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Single-cell RNA-seq denoising using a deep count autoencoder
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- Zhaoxiang Cai & Sofia Apolinário & Ana R. Baião & Clare Pacini & Miguel D. Sousa & Susana Vinga & Roger R. Reddel & Phillip J. Robinson & Mathew J. Garnett & Qing Zhong & Emanuel Gonçalves, 2024. "Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
- Hui Li & Cory R. Brouwer & Weijun Luo, 2022. "A universal deep neural network for in-depth cleaning of single-cell RNA-Seq data," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
- Songming Tang & Xuejian Cui & Rongxiang Wang & Sijie Li & Siyu Li & Xin Huang & Shengquan Chen, 2024. "scCASE: accurate and interpretable enhancement for single-cell chromatin accessibility sequencing data," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
- Lulu Shang & Xiang Zhou, 2022. "Spatially aware dimension reduction for spatial transcriptomics," Nature Communications, Nature, vol. 13(1), pages 1-22, December.
- Yuge Wang & Hongyu Zhao, 2022. "Non-linear archetypal analysis of single-cell RNA-seq data by deep autoencoders," PLOS Computational Biology, Public Library of Science, vol. 18(4), pages 1-31, April.
- Ethan Bahl & Snehajyoti Chatterjee & Utsav Mukherjee & Muhammad Elsadany & Yann Vanrobaeys & Li-Chun Lin & Miriam McDonough & Jon Resch & K. Peter Giese & Ted Abel & Jacob J. Michaelson, 2024. "Using deep learning to quantify neuronal activation from single-cell and spatial transcriptomic data," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
- Ian Covert & Rohan Gala & Tim Wang & Karel Svoboda & Uygar Sümbül & Su-In Lee, 2023. "Predictive and robust gene selection for spatial transcriptomics," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
- Andrea Riba & Attila Oravecz & Matej Durik & Sara Jiménez & Violaine Alunni & Marie Cerciat & Matthieu Jung & Céline Keime & William M. Keyes & Nacho Molina, 2022. "Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
- Joaquim Fernando Pinto da Costa & Manuel Cabral, 2022. "Statistical Methods with Applications in Data Mining: A Review of the Most Recent Works," Mathematics, MDPI, vol. 10(6), pages 1-22, March.
- Xinyi Zhang & Xiao Wang & G. V. Shivashankar & Caroline Uhler, 2022. "Graph-based autoencoder integrates spatial transcriptomics with chromatin images and identifies joint biomarkers for Alzheimer’s disease," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
- Jinhee Park & Hyerin Kim & Jaekwang Kim & Mookyung Cheon, 2020. "A practical application of generative adversarial networks for RNA-seq analysis to predict the molecular progress of Alzheimer's disease," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-20, July.
- Jing Qi & Yang Zhou & Zicen Zhao & Shuilin Jin, 2021. "SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data," PLOS Computational Biology, Public Library of Science, vol. 17(6), pages 1-20, June.
- Zhijian Li & Christoph Kuppe & Susanne Ziegler & Mingbo Cheng & Nazanin Kabgani & Sylvia Menzel & Martin Zenke & Rafael Kramann & Ivan G. Costa, 2021. "Chromatin-accessibility estimation from single-cell ATAC-seq data with scOpen," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
- Viktoria Schuster & Emma Dann & Anders Krogh & Sarah A. Teichmann, 2024. "multiDGD: A versatile deep generative model for multi-omics data," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
- Ziqi Zhang & Xinye Zhao & Mehak Bindra & Peng Qiu & Xiuwei Zhang, 2024. "scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
- Zhenchao Tang & Guanxing Chen & Shouzhi Chen & Jianhua Yao & Linlin You & Calvin Yu-Chian Chen, 2024. "Modal-nexus auto-encoder for multi-modality cellular data integration and imputation," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
- Nicolae Sapoval & Amirali Aghazadeh & Michael G. Nute & Dinler A. Antunes & Advait Balaji & Richard Baraniuk & C. J. Barberan & Ruth Dannenfelser & Chen Dun & Mohammadamin Edrisi & R. A. Leo Elworth &, 2022. "Current progress and open challenges for applying deep learning across the biosciences," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
- Lucy Xia & Christy Lee & Jingyi Jessica Li, 2024. "Statistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
- Xiang Lin & Tian Tian & Zhi Wei & Hakon Hakonarson, 2022. "Clustering of single-cell multi-omics data with a multimodal deep learning method," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
- Yasa Baig & Helena R. Ma & Helen Xu & Lingchong You, 2023. "Autoencoder neural networks enable low dimensional structure analyses of microbial growth dynamics," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
- Xin Tang & Jiawei Zhang & Yichun He & Xinhe Zhang & Zuwan Lin & Sebastian Partarrieu & Emma Bou Hanna & Zhaolin Ren & Hao Shen & Yuhong Yang & Xiao Wang & Na Li & Jie Ding & Jia Liu, 2023. "Explainable multi-task learning for multi-modality biological data analysis," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
- Md Tauhidul Islam & Jen-Yeu Wang & Hongyi Ren & Xiaomeng Li & Masoud Badiei Khuzani & Shengtian Sang & Lequan Yu & Liyue Shen & Wei Zhao & Lei Xing, 2022. "Leveraging data-driven self-consistency for high-fidelity gene expression recovery," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
- Vidhya M. Ravi & Nicolas Neidert & Paulina Will & Kevin Joseph & Julian P. Maier & Jan Kückelhaus & Lea Vollmer & Jonathan M. Goeldner & Simon P. Behringer & Florian Scherer & Melanie Boerries & Marie, 2022. "T-cell dysfunction in the glioblastoma microenvironment is mediated by myeloid cells releasing interleukin-10," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
- Zhuohan Yu & Yanchi Su & Yifu Lu & Yuning Yang & Fuzhou Wang & Shixiong Zhang & Yi Chang & Ka-Chun Wong & Xiangtao Li, 2023. "Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
- George C. Linderman & Jun Zhao & Manolis Roulis & Piotr Bielecki & Richard A. Flavell & Boaz Nadler & Yuval Kluger, 2022. "Zero-preserving imputation of single-cell RNA-seq data," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
- Lingfei Wang, 2021. "Single-cell normalization and association testing unifying CRISPR screen and gene co-expression analyses with Normalisr," Nature Communications, Nature, vol. 12(1), pages 1-13, December.