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Iqbal MS, Ahmad W, Alizadehsani R, Hussain S, Rehman R. Breast Cancer Dataset, Classification and Detection Using Deep Learning. Healthcare (Basel) 2022; 10:2395. [PMID: 36553919 PMCID: PMC9778593 DOI: 10.3390/healthcare10122395] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 11/24/2022] [Accepted: 11/25/2022] [Indexed: 12/05/2022] Open
Abstract
Incorporating scientific research into clinical practice via clinical informatics, which includes genomics, proteomics, bioinformatics, and biostatistics, improves patients' treatment. Computational pathology is a growing subspecialty with the potential to integrate whole slide images, multi-omics data, and health informatics. Pathology and laboratory medicine are critical to diagnosing cancer. This work will review existing computational and digital pathology methods for breast cancer diagnosis with a special focus on deep learning. The paper starts by reviewing public datasets related to breast cancer diagnosis. Additionally, existing deep learning methods for breast cancer diagnosis are reviewed. The publicly available code repositories are introduced as well. The paper is closed by highlighting challenges and future works for deep learning-based diagnosis.
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Affiliation(s)
- Muhammad Shahid Iqbal
- Department of Computer Science and Information Technology, Women University AJK, Bagh 12500, Pakistan
| | - Waqas Ahmad
- Higher Education Department Govt, AJK, Mirpur 10250, Pakistan
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC 3216, Australia
| | - Sadiq Hussain
- Examination Branch, Dibrugarh University, Dibrugarh 786004, India
| | - Rizwan Rehman
- Centre for Computer Science and Applications, Dibrugarh University, Dibrugarh 786004, India
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2
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Zeira R, Land M, Strzalkowski A, Raphael BJ. Alignment and integration of spatial transcriptomics data. Nat Methods 2022; 19:567-575. [PMID: 35577957 PMCID: PMC9334025 DOI: 10.1038/s41592-022-01459-6] [Citation(s) in RCA: 69] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 03/17/2022] [Indexed: 01/05/2023]
Abstract
Spatial transcriptomics (ST) measures mRNA expression across thousands of spots from a tissue slice while recording the two-dimensional (2D) coordinates of each spot. We introduce probabilistic alignment of ST experiments (PASTE), a method to align and integrate ST data from multiple adjacent tissue slices. PASTE computes pairwise alignments of slices using an optimal transport formulation that models both transcriptional similarity and physical distances between spots. PASTE further combines pairwise alignments to construct a stacked 3D alignment of a tissue. Alternatively, PASTE can integrate multiple ST slices into a single consensus slice. We show that PASTE accurately aligns spots across adjacent slices in both simulated and real ST data, demonstrating the advantages of using both transcriptional similarity and spatial information. We further show that the PASTE integrated slice improves the identification of cell types and differentially expressed genes compared with existing approaches that either analyze single ST slices or ignore spatial information.
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Affiliation(s)
- Ron Zeira
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Max Land
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | | | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ, USA.
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3
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Elyanow R, Zeira R, Land M, Raphael BJ. STARCH: copy number and clone inference from spatial transcriptomics data. Phys Biol 2021; 18:035001. [PMID: 33022659 PMCID: PMC9876615 DOI: 10.1088/1478-3975/abbe99] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Tumors are highly heterogeneous, consisting of cell populations with both transcriptional and genetic diversity. These diverse cell populations are spatially organized within a tumor, creating a distinct tumor microenvironment. A new technology called spatial transcriptomics can measure spatial patterns of gene expression within a tissue by sequencing RNA transcripts from a grid of spots, each containing a small number of cells. In tumor cells, these gene expression patterns represent the combined contribution of regulatory mechanisms, which alter the rate at which a gene is transcribed, and genetic diversity, particularly copy number aberrations (CNAs) which alter the number of copies of a gene in the genome. CNAs are common in tumors and often promote cancer growth through upregulation of oncogenes or downregulation of tumor-suppressor genes. We introduce a new method STARCH (spatial transcriptomics algorithm reconstructing copy-number heterogeneity) to infer CNAs from spatial transcriptomics data. STARCH overcomes challenges in inferring CNAs from RNA-sequencing data by leveraging the observation that cells located nearby in a tumor are likely to share similar CNAs. We find that STARCH outperforms existing methods for inferring CNAs from RNA-sequencing data without incorporating spatial information.
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Affiliation(s)
- Rebecca Elyanow
- Center for Computational Molecular Biology, Brown University, Providence, RI 029012, USA
- Department of Computer Science, Princeton University, Princeton, NJ 08540
| | - Ron Zeira
- Department of Computer Science, Princeton University, Princeton, NJ 08540
| | - Max Land
- Department of Computer Science, Princeton University, Princeton, NJ 08540
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He B, Bergenstråhle L, Stenbeck L, Abid A, Andersson A, Borg Å, Maaskola J, Lundeberg J, Zou J. Integrating spatial gene expression and breast tumour morphology via deep learning. Nat Biomed Eng 2020; 4:827-834. [PMID: 32572199 DOI: 10.1038/s41551-020-0578-x] [Citation(s) in RCA: 160] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Accepted: 05/23/2020] [Indexed: 11/09/2022]
Abstract
Spatial transcriptomics allows for the measurement of RNA abundance at a high spatial resolution, making it possible to systematically link the morphology of cellular neighbourhoods and spatially localized gene expression. Here, we report the development of a deep learning algorithm for the prediction of local gene expression from haematoxylin-and-eosin-stained histopathology images using a new dataset of 30,612 spatially resolved gene expression data matched to histopathology images from 23 patients with breast cancer. We identified over 100 genes, including known breast cancer biomarkers of intratumoral heterogeneity and the co-localization of tumour growth and immune activation, the expression of which can be predicted from the histopathology images at a resolution of 100 µm. We also show that the algorithm generalizes well to The Cancer Genome Atlas and to other breast cancer gene expression datasets without the need for re-training. Predicting the spatially resolved transcriptome of a tissue directly from tissue images may enable image-based screening for molecular biomarkers with spatial variation.
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Affiliation(s)
- Bryan He
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | | | - Linnea Stenbeck
- School of Biotechnology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Abubakar Abid
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Alma Andersson
- School of Biotechnology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Åke Borg
- Division of Oncology and Pathology, Lund University, Lund, Sweden
| | - Jonas Maaskola
- School of Biotechnology, KTH Royal Institute of Technology, Stockholm, Sweden.
| | - Joakim Lundeberg
- School of Biotechnology, KTH Royal Institute of Technology, Stockholm, Sweden.
| | - James Zou
- Department of Computer Science, Stanford University, Stanford, CA, USA. .,Department of Electrical Engineering, Stanford University, Stanford, CA, USA. .,Department of Biomedical Data Science, Stanford University, Stanford, CA, USA. .,Chan-Zuckerberg Biohub, San Francisco, CA, USA.
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Camunas-Soler J, Dai XQ, Hang Y, Bautista A, Lyon J, Suzuki K, Kim SK, Quake SR, MacDonald PE. Patch-Seq Links Single-Cell Transcriptomes to Human Islet Dysfunction in Diabetes. Cell Metab 2020; 31:1017-1031.e4. [PMID: 32302527 PMCID: PMC7398125 DOI: 10.1016/j.cmet.2020.04.005] [Citation(s) in RCA: 169] [Impact Index Per Article: 42.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 01/23/2020] [Accepted: 04/02/2020] [Indexed: 12/16/2022]
Abstract
Impaired function of pancreatic islet cells is a major cause of metabolic dysregulation and disease in humans. Despite this, it remains challenging to directly link physiological dysfunction in islet cells to precise changes in gene expression. Here we show that single-cell RNA sequencing combined with electrophysiological measurements of exocytosis and channel activity (patch-seq) can be used to link endocrine physiology and transcriptomes at the single-cell level. We collected 1,369 patch-seq cells from the pancreata of 34 human donors with and without diabetes. An analysis of function and gene expression networks identified a gene set associated with functional heterogeneity in β cells that can be used to predict electrophysiology. We also report transcriptional programs underlying dysfunction in type 2 diabetes and extend this approach to cryopreserved cells from donors with type 1 diabetes, generating a valuable resource for understanding islet cell heterogeneity in health and disease.
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Affiliation(s)
- Joan Camunas-Soler
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Chan Zuckerberg Biohub, San Francisco, CA 94518, USA
| | - Xiao-Qing Dai
- Department of Pharmacology, University of Alberta, Edmonton, AB T6G 2E1, Canada; Alberta Diabetes Institute, University of Alberta, Edmonton, AB T6G 2E1, Canada
| | - Yan Hang
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Austin Bautista
- Department of Pharmacology, University of Alberta, Edmonton, AB T6G 2E1, Canada; Alberta Diabetes Institute, University of Alberta, Edmonton, AB T6G 2E1, Canada
| | - James Lyon
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB T6G 2E1, Canada
| | - Kunimasa Suzuki
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB T6G 2E1, Canada
| | - Seung K Kim
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Diabetes Research Center, Stanford University, Stanford, CA 94305, USA.
| | - Stephen R Quake
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Chan Zuckerberg Biohub, San Francisco, CA 94518, USA; Stanford Diabetes Research Center, Stanford University, Stanford, CA 94305, USA; Department of Applied Physics, Stanford University, Stanford, CA 94305, USA.
| | - Patrick E MacDonald
- Department of Pharmacology, University of Alberta, Edmonton, AB T6G 2E1, Canada; Alberta Diabetes Institute, University of Alberta, Edmonton, AB T6G 2E1, Canada.
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