Expression-based machine learning models for predicting plant tissue identity

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..., We analyzed gene expression data from two sources. The first (Zhang et al., 2020) contains 28,165 Arabidopsis 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 aboveg..., , # 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 model...

拟南芥（Arabidopsis）作为模式生物的选择，在推动基因组科学发展中发挥了举足轻重的作用。当时学界曾提出基于农业或生态学的模式物种选择框架，但最终均未被采纳，转而选择能够助力基因组学研究的物种开展知识积累工作。本研究旨在探究基于拟南芥基因表达数据构建的模型，在其他开花植物中预测组织类型（tissue identity）的能力。通过对比不同机器学习算法，仅在拟南芥数据上训练与测试的模型可获得近乎完美的精确率（precision）与召回率（recall）；而利用拟南芥训练得到的模型跨开花植物预测组织类型时，精确率介于0.69至0.74之间，召回率则介于0.54至0.64之间。地下组织的预测效果优于其他组织类型，且组织类型预测能力与样本与拟南芥的系统发育距离无相关性。K近邻（K-Nearest Neighbors）算法是表现最优的模型，其结果表明……

本研究分析了两个来源的基因表达数据：其一为Zhang等人2020年的数据集，包含37334个基因对应的28165条拟南芥基因表达谱；其二为Palande等人2023年的数据集，包含6327个同源基因簇（orthogroups）对应的2671条开花植物基因表达谱。

最初，基因表达谱根据原始标注被划分为23种组织类型：“花药（anther）”、“心皮（carpel）”、“子叶（cotyledon）”、“花（flower）”、“下胚轴（hypocotyl）”、“花序（inflorescence）”、“节间（internode）”、“叶（leaf）”、“其他（other）”、“花瓣（petal）”、“叶柄（petiole）”、“雌蕊（pistil）”、“其他生殖组织（reproductive-other）”、“根（root）”、“根细胞（root cell）”、“种子（seed）”、“幼苗（seedling）”、“萼片（sepal）”、“茎（shoot）”、“雄蕊（stamen）”、“柱头（stigma）”、“维管组织（vasculature）”或“整株植物（whole plant）”。

由于各类别间样本量差异悬殊，我们将其合并为4个组织类型标签：“地上组织（aboveground）”、“地下组织（below ground）”、“整株植物（whole plant）”与“其他（other）”。上述类别设计兼具包容性，旨在便于对本研究分析的多类实验数据进行准确归类，重点关注地上……

# 基于表达谱的机器学习模型预测植物组织类型
## 拟南芥基因表达数据集
[https://doi.org/10.5061/dryad.4b8gthtn7](https://doi.org/10.5061/dryad.4b8gthtn7)

本数据集包含3个.parquet格式文件：
1. `gene_FPKM_200501.parquet`：原始基因表达数据库下载自拟南芥RNA测序数据库（Arabidopsis RNA-Seq Database，https://plantrnadb.com/athrdb/）（Zhang等，2020，DOI: 10.1016/j.molp.2020.08.001），该原始数据集包含37334个基因对应的28165条拟南芥基因表达谱。
2. `gene_FPKM_transposed.parquet`：为`gene_FPKM_200501.parquet`的转置版本，更符合典型机器学习数据集的格式——样本以行表示。
3. `gene_FPKM_transposed_UMR75.parquet`：对`gene_FPKM_transposed.parquet`中的基因表达谱进行过滤，移除唯一比对率（unique mapped rate）低于75%的样本，该数据集用于训练与测试机器学习模型……