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# == title (data:golub_cola)
# Example ConsensusPartitionList object from Golub dataset
#
# == details
# Following code was used to generate ``golub_cola``:
#
# library(cola)
#
# library(golubEsets) # from bioc
# data(Golub_Merge)
# m = exprs(Golub_Merge)
# colnames(m) = paste0("sample_", colnames(m))
# anno = pData(Golub_Merge)
#
# m[m <= 1] = NA
# m = log10(m)
#
# m = adjust_matrix(m)
#
# library(preprocessCore) # from bioc
# cn = colnames(m)
# rn = rownames(m)
# m = normalize.quantiles(m)
# colnames(m) = cn
# rownames(m) = rn
#
# set.seed(123)
# golub_cola = run_all_consensus_partition_methods(
# m, mc.cores = 2,
# anno = anno[, c("ALL.AML"), drop = FALSE],
# anno_col = c("ALL" = "red", "AML" = "blue")
# )
#
# == seealso
# https://jokergoo.github.io/cola_examples/Golub_leukemia/
#
# == author
# Zuguang Gu <z.gu@dkfz.de>
#
# == example
# data(golub_cola)
# golub_cola
# == title (data:golub_cola_rh)
# Example HierarchicalPartition object from Golub dataset
#
# == details
# Following code was used to generate ``golub_cola_rh``:
#
# library(cola)
# data(golub_cola)
# m = get_matrix(golub_cola)
# set.seed(123)
# golub_cola_rh = hierarchical_partition(
# m, mc.cores = 2,
# anno = get_anno(golub_cola),
# anno_col = get_anno_col(golub_cola)
# )
#
# == author
# Zuguang Gu <z.gu@dkfz.de>
#
# == example
# data(golub_cola_rh)
# golub_cola_rh
# == title (data:golub_cola_ds)
# Example DownSamplingConsensusPartition object from Golub dataset
#
# == details
# Following code was used to generate ``golub_cola_ds``:
#
# library(cola)
# data(golub_cola)
# m = get_matrix(golub_cola)
# set.seed(123)
# golub_cola_ds = consensus_partition_by_down_sampling(
# m, subset = 50, mc.cores = 2,
# anno = get_anno(golub_cola),
# anno_col = get_anno_col(golub_cola),
# )
#
# == author
# Zuguang Gu <z.gu@dkfz.de>
#
# == example
# data(golub_cola_ds)
# golub_cola_ds
# == title (data:cola_rl)
# Example ConsensusPartitionList object
#
# == details
# Following code was used to generate ``cola_rl``:
#
# set.seed(123)
# m = cbind(rbind(matrix(rnorm(20*20, mean = 1, sd = 0.5), nr = 20),
# matrix(rnorm(20*20, mean = 0, sd = 0.5), nr = 20),
# matrix(rnorm(20*20, mean = 0, sd = 0.5), nr = 20)),
# rbind(matrix(rnorm(20*20, mean = 0, sd = 0.5), nr = 20),
# matrix(rnorm(20*20, mean = 1, sd = 0.5), nr = 20),
# matrix(rnorm(20*20, mean = 0, sd = 0.5), nr = 20)),
# rbind(matrix(rnorm(20*20, mean = 0.5, sd = 0.5), nr = 20),
# matrix(rnorm(20*20, mean = 0.5, sd = 0.5), nr = 20),
# matrix(rnorm(20*20, mean = 1, sd = 0.5), nr = 20))
# ) + matrix(rnorm(60*60, sd = 0.5), nr = 60)
# cola_rl = run_all_consensus_partition_methods(data = m, top_n = c(20, 30, 40))
#
# == author
# Zuguang Gu <z.gu@dkfz.de>
#
# == example
# data(cola_rl)
# cola_rl
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