ArticleThe annals of applied statistics2018
A UNIFIED STATISTICAL FRAMEWORK FOR SINGLE CELL AND BULK RNA SEQUENCING DATA.
Article in The annals of applied statistics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 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
40 citing papers in PubMed.
- Bayesian inference of haematopoietic stem/progenitor cell differentiation phenotypic manifolds and their bifurcation points using Gaussian processes and Gibbs sampling.Scientific reports · 2026Article
- Impact of Data Quality on Deep Learning Prediction of Spatial Transcriptomics from Histology Images.bioRxiv : the preprint server for biology · 2025Article
- EMixed: Probabilistic Multi-Omics Cellular Deconvolution of Bulk Omics Data.Journal of data science : JDS · 2025Article
- Learning to quantify uncertainty in off-target activity for CRISPR guide RNAs.Nucleic acids research · 2024Article
- Assessing transcriptomic heterogeneity of single-cell RNASeq data by bulk-level gene expression data.BMC bioinformatics · 2024Article
- CTEC: a cross-tabulation ensemble clustering approach for single-cell RNA sequencing data analysis.Bioinformatics (Oxford, England) · 2024Article
- Article
- Fine-grained cell-type specific association studies with human bulk brain data using a large single-nucleus RNA sequencing based reference panel.Scientific reports · 2023Article
- The Poisson distribution model fits UMI-based single-cell RNA-sequencing data.BMC bioinformatics · 2023Article
- DURIAN: an integrative deconvolution and imputation method for robust signaling analysis of single-cell transcriptomics data.Briefings in bioinformatics · 2022Article
- HiCImpute: A Bayesian hierarchical model for identifying structural zeros and enhancing single cell Hi-C data.PLoS computational biology · 2022Article
- Correlation Imputation for Single-Cell RNA-seq.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- Statistics or biology: the zero-inflation controversy about scRNA-seq data.Genome biology · 2022Review
- An Overview of Algorithms and Associated Applications for Single Cell RNA-Seq Data Imputation.Current genomics · 2021Review
- Inference of tissue relative proportions of the breast epithelial cell types luminal progenitor, basal, and luminal mature.Scientific reports · 2021Article
- Consensus clustering of single-cell RNA-seq data by enhancing network affinity.Briefings in bioinformatics · 2021Article
- Bayesian estimation of cell type-specific gene expression with prior derived from single-cell data.Genome research · 2021Article
- iSMNN: batch effect correction for single-cell RNA-seq data via iterative supervised mutual nearest neighbor refinement.Briefings in bioinformatics · 2021Article
- Separating measurement and expression models clarifies confusion in single-cell RNA sequencing analysis.Nature genetics · 2021Review
- Single-cell mapper (scMappR): using scRNA-seq to infer the cell-type specificities of differentially expressed genes.NAR genomics and bioinformatics · 2021Article
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
Recent advances in technology have enabled the measurement of RNA levels for individual cells. Compared to traditional tissue-level bulk RNA-seq data, single cell sequencing yields valuable insights about gene expression profiles for different cell types, which is potentially critical for understanding many complex human diseases. However, developing quantitative tools for such data remains challenging because of high levels of technical noise, especially the "dropout" events. A "dropout" happens when the RNA for a gene fails to be amplified prior to sequencing, producing a "false" zero in the observed data. In this paper, we propose a Unified RNA-Sequencing Model (URSM) for both single cell and bulk RNA-seq data, formulated as a hierarchical model. URSM borrows the strength from both data sources and carefully models the dropouts in single cell data, leading to a more accurate estimation of cell type specific gene expression profile. In addition, URSM naturally provides inference on the dropout entries in single cell data that need to be imputed for downstream analyses, as well as the mixing proportions of different cell types in bulk samples. We adopt an empirical Bayes' approach, where parameters are estimated using the EM algorithm and approximate inference is obtained by Gibbs sampling. Simulation results illustrate that URSM outperforms existing approaches both in correcting for dropouts in single cell data, as well as in deconvolving bulk samples. We also demonstrate an application to gene expression data on fetal brains, where our model successfully imputes the dropout genes and reveals cell type specific expression patterns.
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.