Dimension reduction techniques are widely used to interpret high-dimensional biological data. Features learned from these methods are used to discover both technical artifacts and novel biological phenomena. Such feature discovery is critically importent in analysis of large single-cell datasets, where lack of a ground truth limits validation and interpretation. Transfer learning (TL) can be used to relate the features learned from one source dataset to a new target dataset to perform biologically driven validation by evaluating their use in or association with additional sample annotations in that independent target dataset.

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projectR：R / Bioconductor软件包，用于通过PCA，NMF，关联和聚类进行转移学习。

降维技术被广泛用于解释高维生物学数据。从这些方法中学到的特征可用于发现技术文物和新颖的生物学现象。在缺乏基本事实限制了验证和解释的大型单细胞数据集的分析中，这种特征发现至关重要。转移学习（TL）可用于将从一个源数据集学习到的特征与新的目标数据集相关联，以通过评估其在该独立目标数据集中的使用或与其他样本注释的关联来执行生物驱动的验证。