Expression‐based machine learning models for predicting plant tissue identity
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Wiley
2024
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| author | Sourabh Palande Jeremy Arsenault Patricia Basurto‐Lozada Andrew Bleich Brianna N. I. Brown Sophia F. Buysse Noelle A. Connors Sikta Das Adhikari Kara C. Dobson Francisco Xavier Guerra‐Castillo Maria F. Guerrero‐Carrillo Sophia Harlow Héctor Herrera‐Orozco Asia T. Hightower Paulo Izquierdo MacKenzie Jacobs Nicholas A. Johnson Wendy Leuenberger Alessandro Lopez‐Hernandez Alicia Luckie‐Duque Camila Martínez‐Avila Eddy J. Mendoza‐Galindo David Cruz Plancarte Jenny M. Schuster Harry Shomer Sidney C. Sitar Anne K. Steensma Joanne Elise Thomson Damián Villaseñor‐Amador Robin Waterman Brandon M. Webster Madison Whyte Sofía Zorilla‐Azcué Beronda L. Montgomery Aman Y. Husbands Arjun Krishnan Sarah Percival Elizabeth Munch Robert VanBuren Daniel H. Chitwood Alejandra Rougon‐Cardoso |
| author_facet | Sourabh Palande Jeremy Arsenault Patricia Basurto‐Lozada Andrew Bleich Brianna N. I. Brown Sophia F. Buysse Noelle A. Connors Sikta Das Adhikari Kara C. Dobson Francisco Xavier Guerra‐Castillo Maria F. Guerrero‐Carrillo Sophia Harlow Héctor Herrera‐Orozco Asia T. Hightower Paulo Izquierdo MacKenzie Jacobs Nicholas A. Johnson Wendy Leuenberger Alessandro Lopez‐Hernandez Alicia Luckie‐Duque Camila Martínez‐Avila Eddy J. Mendoza‐Galindo David Cruz Plancarte Jenny M. Schuster Harry Shomer Sidney C. Sitar Anne K. Steensma Joanne Elise Thomson Damián Villaseñor‐Amador Robin Waterman Brandon M. Webster Madison Whyte Sofía Zorilla‐Azcué Beronda L. Montgomery Aman Y. Husbands Arjun Krishnan Sarah Percival Elizabeth Munch Robert VanBuren Daniel H. Chitwood Alejandra Rougon‐Cardoso Sourabh Palande Jeremy Arsenault Patricia Basurto‐Lozada Andrew Bleich Brianna N. I. Brown Sophia F. Buysse Noelle A. Connors Sikta Das Adhikari Kara C. Dobson Francisco Xavier Guerra‐Castillo Maria F. Guerrero‐Carrillo Sophia Harlow Héctor Herrera‐Orozco Asia T. Hightower Paulo Izquierdo MacKenzie Jacobs Nicholas A. Johnson Wendy Leuenberger Alessandro Lopez‐Hernandez Alicia Luckie‐Duque Camila Martínez‐Avila Eddy J. Mendoza‐Galindo David Cruz Plancarte Jenny M. Schuster Harry Shomer Sidney C. Sitar Anne K. Steensma Joanne Elise Thomson Damián Villaseñor‐Amador Robin Waterman Brandon M. Webster Madison Whyte Sofía Zorilla‐Azcué Beronda L. Montgomery Aman Y. Husbands Arjun Krishnan Sarah Percival Elizabeth Munch Robert VanBuren Daniel H. Chitwood Alejandra Rougon‐Cardoso |
| collection | Wiley Open Access |
| contents | Expression‐based machine learning models for predicting plant tissue identity Sourabh Palande Jeremy Arsenault Patricia Basurto‐Lozada Andrew Bleich Brianna N. I. Brown Sophia F. Buysse Noelle A. Connors Sikta Das Adhikari Kara C. Dobson Francisco Xavier Guerra‐Castillo Maria F. Guerrero‐Carrillo Sophia Harlow Héctor Herrera‐Orozco Asia T. Hightower Paulo Izquierdo MacKenzie Jacobs Nicholas A. Johnson Wendy Leuenberger Alessandro Lopez‐Hernandez Alicia Luckie‐Duque Camila Martínez‐Avila Eddy J. Mendoza‐Galindo David Cruz Plancarte Jenny M. Schuster Harry Shomer Sidney C. Sitar Anne K. Steensma Joanne Elise Thomson Damián Villaseñor‐Amador Robin Waterman Brandon M. Webster Madison Whyte Sofía Zorilla‐Azcué Beronda L. Montgomery Aman Y. Husbands Arjun Krishnan Sarah Percival Elizabeth Munch Robert VanBuren Daniel H. Chitwood Alejandra Rougon‐Cardoso Applications in Plant Sciences Abstract Premise The selection of Arabidopsis as a model organism played a pivotal role in advancing genomic science. The competing frameworks to select an agricultural‐ or ecological‐based model species were rejected, in favor of building knowledge in a species that would facilitate genome‐enabled research. Methods Here, we examine the ability of models based on Arabidopsis gene expression data to predict tissue identity in other flowering plants. Comparing different machine learning algorithms, models trained and tested on Arabidopsis data achieved near perfect precision and recall values, whereas when tissue identity is predicted across the flowering plants using models trained on Arabidopsis data, precision values range from 0.69 to 0.74 and recall from 0.54 to 0.64. Results The identity of belowground tissue can be predicted more accurately than other tissue types, and the ability to predict tissue identity is not correlated with phylogenetic distance from Arabidopsis . k ‐nearest neighbors is the most successful algorithm, suggesting that gene expression signatures, rather than marker genes, are more valuable to create models for tissue and cell type prediction in plants. Discussion Our data‐driven results highlight that the assertion that knowledge from Arabidopsis is translatable to other plants is not always true. Considering the current landscape of abundant sequencing data, we should reevaluate the scientific emphasis on Arabidopsis and prioritize plant diversity. 10.1002/aps3.11621 http://creativecommons.org/licenses/by-nc/4.0/ |
| doi_str_mv | 10.1002/aps3.11621 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_aps3_11621 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by-nc/4.0/ |
| publishDate | 2024 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Expression‐based machine learning models for predicting plant tissue identity Sourabh Palande Jeremy Arsenault Patricia Basurto‐Lozada Andrew Bleich Brianna N. I. Brown Sophia F. Buysse Noelle A. Connors Sikta Das Adhikari Kara C. Dobson Francisco Xavier Guerra‐Castillo Maria F. Guerrero‐Carrillo Sophia Harlow Héctor Herrera‐Orozco Asia T. Hightower Paulo Izquierdo MacKenzie Jacobs Nicholas A. Johnson Wendy Leuenberger Alessandro Lopez‐Hernandez Alicia Luckie‐Duque Camila Martínez‐Avila Eddy J. Mendoza‐Galindo David Cruz Plancarte Jenny M. Schuster Harry Shomer Sidney C. Sitar Anne K. Steensma Joanne Elise Thomson Damián Villaseñor‐Amador Robin Waterman Brandon M. Webster Madison Whyte Sofía Zorilla‐Azcué Beronda L. Montgomery Aman Y. Husbands Arjun Krishnan Sarah Percival Elizabeth Munch Robert VanBuren Daniel H. Chitwood Alejandra Rougon‐Cardoso Applications in Plant Sciences Expression‐based machine learning models for predicting plant tissue identity Sourabh Palande Jeremy Arsenault Patricia Basurto‐Lozada Andrew Bleich Brianna N. I. Brown Sophia F. Buysse Noelle A. Connors Sikta Das Adhikari Kara C. Dobson Francisco Xavier Guerra‐Castillo Maria F. Guerrero‐Carrillo Sophia Harlow Héctor Herrera‐Orozco Asia T. Hightower Paulo Izquierdo MacKenzie Jacobs Nicholas A. Johnson Wendy Leuenberger Alessandro Lopez‐Hernandez Alicia Luckie‐Duque Camila Martínez‐Avila Eddy J. Mendoza‐Galindo David Cruz Plancarte Jenny M. Schuster Harry Shomer Sidney C. Sitar Anne K. Steensma Joanne Elise Thomson Damián Villaseñor‐Amador Robin Waterman Brandon M. Webster Madison Whyte Sofía Zorilla‐Azcué Beronda L. Montgomery Aman Y. Husbands Arjun Krishnan Sarah Percival Elizabeth Munch Robert VanBuren Daniel H. Chitwood Alejandra Rougon‐Cardoso Applications in Plant Sciences Abstract Premise The selection of Arabidopsis as a model organism played a pivotal role in advancing genomic science. The competing frameworks to select an agricultural‐ or ecological‐based model species were rejected, in favor of building knowledge in a species that would facilitate genome‐enabled research. Methods Here, we examine the ability of models based on Arabidopsis gene expression data to predict tissue identity in other flowering plants. Comparing different machine learning algorithms, models trained and tested on Arabidopsis data achieved near perfect precision and recall values, whereas when tissue identity is predicted across the flowering plants using models trained on Arabidopsis data, precision values range from 0.69 to 0.74 and recall from 0.54 to 0.64. Results The identity of belowground tissue can be predicted more accurately than other tissue types, and the ability to predict tissue identity is not correlated with phylogenetic distance from Arabidopsis . k ‐nearest neighbors is the most successful algorithm, suggesting that gene expression signatures, rather than marker genes, are more valuable to create models for tissue and cell type prediction in plants. Discussion Our data‐driven results highlight that the assertion that knowledge from Arabidopsis is translatable to other plants is not always true. Considering the current landscape of abundant sequencing data, we should reevaluate the scientific emphasis on Arabidopsis and prioritize plant diversity. 10.1002/aps3.11621 http://creativecommons.org/licenses/by-nc/4.0/ |
| title | Expression‐based machine learning models for predicting plant tissue identity |
| topic | Applications in Plant Sciences |
| url | https://bsapubs.onlinelibrary.wiley.com/doi/10.1002/aps3.11621 |