# apl_score: Find rows most highly associated with a condition In ClemensKohl/APL: Association Plots

 apl_score R Documentation

## Find rows most highly associated with a condition

### Description

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

### Usage

apl_score(
caobj,
mat = NULL,
dims = caobj@dims,
group = caobj@group,
reps = 10,
quant = 0.99,
python = FALSE,
store_perm = TRUE,
method = "random"
)


### Arguments

 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. 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. method Method to calculate the cutoff. Either "random" for random direction method or "permutation" for the permutation method.

### Details

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}{\tanα}

By default the permutation is repeated 10 times (for random direction min. 300 repetition is recommended!), but for very large matrices this can be reduced. The method "permutation" permutes the columns in each row and calculates AP-coordinates for each such permutation. The cutoff is then taken by the quantile specified by "quan". The "random" method in contrast calculates AP-coordinates for the original data, but by looking into random directions.

If store_perm is TRUE the permuted data is stored in the cacomp object and can be used for future scoring.

### Value

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.

### References

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

### Examples

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)


ClemensKohl/APL documentation built on Dec. 31, 2022, 8:13 a.m.