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References from The impact of dropouts in scRNAseq dense neighborhood analysis. Local targets link to admitted publications; unresolved targets remain external evidence.
A benchmark study of simulation methods for single-cell rna sequencing data
10.1038/s41467-021-27130-w · 2021 · External reference
Stability of scrna-seq analysis workflows is susceptible to preprocessing and is mitigated by regularized or supervised approaches
10.1177/11769343221123050 · 2022 · External reference
10.1093/nar/gkad1021
10.1093/nar/gkad1021 · External reference
Drimpute: imputing dropout events in single cell rna sequencing data
10.1186/s12859-018-2226-y · 2018 · External reference
10.1371/journal.pcbi.1012224
10.1371/journal.pcbi.1012224 · External reference
Unresolved reference
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10.1038/nbt.4091
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10.1038/s41587-023-01767-y
10.1038/s41587-023-01767-y · External reference
10.1093/biostatistics/kxx053
10.1093/biostatistics/kxx053 · External reference
10.1007/bf01908075
10.1007/bf01908075 · External reference
10.1038/nmeth.2967
10.1038/nmeth.2967 · External reference
Characterizing noise structure in single-cell rna-seq distinguishes genuine from technical stochastic allelic expression
10.1038/ncomms9687 · 2015 · External reference
Benchmark and parameter sensitivity analysis of single-cell rna sequencing clustering methods
10.3389/fgene.2019.01253 · 2019 · External reference
Eleven grand challenges in single-cell data science
10.1186/s13059-020-1926-6 · 2020 · External reference
10.1038/s41592-020-0905-x
10.1038/s41592-020-0905-x · External reference
10.1186/s13059-017-1188-0
10.1186/s13059-017-1188-0 · External reference
Shared differential clustering across single-cell rna sequencing datasets with the hierarchical Dirichlet process
2024 · External reference
Expression atlas update: from tissues to single cells
2020 · External reference
Network analysis of microarray data
10.1007/978-1-0716-1839-4_11 · 2022 · External reference
Scikit-learn: machine learning in Python
2011 · External reference
Identification of cell types from single cell data using stable clustering
10.1038/s41598-020-66848-3 · 2020 · External reference
Human protein-coding genes and gene feature statistics in 2019
10.1186/s13104-019-4343-8 · 2019 · External reference
10.1038/s41593-020-00789-y
10.1038/s41593-020-00789-y · External reference
Embracing the dropouts in single-cell rna-seq analysis
10.1038/s41467-020-14976-9 · 2020 · External reference
10.1016/j.csbj.2020.09.014
10.1016/j.csbj.2020.09.014 · External reference
Flexible experimental designs for valid single-cell rna-sequencing experiments allowing batch effects correction
10.1038/s41467-020-16905-2 · 2020 · External reference
10.1038/s41592-019-0425-8
10.1038/s41592-019-0425-8 · External reference
10.1038/s41598-019-41695-z
10.1038/s41598-019-41695-z · External reference
10.1016/j.cell.2018.05.061
10.1016/j.cell.2018.05.061 · External reference
10.1186/s13059-017-1382-0
10.1186/s13059-017-1382-0 · External reference
10.1038/s41467-021-22801-0
10.1038/s41467-021-22801-0 · External reference
A comparison for dimensionality reduction methods of single-cell rna-seq data
10.3389/fgene.2021.646936 · 2021 · External reference
Dimensionality reduction by umap reinforces sample heterogeneity analysis in bulk transcriptomic data
10.1016/j.celrep.2021.109442 · 2021 · External reference
Clustering malignant cell states using universally variable genes
10.1093/bib/bbad460 · 2024 · External reference
Benchmarking clustering algorithms on estimating the number of cell types from single-cell rna-sequencing data
10.1186/s13059-022-02622-0 · 2022 · External reference
10.1261/rna.078965.121
10.1261/rna.078965.121 · External reference
Simulating multiple faceted variability in single cell rna sequencing
10.1038/s41467-019-10500-w · 2019 · External reference
Modeling zero inflation is not necessary for spatial transcriptomics
10.1186/s13059-022-02684-0 · 2022 · External reference
10.1164/rccm.202001-0179le
10.1164/rccm.202001-0179le · External reference
10.1016/j.csbj.2023.09.035 · Admitted local publication
