, School of Public Health, Baltimore, MD, USA.
Ben LangmeadDepartment of Computer Science, Johns Hopkins University, Baltimore, MD, USA.

Funding

Methods for sequencing data analysis and archive-scale data science - RenewalR35GM139602 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI Benjamin Thomas Langmead · 2021 to 2026
$5.0M
Hardening and Scaling Core Genomics SoftwareR01GM118568 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI LANGMEAD, BENJAMIN THOMAS · 2016 to 2020
$2.0M
Statistical Methods for the Normalization and Quantification of Single-Cell RNA-Sequencing DataR00HG009007 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI HICKS, STEPHANIE CARINNE · 2018 to 2020
$725k
NHGRI NIH HHS R00 HG009007NIGMS NIH HHS R01 GM118568NIGMS NIH HHS R35 GM139602
6 · The paper itself

Abstract

Single-cell RNA-sequencing (scRNA-seq) analyses typically begin by clustering a gene-by-cell expression matrix to empirically define groups of cells with similar expression profiles. We describe new methods and a new open source library, minicore, for efficient

Indexed as

clusteringimportance samplingSIMDsingle cell

Identifiers

PMID34778889
PMCPMC8586878

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.