library(markdown) library(knitr) knitr::opts_chunk$set( error = FALSE, tidy = FALSE, message = FALSE, fig.align = "center") options(width = 100) options(rmarkdown.html_vignette.check_title = FALSE) library(cola)
Assume your matrix is stored in an object called mat
, to perform consensus
partitioning with cola, you only need to run following code:
# code only for demonstration mat = adjust_matrix(mat) # optional rl = run_all_consensus_partition_methods(mat, mc.cores = ...) cola_report(rl, output_dir = ..., mc.cores = ...)
In above code, there are three steps:
NA
s are removed. Rows
with very low variance are removed. NA
values are imputed if there are
less than 50% in each row. Outliers are adjusted in each row.hclust
(hierarchical clustering with cutree),
kmeans
(k-means clustering), skmeans::skmeans
(spherical k-means
clustering), cluster::pam
(partitioning around medoids) and
Mclust::mclust
(model-based clustering). The default methods to extract
top n rows are SD
(standard deviation), CV
(coefficient of variation),
MAD
(median absolute deviation) and ATC
(ability to correlate to other
rows). run_all_consensus_partition_methods()
runs multiple methods in sequence, which might
take long time for big datasets. Users can also run consensus partitioining with
a specific top-value methods (e.g. SD) and partitioning methods (e.g. skmeans) by
consensus_partition()
function:
res = consensus_partition(mat, top_value_method = ..., partition_method = ...) cola_report(res, output_dir = ..., mc.cores = ...)
For extremely large datasets, users can run consensus_partition_by_down_sampling()
by randomly
sampling a subset of samples for classification, later the classes of the remaining
samples are predicted by the signatures of the cola classification. More details
can be found in the vignette "Work with Big Datasets".
res = consensus_partition_by_down_sampling(mat, subset = ..., top_value_method = ..., partition_method = ...) cola_report(res, output_dir = ..., mc.cores = ...)
There are examples on real datasets for cola analysis that can be found at https://jokergoo.github.io/cola_collection/.
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.