Version 1
: Received: 24 July 2024 / Approved: 25 July 2024 / Online: 25 July 2024 (12:07:10 CEST)
How to cite:
Oketch, D. J. A.; Giulietti, M.; Piva, F. A Comparison of Tools That Identify Tumor Cells by Inferring CNVs from Single-Cell Experiments in Pancreatic Ductal Adenocarcinoma. Preprints2024, 2024072037. https://doi.org/10.20944/preprints202407.2037.v1
Oketch, D. J. A.; Giulietti, M.; Piva, F. A Comparison of Tools That Identify Tumor Cells by Inferring CNVs from Single-Cell Experiments in Pancreatic Ductal Adenocarcinoma. Preprints 2024, 2024072037. https://doi.org/10.20944/preprints202407.2037.v1
Oketch, D. J. A.; Giulietti, M.; Piva, F. A Comparison of Tools That Identify Tumor Cells by Inferring CNVs from Single-Cell Experiments in Pancreatic Ductal Adenocarcinoma. Preprints2024, 2024072037. https://doi.org/10.20944/preprints202407.2037.v1
APA Style
Oketch, D. J. A., Giulietti, M., & Piva, F. (2024). A Comparison of Tools That Identify Tumor Cells by Inferring CNVs from Single-Cell Experiments in Pancreatic Ductal Adenocarcinoma. Preprints. https://doi.org/10.20944/preprints202407.2037.v1
Chicago/Turabian Style
Oketch, D. J. A., Matteo Giulietti and Francesco Piva. 2024 "A Comparison of Tools That Identify Tumor Cells by Inferring CNVs from Single-Cell Experiments in Pancreatic Ductal Adenocarcinoma" Preprints. https://doi.org/10.20944/preprints202407.2037.v1
Abstract
Single-cell RNA sequencing (scRNA-seq) technique has enabled detailed analysis of gene expression at the single cell level, enhancing the understanding of subtle mechanisms that underly pathologies and drug resistance. To derive such biological meaning from sequencing data in oncology, some critical processing must be performed, including identification of the tumor cells by markers and algorithms that infer copy number variations (CNVs). We compared the performance of sciCNV, InferCNV, CopyKAT and SCEVAN tools that identify tumour cells by inferring CNVs from scRNA-seq data. Sequencing data from Pancreatic Ductal Adenocarcinoma (PDAC) patients, adjacent and healthy tissues were analysed, and the predicted tumor cells were compared to those identified by well-assessed PDAC markers. Results from InferCNV, CopyKAT and SCEVAN overlapped by less than 30% with InferCNV showing the highest sensitivity (0.72) and SCEVAN the highest specificity (0.75). We show that the predictions are highly dependent on the sample and the software used, and that they return so many false positives hence are of little use in verifying or filtering predictions made via tumor biomarkers. We highlight how critical this processing can be, warn against the blind use of these software and point out the great need for more reliable algorithms.
Medicine and Pharmacology, Oncology and Oncogenics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
