Data for: Approaches for handling missing values and their impacts on biological inferences: a molecular rate case study

These data files are associated with the manuscript entitled: "Approaches for handling missing values and their impacts on biological inferences: a molecular rate case study" by Jacqueline A. May, Zeny Feng, and Sarah J. Adamowicz. This project entailed an evaluation of missing data handling approach on inferences using a molecular evolution case study. A target mixed-type dataset was first imputed using a real data-driven strategy for imputation method selection. Both trait-only (non-phylogenetic) and phylogenetic imputation methods were used to impute the dataset. Phylogenetic generalized least squares (PGLS) analyses were then applied to the complete-case and imputed datasets, specifying the traits as predictors and molecular evolutionary rates as the response variable. Those traits that associate significantly with molecular rates were identified and PGLS models compared to determine how the approach for handling missing data impacts biological inferences and conclusions. The files stored here are the trees built for phylogenetic imputation (RAxML tree and ultrametric tree versions) and the corresponding GenBank accession numbers.本数据集文件与题为《缺失值处理方法及其对生物学推断的影响：一项分子速率案例研究》的学术手稿相关联，作者为杰奎琳·A·梅（Jacqueline A. May）、曾妮·冯（Zeny Feng）与莎拉·J·亚当维奇（Sarah J. Adamowicz）。本研究以分子进化为案例场景，旨在评估缺失数据处理方法对生物学推断的影响。研究首先采用基于真实数据的策略筛选适配的插补方法，并对目标混合类型数据集完成插补；随后分别使用仅性状（非系统发育）插补法与系统发育插补法处理该数据集。后续针对完整案例数据集与插补后数据集开展系统发育广义最小二乘（Phylogenetic Generalized Least Squares，PGLS）分析，以性状作为预测变量、分子进化速率作为响应变量。研究筛选出与分子速率显著相关的性状，并通过对比不同PGLS模型，明确缺失值处理方法对生物学推断与研究结论的影响。本仓库存储的文件包括用于系统发育插补构建的进化树（RAxML树与超度量树版本）以及对应的GenBank登录号。