Cell-type specific gene expression profiles are needed for many computational methods operating on bulk RNA-Seq samples, such as deconvolution of cell-type fractions and digital cytometry. However, the gene expression profile of a cell type can vary substantially due to both technical factors and biological differences in cell state and surroundings, reducing the efficacy of such methods. Here, we investigated which factors contribute most to this variation. We evaluated different normalization methods, quantified the variance explained by different factors, evaluated the effect on deconvolution of cell type fractions, and examined the differences between UMI-based single-cell RNA-Seq and bulk RNA-Seq. We investigated a collection of publicly available bulk and single-cell RNA-Seq datasets containing B and T cells, and found that the technical variation across laboratories is substantial, even for genes specifically selected for deconvolution, and this variation has a confounding effect on deconvolution. Tissue of origin is also a substantial factor, highlighting the challenge of using cell type profiles derived from blood with mixtures from other tissues. We also show that much of the differences between UMI-based single-cell and bulk RNA-Seq methods can be explained by the number of read duplicates per mRNA molecule in the single-cell sample. Our work shows the importance of either matching or correcting for technical factors when creating cell-type specific gene expression profiles that are to be used together with bulk samples.

许多对大量 RNA-Seq 样本进行操作的计算方法都需要细胞类型特异性基因表达谱，例如细胞类型组分的反卷积和数字细胞计数。然而，由于技术因素以及细胞状态和环境的生物学差异，细胞类型的基因表达谱可能会有很大差异，从而降低了此类方法的功效。在这里，我们调查了哪些因素对这种变化影响最大。我们评估了不同的归一化方法，量化了不同因素解释的方差，评估了对细胞类型分数反卷积的影响，并检查了基于 UMI 的单细胞 RNA-Seq 和 Bulk RNA-Seq 之间的差异。我们研究了一系列公开的包含 B 和 T 细胞的批量和单细胞 RNA-Seq 数据集，发现实验室之间的技术差异很大，即使对于专门选择用于反卷积的基因也是如此，而且这种差异对反卷积有混杂影响。组织来源也是一个重要因素，凸显了使用源自血液和其他组织混合物的细胞类型谱的挑战。我们还表明，基于 UMI 的单细胞和批量 RNA 测序方法之间的大部分差异可以通过单细胞样本中每个 mRNA 分子的重复读取数量来解释。我们的工作表明，在创建与批量样品一起使用的细胞类型特异性基因表达谱时，匹配或校正技术因素的重要性。