ReviewSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2023
[Imputation method for dropout in single-cell transcriptome data].
Review in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- CPARI: a novel approach combining cell partitioning with absolute and relative imputation to address dropout in single-cell RNA-seq data.Briefings in bioinformatics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell transcriptome sequencing (scRNA-seq) can resolve the expression characteristics of cells in tissues with single-cell precision, enabling researchers to quantify cellular heterogeneity within populations with higher resolution, revealing potentially heterogeneous cell populations and the dynamics of complex tissues. However, the presence of a large number of technical zeros in scRNA-seq data will have an impact on downstream analysis of cell clustering, differential genes, cell annotation, and pseudotime, hindering the discovery of meaningful biological signals. The main idea to solve this problem is to make use of the potential correlation between cells and genes, and to impute the technical zeros through the observed data. Based on this, this paper reviewed the basic methods of imputing technical zeros in the scRNA-seq data and discussed the advantages and disadvantages of the existing methods. Finally, recommendations and perspectives on the use and development of the method were provided.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.