scQCEA: a framework for annotation and quality control report of single-cell RNA-sequencing data

[1]  T. Aittokallio,et al.  Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data , 2022, Nature Communications.

[2]  Clifford A. Meyer,et al.  Fast alignment and preprocessing of chromatin profiles with Chromap , 2021, Nature Communications.

[3]  V. Busskamp,et al.  Automated methods for cell type annotation on scRNA-seq data , 2021, Computational and structural biotechnology journal.

[4]  Zhe Wang,et al.  Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data , 2020, Nature Communications.

[5]  Vincent Rouilly,et al.  SCHNAPPs - Single Cell sHiNy APPlication(s) , 2020, bioRxiv.

[6]  Q. Zou,et al.  Identifying cell types to interpret scRNA-seq data: how, why and more possibilities. , 2020, Briefings in functional genomics.

[7]  P. Klenerman,et al.  Peripheral CD8+ T cell characteristics associated with durable responses to immune checkpoint blockade in patients with metastatic melanoma , 2019, Nature Medicine.

[8]  Quanhu Sheng,et al.  scRNABatchQC: multi-samples quality control for single cell RNA-seq data , 2019, Bioinform..

[9]  Joseph E Powell,et al.  ascend: R package for analysis of single-cell RNA-seq data , 2019, GigaScience.

[10]  Fabian J Theis,et al.  Current best practices in single‐cell RNA‐seq analysis: a tutorial , 2019, Molecular systems biology.

[11]  Allon M Klein,et al.  Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data. , 2019, Cell systems.

[12]  Samantha Riesenfeld,et al.  EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data , 2019, Genome Biology.

[13]  Lai Guan Ng,et al.  Dimensionality reduction for visualizing single-cell data using UMAP , 2018, Nature Biotechnology.

[14]  Mitulkumar V. Patel,et al.  iS-CellR: a user-friendly tool for analyzing and visualizing single-cell RNA sequencing data , 2018, Bioinform..

[15]  Samuel L. Wolock,et al.  A single-cell hematopoietic landscape resolves 8 lineage trajectories and defects in Kit mutant mice. , 2018, Blood.

[16]  J. Marioni,et al.  Differentiation dynamics of mammary epithelial cells revealed by single-cell RNA sequencing , 2017, Nature Communications.

[17]  Qin Zhu,et al.  PIVOT: platform for interactive analysis and visualization of transcriptomics data , 2016, BMC Bioinformatics.

[18]  J. Aerts,et al.  SCENIC: Single-cell regulatory network inference and clustering , 2017, Nature Methods.

[19]  Sandrine Dudoit,et al.  Normalizing single-cell RNA sequencing data: challenges and opportunities , 2017, Nature Methods.

[20]  Thomas K. Wolfgruber,et al.  Granatum: a graphical single-cell RNA-Seq analysis pipeline for genomics scientists , 2017, bioRxiv.

[21]  Vincent Gardeux,et al.  ASAP: a web-based platform for the analysis and interactive visualization of single-cell RNA-seq data , 2016, bioRxiv.

[22]  Grace X. Y. Zheng,et al.  Massively parallel digital transcriptional profiling of single cells , 2016, Nature Communications.

[23]  A. Regev,et al.  Spatial reconstruction of single-cell gene expression , 2015, Nature Biotechnology.

[24]  G. von Heijne,et al.  Tissue-based map of the human proteome , 2015, Science.

[25]  Fabian J Theis,et al.  Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells , 2015, Nature Biotechnology.

[26]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..