Internal function for `cacomp`
run_cacomp.Rd`run_cacomp` performs correspondence analysis on a matrix and returns the transformed data.
Usage
run_cacomp(
obj,
coords = TRUE,
princ_coords = 3,
python = FALSE,
dims = 100,
top = 5000,
inertia = TRUE,
rm_zeros = TRUE,
residuals = "pearson",
cutoff = NULL,
clip = FALSE,
...
)Arguments
- 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.
- princ_coords
Integer. Number indicating whether principal coordinates should be calculated for the rows (=1), columns (=2), both (=3) or none (=0).
- python
DEPRACTED. 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 when calculating the full SVD. This parameter only works when dims==NULL or dims==rank(mat), where caculating a full SVD is demanded.
- dims
Integer. Number of CA dimensions to retain. If NULL: (0.2 * min(nrow(A), ncol(A)) - 1 ).
- top
Integer. Number of most variable rows to retain. Set NULL to keep all.
- inertia
Logical. Whether total, row and column inertias should be calculated and returned.
- rm_zeros
Logical. Whether rows & cols containing only 0s should be removed. Keeping zero only rows/cols might lead to unexpected results.
- residuals
character string. Specifies which kind of residuals should be calculated. Can be "pearson" (default), "freemantukey" or "NB" for negative-binomial.
- cutoff
numeric. Residuals that are larger than cutoff or lower than -cutoff are clipped to cutoff.
- clip
logical. Whether residuals should be clipped if they are higher/lower than a specified cutoff
- ...
Arguments forwarded to methods.
Value
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/genes were retained for the analysis. tot_inertia, row_inertia and col_inertia: Only if inertia = TRUE. Total, row and column inertia respectively.