smooth_via_pca | R Documentation |

Perform PCA, identify significant dimensions, and reverse the rotation using only significant dimensions.

smooth_via_pca( x, elbow_th = 0.025, dims_use = NULL, max_pc = 100, do_plot = FALSE, scale. = FALSE )

`x` |
A data matrix with genes as rows and cells as columns |

`elbow_th` |
The fraction of PC sdev drop that is considered significant; low values will lead to more PCs being used |

`dims_use` |
Directly specify PCs to use, e.g. 1:10 |

`max_pc` |
Maximum number of PCs computed |

`do_plot` |
Plot PC sdev and sdev drop |

`scale.` |
Boolean indicating whether genes should be divided by standard deviation after centering and prior to PCA |

Smoothed data

vst_out <- vst(pbmc) y_smooth <- smooth_via_pca(vst_out$y, do_plot = TRUE)

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