DOI: 10.5061/dryad.4b8gthtn7
title: Expression-based machine learning models for predicting plant tissue identity

description: The selection of Arabidopsis as a model organism played a pivotal role in
 advancing genomic science. Competing frameworks to select an agricultural-
 or ecological-based model species were selected against in favor of
 building knowledge in a species that would facilitate genome-enabled
 research. 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. Below-ground tissue is more
 predictable 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 and suggests that
 gene expression signatures, rather than marker genes, are more valuable in
 creating models for tissue and cell type prediction in plants. 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.
description: We analyzed gene expression data from two sources. The first
 (Zhang et al., 2020) contains 28,165 <em>Arabidopsis</em> gene
 expression profiles across 37,334 genes. The second (Palande et al., 2023)
 contains 2,671 flowering plant gene expression profiles across 6,327
 orthogroups. Originally gene expression profiles were
 classified into 23 tissue types based on their original designations:
 “anther,” “carpel,” “cotyledon,” “flower,” “hypocotyl,” “inflorescence,”
 “internode,” “leaf,” “other,” “petal,” “petiole,” “pistil,”
 “reproductive-other,” “root,” “root cell,” “seed,” “seedling,” “sepal,”
 “shoot,” “stamen,” “stigma,” “vasculature,” or “whole plant.”
 Due to large differences in sample size between these categories,
 they were aggregated into four tissue type labels:
 "aboveground", "below ground", "whole
 plant", and "other". The categories are purposefully
 encompassing and were chosen to facilitate accurate assignment across the
 broad categories of experimental data we analyzed, focusing on aboveground
 and belowground tissue identity as one of the simplest cases to test
 tissue predictability. Samples for which tissue
 identity could not be determined from their description were discarded, as
 they were incompatible with our machine learning pipeline. Additionally,
 we discarded low-quality samples, which we measured by unique mapped rate,
 or the number of uniquely mapping reads divided by the total number of
 reads. After removing samples with missing metadata and samples with a low
 unique mapped rate (&lt;75%), the <em>Arabidopsis</em>
 database was left with 19,415 samples. A conserved
 <em>Arabidopsis</em> database was also constructed by keeping
 only the genes mapped to the orthogroups from the flowering plant
 database. The conserved <em>Arabidopsis</em> database
 contained the same number of samples but with much smaller expression
 profiles across only the 6327 orthogroups shared with the angiosperm
 dataset. References: Zhang, H., F.
 Zhang, Y. Yu, L. I. Feng, J. Jia, B. O. Liu, B. Li, et al. 2020. A
 comprehensive online database for exploring ∼20,000 public Arabidopsis
 RNA-seq libraries. Molecular Plant 13(9): 1231–1233.
 Palande, S., J. A. Kaste, M. D. Roberts, K. S. Aba, C.
 Claucherty, J. Dacon, R. Doko, et al. 2023. The topological shape of gene
 expression across the evolution of flowering plants. PLoS Biology 21(12):
 e 3002397.
description: # Expression-based machine learning models for predicting plant tissue
 identity *Arabidopsis* Gene Expression Dataset
 [https://doi.org/10.5061/dryad.4b8gthtn7](https://doi.org/10.5061/dryad.4b8gthtn7) The dataset contains three `.parquet` files: 1\) `gene_FPKM_200501.parquet`: The original gene expression database was downloaded from the [Arabidopsis RNA-Seq Database](https://plantrnadb.com/athrdb/) ([Zhang et al, 2020)](https://doi.org/10.1016/j.molp.2020.08.001). The original dataset contains 28,165 Arabidopsis gene expression profiles across 37,334 genes. 2\) `gene_FPKM_transposed.parquet`: Simply the transposed version of `gene_FPKM_200501.parquet` which is better aligned with typical machine learning datasets where samples are represented in rows. 3\) `gene_FPKM_transposed_UMR75.parquet`: The gene expression profiles (`gene_FPKM_transposed.parquet`) were filtered to remove samples with a unique mapped rate below 75%. This dataset is used to train and test machine learning models. In addition to the `.parquet` files, the following `.tsv` and `.csv` files are also included in this upload: 4\) `Arabidopsis_metadata.tsv`: This is the metadata accompanying the original gene expression dataset (`gene_FPKM_200501.parquet`). It provides the sample ID, project ID, sample name, PMID, genotype, ecotype, tissue type, total reads, unique mapped rate, and the release date for each sample in the dataset. A "/" is placed in the fields with missing information. In other metadata files "/" is replaced with "NA" for better Python and Pandas compatibility. 5\) `metadata_UMR75.csv`: This file contains the metadata corresponding to the filtered gene expression profiles (`gene_FPKM_transposed_UMR75.parquet`). 6\) `all_tissue_type.csv`: This file lists tissue type labels encountered in the metadata (`Arabidopsis_metadata.tsv`) and the various aggregations thereof which were ultimately used in the analyses. Here is a description of the variables (columns) in the file: * `Tissue`: Contains all unique tissue type labels encountered in the metadata. "/" indicates missing or unknown labels. * `Counts`: Indicates the number of samples with the given `Tissue` label in the gene expression dataset. * `TissueClean`: Sanitized tissue labels based on the original designations. Following 23 distinct tissue labels are adopted: “anther,” “carpel,” “cotyledon,” “flower,” “hypocotyl,” “inflorescence,” “internode,” “leaf,” “other,” “petal,” “petiole,” “pistil,” “reproductive-other,” “root,” “root cell,” “seed,” “seedling,” “sepal,” “shoot,” “stamen,” “stigma,” “vasculature,” or “whole plant”. The missing or unknown labels are indicated by "NA", and the samples with "NA" labels are eventually dropped from the analysis. * `VegetativeRepro`: The sanitized labels are further aggregated into "vegetative", "reproductive", "root", "whole plant", and "hypotocyl". Samples for which tissue identity could not be determined from their description were marked as "NA" and eventually discarded, as they were incompatible with our machine-learning pipeline. * `AboveBelow`: Similarly, the sanitized tissue labels were also aggregated into "above ground", "below ground", "seed", and "whole plant". Once again samples for which tissue identity could not be determined were marked as "NA" and discarded. * `Debatable`: For some samples, the assigned `VegetativeRepro` or `AboveBelow` designations could be debatable. For example, we assigned the "whole plant" designation to "seedlings". Such debatable assignments are marked with a "Yes" in this column. The rest are marked with "NA" to indicate no ambiguity in the label assignment. 7\) `tissue_type_map_UMR75.csv`: This file contains unique tissue type labels encountered in the metadata **after** discarding the samples with a low unique mapped rate. It has the same column headers as `all_tissue_type.csv` with two main differences: 1) Does not have the `Debatable` column anymore, and 2) All samples with "NA" labels are dropped. This file is used to map the aggregated tissue type labels to the gene expression profiles (`gene_FPKM_transposed_UMR75.parquet`). After discarding samples that had a low unique mapped rate (&lt;75%) or samples for which tissue identity could not be determined, the final **Arabidopsis** database was left with 19,415 samples. 8\) `Angiosperm_data_clean.csv`: This database, created by [Palande et al., 2023](https://doi.org/10.1371/journal.pbio.3002397) contains 2,671 flowering plant gene expression profiles across 6,327 orthogroups. For more detailed information about the database, refer to the PLoS Biology article titled "The topological shape of gene expression across the evolution of flowering plants". 9\) `arabidopsis_gene_expression_main.zip`: This archive contains all the code required to perform the analyses and reproduce the results presented in **Expression-based machine learning models for predicting plant tissue identity.** To run the analyses, download the archive and extract all files into the `code` folder under the project directory. Similarly, download all the `.parquet` and `.csv` files into the `data` folder under the project directory. From there, the Python scripts and the Jupyter notebooks should work as intended.  The code is also available on GitHub at: "[https://github.com/PlantsAndPython/arabidopsis-gene-expression](https://github.com/PlantsAndPython/arabidopsis-gene-expression)". To run the analysis, simply clone the GitHub repository, then download the parquet files and put them in the "data" directory of the cloned repository. From there, the scripts and Jupyter Notebooks should work as intended. The GitHub method is preferred because it preserves the directory structure and relative paths.