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Find rows most highly associated with a condition

apl_score.Rd

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,
  dims = caobj@dims,
  group = caobj@group,
  reps = 10,
  quant = 0.99,
  python = FALSE,
  store_perm = TRUE
)

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. 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.

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.

Details

The score is calculated by permuting the values of each row to determine the cutoff angle of the 99 $$S_{alpha}(x,y)=x-\frac{y}{\tan\alpha}$$ 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.

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)
#> Warning: 
#> Parameter top is >nrow(obj) and therefore ignored.

# 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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