ReviewThe international journal of biochemistry & cell biology2017
Delineating biological and technical variance in single cell expression data.
Review in The international journal of biochemistry & cell biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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
13 citing papers in PubMed.
- Single-cell transcriptomics and mouse model phenotyping for biomarker screen of peripheral blood in Huntington's disease.Scientific reports · 2026Article
- A novel gene expression stability metric to unveil homeostasis and regulation.Genome biology · 2025Article
- Single-Cell Transcriptomics Sheds Light on Tumor Evolution: Perspectives from City of Hope's Clinical Trial Teams.Journal of clinical medicine · 2024Article
- Single-cell RNA sequencing in silent corticotroph tumors confirms impaired POMC processing and provides new insights into their invasive behavior.European journal of endocrinology · 2022Article
- Robust Transcriptional Profiling and Identification of Differentially Expressed Genes With Low Input RNA Sequencing of Adult Hippocampal Neural Stem and Progenitor Populations.Frontiers in molecular neuroscience · 2022Article
- A top-down measure of gene-to-gene coordination for analyzing cell-to-cell variability.Scientific reports · 2021Article
- Integrated Metabolomics and Transcriptomics Using an Optimised Dual Extraction Process to Study Human Brain Cancer Cells and Tissues.Metabolites · 2021Article
- Article
- scBFA: modeling detection patterns to mitigate technical noise in large-scale single-cell genomics data.Genome biology · 2019Article
- Defining housekeeping genes suitable for RNA-seq analysis of the human allograft kidney biopsy tissue.BMC medical genomics · 2019Article
- Chasing Mavericks: The quest for defining developmental waves of hematopoiesis.Current topics in developmental biology · 2019Review
- Microparticle Delivery of Protein Markers for Single-Cell Western Blotting from Microwells.Small (Weinheim an der Bergstrasse, Germany) · 2018Article
- Overview of single-cell RNA sequencing analysis and its application to spermatogenesis research.Reproductive medicine and biologyReview
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
Single cell transcriptomics is becoming a common technique to unravel new biological phenomena whose functional significance can only be understood in the light of differences in gene expression between single cells. The technology is still in its early days and therefore suffers from many technical challenges. This review discusses the continuous effort to identify and systematically characterise various sources of technical variability in single cell expression data and the need to further develop experimental and computational tools and resources to help deal with it.
Indexed as
Identifiers
What Socratic 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 Socratic graph.