bioRxivCancer Biology
The Imaging and Molecular Annotation of Xenografts and Tumours (IMAXT) High Throughput Data and Analysis Infrastructure
E. A. Gonzalez-Solares, A. Dariush, C. Gonzalez-Fernandez, A. Küpcü Yoldas, M. Al Sa'd, N. Millar, T. Whitmarsh, N. Chornay, I. Falciatori, A. Fatemi, D. Goodwin, L. Kuett, C. M. Mulvey, M. Paez Ribes, F. Qosaj, A. Roth, I. Vazquez-Garcia, S. S. Watson, J. Windhager, S. Aparicio, B. Bodenmiller, E. Boyden, C. Caldas, O. Harris, S. P. Shah, , N. A. Walton
Abstract
With the aim of producing a 3D representation of tumours, IMAXT uses a large variety of modalities in order to acquire tumour samples and produce a map of every cell in the tumour and its host environment. With the large volume and variety of data produced in the project we develop automatic data workflows and analysis pipelines and introduce a research methodology where scientists connect to a cloud environment to perform analysis close to where data are located instead of bringing data to their local computers. Here we present the data and analysis infrastructure, discuss the unique computational challenges and describe the analysis chains developed and deployed to generate molecularly annotated tumour models. Registration is achieved by use of a novel technique involving spherical fiducial marks that are visible in all imaging modalities used within IMAXT.
- organism: mouse
- assay: spatial transcriptomics
- assay: imaging
- research focus: cell type annotation
- research focus: clustering
- research focus: dimensionality reduction
- approach: deep learning
- approach: statistical model
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