The gene expression profile of a tissue averages the expression profiles of all cells in this tissue. Digital tissue deconvolution addresses the following inverse problem: given the expression profile y of a tissue, what is the cellular composition c of that tissue? If X is a matrix whose columns are reference profiles of individual cell types, the composition c can be computed by minimizing ℒ ( y - X c ) for a given loss function ℒ . Current methods use predefined all-purpose loss functions. They successfully quantify the dominating cells of a tissue, while often falling short in detecting small cell populations. In this study we use training data to learn the loss function ℒ along with the composition c . This allows us to adapt to application-specific requirements such as focusing on small cell populations or distinguishing phenotypically similar cell populations. Our method quantifies large cell fractions as accurately as existing methods and significantly improves the detection of small cell populations and the distinction of similar cell types.

译文


组织的基因表达谱将组织中所有细胞的表达谱平均化。数字组织反卷积解决了以下反问题:给定表达谱
ÿ
组织的细胞组成是什么
C
那张纸巾?如果
X
是一个矩阵,其列是各个细胞类型,组成的参考资料
C
可以通过最小化来计算





ÿ
--
X
C




对于给定的损失函数



。当前的方法使用预定义的通用损失函数。他们成功地量化了组织中占主导地位的细胞,而在检测小细胞群体时常常不尽人意。在这项研究中,我们使用训练数据来学习损失函数



连同组成
C
。这使我们能够适应特定于应用程序的要求,例如专注于小细胞群体或区分表型相似的细胞群体。我们的方法与现有方法一样准确地定量了大细胞部分,并显着改善了小细胞群体的检测和相似细胞类型的区分。

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