Evidence map›Paper›PMID 36418729›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2023

scRNA-seq for Microcephaly Research [III]: Computational Analysis of scRNA-seq Data.

Benjamin Babcock, Daniel Malawsky

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
2.5field-weighted citation impact, top 10% of its field
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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. 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

2 authors at 2 institutions in 2 countries.

Benjamin BabcockDepartment of Medicine, Division of Immunology, Lowance Center for Human Immunology, Emory University School of Medicine, Atlanta, GA, USA. ben.babcock@emory.edu.
Daniel MalawskyDepartment of Neurology, University of North Carolina Medical School, Chapel Hill, NC, USA.
Emory University · USUniversity of North Carolina at Chapel Hill · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Single-cell transcriptomic analysis (scRNA-seq) can enable researchers to explore the gene expression patterns of thousands of individual cells simultaneously. Processing the complex data generated by scRNA-seq requires specialized computational tools. This chapter focuses on the analytical aspect of scRNA-seq workflow, with a focus on resolving biological signals from large-scale scRNA-seq data produced by the Drop-Seq platform.

Indexed as

MicrocephalyExome SequencingHumansResearch PersonnelSingle-Cell AnalysisTranscriptomeComputational methodsLouvain clusteringPCAscRNA-seq

Identifiers

PMID36418729
OpenAlexW4310087922

What OpenQuestion holds

Textmetadata
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.