Single-cell RNA-seq: advances and future challenges

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Sequencing technologies – the next generation

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Landscape of transcription in human cells

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Tissue microarrays for high-throughput molecular profiling of tumor specimens

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mRNA-Seq whole-transcriptome analysis of a single cell

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scAnalyzeR: A Comprehensive Software Package With Graphical User Interface for Single-Cell RNA Sequencing Analysis and its Application on Liver Cancer

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Understanding Listeria monocytogenes Biofilms: Perspectives into Mechanisms of Adaptation and Regulation Under Stress Conditions

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Millefy: visualizing cell-to-cell heterogeneity in read coverage of single-cell RNA sequencing datasetsPreprint

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Single-Cell RNA-Seq Analysis of Diabetic Wound Macrophages in STZ-Induced MicePreprint

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Next Generation Sequencing: Transcriptomics

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A Variational Inference Approach to Single-Cell Gene Regulatory Network Inference using Probabilistic Matrix FactorizationPreprint

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Deciphering deep-sea chemosynthetic symbiosis by single-nucleus RNA-sequencingPreprint

Wang, H., He, K., Zhang, H., Zhang, Q., Cao, L., Li, J., Zhong, Z., Chen, H., Zhou, L., Lian, C., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.29.542712 · Cited by 1

Exploiting fluctuations in gene expression to detect causal interactions between genesPreprint

Joly-Smith, E., Talpur, M. M., Allard, P., Papazotos, F., Potvin-Trottier, L., & Hilfinger, A. (2024) · eLife · doi:10.7554/elife.92497.1 · Cited by 1

New insight on DNA isolation from single automated recovered Ceratocystis platani conidiaPreprint

Luchi, N., Pinzani, P., Salvianti, F., Mancini, I., & Santini, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.20.594938 · Cited by 1

The curses of performing differential expression analysis using single-cell dataPreprint

Wu, C.-H., Zhou, X., & Chen, M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.28.596315 · Cited by 1

Defective interferon signaling in the circulating monocytes of type 2 diabetic micePreprint

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A Graph Curvature-Based Pipeline for Discovering Immune Checkpoint Response BiomarkersPreprint

Bannon, J. J., Cantor, C. R., & Mishra, B. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.04.611306 · Cited by 1

Analyzing cell-type-specific isoform expression using IsoDiffR and long-read single-cell RNA sequencingPreprint

Shi, Z., Ye, Q., Zheng, Z., Zheng, Y., & Dai, Q. (2025) · Research Square · doi:10.21203/rs.3.rs-5695957/v1 · Cited by 1

Exploiting fluctuations in gene expression to detect causal interactions between genesPreprint

Joly-Smith, E., Talpur, M. M., Allard, P., Papazotos, F., Potvin-Trottier, L., & Hilfinger, A. (2025) · eLife · doi:10.7554/elife.92497.2 · Cited by 1

scStudio: A User-Friendly Web Application Empowering Non-Computational Users with Intuitive scRNA-seq Data AnalysisPreprint

Bica, M., Serre, K., & Barbosa‐Morais, N. L. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.17.649161 · Cited by 1

Mycobacterial resuscitation promoting factors: roles and mechanisms in infected macrophages

Iakobachvili, N. (2015) · Leicester Research Archive (University of Leicester)

Log-Sum Heuristic Recovery for Automated Isoform Discovery and Abundance Estimation from RNA-Seq data

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Computational Biology-Driven Genomic and Epigenomic Delineation of Acute Myeloid Leukemia

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Understanding Cellular Specialization Through Functional Genomics

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High Throughput Screening of Gene Expression for the Investigation of Multifactorial Dermatological Disorders

Manczinger, M. (2016) · SZTE Repository of Dissertations (University of Szeged) · doi:10.14232/phd.2857

Analysis of Long Noncoding RNAs in RNA-Seq Data

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An Overview And Comparison of Tools for RNA‐Seq Assembly

Loganantharaj, R., & Randall, T. A. (2016) · doi:10.1002/9781119272182.ch12

Systems Biology Approaches for Elucidation of the Transcriptional Regulation of Pulmonary Maturation

Xu, Y. J., & Whitsett, J. A. (2016) · Translational bioinformatics · doi:10.1007/978-981-10-1104-7_19

Estimating isoform abundance by Particle Swarm Optimization

Zhao, J., & Feng, H. (2016) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2016.7822512

Applications of single-cell sequencing for human lung cancer: the progress and the future perspective

Zhang, M., Lin, S., Xiao, W., Chen, D., Yang, D., & Zhang, Y. (2017) · AIMS Biophysics · doi:10.3934/biophy.2017.2.210

Single Cell Technologies

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Techniques for Genome-Wide Expression Analysis of Noncoding RNA

Onoguchi‐Mizutani, R., Yamada, T., & Akimitsu, N. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-805388-1.00011-0 · Cited by 1

FAMA: A Molecular Link between Stomata and Myrosin

Shirakawa, C., Ueda, H., & Shimada, T. (2016)

Illuminating subcellular consequences of membrane protein production in Lactococcus lactis

van Gijtenbeek, L. A. (2017) · University of Groningen research database (University of Groningen / Centre for Information Technology)

Characterization of HIPSTR highlights the heterogeneous expression pattern of lncRNAs in human embryos and stable cell lines

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Einzelzell-RNA-Sequenzierung beleuchtet den Infektionsprozess

Saliba, A., Westermann, A. J., & Vogel, J. (2017) · BIOspektrum · doi:10.1007/s12268-017-0836-y

Optimized Whole Transcriptome Profiling of Motor Axons

Saal-Bauernschubert, L., Briese, M., & Sendtner, M. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-7231-9_16

Future Directions and Challenges Involved in Cancer Noncoding RNomics

Saha, S. S., & Sengupta, S. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-811022-5.00028-0

Classification of Genetic Variants

Genuardi, M., Holinski-Feder, E., Laner, A., & Martins, A. (2018) · Springer eBooks · doi:10.1007/978-3-319-74259-5_18

Atomic Force Microscopy Tip-enhanced Laser Ablation

Cao, F. (2019) · doi:10.31390/gradschool_dissertations.4793

Computational Methods for Inferring Transcriptome Dynamics

Welch, J. D. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/yazw-zn17

The ex vivo and in vitro transcriptomic profiles of equine endometrium using an explant model to further study endometritis

Barros, M. D. (2019)

Using single-cell rna sequencing to study host-pathogen interactions

Mestre-Farràs, N. (2016)

Single-cell RNAseq Imputation Based on Matrix Completion with Side Information

Wang, T., & Nabavi, S. (2019) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm47256.2019.8983268

Cluster Analysis in R With Big Data Applications

Lamere, A. T. (2020) · Advances in computer and electrical engineering book series · doi:10.4018/978-1-7998-2768-9.ch004

In Silico Edgetic Profiling and Network Analysis of Human Genetic Variants, with an Application to Disease Module Detection

Cui, H. (2020) · Digital WPI

Classifying Drosophila Olfactory Projection Neuron Subtypes by Singlecell RNA SequencingPreprint

Li, H., Horns, F., Wu, B., Xie, Q., Li, J., Li, T., Luginbuhl, D. J., Quake, S. R., & Luo, L. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3155560

Model-based deep siamese autoencoder for clustering single cell rna-seq data

Meng, Z. (2020)

Pathway-based Lineage Analysis of Time-course Single-cell RNA Sequencing Data

Tran, T. N. (2019) · TSpace (University of Toronto)

Single-cell RNA sequencing reveals activated status of peripheral blood monocytes in tissue homeostasisPreprint

Lu, J., Luo, J., Guo, J., Zeng, J., & Zhang, X. (2021) · Research Square · doi:10.21203/rs.3.rs-416878/v1

Ribosomal profiling as a tool for studying translation in plants: main results, problems and future prospects

Afonnikov, D. A., Sinitsyna, O. I., Golubeva, T. S., Shmakov, N. A., & Kochetov, A. V. (2021) · Vavilov Journal of Genetics and Breeding · doi:10.18699/vj21.028

Multi-Dimensional Reduction Clustering of Complex Carbohydrates Reveal Tissue Metabolism, Heterogeneity and HistopathologyPreprint

Conroy, L. R., Allison, D. B., Sun, Q., Young, L. E. A., Hawkinson, T. R., Clarke, H. A., Ferreira, J. E., Hammonds, A. V., McDonald, R. J., Absher, K. J., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.14.448358

Transcriptomics: Next Generation Transcriptome

Johnson, N. V., & Wang, Z. (2017) · Wiley eBooks · doi:10.1002/9780470921920.edm149

A Link-Based Ensemble Cluster Approach for Identification of Cell Types

Lu, X., Gao, Y., Tang, D., & Yuan, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-84529-2_54

Computational analysis of alternative polyadenylation from standard RNA-seq and single-cell RNA-seq data

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Single-cell analysis of mammalian cardiogenesis elucidating an essential role of outflow tract progenitors

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Benchmarking imputation methods for network inference using a novel method of synthetic scRNA-seq data generation

Lasri, A., Shahrezaei, V., & Sturrock, M. (2021) · doi:10.1101/2021.10.13.464275

Understanding the Regulation of Transcription in Mental Illness

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Imputation in Scrna-seq Data Using Supervised Deep Generative Networks

Tang, J., Zou, J., Fan, M., Tian, Q., & Fan, S. (2021) · International Conference on Bioinformatics Research and Applications (ICBRA) · doi:10.1145/3487027.3487028

Cellular Similarity based Imputation for Single cell RNA Sequencing Data

Liu, C., Zhu, Y., & Zhang, H. (2021) · International Conference on Bioinformatics and Biomedical Technology (ICBBT) · doi:10.1145/3473258.3473269

Neuronal identity and maturation

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Introduction

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Faculty Opinions recommendation of Comprehensive functional genomic resource and integrative model for the human brain.

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Somatic Mutations

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Gene Regulatory Network Reconstruction Using Single-Cell RNA-Sequencing

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A Device for Isolation of Selected Single Adherent Cells

Dai, W.-Y., Guo, J.-B., Chen, X. X., & Luo, Z. (2022) · BioMed Research International · doi:10.1155/2022/4303586

Recent Dimensionality Reduction Techniques for High-Dimensional COVID-19 Data

Dallas, I. L., Vrahatis, A. G., Tasoulis, S. K., & Plagianakos, V. P. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20837-9_18

Cell Type-Specific Analysis for High-throughput Data

Li, Z., & Wu, H. (2022) · Springer handbooks of computational statistics · doi:10.1007/978-3-662-65902-1_14

scARMF: Association Rule Mining-based feature selection Framework for Single-Cell transcriptomics data

Seal, D. B., Das, V., & De, R. K. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995220

Integrated analyses of single-cell and bulk transcriptome reveal cell type-specific metastasis- and prognosis-related genes in breast cancerPreprint

Dong, J., & Li, C. (2023) · Research Square · doi:10.21203/rs.3.rs-2439125/v1

Posterior Inference of Differential Expression (DE) State and Evolutionary DistancePreprint

Gu, X. (2023) · Research Square · doi:10.21203/rs.3.rs-2694316/v1

Spatial Transcriptomics in Inflammation: Dissecting the Immune Response in 3D in Complex Tissues

Hu, K. H. (2023) · Progress in inflammation research · doi:10.1007/978-3-031-23661-7_8

Assessment of Total Oocyte Transcripts Representation through Single Ooplasm Biopsy in Bovine with High ReliabilityPreprint

Ambrizi, D. R., Nociti, R. P., De, T. H. C., Paixão, J. V. P., Therrien, J., Mattos, E. C., Ferraz, J. B. S., Chiaratti, M. R., Sangalli, J. R., da Silveira, J. C., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.24.538116

Single-Cell Analysis Approaches in Cartilage Diseases Diagnosis and Therapies

Ghorbaninejad, M., Farahi, S., Mirzaeian, F., Khodabandehloo, F., Hosseini, S., & Eslaminejad, M. B. (2023) · Springer eBooks · doi:10.1007/978-981-99-2452-3_4

Integrative Analysis of Gene Expression and Alternative Polyadenylation from Single-Cell RNA-seq Data

Xu, S., Kang, L., Bi, X., & Wu, X. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-7074-2_24

scNMF-Impute: imputation for single-cell RNA-seq data based on nonnegative matrix factorization

Wang, J., Zhang, N.-N., Shang, J., & Liu, J. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385409

Exploiting fluctuations in gene expression to detect causal interactions between genesPreprint

Joly-Smith, E., Talpur, M. M., Allard, P., Papazotos, F., Potvin-Trottier, L., & Hilfinger, A. (2024) · eLife · doi:10.7554/elife.92497

Cluster Analysis in R With Big Data Applications

Lamere, A. T. (2023) · IGI Global eBooks · doi:10.4018/979-8-3693-3026-5.ch022

Additional file 3 of scRNASequest: an ecosystem of scRNA-seq analysis, visualization, and publishing

Li, K., Sun, Y. H., Ouyang, Z., Negi, S., Gao, Z., Zhu, J. F., Wang, W., Chen, Y., Piya, S., Hu, W., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22735494

Additional file 1 of scRNASequest: an ecosystem of scRNA-seq analysis, visualization, and publishing

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Additional file 2 of scRNASequest: an ecosystem of scRNA-seq analysis, visualization, and publishing

Li, K., Sun, Y. H., Ouyang, Z., Negi, S., Gao, Z., Zhu, J., Wang, W., Chen, Y., Piya, S., Hu, W., et al. (2023) · Figshare · doi:10.6084/m9.figshare.22735491

Deciphering deep-sea chemosynthetic symbiosis by single-nucleus RNA-sequencingPreprint

Wang, H., He, K., Zhang, H., Zhang, Q., Cao, L., Li, J., Zhong, Z., Chen, H., Li, Z., Lian, C., et al. (2024) · eLife · doi:10.7554/elife.88294.2

Advancing ScRNA-Seq Data Integration via a Novel Gene Selection Method

Lazaros, K., Exarchos, T., Maglogiannis, I. G., Vlamos, P., & Vrahatis, A. G. (2024) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-63211-2_3

Reviewer #3 (Public Review): Deciphering deep-sea chemosynthetic symbiosis by single-nucleus RNA-sequencing

(2024) · eLife · doi:10.7554/elife.88294.3.sa1

Reviewer #2 (Public Review): Deciphering deep-sea chemosynthetic symbiosis by single-nucleus RNA-sequencing

(2024) · eLife · doi:10.7554/elife.88294.3.sa2

Author response: Deciphering deep-sea chemosynthetic symbiosis by single-nucleus RNA-sequencing

Wang, H., He, K., Zhang, H., Zhang, Q., Cao, L., Li, J., Zhong, Z., Chen, H., Zhou, L., Lian, C., et al. (2024) · eLife · doi:10.7554/elife.88294.3.sa0

eLife Assessment: Deciphering deep-sea chemosynthetic symbiosis by single-nucleus RNA-sequencing

Paz‐García, D. A. (2024) · eLife · doi:10.7554/elife.88294.3.sa4

Reviewer #1 (Public Review): Deciphering deep-sea chemosynthetic symbiosis by single-nucleus RNA-sequencing

(2024) · eLife · doi:10.7554/elife.88294.3.sa3

Deciphering deep-sea chemosynthetic symbiosis by single-nucleus RNA-sequencingPreprint

Wang, H., He, K., Zhang, H., Zhang, Q., Cao, L., Li, J., Zhong, Z., Chen, H., Zhou, L., Lian, C., et al. (2024) · eLife · doi:10.7554/elife.88294.3

CI-VAE for Single-Cell: Leveraging Generative-AI to Enhance Disease Understanding

Nabian, M., Eftekhari, Z., & Wong, C. W. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-66538-7_36

Creation of single App knock-in mouse models and of single MAPT knock-in models: Demonstration of Aβ-tau axis

Saido, T. C. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-28979-8.00023-1

A novel Bayesian model for assessing intratumor heterogeneity of tumor infiltrating leukocytes with multiregion gene expression sequencing

Peng, Y., Hubert, S. M., Futreal, P. A., Song, X., Zhang, J., Lee, J. J., Wistuba, I. I., Yuan, Y., Zhang, J., & Li, Z. (2024) · The Annals of Applied Statistics · doi:10.1214/23-aoas1862

RNA Sequencing Approach for Genome-Wide Transcriptome Profiling in Ethnomedicinal Plants

Chandrakanth, R., Sunil, L., & Sadashivaiah (2024) · Springer eBooks · doi:10.1007/978-981-97-3405-4_16

Incorporating Practical Single Cell and Spatial Transcriptomics Analysis in a Bioinformatics Course

Smiljković, L., Mišić, M. J., Obradović, P., & Kovačević, V. (2024) · International Conference on Electrical, Electronic and Computing Engineering (IcETRAN) · doi:10.1109/icetran62308.2024.10645128

A statistical-based method for the construction and analysis of gene network: application to bacteriaPreprint

Zhang, Z., Chen, G., & Li, E. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.03.611021

scADCA: An Anomaly Detection-Based scRNA-seq Dataset Cell Type Annotation Method for Identifying Novel Cells

Shi, Y., Ma, Y., Chen, X. '., & Gao, J. (2024) · Current Bioinformatics · doi:10.2174/0115748936334071240903064630

RobustCell: a model attack-defense framework for robust transcriptomic data analysis

Liu, T., Kang, Q., Luo, X., Luo, X., & Zhao, H. (2026) · npj Systems Biology and Applications · doi:10.1038/s41540-026-00799-9

Bioinformatics in Primary Immunodeficiencies: Towards a “Computational Immunology” Era

Vásquez, J. D. P. M., & Mentis, A. A. (2024) · Rare diseases of the immune system · doi:10.1007/978-3-031-78864-2_3

MuST: Maximizing the Latent Capacity of Spatial Transcriptomics Data with Multi-modality Structure Transformation

Zang, Z., Li, L., Xu, Y., Li, J., Duan, C., Sun, Y., Lei, Z., & Li, S. Z. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822193

Fast Parallel CPU-GPU Approximate Spectral Clustering for Transcriptomics Data

Branković, S., Smiljković, L., Obradović, P., Radonjiić, M., & Mišić, M. J. (2025) · International Journal of Parallel Programming · doi:10.1007/s10766-025-00783-6

Psychiatric Genomics 2025

Liu, C., Zhang, C., & Glatt, S. J. (2025) · Psychiatric Clinics of North America · doi:10.1016/j.psc.2025.01.002 · Cited by 1

Viral Infection Detector: Ensemble Learning for Predicting Viral Infection in Single-cell Transcriptomics of Virus-Induced CancersPreprint

Han, W., Hu, J., Toh, K., Ni, M., Tan, R., Wu, L., Jin, X., Jian, M., Xu, M., & Chen, H. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.27.639934

Transcript Identification Using Arrayed Hydrogels With TrapFISH

Morse, D. B., Toprakcioglu, Z., Denduluri‐Marthi, A., Brenton, J. D., Thomas, C. J., & Knowles, T. P. J. (2025) · Advanced Materials Technologies · doi:10.1002/admt.202401953

SMTFusion: Multi-order Topological Cell Graphs for Single-Cell Multi-omics Clustering

Yuan, Y., Yang, J., Wang, X., Wang, P., & Ge, D. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-0027-7_43

New Insight Into DNA Isolation From Ceratocystis platani Conidia Using Automated Recovery

Luchi, N., Pinzani, P., Salvianti, F., Mancini, I., & Santini, A. (2025) · Plant Pathology · doi:10.1111/ppa.70040 · Cited by 1

SURVEY AND SUMMARY Single-cell RNA-seq: advances and future challenges

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Methods, Challenges and Potentials of Single Cell RNA-seq

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