Description Usage Arguments Details Value References

'run_cacomp' performs correspondence analysis on a matrix and returns the transformed data.

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`obj` |
A numeric matrix or Seurat/SingleCellExperiment object. For sequencing a count matrix, gene expression values with genes in rows and samples/cells in columns. Should contain row and column names. |

`coords` |
Logical. Indicates whether CA standard coordinates should be calculated. Default TRUE |

`princ_coords` |
Integer. Number indicating whether principal coordinates should be calculated for the rows (=1), columns (=2), both (=3) or none (=0). Default 1. |

`python` |
A logical value indicating whether to use singular-value decomposition from the python package torch. This implementation dramatically speeds up computation compared to 'svd()' in R. |

`dims` |
Integer. Number of CA dimensions to retain. Default NULL (keeps all dimensions). |

`top` |
Integer. Number of most variable rows to retain. Default NULL. |

`inertia` |
Logical.. Whether total, row and column inertias should be calculated and returned. Default TRUE. |

`rm_zeros` |
Logical. Whether rows & cols containing only 0s should be removed. Keeping zero only rows/cols might lead to unexpected results. Default TRUE. |

`...` |
Arguments forwarded to methods. |

The calculation is performed according to Greenacre. Singular value decomposition can be performed either with the base R function 'svd' or preferably by the much faster pytorch implementation (python = TRUE). When working on large matrices, CA coordinates and principal coordinates should only computed when needed to save computational time.

Returns a named list of class "cacomp" with components U, V and D: The results from the SVD. row_masses and col_masses: Row and columns masses. top_rows: How many of the most variable rows were retained for the analysis. tot_inertia, row_inertia and col_inertia: Only if inertia = TRUE. Total, row and column inertia respectively.

Greenacre, M. Correspondence Analysis in Practice, Third Edition, 2017.

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