Table 2.
List of top 15 cited articles in scRNA-seq
| Study | Value | Citations | LC/GC ratio | Year | |
|---|---|---|---|---|---|
|
| |||||
| Local | Global | ||||
| Integrating single-cell transcriptomic data across different conditions, technologies, and species (14) | Presents a methodology for the comprehensive analysis and integration of scRNA-seq data, enabling the identification of shared populations across data sets and downstream analysis | 443 | 4,123 | 10.74 | 2018 |
| Comprehensive integration of single-cell data (15) | Develops a strategy to “anchor” various datasets simultaneously, allowing scientists to integrate single-cell across different modalities | 352 | 4,308 | 8.17 | 2019 |
| The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells (16) | Introduces Monocle, an unsupervised algorithm that enhances the resolution of transcriptome dynamics in cellular processes such as differentiation | 297 | 2,415 | 12.3 | 2014 |
| SC3: consensus clustering of single-cell RNA-seq data (17) | Proposes a consensus clustering algorithm specifically designed for scRNA-seq data, improving the accuracy of cell type identification | 188 | 683 | 27.53 | 2017 |
| Full-length RNA-seq from single cells using Smart-seq2 (18) | Describes an improved protocol for full-length RNA sequencing from single cells, enabling more detailed transcriptome analyses, especially for stem cell research | 182 | 1,942 | 9.37 | 2014 |
| Smart-seq2 for sensitive full-length transcriptome profiling in single cells (19) | Enhances the sensitivity and accuracy of single-cell transcriptome profiling with the Smart-seq2 technology | 172 | 1,216 | 14.14 | 2013 |
| Comparative analysis of single-cell RNA sequencing methods (20) | Offers a comparative study of various scRNA-seq methodologies, evaluating six prominent methods | 152 | 728 | 20.88 | 2017 |
| Computational and analytical challenges in single-cell transcriptomics (21) | Discusses the key computational and analytical challenges in single-cell transcriptomics, proposing solutions to address these issues | 144 | 691 | 20.84 | 2015 |
| Quantitative single-cell RNA-seq with unique molecular identifiers (22) | Introduces a quantitative approach to scRNA-seq that uses unique molecular identifiers, improving data accuracy | 138 | 729 | 18.93 | 2014 |
| Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris (23) | Presents a comprehensive single-cell transcriptomic atlas of mouse, providing insights into organ-specific cell types and states | 129 | 925 | 13.95 | 2018 |
| Current best practices in single-cell RNA-seq analysis: a tutorial (24) | Offers a guide on best practices for analysing scRNA-seq data, from preprocessing to downstream analysis | 119 | 610 | 19.51 | 2019 |
| Splatter: simulation of single-cell RNA sequencing data (25) | Provides a tool for simulating scRNA-seq data, aiding in the development and testing of analytical methods | 118 | 324 | 36.42 | 2017 |
| Single-cell RNA sequencing technologies and bioinformatics pipelines (26) | Reviews the latest technologies and bioinformatics pipelines for scRNA-seq, highlighting their advantages and limitations | 110 | 665 | 16.54 | 2018 |
| Recovering gene interactions from single-cell data using data diffusion (27) | Proposes a data diffusion approach called MAGIC a method that shares information across similar cells to denoise the cell count matrix and fill in missing transcripts | 109 | 590 | 18.47 | 2018 |
| Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R (28) | Introduces an R package for comprehensive preprocessing, quality control, normalization, and visualization of scRNA-seq data | 106 | 620 | 17.1 | 2017 |
Analyses thetop 15 locally cited articles and their contribution in the field of single-cell RNA sequencing (scRNA-Seq) analysis, considering both their global citations and the local/global citation (LC/GC) ratio. The “LC/GC ratio” field signifies the ratio between local and global citations, providing a measure of the extent to which these articles are cited within their immediate research community relative to their global reach. Additionally, the “Year” field indicates the year of publication foreach article. The analysis of these parameters provides valuable information on the local and global recognition of the top 15 articles in scRNA-Seq analysis, allowing for a comprehensive understanding of their significance within the field.