Data and code from: Severity of charcoal rot disease in soybean genotypes inoculated with <i>Macrophomina phaseolina</i> isolates differs among growth environments

This dataset includes all the raw data and all the R statistical software code that we used to analyze the data and produce all the outputs that are in the figures, tables, and text of the associated manuscript:Mengistu, A., Q. D. Read, C. R. Little, H. M. Kelly, P. M. Henry, and N. Bellaloui. 2025. Severity of charcoal rot disease in soybean genotypes inoculated with <i>Macrophomina phaseolina</i> isolates differs among growth environments. <i>Plant Disease</i>. DOI: 10.1094/PDIS-10-24-2230-RE.<br>The data included here come from a series of tests designed to evaluate methods for identifying soybean genotypes that are resistant or susceptible to charcoal rot, a widespread and economically significant disease. Four independent experiments were performed to determine the variability in disease severity by soybean genotype and by isolated variant of the charcoal rot fungus: two field tests, a greenhouse test, and a growth chamber test. The tests differed in the number of genotypes and isolates used, as well as the method of inoculation. The accuracy of identifying resistant and susceptible genotypes varied by study, and the same isolate tested across different studies often had highly variable disease severity. Our results indicate that the non-field methods are not reliable ways to identify sources of charcoal rot resistance in soybean.The models fit in the R script archived here are Bayesian general linear mixed models with AUDPC (area under the disease progress curve) as the response variable. One-dimensional clustering is used to divide the genotypes into resistant and susceptible based on their model-predicted AUDPC values, and this result is compared with the preexisting resistance classification. Posterior distributions of the marginal means for different combinations of genotype, isolate, and other covariates are estimated and compared. Code to reproduce the tables and figures of the manuscript is also included.The following files are included:<b>README.pdf</b>: Full description, with column metadata for the data spreadsheets and text description of each R script<b>data2023-04-18.xlsx</b>: Excel sheet with data from three of the four trials<b>cleaned_data.RData</b>: all data in analysis-ready format; generates a set of data frames when imported into an R environment<b>Modified Cut-Tip Inoculation on DT974290 and LS980358 on first 32 isolates.xlsx</b>: Excel spreadsheet with data from the fourth trial<b>data_cleaning.R</b>: Script required to format data from .xlsx files into analysis-ready format (running this script is not necessary to reproduce the analysis; instead you may begin with the following script importing the cleaned .RData object)<b>AUDPC_fits.R</b>: Script containing code for all model fitting, model predictions and comparisons, and figure and table generation<br>本数据集包含我们用于数据分析并生成相关手稿中所有图表、表格及正文所需的全部原始数据与R统计软件（R statistical software）代码，相关手稿为：Mengistu, A., Q. D. Read, C. R. Little, H. M. Kelly, P. M. Henry, 与 N. Bellaloui. 2025. 大豆基因型接种<i>Macrophomina phaseolina</i>（菜豆壳球孢菌）菌株后的炭腐病严重度因生长环境而异。《植物病害》（Plant Disease）. DOI: 10.1094/PDIS-10-24-2230-RE. 本数据集收录的数据源自一系列旨在评估鉴定大豆抗/感炭腐病（一种分布广泛且经济影响重大的病害）基因型的方法的试验。我们开展了四项独立实验，以探究不同大豆基因型及炭腐病菌株的病害严重度变异情况：两项田间试验、一项温室试验及一项生长箱试验。各试验在所使用的基因型与菌株数量、接种方法上均存在差异。不同研究中，抗、感基因型的鉴定准确度存在差异，且同一菌株在不同研究中测得的病害严重度往往差异显著。本研究结果表明，非田间方法无法可靠地用于鉴定大豆炭腐病抗源。 本数据集附带的R脚本中拟合的模型为以病害进展曲线下面积（AUDPC, area under the disease progress curve）为响应变量的贝叶斯广义线性混合模型（Bayesian general linear mixed models）。基于模型预测的AUDPC值，我们采用一维聚类分析将基因型划分为抗病与感病两类，并将该结果与预先存在的抗性分类结果进行对比。研究人员对基因型、菌株及其他协变量的边际均值的后验分布进行了估计与比较。本数据集同时包含用于复现手稿中表格与图表的代码。 本数据集包含以下文件： <b>README.pdf</b>：完整说明文档，包含数据电子表格的列元数据及各R脚本的文字说明 <b>data2023-04-18.xlsx</b>：包含四项试验中三项试验数据的Excel表格 <b>cleaned_data.RData</b>：格式适配分析的完整数据集；导入R环境后可生成一组数据框 <b>Modified Cut-Tip Inoculation on DT974290 and LS980358 on first 32 isolates.xlsx</b>：包含第四项试验数据的Excel电子表格 <b>data_cleaning.R</b>：用于将.xlsx文件中的数据格式化为适配分析格式的脚本（运行该脚本并非复现分析的必要步骤，您也可直接通过下述脚本导入已清理的.RData对象开始分析） <b>AUDPC_fits.R</b>：包含所有模型拟合、模型预测与比较，以及图表与表格生成代码的脚本