BioinformaticsGenomicsPeer-reviewed journal article
Nebulosa recovers single cell gene expression signals by kernel density estimation
Abstract
SummaryData sparsity in single-cell experiments prevents an accurate assessment of gene expression when visualised in a low-dimensional space. Here, we introduce Nebulosa, an R package that uses weighted kernel density estimation to recover signals lost through drop-out or low expression. Availability and implementationNebulosa can be easily installed from www.github.com/powellgenomicslab/Nebulosa
- Citations
- 381
- FWCI
- 16.24
- Field percentile
- top 1%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Mapping the gene space at single-cell resolution with gene signal pattern analysis
Aarthi Venkat, Sam Leone, … Smita Krishnaswamy·Nature Computational SciencePeer-reviewed·Dec 20, 2024
- singleCellHaystack: A clustering-independent method for finding differentially expressed genes in single-cell transcriptome data
Alexis Vandenbon, Diego Díez·Nature CommunicationsPeer-reviewed·Aug 28, 2020
- Visualizing and interpreting single-cell gene expression datasets with Similarity Weighted Nonnegative Embedding
Yan Wu, Pablo Tamayo, Kun Zhang·Cell SystemsPeer-reviewed·Dec 1, 2018
- destiny – diffusion maps for large-scale single-cell data in R
Philipp Angerer, Laleh Haghverdi, … Florian Buettner·bioRxiv·Oct 9, 2015
- Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data
Andreas Tjärnberg, Omar Mahmood, … Richard Bonneau·PLoS Computational BiologyPeer-reviewed·Jan 7, 2021
- G2S3: a gene graph-based imputation method for single-cell RNA sequencing data
Weimiao Wu, Qile Dai, … Zuoheng Wang·PLoS Computational BiologyPeer-reviewed·May 18, 2021