On the public citation graph

Accurate denoising of single-cell RNA-Seq data using unbiased principal component analysis

0 signed · 61 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/1873-3468.12684openalex-snapshot

Computational approaches for interpreting sc RNA ‐seq data

inferredcites →https://doi.org/10.1007/s10479-011-0841-3openalex-snapshot

Multiobjective evolutionary algorithms to identify highly autocorrelated areas: the case of spatial distribution in financially compromised farms

inferredcites →https://doi.org/10.1016/j.cell.2015.04.044openalex-snapshot

Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells

inferredcites →https://doi.org/10.1016/j.cell.2018.05.061openalex-snapshot

Recovering Gene Interactions from Single-Cell Data Using Data Diffusion

inferredcites →https://doi.org/10.1016/j.molcel.2017.01.023openalex-snapshot

Comparative Analysis of Single-Cell RNA Sequencing Methods

inferredcites →https://doi.org/10.1016/j.molmed.2017.04.006openalex-snapshot

Single-Cell RNA Sequencing: Unraveling the Brain One Cell at a Time

inferredcites →https://doi.org/10.1016/j.tig.2018.07.007openalex-snapshot

Co-expression in Single-Cell Analysis: Saving Grace or Original Sin?

inferredcites →https://doi.org/10.1038/nbt.4314openalex-snapshot

Dimensionality reduction for visualizing single-cell data using UMAP

inferredcites →https://doi.org/10.1038/nmeth.2930openalex-snapshot

Validation of noise models for single-cell transcriptomics

inferredcites →https://doi.org/10.1038/nprot.2017.149openalex-snapshot

Exponential scaling of single-cell RNA-seq in the past decade

inferredcites →https://doi.org/10.1038/nrc.2017.58openalex-snapshot

Unravelling biology and shifting paradigms in cancer with single-cell sequencing

inferredcites →https://doi.org/10.1038/nri.2017.76openalex-snapshot

Single-cell RNA sequencing to explore immune cell heterogeneity

inferredcites →https://doi.org/10.1038/s41467-018-03405-7openalex-snapshot

An accurate and robust imputation method scImpute for single-cell RNA-seq data

inferredcites →https://doi.org/10.1038/s41467-018-07931-2openalex-snapshot

Single-cell RNA-seq denoising using a deep count autoencoder

inferredcites →https://doi.org/10.1038/s41587-019-0071-9openalex-snapshot

A comparison of single-cell trajectory inference methods

inferredcites →https://doi.org/10.1038/s41592-018-0033-zopenalex-snapshot

SAVER: gene expression recovery for single-cell RNA sequencing

inferredcites →https://doi.org/10.1038/s41598-018-34688-xopenalex-snapshot

AutoImpute: Autoencoder based imputation of single-cell RNA-seq data

inferredcites →https://doi.org/10.1073/pnas.1721085115openalex-snapshot

Gene expression distribution deconvolution in single-cell RNA sequencing

inferredcites →https://doi.org/10.1073/pnas.95.25.14863openalex-snapshot

Cluster analysis and display of genome-wide expression patterns

inferredcites →https://doi.org/10.1101/217737openalex-snapshot

K-nearest neighbor smoothing for high-throughput single-cell RNA-Seq data

inferredcites →https://doi.org/10.1101/397588openalex-snapshot

Zero-preserving imputation of scRNA-seq data using low-rank approximation

inferredcites →https://doi.org/10.1101/632216openalex-snapshot

Systematic comparative analysis of single cell RNA-sequencing methods

inferredcites →https://doi.org/10.1101/653907openalex-snapshot

Deep learning does not outperform classical machine learning for cell-type annotation

inferredcites →https://doi.org/10.1101/gr.190595.115openalex-snapshot

Defining cell types and states with single-cell genomics

inferredcites →https://doi.org/10.1126/science.aat1699openalex-snapshot

Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors

inferredcites →https://doi.org/10.1186/s12859-018-2226-yopenalex-snapshot

DrImpute: imputing dropout events in single cell RNA sequencing data

inferredcites →https://doi.org/10.1186/s13059-016-0938-8openalex-snapshot

CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq

inferredcites →https://doi.org/10.12688/f1000research.15666.2openalex-snapshot

A systematic performance evaluation of clustering methods for single-cell RNA-seq data

inferredcites →https://doi.org/10.12688/f1000research.16613.1openalex-snapshot

False signals induced by single-cell imputation

inferredcites →https://doi.org/10.12688/f1000research.16613.2openalex-snapshot

False signals induced by single-cell imputation

inferredcites →https://doi.org/10.15252/msb.20178046openalex-snapshot

Using single‐cell genomics to understand developmental processes and cell fate decisions

inferredcites →https://doi.org/10.48550/arxiv.1802.03426openalex-snapshot

UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/s00439-020-02116-8openalex-snapshot

Evaluating genetic causes of azoospermia: What can we learn from a complex cellular structure and single-cell transcriptomics of the human testis?

inferred← citeshttps://doi.org/10.1038/s41419-020-03356-2openalex-snapshot

PU.1 drives specification of pluripotent stem cell-derived endothelial cells to LSEC-like cells

inferred← citeshttps://doi.org/10.1073/pnas.2100293118openalex-snapshot

Detection of differentially abundant cell subpopulations in scRNA-seq data

inferred← citeshttps://doi.org/10.1093/bib/bbaa222openalex-snapshot

A review of computational strategies for denoising and imputation of single-cell transcriptomic data

inferred← citeshttps://doi.org/10.1093/bib/bbab085openalex-snapshot

WEDGE: imputation of gene expression values from single-cell RNA-seq datasets using biased matrix decomposition

inferred← citeshttps://doi.org/10.1093/bib/bbad384openalex-snapshot

scMultiGAN: cell-specific imputation for single-cell transcriptomes with multiple deep generative adversarial networks

inferred← citeshttps://doi.org/10.1093/gigascience/giad098openalex-snapshot

Imputation method for single-cell RNA-seq data using neural topic model

inferred← citeshttps://doi.org/10.1093/nar/gkaa506openalex-snapshot

scIGANs: single-cell RNA-seq imputation using generative adversarial networks

inferred← citeshttps://doi.org/10.1101/2020.01.20.913384openalex-snapshot

scIGANs: single-cell RNA-seq imputation using generative adversarial networks

inferred← citeshttps://doi.org/10.1101/2020.06.08.140673openalex-snapshot

Monet: An open-source Python package for analyzing and integrating scRNA-Seq data using PCA-based latent spaces

inferred← citeshttps://doi.org/10.1101/2021.04.02.438193openalex-snapshot

Benchmarking scRNA-seq imputation tools with respect to network inference highlights deficits in performance at high levels of sparsity

inferred← citeshttps://doi.org/10.1101/2022.06.11.495771openalex-snapshot

Monod : model-based discovery and integration through fitting stochastic transcriptional dynamics to single-cell sequencing data

inferred← citeshttps://doi.org/10.1101/689851openalex-snapshot

A novel metric reveals previously unrecognized distortion in the analysis of scRNA-seq data

inferred← citeshttps://doi.org/10.1101/711929openalex-snapshot

Detection of differentially abundant cell subpopulations discriminates biological states in scRNA-seq data

inferred← citeshttps://doi.org/10.1101/770388openalex-snapshot

Straightforward clustering of single-cell RNA-Seq data with t-SNE and DBSCAN

inferred← citeshttps://doi.org/10.1101/864488openalex-snapshot

WEDGE: imputation of gene expression values from single-cell RNA-seq datasets using biased matrix decomposition

inferred← citeshttps://doi.org/10.1109/bigdata55660.2022.10020732openalex-snapshot

Preserving Cluster Features in Imputing High Dimensional Data with Extensive Missing Rate

inferred← citeshttps://doi.org/10.1109/cscloud-edgecom58631.2023.00059openalex-snapshot

scIAMC:Single-Cell Imputation via adaptive matrix completion

inferred← citeshttps://doi.org/10.1109/tcbb.2024.3404013openalex-snapshot

Collaborative Structure-Preserved Missing Data Imputation for Single-Cell RNA-Seq Clustering

inferred← citeshttps://doi.org/10.1126/sciimmunol.abd5778openalex-snapshot

Contribution of resident and circulating precursors to tumor-infiltrating CD8 + T cell populations in lung cancer

inferred← citeshttps://doi.org/10.1186/s12859-022-04967-6openalex-snapshot

Combining denoising of RNA-seq data and flux balance analysis for cluster analysis of single cells

inferred← citeshttps://doi.org/10.1186/s12859-025-06138-9openalex-snapshot

Scmaskgan: masked multi-scale CNN and attention-enhanced GAN for scRNA-seq dropout imputation

inferred← citeshttps://doi.org/10.1186/s13059-020-1926-6openalex-snapshot

Eleven grand challenges in single-cell data science

inferred← citeshttps://doi.org/10.15252/msb.20199005openalex-snapshot

Combinatorial prediction of marker panels from single‐cell transcriptomic data

inferred← citeshttps://doi.org/10.26508/lsa.202201591openalex-snapshot

Resolution of the curse of dimensionality in single-cell RNA sequencing data analysis

inferred← citeshttps://doi.org/10.3389/fgene.2021.655536openalex-snapshot

Machine Intelligence in Single-Cell Data Analysis: Advances and New Challenges

inferred← citeshttps://doi.org/10.3390/app122010684openalex-snapshot

Imputation Methods for scRNA Sequencing Data

inferred← citeshttps://doi.org/10.3410/f.736811651.793568845openalex-snapshot

Faculty Opinions recommendation of Combinatorial prediction of marker panels from single-cell transcriptomic data.

inferred← citeshttps://doi.org/10.6084/m9.figshare.21397935openalex-snapshot

Additional file 1 of Combining denoising of RNA-seq data and flux balance analysis for cluster analysis of single cells

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.