ArticlePatterns (New York, N.Y.)2022
Effect of imputation on gene network reconstruction from single-cell RNA-seq data.
Article in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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
18 citing papers in PubMed.
- Single cell transcriptomic analysis by deep learning identifies novel microglial transcription regulations in Alzheimer's Disease.AI in neuroscience · 2026Article
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- AUGMENTED DOUBLY ROBUST POST-IMPUTATION INFERENCE FOR PROTEOMIC DATA.The annals of applied statistics · 2025Article
- AUGMENTED DOUBLY ROBUST POST-IMPUTATION INFERENCE FOR PROTEOMIC DATA.bioRxiv : the preprint server for biology · 2025Article
- SIGRN: Inferring Gene Regulatory Network with Soft Introspective Variational Autoencoders.International journal of molecular sciences · 2024Article
- Scalable identification of lineage-specific gene regulatory networks from metacells with NetID.Genome biology · 2024Article
- Mosaic integration and knowledge transfer of single-cell multimodal data with MIDAS.Nature biotechnology · 2024Article
- COFFEE: consensus single cell-type specific inference for gene regulatory networks.Briefings in bioinformatics · 2024Article
- Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights.BioEssays : news and reviews in molecular, cellular and developmental biology · 2024Review
- COFFEE: Consensus Single Cell-Type Specific Inference for Gene Regulatory Networks.bioRxiv : the preprint server for biology · 2024Article
- EAGS: efficient and adaptive Gaussian smoothing applied to high-resolved spatial transcriptomics.GigaScience · 2024Article
- Evaluating Proteomics Imputation Methods with Improved Criteria.Journal of proteome research · 2023Article
- Gene regulatory network inference in the era of single-cell multi-omics.Nature reviews. Genetics · 2023Review
- Computational approaches to understand transcription regulation in development.Biochemical Society transactions · 2023Review
- scHumanNet: a single-cell network analysis platform for the study of cell-type specificity of disease genes.Nucleic acids research · 2023Article
- Imputation of single-cell transcriptome data enables the reconstruction of networks predictive of breast cancer metastasis.Computational and structural biotechnology journal · 2023Article
- In search of a Drosophila core cellular network with single-cell transcriptome data.G3 (Bethesda, Md.) · 2022Article
- Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation.BMC bioinformatics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite the advances in single-cell transcriptomics, the reconstruction of gene regulatory networks remains challenging. Both the large amount of zero counts in experimental data and the lack of a consensus preprocessing pipeline for single-cell RNA sequencing (scRNA-seq) data make it hard to infer networks. Imputation can be applied in order to enhance gene-gene correlations and facilitate downstream analysis. However, it is unclear what consequences imputation methods have on the reconstruction of gene regulatory networks. To study this, we evaluate the differences on the performance and structure of reconstructed networks before and after imputation in single-cell data. We observe an inflation of gene-gene correlations that affects the predicted network structures and may decrease the performance of network reconstruction in general. However, within the modest limits of achievable results, we also make a recommendation as to an advisable combination of algorithms while warning against the indiscriminate use of imputation before network reconstruction in general.
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.