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An S4 class that contains all elements needed for CA.

cacomp-class.Rd

This class contains elements necessary to computer CA coordinates or Association Plot coordinates, as well as other informative data such as row/column inertia, gene-wise APL-scores, etc. ...

Creates new cacomp object.

Usage

new_cacomp(...)

Arguments

...

slot names and objects for new cacomp object.

Value

cacomp object

Slots

U

class "matrix". Left singular vectors of the original input matrix.

V

class "matrix". Right singular vectors of the original input matrix.

D

class "numeric". Singular values of the original inpt matrix.

std_coords_rows

class "matrix". Standardized CA coordinates of the rows.

std_coords_cols

class "matrix". Standardized CA coordinates of the columns.

prin_coords_rows

class "matrix". Principal CA coordinates of the rows.

prin_coords_cols

class "matrix". Principal CA coordinates of the columns.

apl_rows

class "matrix". Association Plot coordinates of the rows for the direction defined in slot "group"

apl_cols

class "matrix". Association Plot coordinates of the columns for the direction defined in slot "group"

APL_score

class "data.frame". Contains rows sorted by the APL score. Columns: Rowname (gene name in the case of gene expression data), APL score calculated for the direction defined in slot "group", the original row number and the rank of the row as determined by the score.

dims

class "numeric". Number of dimensions in CA space.

group

class "numeric". Indices of the chosen columns for APL calculations.

row_masses

class "numeric". Row masses of the frequency table.

col_masses

class "numeric". Column masses of the frequency table.

top_rows

class "numeric". Number of most variable rows chosen.

tot_inertia

class "numeric". Total inertia in CA space.

row_inertia

class "numeric". Row-wise inertia in CA space.

col_inertia

class "numeric". Column-wise inertia in CA space.

permuted_data

class "list". Storage slot for permuted data.

params

class "list". List of parameters.

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))

res <-  APL:::comp_std_residuals(mat=cnts)
SVD <- svd(res$S)
names(SVD) <- c("D", "U", "V")
SVD <- SVD[c(2, 1, 3)]

ca <- new_cacomp(U = SVD$U,
                 V = SVD$V,
                 D = SVD$D,
                 row_masses = res$rowm,
                 col_masses = res$colm)