moss_signatures: Returns signatures of features by groups of subjects

In MOSS: Multi-Omic Integration via Sparse Singular Value Decomposition

Returns signatures of features by groups of subjects

Description

This function is meant to used after moss_select. Its main purpose is to visualize how each selected feature ( non-zero loading feature) contributes to each group of subjects by latent dimension.

Usage

moss_signatures(
  data.blocks,
  moss_select.out,
  clus_lab = NULL,
  plot = FALSE,
  feature.labels = NULL,
  th = 1,
  only.candidates = FALSE
)

Arguments

data.blocks	A list of omic blocks as provided to moss.
moss_select.out	The output of moss_select.
clus_lab	A vector of same length than number of subjects with labels used to visualize clusters. Defaults to NULL.
plot	Should the results be plotted? Logical. Defaults to FALSE
feature.labels	List with with features names for each omic. Defaults to NULL.
th	Show the th Default to th=1 (all the features). Numeric.
only.candidates	Should we plot only candidate features? Logical.

Value

Returns a list with 'signatures', and if plot=TRUE, a ggplot object named 'sig_plot'. The element 'signatures' is a data frame with columns corresponding to 'Cluster' (groups of subjects), 'Omic', 'Dim' (PC index or latent dimension), 'Feature_name', 'Feature_pos' (column index of the selected feature within the corresponding omic), 'Loadings' (non-zero loadings from moss), 'Means', 'L1' and 'L2' (mean +/- standard error of the selected feature values within an omic).

Examples

library("MOSS")
# Extracting simulated omic blocks.
sim_data <- simulate_data()
sim_blocks <- sim_data$sim_blocks

# Extracting subjects and features labels.
lab.sub <- sim_data$labels$lab.sub

out <- moss(sim_blocks[-4],
  method = "pca",
  nu.v = 10,
  exact.dg = TRUE,
  plot = TRUE,
  alpha.v = 0.5
)
out2 <- moss_select(data.blocks = sim_blocks[-4],
                    SVD = out$sparse,
                    plot = TRUE)

# Display signature plots.
out3 <- moss_signatures(data.blocks = sim_blocks[-4],
                        clus_lab=lab.sub,
                        moss_select.out = out2,
                        plot = TRUE)
out3$sig_plot 
                               

MOSS documentation built on March 26, 2022, 1:10 a.m.