Package 'ctl' reference manual

Title:	Correlated Trait Locus Mapping
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>.
Authors:	Danny Arends <[email protected]>, Yang Li, Gudrun A Brockmann, Ritsert C Jansen, Robert W Williams, and Pjotr Prins
Maintainer:	Danny Arends <[email protected]>
License:	GPL-3
Version:	1.0.0-10
Built:	2024-11-12 06:28:42 UTC
Source:	CRAN

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:

CTLscan - Main function to scan for CTL.
CTLsignificant - Significant interactions from a CTLscan.
CTLnetwork - Create a CTL network from a CTLscan.
image.CTLobject - Heatmap overview of a CTLscan.
plot.CTLscan - Plot the CTL curve for a single trait.
ctl.circle - Circle plot CTLs on single and multiple traits.
ctl.lineplot - Line plot CTLs on single and multiple traits.
CTLprofiles - Extract CTL interaction profiles.

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:

ath.metabolites - Metabolite expression data from Arabidopsis Thaliana
ath.churchill - Metabolite expression data from Arabidopsis Thaliana
yeast.brem - Gene expression data from Saccharomyces cerevisiae

Details

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

basic.qc - Some basic quality checks for phenotype and genotype data
CTLscan.cross - Use an R/qtl cross object with CTLscan

Author(s)

Danny Arends [email protected]
Maintainer: Danny Arends [email protected]
Contributions from: Bruno Tesson, Pjotr Prins and Ritsert C. Jansen

References

TODO

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)
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, ]
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)
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

Keurentjes, J. J. and Fu, J. and de Vos, C. H. and Lommen, A. and Hall, R. D. and Bino, R. J. and van der Plas, L. H. and Jansen, R. C. and Vreugdenhil, D. and Koornneef, M. (2006), The genetics of plant metabolism. Nature Genetics. 38-7, 842–849.
Alonso-Blanco, C. and Peeters, A. J. and Koornneef, M. and Lister, C. and Dean, C. and van den Bosch, N. and Pot, J. and Kuiper, M. T. (1998), Development of an AFLP based linkage map of Ler, Col and Cvi Arabidopsis thaliana ecotypes and construction of a Ler/Cvi recombinant inbred line population. Plant J. 14(2), 259–271.

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, ]
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)
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

References

Keurentjes, J. J. and Fu, J. and de Vos, C. H. and Lommen, A. and Hall, R. D. and Bino, R. J. and van der Plas, L. H. and Jansen, R. C. and Vreugdenhil, D. and Koornneef, M. (2006), The genetics of plant metabolism. Nature Genetics. 38-7, 842–849.
Alonso-Blanco, C. and Peeters, A. J. and Koornneef, M. and Lister, C. and Dean, C. and van den Bosch, N. and Pot, J. and Kuiper, M. T. (1998), Development of an AFLP based linkage map of Ler, Col and Cvi Arabidopsis thaliana ecotypes and construction of a Ler/Cvi recombinant inbred line population. Plant J. 14(2), 259–271.

Examples

data(ath.result)           # Arabidopsis thaliana dataset
ath.result[[1]]            # Print the QCLscan summary of the phenotype 1
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)
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 [email protected]
Maintainer: Danny Arends [email protected]

References

TODO

Examples

  #TODO
#TODO

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)
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 [email protected]
Maintainer: Danny Arends [email protected]

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)
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)
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 [email protected]
Maintainer: Danny Arends [email protected]

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)
  }
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)
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:

$ctls - Matrix of differential correlation scores for each trait at each marker
$qtl - Vector of QTL lodscores for each marker (if a QTL scan was perfomed -qtl)
$p - Vector of maximum scores per marker obtained during permutations
$l - Matrix of LOD scores for CTL likelihood

Note

TODO

Author(s)

Danny Arends [email protected]
Maintainer: Danny Arends [email protected]

References

TODO

Examples

  library(ctl)         # Load CTL library
library(ctl)         # Load CTL library

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)
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 [email protected]
Maintainer: Danny Arends [email protected]

References

TODO

Examples

  #TODO
#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)
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

NOTE: Main bottleneck of the algorithm is the RAM available to the system

Value

CTLscan, a list of:

$dcor - Matrix of differential correlation scores for each trait at each marker
$perms - Vector of maximums per marker obtained during permutations
$ctls - Matrix of LOD scores for CTL likelihood

Note

TODO

Author(s)

Danny Arends [email protected]
Maintainer: Danny Arends [email protected]

References

TODO

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
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)
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:

ctlnet<FILE>.sif - Shows CTL connections from Trait to Marker with edge descriptions
ctlnet<FILE>.nodes - Attributes of the nodes (Traits and Genetic markers) nodes to this file can be used to either color chromosomes, or add chromosome locations.

Value

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

TRAIT1 - Trait ID of the origin trait
MARKER - Marker ID at which the CTL was found
TRAIT2 - Trait ID of the target trait
LOD_C - LOD score of the CTL interaction
CAUSAL - Type of edge determined by QTL LOD-drop:
- NA - CTL/QTL for TRAIT1 and/or TRAIT2 not found
- -1 - TRAIT1 is DOWNSTREAM of TRAIT2
- 0 - UNDETERMINED Edge
- 1 - TRAIT1 is UPSTREAM of TRAIT2
LOD_T1 - QTL LOD-score of TRAIT1 at MARKER
LOD_T2 - QTL LOD-score of TRAIT2 at MARKER

Note

TODO

Author(s)

Danny Arends [email protected]
Maintainer: Danny Arends [email protected]

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)
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)
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 [email protected]
Maintainer: Danny Arends [email protected]

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")
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)
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 [email protected]
Maintainer: Danny Arends [email protected]

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)
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)
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:

RAM available to the system with large number of markers (100K+) and/or phenotypes (100K+).
Computational time with large sample sizes (5000+) and/or huge amount of phenotype data (100K+).
Very very huge amounts of genotype markers (1M+)

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:

$dcor - Matrix of Z scores (method=Exact), or Power/Adjacency Z scores or for each trait at each marker (n.markers x n.phenotypes)
$perms - Vector of maximum scores obtained during permutations (n.perms)
$ctl - Matrix of LOD scores for CTL likelihood of phenotype i (n.markers x n.phenotypes)
$qtl - Vector of LOD scores for QTL likelihood of phenotype i (n.markers)

Author(s)

Danny Arends [email protected]
Maintainer: Danny Arends [email protected]

References

TODO

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
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, ...)
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

NOTE: Main bottleneck of the algorithm is the RAM available to the system

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 [email protected]
Maintainer: Danny Arends [email protected]

References

TODO

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
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"))
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 [email protected]
Maintainer: Danny Arends [email protected]

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

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)
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 [email protected]
Maintainer: Danny Arends [email protected]

References

TODO

Examples

  #TODO
#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, ...)
## 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 [email protected]
Maintainer: Danny Arends [email protected]

Examples

  library(ctl)                      # Load CTL library
  data(ath.result)
  hist(ath.result, phenocol = 1:3)  # Compare the results of the first 3 scans
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), ...)
## 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 phenotypemarker or phenotypes: the phenotypephenotypes 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 [email protected]
Maintainer: Danny Arends [email protected]

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")
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), ...)
## 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 [email protected]
Maintainer: Danny Arends [email protected]

Examples

  library(ctl)
  data(ath.result) # Arabidopsis Thaliana dataset
  plot(ath.result)
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", ...)
## 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 [email protected]
Maintainer: Danny Arends [email protected]

Examples

  library(ctl)
  data(ath.result) # Arabidopsis Thaliana dataset
  plot(ath.result[[1]]$perms)
library(ctl)
  data(ath.result) # Arabidopsis Thaliana dataset
  plot(ath.result[[1]]$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)", ...)
## 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 [email protected]
Maintainer: Danny Arends [email protected]

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")
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)
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 [email protected]
Maintainer: Danny Arends [email protected]

References

TODO

Examples

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

  plotTraits(ath.metab$genotypes, ath.metab$phenotypes, marker=75, doRank = TRUE)
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, ...)
## 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 [email protected]
Maintainer: Danny Arends [email protected]

References

TODO

Examples

  #TODO
#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, ...)
## 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 [email protected]
Maintainer: Danny Arends [email protected]

References

TODO

Examples

  #TODO
#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, ...)
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 [email protected]
Maintainer: Danny Arends [email protected]

Examples

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

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)
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

NOTE: Slow approach, it is adviced to use your own QTL mapping data

Value

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

Note

TODO

Author(s)

Danny Arends [email protected]
Maintainer: Danny Arends [email protected]

References

TODO

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
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

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)
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 [email protected]
Maintainer: Danny Arends [email protected]

References

TODO

Examples

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

  sds <- scanSD(pull.geno(multitrait),pull.pheno(multitrait))
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)
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 [email protected]
Maintainer: Danny Arends [email protected]

References

TODO

Examples

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

  sds <- scanSD.cross(multitrait)
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)
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 [email protected]
Maintainer: Danny Arends [email protected]

References

TODO

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))
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)
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 [email protected]
Maintainer: Danny Arends [email protected]

References

TODO

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))
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)
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, ]
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, ]

Package 'ctl'

Help Index

CTL - CTL mapping in experimental crosses

Description

Details

Author(s)

References

See Also

Example metabolite expression data from Arabidopsis Thaliana on 9 metabolites.

Description

Usage

Format

Details

Source

References

Examples

Example metabolite expression data from Arabidopsis Thaliana on 24 metabolites.

Description

Usage

Format

Details

Source

References

Examples

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

Description

Usage

Format

Details

Source

References

Examples

Create quality control plots.

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Circleplot CTL on multiple traits

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Lineplot CTL on multiple traits

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

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

Description

Usage

Arguments

Details

Value

Note

Author(s)

References

Examples

CTLhelper - Helper functions for CTL mapping

Description

Usage

Arguments

Details

Value

Note

Author(s)

References

Examples