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Plot the conditional CDFs of every gene in a gene set

Source: R/plot.cit_gsa.R
plot.cit_gsa.Rd

Draws, for one gene set, the conditional CDF of each gene given X (and Z) as a semi-transparent step function, overlaid with a bold gene-set summary curve per level of X. Layout, colors, reference curve and faceting follow plot_compare_ccdf, of which this is the many-genes counterpart.

Usage

# S3 method for class 'cit_gsa'
plot(
  x,
  M,
  X,
  Z = NULL,
  geneset,
  which_set = 1,
  method = c("OLS", "logistic"),
  fast = TRUE,
  space_y = FALSE,
  number_y = 20,
  discretize = !is.factor(X[, 1]) || (!is.null(Z) && !is.factor(Z[, 1])),
  probs = c(0, 0.25, 0.5, 0.75, 1),
  bin_labels = c("Q1", "Q2", "Q3", "Q4"),
  summary_fun = stats::median,
  alpha = 0.25,
  linewidth = 0.9,
  n_grid = 200,
  ...
)

Arguments

x

an object of class cit_gsa, as returned by cit_gsa.

M

a numeric matrix or data frame of size n x r containing the preprocessed expressions, with gene names as column names. Required: cit_gsa() does not retain the data it was called on.

X

a data frame whose first column is the variable of interest.

Z

a data frame whose first column is the covariate, or NULL.

geneset

the gene set to draw: a character vector of gene names, or a named list of such vectors, in which case which_set picks one.

which_set

index or name selecting the gene set when geneset is a list. Default is the first.

method, fast, space_y, number_y

passed to ccdf.

discretize

a logical flag, following the same logic as in plot_compare_ccdf: continuous variables are quartile-binned and combined into a single interaction factor, so every curve drawn is an exact empirical CDF. Because a pointwise median of monotonic curves is itself monotonic, the summary curve then needs no monotonicity correction. Default is FALSE when X (and Z) are already factors, and TRUE otherwise.

probs, bin_labels

passed to the quartile binning, exactly as in plot_compare_ccdf.

summary_fun

the function used to summarize across genes at each expression value. Default is median.

alpha

opacity of the individual gene curves. Default 0.25.

linewidth

width of the summary and reference curves. Default 0.9.

n_grid

number of points on the shared grid used to summarize across genes. Genes in a set do not share expression values, so all curves are re-evaluated on a common grid first. Default 200.

...

further arguments to be passed.

Value

a ggplot object; when Z is supplied, a patchwork composition stacking the CCDF given X alone (panel A) above the CCDF given X and Z (panel B, faceted by Z).

See also

plot_compare_ccdf for the single-gene version.

Examples

set.seed(123)
n <- 60
X <- data.frame(X = as.factor(rbinom(n, size = 1, prob = 0.5)))

# 20 genes: two sets of 10. Only the genes of set1 depend on X.
M <- matrix(rnorm(n * 20), nrow = n,
  dimnames = list(NULL, paste0("g", 1:20)))
M[, 1:10] <- M[, 1:10] + 1.5 * (as.numeric(X$X) - 1)
geneset <- list(set1 = paste0("g", 1:10), set2 = paste0("g", 11:20))

res <- cit_gsa(M = M, X = X, geneset = geneset,
  test = "asymptotic", parallel = FALSE)

# set1: the two summary curves separate, and the individual genes with them
plot(res, M = M, X = X, geneset = geneset, which_set = "set1")


# \donttest{
# set2 is null: the two summary curves stay close to each other and to the
# reference CDF
plot(res, M = M, X = X, geneset = geneset, which_set = "set2")


# with a continuous covariate: quartile-binned and faceted by Z
Z <- data.frame(Z = rnorm(n))
plot(res, M = M, X = X, Z = Z, geneset = geneset, which_set = 1)

plot(res, M = M, X = X, Z = Z, geneset = geneset, which_set = 2)

# }