R/get_group_comparison.R
get_group_comparison.Rd
Compare genotypes/phenotypes based on signature groups (samples are assigned to
several groups). For categorical
type, calculate fisher p value (using stats::fisher.test) and count table.
In larger than 2 by 2 tables, compute p-values by Monte Carlo simulation.
For continuous type, calculate anova p value (using stats::aov),
summary table and Tukey Honest significant difference (using stats::TukeyHSD).
The result of this function can be plotted by show_group_comparison()
.
get_group_comparison( data, col_group, cols_to_compare, type = "ca", NAs = NA, verbose = FALSE )
data | a |
---|---|
col_group | column name of signature groups. |
cols_to_compare | column names of genotypes/phenotypes want to summarize based on groups. |
type | a characater vector with length same as |
NAs | default is |
verbose | if |
a list
contains data, summary, p value etc..
Shixiang Wang w_shixiang@163.com
# \donttest{ load(system.file("extdata", "toy_copynumber_signature_by_W.RData", package = "sigminer", mustWork = TRUE )) # Assign samples to clusters groups <- get_groups(sig, method = "k-means") set.seed(1234) groups$prob <- rnorm(10) groups$new_group <- sample(c("1", "2", "3", "4", NA), size = nrow(groups), replace = TRUE) # Compare groups (filter NAs for categorical coloumns) groups.cmp <- get_group_comparison(groups[, -1], col_group = "group", cols_to_compare = c("prob", "new_group"), type = c("co", "ca"), verbose = TRUE ) # Compare groups (Set NAs of categorical columns to 'Rest') groups.cmp2 <- get_group_comparison(groups[, -1], col_group = "group", cols_to_compare = c("prob", "new_group"), type = c("co", "ca"), NAs = "Rest", verbose = TRUE ) # }