In redsnic/CIMICE: CIMICE-R: (Markov) Chain Method to Inferr Cancer Evolution

knitr::opts_chunk$set(echo = TRUE)
library(CIMICE)
library(dplyr)
library(igraph)
library(Matrix)
library(purrr)

Reference for quick CIMICE analysis

In this short tutorial it is shown how to execute CIMICE's analysis

Read input

To load the input use CIMICE::read family functions or the pair CIMICE::make_dataset, CIMICE::update_df in the following manner:

# read from file
read_CAPRI(system.file("extdata", "example.CAPRI", package = "CIMICE", mustWork = TRUE))

# from a string
read_CAPRI_string("
s\\g A B C D
S1 0 0 0 1
S2 1 0 0 0
S3 1 0 0 0
S4 1 0 0 1
S5 1 1 0 1
S6 1 1 0 1
S7 1 0 1 1
S8 1 1 0 1
")

# using CIMICE::make_dataset and CIMICE::update_df
# genes
make_dataset(A,B,C,D) %>% 
    # samples
    update_df("S1", 0, 0, 0, 1) %>%
    update_df("S2", 1, 0, 0, 0) %>%
    update_df("S3", 1, 0, 0, 0) %>%
    update_df("S4", 1, 0, 0, 1) %>%
    update_df("S5", 1, 1, 0, 1) %>%
    update_df("S6", 1, 1, 0, 1) %>%
    update_df("S7", 1, 0, 1, 1) %>%
    update_df("S8", 1, 1, 0, 1)

This last dataset will be use in this example, under the name of example_dataset(). Input dataset analysis and feature selection should be done at this point.

Preprocess dataset

This early phase reorganizes the dataset to simplify further analysis.

preproc <- dataset_preprocessing(example_dataset())
samples <- preproc[["samples"]]
freqs   <- preproc[["freqs"]]
labels  <- preproc[["labels"]]
genes   <- preproc[["genes"]]

Build topology

The default method to organize genotypes in a graph in CIMICE is based on the "subset" relation among them.

g <- graph_non_transitive_subset_topology(samples,labels)

V(g)$vertex.size <- rep(10, length(V(g)))
plot(g, vertex.size=rep(55, length(V(g))))

Compute Weights

CIMICE provides a strategy to estimate transition probabilities among genotypes. This phase is divided in four different subphases that can be executed together in this way:

W <- compute_weights_default(g, freqs)

Visualize output:

CIMICE supports many different output options, the recommended one is based on "visNetwork".

out <- quick_run(example_dataset()) # quick_run summarizes all the previous steps
draw_visNetwork(out)

redsnic/CIMICE documentation built on March 30, 2022, 2:46 a.m.