Next generation and single cell sequencing have ushered in an era of big data in biology. These data present an unprecedented opportunity to learn new mechanisms and ask unasked questions. Matrix factorization (MF) techniques can reveal low-dimensional structure from high-dimensional data to uncover new biological knowledge. The knowledge of gained from low dimensional features in training data can also be transferred to new datasets to relate disparate model systems and data modalities. We illustrate the power of these techniques for interpretation of high dimensional data through case studies in postmortem tissues from GTEx, acquired therapeutic resistance in cancer, and developmental biology.