Component: PyCloneRC

PyCloneRC

Reclustering tool for PyClone variant clustering. Work in progress. This tool supplants the PyClone analysis and requires PyClone version 0.13. For more information about PyClone, see https://bitbucket.org/aroth85/pyclone/wiki/Home and Roth et al., Nat Methods 11: 396, 2014.

Version	1.0
Bundle	sequencing
Categories	Analysis
Authors	Antti Hakkinen (antti.e.hakkinen@helsinki.fi)
Issue tracker	View/Report issues
Requires	Python ; PyClone
Source files	component.xml launcher.py
Usage	Example with default values PyCloneRC( config = YAML, //trace = BinaryFolder, //mutationFiles = YAML, burnin = 0, clusterPValues = false, infCoverage = 1e100, meshSize = 101, minAssignLr = 0, minClusterSize = 1, minCreateLr = 0, plotType = "", thin = 1, trim = 0 )

Version

1.0

Bundle

sequencing

Categories

Analysis

Authors

Antti Hakkinen (antti.e.hakkinen@helsinki.fi)

Issue tracker

View/Report issues

Requires

Python ; PyClone

Source files

component.xml launcher.py

Usage

Example with default values

Inputs

Name	Type	Mandatory	Description
config	YAML	Mandatory	PyClone configuration file
trace	BinaryFolder	Optional	PyClone trace directory (overrides that specified in the configuration file)
mutationFiles	Array<YAML>	Optional	Mutation files in the YAML format

Name

Type

Mandatory

Description

config

YAML

Mandatory

PyClone configuration file

trace

BinaryFolder

Optional

PyClone trace directory (overrides that specified in the configuration file)

mutationFiles

Array<YAML>

Optional

Mutation files in the YAML format

Name	Type	Description
clusteringResults	CSVList	Results of clustering
plots	BinaryFolder	Output directory for the plots

Name

Type

Description

clusteringResults

CSVList

Results of clustering

plots

BinaryFolder

Output directory for the plots

Parameters

Name	Type	Default	Description
burnin	int	0	Number of MCMC samples to discard from the beginning. Prior to convergence, the MCMC series features an initial transient which is controlled by the initial parameters and not the data. This initial transient should be specified such that it can be discarded from the subsequent analysis.
clusterPValues	boolean	false	If enabled, computes p-values for each locus to belong into its assigned cluster. The values are output in the loci table.
infCoverage	float	1e100	Variants with coverage no smaller than the specified will be considered as known, and will be spliced off of clusters with unequal values.
meshSize	int	101	Number of mesh points for density estimation. The default of 101 allows subdivision of 1% in cellular prevalences.
minAssignLr	float	0	Minimum likelihood ratio to assign into a cluster. A value of 0 will assign all variants to a cluster, while a larger value eliminates uncertain assignments.
minClusterSize	int	1	Minimum number of variants in a cluster.
minCreateLr	float	0	Minimum likelihood ratio to create a cluster. A value of 0 will keep all clusters, while for a larger value clusters with low evidence of cluster linkage are rejected.
plotType	string	""	Comma separated list of the requested plot types. Options: "similarity_matrix", "filter_metrics", "cluster_density".
thin	int	1	Thinning ratio for the MCMC series in samples. In an MCMC series, the consecutive samples are correlated. These correlations will skew higher-order statistics such as variance estimates. This can be mitigated by specifying T such that each T-sample string is thinned into a single sample.
trim	float	0	Fraction of data to be trimmed from the summary statistics. A value of 0 produces regular mean/std, while a larger value excludes a specified fraction of outliers.

Name

Type

Default

Description

burnin

int

Number of MCMC samples to discard from the beginning. Prior to convergence, the MCMC series features an initial transient which is controlled by the initial parameters and not the data. This initial transient should be specified such that it can be discarded from the subsequent analysis.

clusterPValues

boolean

false

If enabled, computes p-values for each locus to belong into its assigned cluster. The values are output in the loci table.

infCoverage

float

1e100

Variants with coverage no smaller than the specified will be considered as known, and will be spliced off of clusters with unequal values.

meshSize

int

101

Number of mesh points for density estimation. The default of 101 allows subdivision of 1% in cellular prevalences.

minAssignLr

float

Minimum likelihood ratio to assign into a cluster. A value of 0 will assign all variants to a cluster, while a larger value eliminates uncertain assignments.

minClusterSize

int

Minimum number of variants in a cluster.

minCreateLr

float

Minimum likelihood ratio to create a cluster. A value of 0 will keep all clusters, while for a larger value clusters with low evidence of cluster linkage are rejected.

plotType

string

Comma separated list of the requested plot types. Options: "similarity_matrix", "filter_metrics", "cluster_density".

thin

int

Thinning ratio for the MCMC series in samples. In an MCMC series, the consecutive samples are correlated. These correlations will skew higher-order statistics such as variance estimates. This can be mitigated by specifying T such that each T-sample string is thinned into a single sample.

trim

float

Fraction of data to be trimmed from the summary statistics. A value of 0 produces regular mean/std, while a larger value excludes a specified fraction of outliers.

Test cases

Test case	Parameters▼	IN config	IN trace	IN mutationFiles	OUT clusteringResults	OUT plots
default	properties	config	trace	mutationFiles	clusteringResults	plots
	minCreateLr = 0.5, minAssignLr = 0.5, trim = .75, clusterPValues = true

Test case

Parameters▼

IN
config

IN
trace

IN
mutationFiles

OUT
clusteringResults

OUT
plots

default