Nothing
# == title
# Test whether a list of factors are correlated
#
# == param
# -x A data frame or a vector which contains discrete or continuous variables.
# if ``y`` is omit, pairwise testing for all columns in ``x`` is performed.
# -y A data frame or a vector which contains discrete or continuous variables.
# -all_factors Are all columns in ``x`` and ``y`` enforced to be factors?
# -verbose Whether to print messages.
#
# == details
# Pairwise test is applied to every two columns in the data frames. Methods are:
#
# - two numeric variables: correlation test by `stats::cor.test` is applied (Spearman method);
# - two character or factor variables: `stats::chisq.test` is applied;
# - one numeric variable and one character/factor variable: oneway ANOVA test by `stats::oneway.test` is applied.
#
# This function can be used to test the correlation between the predicted classes and other known factors.
#
# == return
# A matrix of p-values. If there are NA values, basically it means there are no efficient data points to perform the test.
#
# == author
# Zuguang Gu <z.gu@dkfz.de>
#
# == examples
# df = data.frame(
# v1 = rnorm(100),
# v2 = sample(letters[1:3], 100, replace = TRUE),
# v3 = sample(LETTERS[5:6], 100, replace = TRUE)
# )
# test_between_factors(df)
# x = runif(100)
# test_between_factors(x, df)
test_between_factors = function(x, y = NULL, all_factors = FALSE, verbose = FALSE) {
if(is.null(y)) {
df = x
if(is.matrix(df)) {
df = as.data.frame(df)
}
if(all_factors) {
for(i in seq_len(ncol(df))) {
df[[i]] = factor(df[[i]])
}
}
nm = colnames(df)
n = ncol(df)
p.value = matrix(NA, nrow = n, ncol = n, dimnames = list(nm, nm))
for(i in seq_len(n-1)) {
if(length(df[[i]]) < 2) {
p.value[i, ] = NA
} else {
for(j in (i+1):n) {
if(is.numeric(df[[i]]) && is.numeric(df[[j]])) {
if(verbose) qqcat("@{nm[i]} ~ @{nm[j]}: correlation test\n")
p.value[i, j] = cor.test(df[[i]], df[[j]], method = "spearman")$p.value
} else if(is.numeric(df[[i]]) && (is.character(df[[j]]) || is.factor(df[[j]]))) {
if(verbose) qqcat("@{nm[i]} ~ @{nm[j]}: oneway ANOVA test\n")
l = df[[j]] %in% df[[j]][duplicated(df[[j]])]
try({p.value[i, j] = oneway.test(df[[i]][l] ~ df[[j]][l])$p.value}, silent = TRUE)
} else if(is.numeric(df[[j]]) && (is.character(df[[i]]) || is.factor(df[[i]]))) {
if(verbose) qqcat("@{nm[i]} ~ @{nm[j]}: oneway ANOVA test\n")
l = df[[i]] %in% df[[i]][duplicated(df[[i]])]
try({p.value[i, j] = oneway.test(df[[j]][l] ~ df[[i]][l])$p.value}, silent = TRUE)
} else if ((is.character(df[[i]]) || is.factor(df[[i]])) && (is.character(df[[j]]) || is.factor(df[[j]]))) {
if(verbose) qqcat("@{nm[i]} ~ @{nm[j]}: Chi-squared test\n")
try({p.value[i, j] = chisq.test(df[[i]], df[[j]])$p.value}, silent = TRUE)
}
p.value[j, i] = p.value[i, j]
}
}
}
diag(p.value) = 0
} else {
if(is.matrix(x)) {
x = as.data.frame(x)
}
if(is.matrix(y)) {
y = as.data.frame(y)
}
if(is.atomic(x)) {
df1 = data.frame(x)
colnames(df1) = deparse(substitute(x))
} else {
df1 = x
}
if(is.atomic(y)) {
df2 = data.frame(y)
colnames(df2) = deparse(substitute(y))
} else {
df2 = y
}
if(all_factors) {
for(i in seq_len(ncol(df1))) {
df1[[i]] = factor(df1[[i]])
}
for(i in seq_len(ncol(df2))) {
df2[[i]] = factor(df2[[i]])
}
}
nm1 = colnames(df1)
nm2 = colnames(df2)
n1 = ncol(df1)
n2 = ncol(df2)
p.value = matrix(NA, nrow = n1, ncol = n2, dimnames = list(nm1, nm2))
for(i in seq_len(n1)) {
if(length(df1[[i]]) < 2) {
p.value[i, ] = NA
} else {
for(j in seq_len(n2)) {
if(is.numeric(df1[[i]]) && is.numeric(df2[[j]])) {
if(verbose) qqcat("@{nm1[i]} ~ @{nm2[j]}: correlation test\n")
p.value[i, j] = cor.test(df1[[i]], df2[[j]])$p.value
} else if(is.numeric(df1[[i]]) && (is.character(df2[[j]]) || is.factor(df2[[j]]))) {
if(verbose) qqcat("@{nm1[i]} ~ @{nm2[j]}: oneway ANOVA test\n")
l = df2[[j]] %in% df2[[j]][duplicated(df2[[j]])]
try({p.value[i, j] = oneway.test(df1[[i]][l] ~ df2[[j]][l])$p.value}, silent = TRUE)
} else if((is.character(df1[[i]]) || is.factor(df1[[i]])) && is.numeric(df2[[j]])) {
if(verbose) qqcat("@{nm2[j]} ~ @{nm1[i]}: oneway ANOVA test\n")
l = df1[[i]] %in% df1[[i]][duplicated(df1[[i]])]
try({p.value[i, j] = oneway.test(df2[[j]][l] ~ df1[[i]][l])$p.value}, silent = TRUE)
} else if ((is.character(df1[[i]]) || is.factor(df1[[i]])) && (is.character(df2[[j]]) || is.factor(df2[[j]]))) {
if(verbose) qqcat("@{nm1[i]} ~ @{nm2[j]}: Chi-squared test\n")
try({suppressWarnings(p.value[i, j] <- chisq.test(df1[[i]], df2[[j]])$p.value)}, silent = TRUE)
}
}
}
}
}
return(p.value)
}
# == title
# Test correspondance between predicted subgroups and known factors
#
# == param
# -object A `ConsensusPartition-class` object.
# -k Number of subgroups. It uses all ``k`` if it is not specified.
# -known A vector or a data frame with known factors. By default it is the annotation table set in `consensus_partition` or `run_all_consensus_partition_methods`.
# -silhouette_cutoff Cutoff for sihouette scores. Samples with value less than it are omit.
# -verbose Whether to print messages.
#
# == details
# The test is performed by `test_between_factors` between the predicted classes and user's annotation table.
#
# == value
# A data frame with the following columns:
#
# - number of samples used to test after filtered by ``silhouette_cutoff``,
# - p-values from the tests,
# - number of subgroups.
#
# == author
# Zuguang Gu <z.gu@dkfz.de>
#
# == example
# data(golub_cola)
# res = golub_cola["ATC:skmeans"]
# anno = get_anno(res)
# anno
# test_to_known_factors(res, k = 3)
# # or explicitly specify known argument
# test_to_known_factors(res, k = 3, known = anno)
setMethod(f = "test_to_known_factors",
signature = "ConsensusPartition",
definition = function(object, k, known = get_anno(object),
silhouette_cutoff = 0.5, verbose = FALSE) {
if(missing(k)) {
all_k = object@k
m = do.call("rbind", lapply(all_k, function(k) test_to_known_factors(object, k, known = known,
silhouette_cutoff = silhouette_cutoff, verbose = verbose)))
return(m)
}
class_df = get_classes(object, k)
l = class_df$silhouette >= silhouette_cutoff
class = as.character(class_df$class)[l]
if(is.null(known)) {
stop_wrap("`known` should not be NULL.")
}
if(is.data.frame(known)) {
known = known[l, , drop = FALSE]
} else if(is.matrix(known)) {
known = known[l, ,drop = FALSE]
} else {
known = known[l]
}
m = test_between_factors(class, known, verbose = verbose)
rownames(m) = paste(object@top_value_method, object@partition_method, sep = ":")
colnames(m) = paste0(colnames(m), "(p-value)")
m = cbind(n_sample = sum(l), m, k = k)
return(m)
})
# == title
# Test correspondance between predicted classes and known factors
#
# == param
# -object A `ConsensusPartitionList-class` object.
# -k Number of subgroups. It uses all ``k`` if it is not set.
# -known A vector or a data frame with known factors. By default it is the annotation table set in `consensus_partition` or `run_all_consensus_partition_methods`.
# -silhouette_cutoff Cutoff for sihouette scores. Samples with value less than this are omit.
# -verbose Whether to print messages.
#
# == details
# The function basically sends each `ConsensusPartition-class` object to
# `test_to_known_factors,ConsensusPartition-method` and merges results afterwards.
#
# == value
# A data frame with the following columns:
#
# - number of samples used to test after filtered by ``silhouette_cutoff``,
# - p-values from the tests,
# - number of subgroups.
#
# If there are NA values, basically it means there are no efficient data points to perform the test.
#
# == seealso
# `test_between_factors`, `test_to_known_factors,ConsensusPartition-method`
#
# == author
# Zuguang Gu <z.gu@dkfz.de>
#
# == example
# data(golub_cola)
# test_to_known_factors(golub_cola)
setMethod(f = "test_to_known_factors",
signature = "ConsensusPartitionList",
definition = function(object, k, known = get_anno(object),
silhouette_cutoff = 0.5, verbose = FALSE) {
if(missing(k)) {
m_list = lapply(object@list, function(x) {
test_to_known_factors(x, known = known, silhouette_cutoff = silhouette_cutoff, verbose = verbose)
})
} else {
m_list = lapply(object@list, function(x) {
test_to_known_factors(x, k = k, known = known, silhouette_cutoff = silhouette_cutoff, verbose = verbose)
})
}
do.call("rbind", m_list)
})
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