样本反卷积方法可估计大量组织样本中的细胞类型比例和基因表达，但其性能和生物学应用仍未得到探索，特别是在人脑转录组数据中。在这里，使用来自批量组织 RNA 测序 (RNA-seq)、单细胞/细胞核 (sc/sn) RNA-seq 和免疫组织化学的样本匹配数据评估了九种反卷积方法。使用来自 149 个成人死后大脑和 72 个类器官样本的每个细胞总共 1,130,767 个细胞核。结果显示，dtangle 在估计细胞比例方面表现最佳，bMIND 在估计样本细胞类型基因表达方面表现最佳。对于 8 种脑细胞类型，通过解卷积表达 (decon-eQTL) 鉴定了 25,273 个细胞类型 eQTL。结果表明，decon-eQTL 比单独的大块组织或单细胞 eQTL 更能解释精神分裂症 GWAS 遗传力。还使用解卷积数据检查了与阿尔茨海默病、精神分裂症和大脑发育相关的差异基因表达。我们的研究结果在大量组织和单细胞数据中得到了重复，为解卷积数据在多种脑部疾病中的生物学应用提供了见解。

Sample-wise deconvolution methods estimate cell-type proportions and gene expressions in bulk tissue samples, yet their performance and biological applications remain unexplored, particularly in human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk tissue RNA sequencing (RNA-seq), single-cell/nuclei (sc/sn) RNA-seq, and immunohistochemistry. A total of 1,130,767 nuclei per cells from 149 adult postmortem brains and 72 organoid samples were used. The results showed the best performance of dtangle for estimating cell proportions and bMIND for estimating sample-wise cell-type gene expressions. For eight brain cell types, 25,273 cell-type eQTLs were identified with deconvoluted expressions (decon-eQTLs). The results showed that decon-eQTLs explained more schizophrenia GWAS heritability than bulk tissue or single-cell eQTLs did alone. Differential gene expressions associated with Alzheimer's disease, schizophrenia, and brain development were also examined using the deconvoluted data. Our findings, which were replicated in bulk tissue and single-cell data, provided insights into the biological applications of deconvoluted data in multiple brain disorders.