RNA profiles are an informative phenotype of cellular and tissue states but can be costly to generate at massive scale. Here, we describe how gene expression levels can be efficiently acquired with random composite measurements-in which abundances are combined in a random weighted sum. We show (1) that the similarity between pairs of expression profiles can be approximated with very few composite measurements; (2) that by leveraging sparse, modular representations of gene expression, we can use random composite measurements to recover high-dimensional gene expression levels (with 100 times fewer measurements than genes); and (3) that it is possible to blindly recover gene expression from composite measurements, even without access to training data. Our results suggest new compressive modalities as a foundation for massive scaling in high-throughput measurements and new insights into the interpretation of high-dimensional data.

中文翻译：

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通过随机复合测量有效生成转录组图谱。

RNA 谱是细胞和组织状态的信息表型，但大规模生成成本高昂。在这里，我们描述了如何通过随机复合测量有效地获取基因表达水平，其中丰度以随机加权和的形式组合。我们证明（1）表达谱对之间的相似性可以用很少的复合测量来近似；(2) 通过利用基因表达的稀疏、模块化表示，我们可以使用随机复合测量来恢复高维基因表达水平（测量值比基因少 100 倍）；(3)即使无法获得训练数据，也可以从复合测量中盲目地恢复基因表达。我们的结果表明新的压缩模式可以作为高通量测量大规模扩展的基础，并为高维数据的解释提供新的见解。