Evidence map›Paper›PMID 39294566›Full record

ArticleBMC genomics2024

Mouse blood cells types and aging prediction using penalized Latent Dirichlet Allocation.

Xiaotian Wu, Yee Voan Teo, Nicola Neretti, Zhijin Wu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC genomics, 2024. 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
–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

1 citing paper in PubMed.

  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

4 authors.

Xiaotian WuDepartment of Biostatistics, Brown University, Providence, RI, USA.
Yee Voan TeoDepartment of Molecular Biology, Cell Biolgy, and Biochemistry, Brown University, Providence, RI, USA.
Nicola NerettiDepartment of Molecular Biology, Cell Biolgy, and Biochemistry, Brown University, Providence, RI, USA.
Zhijin WuDepartment of Biostatistics, Brown University, Providence, RI, USA. zhijin_wu@brown.edu.ORCID http://orcid.org/0000-0002-9596-9134

Funding

Statistical Methods for Single-Cell RNA-SeqR01GM122083 · NIGMS · EMORY UNIVERSITY · PI WU, HAO · 2016 to 2020
$1.7M
NIGMS NIH HHS R01 GM122083NIH HHS R01GM122083
6 · The paper itself

Abstract

backgroundAging is a complex, heterogeneous process that has multiple causes. Knowledge on genomic, epigenomic and transcriptomic changes during the aging process shed light on understanding the aging mechanism. A recent breakthrough in biotechnology, single cell RNAseq, is revolutionizing aging study by providing gene expression profile of the entire transcriptome of individual cells. Many interesting information could be inferred from this new type of data with the help of novel computational methods.

resultsIn this manuscript a novel statistical method, penalized Latent Dirichlet Allocation (pLDA), is applied to an aging mouse blood scRNA-seq data set. A pipeline is built for cell type and aging prediction. The sequence of models in the pipeline take scRNA-seq expression counts as input, preprocess the data using pLDA and predict the cell type and aging status.

conclusionspLDA learns a dimension reduced representation of the expression profile. This representation allows identification of cell types and has predictability of the age of cells.

Indexed as

AgingAlgorithmsAnimalsBlood CellsComputational BiologyGene Expression ProfilingMiceSingle-Cell AnalysisTranscriptomeAgingBlood cellsPenalized LDASingle cell RNA seq

Identifiers

PMID39294566
PMCPMC11409595

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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.