apl_score | R Documentation |

Ranks rows by a calculated score which balances the association of the row with the condition and how associated it is with other conditions.

```
apl_score(
caobj,
mat,
dims = caobj@dims,
group = caobj@group,
reps = 10,
quant = 0.99,
python = FALSE,
store_perm = TRUE
)
```

`caobj` |
A "cacomp" object with principal row coordinates and standardized column coordinates calculated. |

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

`dims` |
Integer. Number of CA dimensions to retain. Needs to be the same as in caobj! |

`group` |
Vector of indices of the columns to calculate centroid/x-axis direction. |

`reps` |
Integer. Number of permutations to perform. Default = 10. |

`quant` |
Numeric. Single number between 0 and 1 indicating the quantile used to calculate the cutoff. Default 0.99. |

`python` |
A logical value indicating whether to use singular-value decomposition from the python package torch. |

`store_perm` |
Logical. Whether permuted data should be stored in the CA object. This implementation dramatically speeds up computation compared to 'svd()' in R. |

The score is calculated by permuting the values of each row to determine the cutoff angle of the 99

`S_{alpha}(x,y)=x-\frac{y}{\tan\alpha}`

By default the permutation is repeated 10 times, but for very large matrices this can be reduced. If store_perm is TRUE the permuted data is stored in the cacomp object and can be used for future scoring.

Returns the input "cacomp" object with "APL_score" component added. APL_score contains a data frame with ranked rows, their score and their original row number.

Association Plots: Visualizing associations in high-dimensional
correspondence analysis biplots

Elzbieta Gralinska, Martin Vingron

bioRxiv 2020.10.23.352096; doi: https://doi.org/10.1101/2020.10.23.352096

```
set.seed(1234)
# Simulate counts
cnts <- mapply(function(x){rpois(n = 500, lambda = x)},
x = sample(1:20, 50, replace = TRUE))
rownames(cnts) <- paste0("gene_", 1:nrow(cnts))
colnames(cnts) <- paste0("cell_", 1:ncol(cnts))
# Run correspondence analysis.
ca <- cacomp(obj = cnts, princ_coords = 3)
# Calculate APL coordinates:
ca <- apl_coords(ca, group = 1:10)
# Rank genes by S-alpha score
ca <- apl_score(ca, mat = cnts)
```

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