ArticleBriefings in bioinformatics2024
Detecting expressed genes in cell populations at the single-cell level with scGeneXpress.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Determining whether genes are expressed or not remains a challenge in single-cell RNAseq experiments due to their different expression spectra, which are influenced by genetics, the microenvironment and gene length. Current approaches for addressing this issue fail to provide a comprehensive landscape of expressed genes, since they neglect the inherent differences in the expression ranges and distributions of genes. Here, we present scGeneXpress, a method for detecting expressed genes in cell populations of single-cell RNAseq samples based on gene-specific reference distributions. We demonstrate that scGeneXpress accurately detects expressed cell markers and identity genes in 34 human and mouse tissues and can be employed to improve differential expression analysis of single-cell RNAseq data.
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.