Learn R Programming

QuantumClone (version 1.0.0.6)

Clustering Mutations using High Throughput Sequencing (HTS) Data

Description

Using HTS data, clusters mutations in order to recreate putative clones from the data provided. It requires genotype at the location of the variant as well as the depth of coverage and number of reads supporting the mutation. Additional information may be provided, such as the contamination in the tumor sample. This package also provides a function QuantumCat() which simulates data obtained from tumor sequencing.

Copy Link

Version

Install

install.packages('QuantumClone')

Monthly Downloads

13

Version

1.0.0.6

License

GPL-2

Issues

Pull Requests

Stars

Forks

Maintainer

Paul Deveau

Last Published

November 13th, 2017

Functions in QuantumClone (1.0.0.6)

Cluster_plot_from_cell

Cellularity clustering
Compute_NMI

Normalized Mutual Information
One_step_clustering

Cellularity clustering
Patient_schrodinger_cellularities

Patient Schrodinger Cellularities
QuantumCat

Data generation
QuantumClone

One step analysis function
filter_on_fik

Data filter
find_x_position

Graphic position
multiplot_trees

Plots multiple trees
parallelEM

Expectation Maximization algorithm
Compute_objective

Compute value of objective function
Create_prior_cutTree

Create priors from hierarchical clustering
NMI_cutree

NMI
One_D_plot

Plots
Tidy_output

Tidying output from EM
Tree

Example of output by Tree_generation
check_leaf

Check created leaf
check_split

Check
list_prod

List product
longueur

Length
plot_cell_from_Return_out

Plot cellularity
plot_with_margins_densities

Plot with margin densities
Cellular_preclustering

Preclustering method
CellularitiesFromFreq

Cellularities from allele frequency
Input_Example

Example generated by QuantumCat
MajorityVote

Majority vote
Precision_Recall

Precision
ProbDistMatrix

Distance
grbase

Computes gradient of function
grzero

Gradient 0
phylo_tree_generation

Data generation
plot_QC_out

Plot QC_output
zscore

Z-score
BIC_criterion

Bayesian Information Criterion
BIC_criterion_FLASH

Compute criterion FLASH
FLASH_main

Flash core
FlashQC

Flash QuantumClone
Tree_generation

Phylogenetic tree
add_leaf_list

Phylogenetic tree leaf
create_priors

Clonal fraction prior creation
e.step

Expectation step calculation
hard.clustering

Hard clustering based on EM output
is_included

Group theory
From_freq_to_cell

Wrap-up function
FullEM

Expectation Maximization algorithm
Probability.to.belong.to.clone

Probability
QC_output

Example of output generated by clustering of Input_Example
EM.algo

Expectation Maximization algorithm
EM_clustering

Expectation Maximization
eval.fik.m

Eval probability for M step Computes the log directly as log density is faster to compute
evolution_plot

Evolution plot
m.step

Maximization step
manual_plot_trees

Plot tree
strcount

String count
update_probs

Update proportions