Description Usage Arguments Details Examples

This function will do dimensionality reduction.

1 2 3 4 5 6 7 8 |

`input` |
the input ex_sc |

`genelist` |
the subset of genes to perform dimensionality reduction on |

`pre_reduce` |
the algorithm choice for reduction before tSNE (either "ICA", "PCA", "iPCA"). |

`nComp` |
the number of components to reduce too before tSNE, 5-20 recommended. |

`tSNE_perp` |
number of cells expressed above threshold for a given gene, 10-100 recommended. |

`iterations` |
The number of iterations for tSNE to perform. |

`print_progress` |
will print progress if TRUE |

`nVar` |
cutoff for percent of variance explained from PCs |

If the method is ICA, independent component analysis will be performed, and then tSNE will do the final dimension reduction. If PCA is selected, PCA will be performed before on the expression matrix transpose before tSNE. This PCA will use the cells positions on the principal components. If iPCA is selected, PCA will be be performed but without transposing the data. This will create "meta cells" instead of meta genes created in the typical PCA. Then tSNE will be performed on each cells contribution (loading) to the meta cell. We find that iPCA is much more robust and leads to cleaner clusters than traditional PCA.

1 | ```
ex_sc_example <- dim_reduce(input = ex_sc_example, genelist = gene_subset, pre_reduce = "iPCA", nComp = 15, tSNE_perp = 30, iterations = 500, print_progress=TRUE)
``` |

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