Interpreting Raman spectra using machine learning: towards a non-invasive method of characterizing single cells
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2021
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2021
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| Online Access: | https://hdl.handle.net/1721.1/130714 |
| _version_ | 1811085608683044864 |
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| author | Sorenson, Taylor(Taylor M.) |
| author2 | Aviv Regev and Tommaso Biancalani. |
| author_facet | Aviv Regev and Tommaso Biancalani. Sorenson, Taylor(Taylor M.) |
| author_sort | Sorenson, Taylor(Taylor M.) |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2021 |
| first_indexed | 2024-09-23T13:12:18Z |
| format | Thesis |
| id | mit-1721.1/130714 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T13:12:18Z |
| publishDate | 2021 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1307142021-05-25T03:21:56Z Interpreting Raman spectra using machine learning: towards a non-invasive method of characterizing single cells Sorenson, Taylor(Taylor M.) Aviv Regev and Tommaso Biancalani. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Electrical Engineering and Computer Science. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2021 Cataloged from the official PDF of thesis. Includes bibliographical references (pages 89-93). Raman microscopy has the potential to non-destructively measure the biomolecular changes in single cells in a label-free manner. However, the extent to which Raman spectra can effectively infer biologically-relevant information is not well understood. In this thesis, we use machine learning methods to explore the ability of Raman microscopy data to infer cell states in microbes and the gene expression values of ten genes in mouse embryonic fibroblasts (MEFs) undergoing a dynamic cellular reprogramming process. Using a multi-modal, supervised learning approach, we provide evidence that Raman spectra can accurately resolve microbial cell types. This thesis also presents a robust computational pipeline to preprocess Raman spectra, calibrate multi-modal data, and segment nuclei; an analysis of methods to increase the signal-to-noise ratio of Raman spectra; and an analysis of Raman spectral features important for predicting microbial cell-type. Together, the results suggest Raman microscopy be considered as a useful modality for distinguishing cell-types and potentially tracking cellular dynamics, a common goal of many consortia including the Human Cell Atlas. by Taylor Sorenson. M. Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 2021-05-24T19:52:43Z 2021-05-24T19:52:43Z 2021 2021 Thesis https://hdl.handle.net/1721.1/130714 1251801775 eng MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. http://dspace.mit.edu/handle/1721.1/7582 93 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Sorenson, Taylor(Taylor M.) Interpreting Raman spectra using machine learning: towards a non-invasive method of characterizing single cells |
| title | Interpreting Raman spectra using machine learning: towards a non-invasive method of characterizing single cells |
| title_full | Interpreting Raman spectra using machine learning: towards a non-invasive method of characterizing single cells |
| title_fullStr | Interpreting Raman spectra using machine learning: towards a non-invasive method of characterizing single cells |
| title_full_unstemmed | Interpreting Raman spectra using machine learning: towards a non-invasive method of characterizing single cells |
| title_short | Interpreting Raman spectra using machine learning: towards a non-invasive method of characterizing single cells |
| title_sort | interpreting raman spectra using machine learning towards a non invasive method of characterizing single cells |
| topic | Electrical Engineering and Computer Science. |
| url | https://hdl.handle.net/1721.1/130714 |
| work_keys_str_mv | AT sorensontaylortaylorm interpretingramanspectrausingmachinelearningtowardsanoninvasivemethodofcharacterizingsinglecells |