Trajectory-informed gene feature selection in single-cell analysis with SEEK-VFI
Danning, R.; Ke, Z. T.; Lin, X.; Ma, R.
Show abstract
The prioritization of highly-variable genes is an important step in single-cell trajectory inference. However, when variability arises from a continuous latent cell development trajectory, standard methods may fail to differentiate trajectory-relevant from uninformative genes. SEEK-VFI is an ensemble topic-modeling machine learning algorithm for trajectory inference preprocessing that prioritizes trajectory-relevant genes. It outperforms existing methods, and identifies key genes that improve trajectory topology reconstruction, enhance visualization, and augment downstream trajectory analyses.
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