Version: 1.0.0-10
Date: 2024-01-15
Title: Correlated Trait Locus Mapping
Author: Danny Arends <Danny.Arends@gmail.com>, Yang Li, Gudrun A Brockmann, Ritsert C Jansen, Robert W Williams, and Pjotr Prins
Maintainer: Danny Arends <Danny.Arends@gmail.com>
Depends: R (≥ 2.10), MASS, parallel, qtl
Description: Identification and network inference of genetic loci associated with correlation changes in quantitative traits (called correlated trait loci, CTLs). Arends et al. (2016) <doi:10.21105/joss.00087>.
Encoding: UTF-8
ByteCompile: true
License: GPL-3
NeedsCompilation: yes
Packaged: 2024-01-15 15:27:28 UTC; rqdt9
Repository: CRAN
Date/Publication: 2024-01-16 12:10:02 UTC

CTLhelper - Helper functions for CTL mapping

Description

Helper functions for Correlated Trait Locus (CTL) mapping

Usage

ctl.names(CTLobject)
ctl.qtlmatrix(CTLobject)

ctl.name(CTLscan)
ctl.ctlmatrix(CTLscan)
ctl.dcormatrix(CTLscan)
ctl.qtlprofile(CTLscan)

Arguments

CTLobject

An object of class "CTLobject", as output by CTLscan.

CTLscan

An object of class "CTLscan". This is a single element from an "CTLobject", as output by CTLscan.

Details

TODO

Value

TODO

Note

TODO

Author(s)

Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com

References

TODO

Examples

  #TODO

CTLmapping - Scan for correlated trait loci (CTL)

Description

Scan for correlated trait loci (CTL)

Usage

CTLmapping(genotypes, phenotypes, phenocol = 1, nperm = 100, nthreads = 1,
strategy = c("Exact", "Full", "Pairwise"), adjust = TRUE, qtl = TRUE, verbose = FALSE)

Arguments

genotypes

Matrix of genotypes. (individuals x markers)

phenotypes

Matrix of phenotypes. (individuals x phenotypes)

phenocol

Which phenotype column(s) should we analyse. Default: Analyse a single phenotype.

nperm

Number of permutations to perform. This parameter is not used when method="Exact".

nthreads

Number of CPU cores to use during the analysis.

strategy

The permutation strategy to use, either

  • Exact: Uses exact calculations to calculate the likelihood of a difference in correlation: Cor(AA) - Cor(BB). Using a Bonferroni correction.

  • Full: Most powerful analysis method - Compensate for marker and trait correlation structure (Breitling et al.).

  • Pairwise: Suitable when we have a lot of markers and only a few traits (< 50) (human GWAS)- Compensates only for marker correlation structure.

Note: Exact is the default and fastest option it uses a normal distribution for estimating p-values and uses bonferoni correction. It has however the least power to detect CTLs, the two other methods (Full and Pairwise) perform permutations to assign significance.

adjust

Adjust p-values for multiple testing (only used when strategy = Exact).

qtl

Use the internal slow QTL mapping method to map QTLs.

verbose

Be verbose.

Details

TODO

Value

CTLscan, a list of:

Note

TODO

Author(s)

Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com

References

TODO

See Also

Examples

  library(ctl)
  data(ath.metabolites) # Arabidopsis Thaliana dataset
  singlescan <- CTLmapping(ath.metab$genotypes, ath.metab$phenotypes, phenocol = 23)

  plot(singlescan)      # Plot the results of the CTL scan for the phenotype

  summary <- CTLsignificant(singlescan)
  summary               # Get a list of significant CTLs

CTLnetwork - Interaction network from a genome-wide CTLscan of multiple traits

Description

Create a file containing the interaction network from a genome-wide CTLscan of multiple traits.

Usage

CTLnetwork(CTLobject, mapinfo, significance = 0.05, LODdrop = 2, 
what = c("names", "ids"), short = FALSE, add.qtls = FALSE, file = "", verbose = TRUE)

Arguments

CTLobject

An object of class "CTLobject", as output by CTLscan.

mapinfo

The mapinfo matrix with 3 columns: "Chr" - the chromosome number, "cM" - the location of the marker in centiMorgans and the 3rd column "Mbp" - The location of the marker in Mega basepairs. If supplied the marker names (rownames) should match those in the CTLobject (only significant markers will be annotated).

significance

Significance threshold for a genome wide false discovery rate (FDR).

LODdrop

Drop in LOD score needed before we assign an edge type.

what

Return trait and marker names or column numbers (for indexing).

short

Edges are markers when TRUE, otherwise markers are nodes (default).

add.qtls

Should marker QTL trait interactions be added to the generated sif network file, QTLs are included when they are above -log10(significance/n.markers).

file

A connection, or a character string naming the file to print to. If "" (the default), CTLnetwork prints to the standard output connection, the console unless redirected by sink.

verbose

Be verbose.

Details

Outputs a sif network file, and a node attribute file:

Value

A matrix with significant CTL interactions and information in 5 Columns:

Note

TODO

Author(s)

Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com

References

TODO

Examples

  library(ctl)
  data(ath.result)       # Arabidopsis Thaliana results
  data(ath.metabolites)  # Arabidopsis Thaliana data set

  ctls <- CTLnetwork(ath.result, significance = 0.1)
  op <- par(mfrow = c(2,1))
  plot(ctls)
  ctl.lineplot(ath.result, ath.metab$map, significance=0.1)

CTLprofiles - Extract CTL interaction profiles

Description

Extract the CTL interaction profiles: phenotype x marker (p2m matrix) and phenotype x phenotype (p2p matrix) from a CTLscan.

Usage

CTLprofiles(CTLobject, against = c("markers","phenotypes"), significance = 0.05, 
verbose=FALSE)

Arguments

CTLobject

An object of class "CTLobject", as output by CTLscan.

against

Plot the CTL against either: markers or phenotypes.

significance

Significance threshold to set a genome wide False Discovery Rate (FDR).

verbose

Be verbose.

Details

These matrices can be combined with QTL information to perform de novo reconstruction of interaction networks.

The 'against' parameter is by default set to "markers" which returns a phenotype x markers matrix (p2m matrix), which should be comparible to the QTL profiles of the traits.

When the 'against' parameter is set to "phenotypes" a phenotype x phenotype matrix (p2p matrix) is returned, showing the interactions between the phenotypes.

Value

Matrix: phenotypes x marker or phenotypes x phenotypes

Note

TODO

Author(s)

Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com

References

TODO

Examples

  library(ctl)         # Load CTL library
  data(ath.result)     # Arabidopsis Thaliana results
  p2m_matrix <- CTLprofiles(ath.result, against="markers")
  p2p_matrix <- CTLprofiles(ath.result, against="phenotypes")

CTLregions - Get all significant interactions from a genome-wide CTLscan

Description

Get all significant interactions from a genome-wide CTLscan.

Usage

CTLregions(CTLobject, mapinfo, phenocol = 1, significance = 0.05, verbose = TRUE)

Arguments

CTLobject

An object of class "CTLobject", as output by CTLscan.

mapinfo

The mapinfo matrix with 3 columns: "Chr" - the chromosome number, "cM" - the location of the marker in centiMorgans and the 3rd column "Mbp" - The location of the marker in Mega basepairs. If supplied the marker names (rownames) should match those in the CTLobject.

phenocol

Which phenotype column should we analyse.

significance

Significance threshold to set a genome wide False Discovery Rate (FDR).

verbose

Be verbose.

Details

TODO

Value

A matrix significant CTL interactions with 4 columns: trait, marker, trait, lod

Note

TODO

Author(s)

Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com

References

TODO

Examples

  library(ctl)
  
  data(ath.metabolites)                 # Arabidopsis Thaliana data set
  data(ath.result)                      # Arabidopsis Thaliana CTL results
  regions <- CTLregions(ath.result, ath.metab$map)

CTLscan - Scan for Correlated Trait Locus (CTL)

Description

Scan for Correlated Trait Locus (CTL) in populations

Usage

CTLscan(genotypes, phenotypes, phenocol, nperm=100, nthreads = 1, 
strategy = c("Exact", "Full", "Pairwise"),
parametric = FALSE, adjust=TRUE, qtl = TRUE, verbose = FALSE)

Arguments

genotypes

Matrix of genotypes. (individuals x markers)

phenotypes

Matrix of phenotypes. (individuals x phenotypes)

phenocol

Which phenotype column(s) should we analyse. Default: Analyse all phenotypes.

nperm

Number of permutations to perform. This parameter is not used when method="Exact".

nthreads

Number of CPU cores to use during the analysis.

strategy

The permutation strategy to use, either

  • Exact - Uses exact calculations to calculate the likelihood of a difference in correlation: Cor(AA) - Cor(BB). Using a Bonferroni correction.

  • Full - Most powerful analysis method - Compensate for marker and trait correlation structure (Breitling et al.).

  • Pairwise - Suitable when we have a lot of markers and only a few traits (< 50) (human GWAS)- Compensates only for marker correlation structure.

Note: Exact is the default and fastest option it uses a normal distribution for estimating p-values and uses bonferoni correction. It has however the least power to detect CTLs, the two other methods (Full and Pairwise) perform permutations to assign significance.

parametric

Use non-parametric testing (Spearman) or parametric testing (Pearson). The DEFAULT is to use non-parametric tests which are less sensitive to outliers in the phenotype data.

adjust

Adjust p-values for multiple testing (only used when strategy = Exact).

qtl

Use the internal slow QTL mapping method to map QTLs.

verbose

Be verbose.

Details

By default the algorithm will not do QTL mapping, the qtl component of the output is an vector of 0 scores for LOD. This is to remove some computational burden, please use the have.qtls parameter to provide QTL data. Some computational bottleneck of the algorithm are:

Some way of avoiding these problems are: CTL mapping using only a single chromosome at a time and / or selecting a smaller subsets of phenotype data for analysis.

Value

CTLobject, a list with at each index (i) an CTLscan object:

Author(s)

Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com

References

TODO

See Also

Examples

  library(ctl)
  data(ath.metabolites)                 # Arabidopsis Thaliana data set

  ctlscan <- CTLscan(ath.metab$genotypes, ath.metab$phenotypes, phenocol=1:4)
  ctlscan

  # Genetic regions with significant CTLs found for the first phenotype
  CTLregions(ctlscan, ath.metab$map, phenocol = 1)
  
  summary <- CTLsignificant(ctlscan)    # Matrix of Trait, Marker, Trait interactions 
  summary                               # Get a list of significant CTLs

  nodes <- ctl.lineplot(ctlscan, ath.metab$map)  # Line plot the phenotypes
  nodes

CTLscan.cross - Scan for Correlated Trait Locus (CTL) (R/qtl cross object)

Description

Scan for Correlated Trait Locus (CTL) in populations (using an R/qtl cross object)

Usage

CTLscan.cross(cross, ...)

Arguments

cross

An object of class cross. See read.cross for details.

...

Passed to CTLscan function:

  • phenocol - Which phenotype column should we analyse.

  • method - We provide 3 ways of mapping correlation differences across the genome:

    • Exact: Uses a Correlation to Z score transformation to calculate the likelihood of a difference in correlation: Cor(AA) - Cor(BB)

    • Power: More powerful analysis method using the squared difference in correlation: (Cor(AA) - Cor(BB))^2

    • Adjacency: Adjacency method which using the squared difference in squared correlation, but keeping the sign of correlation: (sign*Cor(AA)^2 - sign*Cor(BB)^2)^2

    Note: Exact is the default and fastest option it uses a normal distribution for estimating p-values and uses bonferoni correction. It has however the least power to detect CTLs, the two other methods (Power and Adjacency) perform permutations to assign significance.

  • n.perm - Number of permutations to perform.

  • strategy - The permutation strategy to use, either Full (Compensate for marker and trait correlation structure) or Pairwise (Compensate for marker correlation structure). This parameter is not used when method="Exact".

  • conditions - A vector of experimental conditions applied during the experiment. These conditions will be used as covariates in the QTL modeling step.

  • n.cores - Number of CPU cores to use during the analysis.

  • verbose - Be verbose.

Details

TODO

Value

CTLscan object, a list with at each index a CTL matrix (Rows: Phenotypes, Columns: Genetic markers) for the phenotype.

Note

TODO

Author(s)

Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com

References

TODO

See Also

Examples

  library(ctl)
  data(multitrait)      # Arabidopsis Thaliana (R/qtl cross object)

  mtrait <- calc.genoprob(multitrait)          # Calculate genotype probabilities
  qtls   <- scanone(mtrait, pheno.col = 1)     # Scan for QTLS using R/qtl

  ctls   <- CTLscan.cross(mtrait, phenocol = 1, qtl = FALSE)
  ctls[[1]]$qtl <- qtls[,3]

  ctl.lineplot(ctls, qtls[,1:2], significance = 0.05) # Line plot all the phenotypes

  summary <- CTLsignificant(ctls)              # Get a list of significant CTLs
  summary

CTLsignificant - Get all significant interactions from a genome-wide CTLscan

Description

Get all significant interactions from a genome-wide CTLscan.

Usage

CTLsignificant(CTLobject, significance = 0.05, what = c("names","ids"))

Arguments

CTLobject

An object of class "CTLobject", as output by CTLscan.

significance

Significance threshold to set a genome wide False Discovery Rate (FDR).

what

Return trait and marker names or column numbers (for indexing).

Details

TODO

Value

A matrix significant CTL interactions with 4 columns: trait, marker, trait, lod

Note

TODO

Author(s)

Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com

References

TODO

Examples

  library(ctl)
  data(ath.result)
  all_interactions <- CTLsignificant(ath.result)
  all_interactions[1:10, ]
  trait1_interactions <- CTLsignificant(ath.result[[1]])
  trait1_interactions

QTLmapping - QTL mapping method for CTL analysis

Description

Internal QTL mapping method used by the CTL analysis, associates every column in the genotypes with a single phenotype

Usage

QTLmapping(genotypes, phenotypes, phenocol = 1, verbose = TRUE)

Arguments

genotypes

Matrix of genotypes. (individuals x markers)

phenotypes

Matrix of phenotypes. (individuals x phenotypes)

phenocol

Which phenotype column(s) should we analyse. Default: Analyse a single phenotype.

verbose

Be verbose.

Details

TODO

Value

vector of LOD scores for each genotype column, for phenotype column phenocol

Note

TODO

Author(s)

Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com

References

TODO

See Also

Examples

  library(ctl)
  data(ath.metabolites) # Arabidopsis Thaliana dataset
  qtldata <- QTLmapping(ath.metab$genotypes, ath.metab$phenotypes, phenocol = 23)
  plot(qtldata)         # Plot the results of the QTL scan for the phenotype

Example metabolite expression data from Arabidopsis Thaliana on 9 metabolites.

Description

Arabidopsis recombinant inbred lines by selfing. There are 403 lines, 9 phenotypes, and 69 markers on 5 chromosomes stored as a list with 3 matrices: genotypes, phenotypes, map

Usage

  data(ath.churchill)

Format

Data stored in a list holding 3 matrices genotypes, phenotypes and map

Details

Arabidopsis recombinant inbred lines by selfing. There are 403 lines, 9 metabolic phenotypes, and 69 markers on 5 chromosomes.

Source

Arabidopsis Bay-0 x Sha metabolite data from XX, senior author: Gary Churchill 2012, Published in: Plos

References

TODO

Examples

  library(ctl)
  data(ath.churchill)           # Arabidopsis thaliana dataset
  
  ath.gary$genotypes[1:5, 1:5]  # ath.gary is the short name
  ath.gary$phenotypes[1:5, 1:5]
  ath.gary$map[1:5, ]

Example metabolite expression data from Arabidopsis Thaliana on 24 metabolites.

Description

Arabidopsis recombinant inbred lines by selfing. There are 162 lines, 24 phenotypes, and 117 markers on 5 chromosomes stored as a list with 3 matrices: genotypes, phenotypes, map

Usage

  data(ath.metabolites)

Format

Data stored in a list holding 3 matrices genotypes, phenotypes and map

Details

Arabidopsis recombinant inbred lines by selfing. There are 162 lines, 24 phenotypes, and 117 markers on 5 chromosomes.

Source

Part of the Arabidopsis RIL selfing experiment with Landsberg Erecta (Ler) and Cape Verde Islands (Cvi) with 162 individuals scored (with errors) at 117 markers. Dataset obtained from GBIC - Groningen BioInformatics Centre, University of Groningen.

References

Examples

  library(ctl)
  data(ath.metabolites)           # Arabidopsis thaliana dataset

  ath.metab$genotypes[1:5, 1:5]   # ath.metab is the short name
  ath.metab$phenotypes[1:5, 1:5]
  ath.metab$map[1:5, ]

Output of QCLscan after 5000 permutations on the metabolite expression data from Arabidopsis Thaliana.

Description

Results from a QCLscan on Arabidopsis recombinant inbred lines by selfing. There are 162 lines, 24 phenotypes, and 117 markers on 5 chromosomes stored as a list with 3 matrices: genotypes, phenotypes, map

Usage

  data(ath.result)

Format

Cross object from R/QTL

Details

Results from a QCLscan on Arabidopsis recombinant inbred lines by selfing. There are 162 lines, 24 phenotypes, and 117 markers on 5 chromosomes. the QCLscan also includes 5000 permutations

Source

Part of the Arabidopsis RIL selfing experiment with Landsberg Erecta (Ler) and Cape Verde Islands (Cvi) with 162 individuals scored (with errors) at 117 markers. Dataset obtained from GBIC - Groningen BioInformatics Centre, University of Groningen.

References

Examples

data(ath.result)           # Arabidopsis thaliana dataset
ath.result[[1]]            # Print the QCLscan summary of the phenotype 1

Create quality control plots.

Description

Create quality control plots, used in the examples of CTL mapping.

Usage

  basic.qc(genotypes, phenotypes, map_info)

Arguments

genotypes

Matrix of genotypes. (individuals x markers)

phenotypes

Matrix of phenotypes. (individuals x phenotypes)

map_info

Matrix of genetic map information

Details

None.

Value

None.

Author(s)

Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com

References

TODO

See Also

Examples

  #TODO

Internal CTL functions

Description

Internal CTL functions. These are generally not to be called by the user.

Author(s)

Danny Arends, Danny.Arends@Gmail.com

CTL - CTL mapping in experimental crosses

Description

Analysis of experimental crosses to identify genetic markers associated with correlation changes in quantitative traits (CTL). The additional correlation information obtained can be combined with QTL information to perform de novo reconstruction of interaction networks.

For more background information about the method we refer to the methodology article published in XX (201X).

The R package is a basic iomplementation and it includes the following core functionality:

For all these functions we also provide examples and demonstrations on real genetical genomics data. We thank all contributors for publishing their data online and will accept submissions of intrestion datasets, currently ctl provides:

Details

More detailed information and/or examples are given per function as needed. Some additional functionality:

Author(s)

Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com
Contributions from: Bruno Tesson, Pjotr Prins and Ritsert C. Jansen

References

See Also

Circleplot CTL on multiple traits

Description

Plot the CTL for genome-wide CTL on multiple traits (the output of CTLscan).

Usage

ctl.circle(CTLobject, mapinfo, phenocol, significance = 0.05, gap = 50, cex = 1,
verbose = FALSE)

Arguments

CTLobject

An object of class "CTLobject", as output by CTLscan.

mapinfo

The mapinfo matrix with 3 columns: "Chr" - the chromosome number, "cM" - the location of the marker in centiMorgans and the 3rd column "Mbp" - The location of the marker in Mega basepairs. If supplied the marker names (rownames) should match those in the CTLobject.

phenocol

Which phenotype results to plot. Defaults to plot all phenotypes.

significance

Significance threshold to set a genome wide False Discovery Rate (FDR).

gap

Gap between chromosomes in cM.

cex

Global magnificantion factor for the image elements.

verbose

Be verbose.

Details

None.

Value

None.

Author(s)

Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com

See Also

Examples

  library(ctl)
  data(ath.result)       # Arabidopsis Thaliana results
  data(ath.metabolites)  # Arabidopsis Thaliana data set

  ctl.circle(ath.result, ath.metab$map, sign=0.001)
  ctl.circle(ath.result, ath.metab$map, phenocol = 1:6, sign = 0.01)

Lineplot CTL on multiple traits

Description

Plot the CTL for genome-wide CTL on multiple traits (the output of CTLscan).

Usage

ctl.lineplot(CTLobject, mapinfo, phenocol, significance = 0.05, gap = 50, 
col = "orange", bg.col = "lightgray", cex = 1, verbose = FALSE)

Arguments

CTLobject

An object of class "CTLobject", as output by CTLscan.

mapinfo

The mapinfo matrix with 3 columns: "Chr" - the chromosome number, "cM" - the location of the marker in centiMorgans and the 3rd column "Mbp" - The location of the marker in Mega basepairs. If supplied the marker names (rownames) should match those in the CTLobject (only significant markers will be annotated).

phenocol

Which phenotype results to plot. Defaults to plot all phenotypes.

significance

Significance threshold to set a genome wide False Discovery Rate (FDR).

gap

The gap between chromosomes in cM.

col

Line color used.

bg.col

Node background color.

cex

Global magnificantion factor for the image elements.

verbose

Be verbose.

Details

None.

Value

None.

Author(s)

Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com

See Also

Examples

  require(ctl)
  data(ath.result)       # Arabidopsis Thaliana results
  data(ath.metabolites)  # Arabidopsis Thaliana data set

  todo <- c(1,3,4,5,6,8,9,10,11,12,14,17,18,19,22,23)
  op   <- par(mfrow = c(4,4))
  op   <- par(oma = c(0.1,0.1,0.1,0.1))
  op   <- par(mai = c(0.1,0.1,0.1,0.1))
  for(x in todo){ # Overview of the 16 traits with CTLs
    ctl.lineplot(ath.result, ath.metab$map, phenocol = x, sign=0.1)
  }

ctl.load - Load CTLs calculated by the D2.0 version

Description

Load CTLs calculated by the D2.0 version

Usage

ctl.load(genotypes = "ngenotypes.txt", phenotypes = "nphenotypes.txt",
output = "ctlout", from=1, to, verbose = FALSE)

Arguments

genotypes

Original datafile containing the genotypes scanned.

phenotypes

Original datafile containing the phenotypes scanned.

output

Directory containing the output files.

from

Start loading at which phenotype.

to

Continue loading untill this phenotype.

verbose

Be verbose.

Details

TODO

Value

CTLobject, a list with at each index a CTLscan object:

Note

TODO

Author(s)

Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com

References

TODO

Examples

  library(ctl)         # Load CTL library

detect.peaks - Peak detection algorithm to 'flatten' data above a certain threshold

Description

Peak detection algorithm to 'flatten' data above a certain threshold

Usage

detect.peaks(data, chrEdges = c(1), threshold = 4, verbose = FALSE)

Arguments

data

A vector of scores per marker/locus.

chrEdges

Start positions of the chromosomes.

threshold

Threshold to determine regions.

verbose

Be verbose.

Details

TODO

Value

TODO

Note

TODO

Author(s)

Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com

References

TODO

See Also

Examples

  #TODO

Plot histogram of CTL permutations

Description

Plot histogram of CTL permutations (the output of CTLscan).

Usage

## S3 method for class 'CTLobject'
hist(x, phenocol=1, ...)

Arguments

x

An object of class "CTLscan", as output by CTLscan.

phenocol

Which phenotype column(s) should we analyse. Defaults to analyse all phenotype columns

...

Passed to the function image when it is called.

Details

None.

Value

For a detailed description, see CTLprofiles

Author(s)

Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com

See Also

Examples

  library(ctl)                      # Load CTL library
  data(ath.result)
  hist(ath.result, phenocol = 1:3)  # Compare the results of the first 3 scans

Plot genome-wide CTL on multiple traits

Description

Plot the CTL for genome-wide CTL on multiple traits (the output of CTLscan).

Usage

## S3 method for class 'CTLobject'
image(x, marker_info, against = c("markers","phenotypes"), significance = 0.05,
col=whiteblack, do.grid=TRUE, grid.col = "white", verbose = FALSE, add=FALSE, 
breaks = c(0, 1, 2, 3, 10, 10000), ...)

Arguments

x

An object of class "CTLscan", as output by CTLscan.

marker_info

Information used to plot chromosome lines.

against

Plot which interaction matrice, options are: markers: the phenotype*marker or phenotypes: the phenotype*phenotypes matrix.

significance

Significance threshold to set a genome wide False Discovery Rate (FDR).

col

Color-range used in plotting.

do.grid

When TRUE, grid lines are added to the plot.

grid.col

Color used for the grid lines, only used when do.grid = TRUE.

verbose

Be verbose.

add

Add this plot to a previously opened plot window.

breaks

See par.

...

Passed to the function image when it is called.

Details

None.

Value

For a detailed description, see CTLprofiles

Author(s)

Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com

See Also

Examples

  library(ctl)
  data(ath.result)       # Arabidopsis Thaliana results
  #Phenotype to phenotype matrix
  p2p_matrix <- image(ath.result, against="phenotypes")
  #Phenotype to marker matrix
  p2m_matrix <- image(ath.result, against="markers")

Plot CTL curves or heatmaps

Description

Plot the CTL curve or heatmaps for a genome scan (the output of CTLscan).

Usage

## S3 method for class 'CTLobject'
plot(x, phenocol = 1:length(x), ...)

Arguments

x

An object of class "CTLobject", as output by CTLscan.

phenocol

Which phenotype column(s) should we plot. Defaults to creating an image of all phenotype columns

...

Passed to the function image.CTLobject plot.CTLscan when it is called.

Details

None.

Value

None.

Author(s)

Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com

See Also

Examples

  library(ctl)
  data(ath.result) # Arabidopsis Thaliana dataset
  plot(ath.result)

Differential correlation versus likelihood plotted in curves

Description

Differential correlation versus likelihood plot curves.

Usage

## S3 method for class 'CTLpermute'
plot(x, type="s", ...)

Arguments

x

An object of class "CTLscan".

type

What type of plot should be drawn. for possible options see plot.

...

Passed to the function plot when it is called.

Details

None.

Value

None.

Author(s)

Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com

See Also

Examples

  library(ctl)
  data(ath.result) # Arabidopsis Thaliana dataset
  plot(ath.result[[1]]$perms)

Plot CTL results as bar, line or GWAS plot.

Description

Plot the CTL results for a genome scan (the output of CTLscan) as a barplot, curved line or GWAS plot.

Usage

## S3 method for class 'CTLscan'
plot(x, mapinfo = NULL, type = c("barplot","gwas","line"), 
onlySignificant = TRUE, significance = 0.05, gap = 25, plot.cutoff = FALSE, 
do.legend=TRUE, legend.pos = "topleft", cex.legend=1.0, ydim=NULL, 
ylab="-log10(P-value)", ...)

Arguments

x

An object of class "CTLscan", as output by CTLscan.

mapinfo

The mapinfo matrix with 3 columns: "Chr" - the chromosome number, "cM" - the location of the marker in centiMorgans and the 3rd column "Mbp" - The location of the marker in Mega basepairs. If supplied the marker names (rownames) should match those in the CTLobject.

type

Type of plot: Summed barplot, GWAS style plot or a basic line plot.

onlySignificant

Plot only the significant contributions to the CTL profile.

significance

Significance threshold for setting a genomewide FDR.

gap

Gap in Cm between chromosomes.

plot.cutoff

Adds a line at -log10(significance) and adds a legend showing the significance level.

do.legend

Adds a legend showing which phenotypes contribute to the CTL profile.

legend.pos

Position of the legend in the plot window.

cex.legend

Maginification of the text in the legend.

ydim

Dimension of the y-axis, if NULL then it will be calculated.

ylab

Label for the y-axis.

...

Passed to the function plot when it is called.

Details

None.

Value

None.

Author(s)

Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com

See Also

Examples

  library(ctl)
  data(ath.result)       # Arabidopsis thaliana results
  data(ath.metabolites)  # Arabidopsis thaliana data (phenotypes, genotypes and mapinfo

  plot(ath.result[[3]])
  plot(ath.result[[2]], mapinfo = ath.metab[[3]])
  plot(ath.result[[1]], mapinfo = ath.metab[[3]])
  plot(ath.result[[3]], mapinfo = ath.metab[[3]])
  plot(ath.result[[3]], mapinfo = ath.metab[[3]], type="gwas")
  plot(ath.result[[3]], mapinfo = ath.metab[[3]], type="line")

plotTraits - Trait vs Trait scatterplot, colored by the selected genetic locus

Description

Trait vs Trait scatterplot, colored by the selected genetic locus

Usage

plotTraits(genotypes, phenotypes, phenocol = c(1, 2), marker = 1, doRank = FALSE)

Arguments

genotypes

Matrix of genotypes. (individuals x markers)

phenotypes

Matrix of phenotypes. (individuals x phenotypes)

phenocol

Which phenotype column(s) should be plotted against each other, Default: phenotype 1 versus 2

marker

Which marker (column in genotypes) should be used to add genotype as a color of the dots.

doRank

Transform quantitative data into ranked data before analyzing the slope.

Details

TODO

Value

TODO

Note

TODO

Author(s)

Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com

References

TODO

See Also

Examples

  library(ctl)
  data(ath.metabolites)                 # Arabidopsis Thaliana data set

  plotTraits(ath.metab$genotypes, ath.metab$phenotypes, marker=75, doRank = TRUE)

Print the results of a CTL genome scan

Description

Print the results of a multiple phenotype CTL genome scan produced by CTLscan.

Usage

  ## S3 method for class 'CTLobject'
print(x, ...)

Arguments

x

An object of class "CTLobject", as output by CTLscan.

...

Ignored.

Details

None.

Value

None.

Author(s)

Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com

References

TODO

See Also

Examples

  #TODO

Print the results of a single phenotype CTL scan

Description

Print the results of a single phenotype CTL scan produced by either CTLmapping (Single phenotype scan) or CTLscan (Multi phenotype scan).

Usage

## S3 method for class 'CTLscan'
print(x, ...)

Arguments

x

An object of class "CTLscan". This is a single element from an "CTLobject", as output by CTLscan.

...

Ignored.

Details

None.

Value

None.

Author(s)

Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com

References

TODO

See Also

Examples

  #TODO

Plot a QTL heatmap of the phenotypes scanned by CTLscan

Description

Plots the QTL heatmap of a genome wide QTL scan (part of the output of CTLscan).

Usage

  qtlimage(x, marker_info, do.grid=TRUE, grid.col="white", verbose=FALSE, ...)

Arguments

x

An object of class "CTLobject", as output by CTLscan.

marker_info

Information used to plot chromosome lines.

do.grid

When TRUE, grid lines are added to the plot.

grid.col

Color used for the grid lines, only used when do.grid = TRUE.

verbose

Be verbose.

...

Passed to the function plot when it is called.

Details

None.

Value

None.

Author(s)

Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com

See Also

Examples

  library(ctl)          # Load CTL library
  data(ath.metabolites) # Arabidopsis Thaliana data
  data(ath.result)      # Arabidopsis Thaliana results
  qtlimage(ath.result, ath.metab$map)  # Plot only the qtls

scanSD - Analyze the differences in Standard Deviation between genotypes between two traits

Description

Analyze the differences in Standard Deviation between genotypes between two traits

Usage

scanSD(genotypes, phenotypes, phenocol=c(1,2), doRank = FALSE)

Arguments

genotypes

Matrix of genotypes. (individuals x markers)

phenotypes

Matrix of phenotypes. (individuals x phenotypes)

phenocol

Which phenotype column(s) should be plotted against each other, Default: phenotype 1 versus 2

doRank

Transform quantitative data into ranked data before analyzing the slope.

Details

TODO

Value

TODO

Note

TODO

Author(s)

Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com

References

TODO

See Also

Examples

  library(ctl)
  data(multitrait)      # Arabidopsis Thaliana (R/qtl cross object)

  sds <- scanSD(pull.geno(multitrait),pull.pheno(multitrait))

scanSD.cross - Analyze the differences in standard deviation between two traits at a certain genetic marker (R/qtl cross object)

Description

Analyze the differences in standard deviation between two traits at a certain genetic marker

Usage

scanSD.cross(cross, phenocol = c(1,2), doRank = FALSE)

Arguments

cross

An object of class cross. See read.cross for details.

phenocol

Which phenotype column(s) should be plotted against each other, Default: phenotype 1 versus 2

doRank

Transform quantitative data into ranked data before analyzing the slope.

Details

TODO

Value

TODO

Note

TODO

Author(s)

Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com

References

TODO

See Also

Examples

  library(ctl)
  data(multitrait)      # Arabidopsis Thaliana (R/qtl cross object)

  sds <- scanSD.cross(multitrait)

scanSlopes - Create a slope difference profile between two traits

Description

Create a slope difference profile between two traits

Usage

scanSlopes(genotypes, phenotypes, phenocol = 1, doRank = FALSE, verbose = FALSE)

Arguments

genotypes

Matrix of genotypes. (individuals x markers)

phenotypes

Matrix of phenotypes. (individuals x phenotypes)

phenocol

Which phenotype column(s) should we analyse. Default: Analyse phenotype 1.

doRank

Transform quantitative data into ranked data before analyzing the slope.

verbose

Be verbose.

Details

TODO

Value

TODO

Note

TODO

Author(s)

Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com

References

TODO

See Also

Examples

  library(ctl)
  data(ath.metabolites)                 # Arabidopsis Thaliana data set

  slopes <- scanSlopes(ath.metab$genotypes, ath.metab$phenotypes[,1:4], phenocol = 2)
  image(1:nrow(slopes), 1:ncol(slopes), -log10(slopes))

scanSlopes.cross - Create a slope difference profile between two traits (R/qtl cross object)

Description

Create a slope difference profile between two traits (using an R/qtl cross object)

Usage

scanSlopes.cross(cross, phenocol = 1, doRank = FALSE, verbose = FALSE)

Arguments

cross

An object of class cross. See read.cross for details.

phenocol

Which phenotype column(s) should we analyse. Default: Analyse phenotype 1

doRank

Transform quantitative data into ranked data before analyzing the slope.

verbose

Be verbose.

Details

TODO

Value

TODO

Note

TODO

Author(s)

Danny Arends Danny.Arends@gmail.com
Maintainer: Danny Arends Danny.Arends@gmail.com

References

TODO

See Also

Examples

  library(ctl)
  data(multitrait)      # Arabidopsis Thaliana (R/qtl cross object)
  multitrait$pheno <- multitrait$pheno[,1:4]
  slopes <- scanSlopes.cross(multitrait)
  image(1:nrow(slopes), 1:ncol(slopes), -log10(slopes))

Example gene expression data from Saccharomyces cerevisiae on 301 RNA expressions.

Description

Saccharomyces recombinant inbred lines. There are 109 lines, 301 phenotypes, genotyped at 282 markers on 16 chromosomes stored as a list with 3 matrices: genotypes, phenotypes and map

Usage

  data(yeast.brem)

Format

Data stored in a list holding 3 matrices genotypes, phenotypes and map

Details

Saccharomyces recombinant inbred lines. There are 109 lines, 301 RNA expression phenotypes. The individuals are genotyped at 282 markers on 16 chromosomes.

Source

Saccharomyces cerevisiae RNA expression data from XX, senior author: Rachel Brem 20XX, Published in: Plos

References

TODO

Examples

  library(ctl)
  data(yeast.brem)              # Yeast data set
  
  yeast.brem$genotypes[1:5, 1:5]
  yeast.brem$phenotypes[1:5, 1:5]
  yeast.brem$map[1:5, ]