Research Data Leeds Repository

Dataset for 'Single-cell nanobiopsy enables multigenerational longitudinal transcriptomics of cancer cells'

Marcuccio, Fabio and Chau, Chalmers C. and Tanner, Georgette and Elpidorou, Marilena and Finetti, Martina A. and Ajaib, Shoaib and Taylor, Morag and Lascelles, Carolina and Carr, Ian and Macaulay, Iain and Stead, Lucy F. and Actis, Paolo (2024) Dataset for 'Single-cell nanobiopsy enables multigenerational longitudinal transcriptomics of cancer cells'. University of Leeds. [Dataset] https://doi.org/10.5518/1464

Dataset description

The dataset contains the following: 1) Main: contains the data used to generate the images, graphs and plots of the figures in the main manuscript. This folder contains the raw data. To learn about the methods, please refer to the main manuscript and supporting information file, where methods used for the experiments and data analysis are comprehensively described. 2) SI: contains the data used to generate the images, graphs, and plots of the figures in the Supporting Information file. This folder contains the raw data. To learn about the methods, please refer to the main manuscript and supporting information file, where methods used for the experiments and data analysis are comprehensively described. 3) DATA-Bioinformatics: contains the Sequencing Data, scripts and pipelines used to generate the graphs and plots for the nanobiopsy and the whole-cell datasets. These data are also freely accessible on the GitHub repository at the following link link: https://github.com/GliomaGenomics/GBM_NB

Keywords:	Nanobiopsy, Cancer, Transcriptomic
Subjects:	H000 - Engineering > H600 - Electronic & electrical engineering
Divisions:	Faculty of Engineering and Physical Sciences > School of Electronic and Electrical Engineering
License:	Creative Commons Attribution 4.0 International (CC BY 4.0)
Date deposited:	30 Jan 2024 21:06
URI:	http://archive.researchdata.leeds.ac.uk/id/eprint/1232
Additional details Additional details Creators: Creators ORCID Other ID Email Primary affiliation Primary affiliation ID Primary affiliation ID type Secondary affiliation Secondary affiliation ID Secondary affiliation ID type Marcuccio, Fabio https://orcid.org/0000-0003-4816-2896 Imperial College London https://ror.org/041kmwe10 ror University of Leeds https://ror.org/024mrxd33 ror Chau, Chalmers C. https://orcid.org/0000-0002-3134-6798 University of Leeds https://ror.org/024mrxd33 ror Tanner, Georgette https://orcid.org/0000-0001-9706-3590 University of Leeds https://ror.org/024mrxd33 ror Elpidorou, Marilena https://orcid.org/0000-0002-5533-2251 University of Leeds https://ror.org/024mrxd33 ror Finetti, Martina A. https://orcid.org/0000-0003-4589-3343 University of Leeds https://ror.org/024mrxd33 ror Ajaib, Shoaib University of Leeds https://ror.org/024mrxd33 ror Taylor, Morag https://orcid.org/0000-0002-3907-1290 University of Leeds https://ror.org/024mrxd33 ror Lascelles, Carolina University of Leeds https://ror.org/024mrxd33 ror Carr, Ian https://orcid.org/0000-0001-9544-1068 University of Leeds https://ror.org/024mrxd33 ror Macaulay, Iain https://orcid.org/0000-0002-6761-757X Earlham Institute https://ror.org/018cxtf62 ror Stead, Lucy F. https://orcid.org/0000-0002-9550-4150 University of Leeds https://ror.org/024mrxd33 ror Actis, Paolo https://orcid.org/0000-0002-7146-1854 University of Leeds https://ror.org/024mrxd33 ror Type of data: Dataset Research funders: The Brain Tumour Charity, European Commision H2020 grant, UKRI Grant numbers: GN-000482, 812398, MR/T020504/1 Publication date: 2024 Resource language: English Publisher: University of Leeds Contact Email Address: l.f.stead@leeds.ac.uk, p.actis@leeds.ac.uk Last Modified: 31 Jan 2024 16:34

Files

Documentation

README_Marcuccio_2024.txt [3kB] README_Marcuccio_2024.txt [3kB] README_Marcuccio_2024.txt [3kB] Visible to: Anyone File name: README_Marcuccio_2024.txt Content type: Documentation Mime-Type: text/plain License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 3kB

Data

Main [3GB] Main [3GB] Main [3GB] Visible to: Anyone File name: Main.zip Content type: Data Mime-Type: application/zip License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 3GB

SI [1GB] SI [1GB] SI [1GB] Visible to: Anyone File name: SI.zip Content type: Data Mime-Type: application/zip License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 1GB

DATA - Bioinformatics [55MB] DATA - Bioinformatics [55MB] DATA - Bioinformatics [55MB] Visible to: Anyone File name: DATA_Bioinformatics.zip Content type: Data Mime-Type: application/zip License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 55MB