| Title: | Lining Up Two Sets of Measurements |
|---|---|
| Description: | Tools for detecting and correcting sample mix-ups between two sets of measurements, such as between gene expression data on two tissues. Broman et al. (2015) <doi:10.1534/g3.115.019778>. |
| Authors: | Karl W Broman [aut, cre]
|
| Maintainer: | Karl W Broman <[email protected]> |
| License: | GPL-3 |
| Version: | 0.44 |
| Built: | 2024-11-12 04:27:06 UTC |
| Source: | https://github.com/kbroman/lineup |
For gene expression data with physical positions of the genes, calculate the LOD score at those positions to assess evidence for local eQTL.
calc.locallod( cross, pheno, pmark, addcovar = NULL, intcovar = NULL, verbose = TRUE, n.cores = 1 )calc.locallod( cross, pheno, pmark, addcovar = NULL, intcovar = NULL, verbose = TRUE, n.cores = 1 )
cross |
An object of class |
pheno |
A data frame of phenotypes (generally gene expression data), stored as individuals x phenotypes. The row names must contain individual identifiers. |
pmark |
Pseudomarkers that are closest to the genes in |
addcovar |
Additive covariates passed to |
intcovar |
Interactive covariates passed to |
verbose |
If TRUE, print tracing information. |
n.cores |
Number of CPU cores to use in the calculations. With
|
cross and pheno must contain exactly the same individuals in
the same order. (Use findCommonID() to line them up.)
We consider the expression phenotypes in batches: those whose closest pseudomarker is the same.
We use Haley-Knott regression to calculate the LOD scores.
Actually, we use a bit of a contortion of the data to force the
qtl::scanone() function in R/qtl to calculate the LOD score at a
single position.
We omit any transcripts that map to the X chromosome; we can only handle autosomal loci for now.
A vector of LOD scores. The names indicate the gene names (columns in
pheno).
Karl W Broman, [email protected]
find.gene.pseudomarker(), plotEGclass(),
findCommonID(), disteg()
data(f2cross, expr1, genepos, pmap) library(qtl) # calc QTL genotype probabilities f2cross <- calc.genoprob(f2cross, step=1) # find nearest pseudomarkers pmark <- find.gene.pseudomarker(f2cross, pmap, genepos, "prob") # line up f2cross and expr1 id <- findCommonID(f2cross, expr1) # calculate LOD score for local eQTL locallod <- calc.locallod(f2cross[,id$first], expr1[id$second,], pmark)data(f2cross, expr1, genepos, pmap) library(qtl) # calc QTL genotype probabilities f2cross <- calc.genoprob(f2cross, step=1) # find nearest pseudomarkers pmark <- find.gene.pseudomarker(f2cross, pmap, genepos, "prob") # line up f2cross and expr1 id <- findCommonID(f2cross, expr1) # calculate LOD score for local eQTL locallod <- calc.locallod(f2cross[,id$first], expr1[id$second,], pmark)
Combine multiple distance matrices into a single distance matrix providing an overall summary
combinedist(..., method = c("median", "mean"))combinedist(..., method = c("median", "mean"))
... |
Set of distance matrices, as calculated by |
method |
Indicates whether to summarize using the median or the mean. |
The row and column names of the input distance matrices define the individual IDs.
If the input distance matrices all have an attribute "denom" (for
denominator) and method="mean", we use a weighted mean, weighted by
the denominators. This could be used to calculate an overall proportion.
A distance matrix, with class "lineupdist". The individual
IDs are in the row and column names.
Karl W Broman, [email protected]
distee(), disteg(),
summary.lineupdist()
library(qtl) # load example data data(f2cross, expr1, expr2, pmap, genepos) # calculate QTL genotype probabilities f2cross <- calc.genoprob(f2cross, step=1) # find nearest pseudomarkers pmark <- find.gene.pseudomarker(f2cross, pmap, genepos) # line up individuals id1 <- findCommonID(f2cross, expr1) id2 <- findCommonID(f2cross, expr2) # calculate LOD score for local eQTL locallod1 <- calc.locallod(f2cross[,id1$first], expr1[id1$second,], pmark) locallod2 <- calc.locallod(f2cross[,id2$first], expr2[id2$second,], pmark) # take those with LOD > 25 expr1s <- expr1[,locallod1>25,drop=FALSE] expr2s <- expr2[,locallod2>25,drop=FALSE] # calculate distance between individuals # (prop'n mismatches between obs and inferred eQTL geno) d1 <- disteg(f2cross, expr1s, pmark) d2 <- disteg(f2cross, expr2s, pmark) # combine distances d <- combinedist(d1, d2) # summary of problem samples summary(d)library(qtl) # load example data data(f2cross, expr1, expr2, pmap, genepos) # calculate QTL genotype probabilities f2cross <- calc.genoprob(f2cross, step=1) # find nearest pseudomarkers pmark <- find.gene.pseudomarker(f2cross, pmap, genepos) # line up individuals id1 <- findCommonID(f2cross, expr1) id2 <- findCommonID(f2cross, expr2) # calculate LOD score for local eQTL locallod1 <- calc.locallod(f2cross[,id1$first], expr1[id1$second,], pmark) locallod2 <- calc.locallod(f2cross[,id2$first], expr2[id2$second,], pmark) # take those with LOD > 25 expr1s <- expr1[,locallod1>25,drop=FALSE] expr2s <- expr2[,locallod2>25,drop=FALSE] # calculate distance between individuals # (prop'n mismatches between obs and inferred eQTL geno) d1 <- disteg(f2cross, expr1s, pmark) d2 <- disteg(f2cross, expr2s, pmark) # combine distances d <- combinedist(d1, d2) # summary of problem samples summary(d)
For matrices x and y, calculate the correlation between columns of x and columns of y.
corbetw2mat( x, y, what = c("paired", "bestright", "bestpairs", "all"), corthresh = 0.9 )corbetw2mat( x, y, what = c("paired", "bestright", "bestpairs", "all"), corthresh = 0.9 )
x |
A numeric matrix. |
y |
A numeric matrix with the same number of rows as |
what |
Indicates which correlations to calculate and return. See value, below. |
corthresh |
Threshold on correlations if |
Missing values (NA) are ignored, and we calculate the correlation
using all complete pairs, as in stats::cor() with
use="pairwise.complete.obs".
If what="paired", the return value is a vector of
correlations, between columns of x and the corresponding column of
y. x and y must have the same number of columns.
If what="bestright", we return a data frame of size ncol(x) by
3, with the th row being the maximum correlation between
column of x and a column of y, and then the
y-column index and y-column name with that correlation. (In
case of ties, we give the first one.)
If what="bestpairs", we return a data frame with five columns,
containing all pairs of columns (with one in x and one in y)
with correlation corthresh. Each row corresponds to a
column pair, and contains the correlation and then the x- and
y-column indices followed by the x- and y-column names.
If what="all", the output is a matrix of size ncol(x) by
ncol(y), with all correlations between columns of x and
columns of y.
Karl W Broman, [email protected]
data(expr1, expr2) # correlations with paired columns r <- corbetw2mat(expr1, expr2) # top 10, by absolute value r[order(abs(r), decreasing=TRUE)[1:10]] # all pairs of columns with correlation >= 0.8 r_allpairs <- corbetw2mat(expr1, expr2, what="bestpairs", corthresh=0.6) # for each column in left matrix, most-correlated column in right matrix r_bestright <- corbetw2mat(expr1, expr2, what="bestright")data(expr1, expr2) # correlations with paired columns r <- corbetw2mat(expr1, expr2) # top 10, by absolute value r[order(abs(r), decreasing=TRUE)[1:10]] # all pairs of columns with correlation >= 0.8 r_allpairs <- corbetw2mat(expr1, expr2, what="bestpairs", corthresh=0.6) # for each column in left matrix, most-correlated column in right matrix r_bestright <- corbetw2mat(expr1, expr2, what="bestright")
Calculate a distance between all pairs of individuals for two gene expression data sets
distee( e1, e2 = NULL, d.method = c("rmsd", "cor"), labels = c("e1", "e2"), verbose = TRUE )distee( e1, e2 = NULL, d.method = c("rmsd", "cor"), labels = c("e1", "e2"), verbose = TRUE )
e1 |
Numeric matrix of gene expression data, as individuals x genes. The row and column names must contain individual and gene identifiers. |
e2 |
(Optional) Like |
d.method |
Calculate inter-individual distance as RMS difference or as correlation. |
labels |
Two character strings, to use as labels for the two data matrices in subsequent output. |
verbose |
if TRUE, give verbose output. |
We calculate the pairwise distance between all individuals (rows) in
e1 and all individuals in e2. This distance is either the RMS
difference (d.method="rmsd") or the correlation
(d.method="cor").
A matrix with nrow(e1) rows and nrow(e2) columns,
containing the distances. The individual IDs are in the row and column
names. The matrix is assigned class "lineupdist".
Karl W Broman, [email protected]
pulldiag(), omitdiag(),
summary.lineupdist(), plot2dist(),
disteg(), corbetw2mat()
# load the example data data(expr1, expr2) # find samples in common id <- findCommonID(expr1, expr2) # calculate correlations between cols of x and cols of y thecor <- corbetw2mat(expr1[id$first,], expr2[id$second,]) # subset at genes with corr > 0.8 and scale values expr1s <- expr1[,thecor > 0.8]/1000 expr2s <- expr2[,thecor > 0.8]/1000 # calculate distance (using "RMS difference" as a measure) d1 <- distee(expr1s, expr2s, d.method="rmsd", labels=c("1","2")) # calculate distance (using "correlation" as a measure...really similarity) d2 <- distee(expr1s, expr2s, d.method="cor", labels=c("1", "2")) # pull out the smallest 8 self-self correlations sort(pulldiag(d2))[1:8] # summary of results summary(d1) summary(d2) # plot histograms of RMS distances plot(d1) # plot histograms of correlations plot(d2) # plot distances against one another plot2dist(d1, d2)# load the example data data(expr1, expr2) # find samples in common id <- findCommonID(expr1, expr2) # calculate correlations between cols of x and cols of y thecor <- corbetw2mat(expr1[id$first,], expr2[id$second,]) # subset at genes with corr > 0.8 and scale values expr1s <- expr1[,thecor > 0.8]/1000 expr2s <- expr2[,thecor > 0.8]/1000 # calculate distance (using "RMS difference" as a measure) d1 <- distee(expr1s, expr2s, d.method="rmsd", labels=c("1","2")) # calculate distance (using "correlation" as a measure...really similarity) d2 <- distee(expr1s, expr2s, d.method="cor", labels=c("1", "2")) # pull out the smallest 8 self-self correlations sort(pulldiag(d2))[1:8] # summary of results summary(d1) summary(d2) # plot histograms of RMS distances plot(d1) # plot histograms of correlations plot(d2) # plot distances against one another plot2dist(d1, d2)
Calculate a distance between all pairs of individuals for two gene expression data sets
disteg( cross, pheno, pmark, min.genoprob = 0.99, k = 20, min.classprob = 0.8, classprob2drop = 1, repeatKNN = TRUE, max.selfd = 0.3, phenolabel = "phenotype", weightByLinkage = FALSE, map.function = c("haldane", "kosambi", "c-f", "morgan"), verbose = TRUE )disteg( cross, pheno, pmark, min.genoprob = 0.99, k = 20, min.classprob = 0.8, classprob2drop = 1, repeatKNN = TRUE, max.selfd = 0.3, phenolabel = "phenotype", weightByLinkage = FALSE, map.function = c("haldane", "kosambi", "c-f", "morgan"), verbose = TRUE )
cross |
An object of class |
pheno |
A data frame of phenotypes (generally gene expression data), stored as individuals x phenotypes. The row names must contain individual identifiers. |
pmark |
Pseudomarkers that are closest to the genes in |
min.genoprob |
Threshold on genotype probabilities; if maximum
probability is less than this, observed genotype taken as |
k |
Number of nearest neighbors to consider in forming a k-nearest neighbor classifier. |
min.classprob |
Minimum proportion of neighbors with a common class to make a class prediction. |
classprob2drop |
If an individual is inferred to have a genotype mismatch with classprob > this value, treat as an outlier and drop from the analysis and then repeat the KNN construction without it. |
repeatKNN |
If TRUE, repeat k-nearest neighbor a second time, after omitting individuals who seem to not be self-self matches |
max.selfd |
Min distance from self (as proportion of mismatches between observed and predicted eQTL genotypes) to be excluded from the second round of k-nearest neighbor. |
phenolabel |
Label for expression phenotypes to place in the output distance matrix. |
weightByLinkage |
If TRUE, weight the eQTL to account for their relative positions (for example, two tightly linked eQTL would each count about 1/2 of an isolated eQTL) |
map.function |
Used if |
verbose |
if TRUE, give verbose output. |
We consider the expression phenotypes in batches, by which pseudomarker they
are closest to. For each batch, we pull the genotype probabilities at the
corresponding pseudomarker and use the individuals that are in common
between cross and pheno and whose maximum genotype probability
is above min.genoprob, to form a classifier of eQTL genotype from
expression values, using k-nearest neighbor (the function
class::knn()). The classifier is applied to all individuals with
expression data, to give a predicted eQTL genotype. (If the proportion of
the k nearest neighbors with a common class is less than
min.classprob, the predicted eQTL genotype is left as NA.)
If repeatKNN is TRUE, we repeat the construction of the k-nearest
neighbor classifier after first omitting individuals whose proportion of
mismatches between observed and inferred eQTL genotypes is greater than
max.selfd.
Finally, we calculate the distance between the observed eQTL genotypes for
each individual in cross and the inferred eQTL genotypes for each
individual in pheno, as the proportion of mismatches between the
observed and inferred eQTL genotypes.
If weightByLinkage is TRUE, we use weights on the mismatch
proportions for the various eQTL, taking into account their linkage. Two
tightly linked eQTL will each be given half the weight of a single isolated
eQTL.
A matrix with nind(cross) rows and nrow(pheno)
columns, containing the distances. The individual IDs are in the row and
column names. The matrix is assigned class "lineupdist".
The names of the genes that were used to construct the classifier are saved
in an attribute "retained".
The observed and inferred eQTL genotypes are saved as attributes
"obsg" and "infg".
The denominators of the proportions that form the inter-individual distances
are in the attribute "denom".
Karl W Broman, [email protected]
distee(), summary.lineupdist(),
pulldiag(), omitdiag(), findCommonID(),
find.gene.pseudomarker(), calc.locallod(),
plot.lineupdist(), class::knn(),
plotEGclass()
library(qtl) # load example data data(f2cross, expr1, pmap, genepos) # calculate QTL genotype probabilities f2cross <- calc.genoprob(f2cross, step=1) # find nearest pseudomarkers pmark <- find.gene.pseudomarker(f2cross, pmap, genepos) # line up individuals id <- findCommonID(f2cross, expr1) # calculate LOD score for local eQTL locallod <- calc.locallod(f2cross[,id$first], expr1[id$second,], pmark) # take those with LOD > 25 expr1s <- expr1[,locallod>25,drop=FALSE] # calculate distance between individuals # (prop'n mismatches between obs and inferred eQTL geno) d <- disteg(f2cross, expr1s, pmark) # plot distances plot(d) # summary of apparent mix-ups summary(d) # plot of classifier for and second eQTL par(mfrow=c(2,1), las=1) plotEGclass(d) plotEGclass(d, 2)library(qtl) # load example data data(f2cross, expr1, pmap, genepos) # calculate QTL genotype probabilities f2cross <- calc.genoprob(f2cross, step=1) # find nearest pseudomarkers pmark <- find.gene.pseudomarker(f2cross, pmap, genepos) # line up individuals id <- findCommonID(f2cross, expr1) # calculate LOD score for local eQTL locallod <- calc.locallod(f2cross[,id$first], expr1[id$second,], pmark) # take those with LOD > 25 expr1s <- expr1[,locallod>25,drop=FALSE] # calculate distance between individuals # (prop'n mismatches between obs and inferred eQTL geno) d <- disteg(f2cross, expr1s, pmark) # plot distances plot(d) # summary of apparent mix-ups summary(d) # plot of classifier for and second eQTL par(mfrow=c(2,1), las=1) plotEGclass(d) plotEGclass(d, 2)
Matrices of simulated gene expression data, each for 98 individuals
at 5,000 genes. Think of expr1 and expr2 as
expression data on two different tissues.
data(expr1) data(expr2)data(expr1) data(expr2)
A matrix of integers, individuals as rows and genes as columns.
data(expr1) data(expr2) # identify the common individuals id <- findCommonID(rownames(expr1), rownames(expr2)) # correlation between tissues for each gene rho <- corbetw2mat(expr1[id$first,], expr2[id$second,]) hist(rho, breaks=100)data(expr1) data(expr2) # identify the common individuals id <- findCommonID(rownames(expr1), rownames(expr2)) # correlation between tissues for each gene rho <- corbetw2mat(expr1[id$first,], expr2[id$second,]) hist(rho, breaks=100)
Simulated experimental cross data with some sample mix-ups. The only phenotype is an individual ID. There are 100 individuals genotyped at 1000 markers on 19 autosomes.
data(f2cross)data(f2cross)
An object of class "cross". See
qtl::read.cross() in the R/qtl package for details.
expr1(), expr2(), genepos(), pmap()
library(qtl) data(f2cross) summary(f2cross)library(qtl) data(f2cross) summary(f2cross)
Pull out the pseudomarker that is closest to the position of each of a series of genes.
find.gene.pseudomarker(cross, pmap, geneloc, where = c("prob", "draws"))find.gene.pseudomarker(cross, pmap, geneloc, where = c("prob", "draws"))
cross |
An object of class |
pmap |
A physical map of the markers in |
geneloc |
A data frame specifying the physical locations of the genes.
There should be two columns, |
where |
Indicates whether to pull pseudomarkers from the genotype
probabilities (produced by |
We first convert positions (by interpolation) from those contained within
cross to physical coordinates contained in pmap. We then use
qtl::find.pseudomarker() to identify the closest pseudomarker to
each gene location.
We also include the positions of the pseudomarkers, and we print a warning message if pseudomarkers are > 2 Mbp from the respective gene.
A data frame with columns chr (the chromosome) and
pmark (the name of the pseudomarker). The third column pos
contains the Mbp position of the pseudomarker. The final column is the
signed distance between the gene and the pseudomarker. The rownames
indicate the gene names.
Karl W Broman, [email protected]
qtl::find.pseudomarker(),
qtl::find.pseudomarkerpos(), plotEGclass(),
disteg(), calc.locallod()
data(f2cross, expr1, genepos, pmap) library(qtl) # calc QTL genotype probabilities f2cross <- calc.genoprob(f2cross, step=1) # find nearest pseudomarkers pmark <- find.gene.pseudomarker(f2cross, pmap, genepos, "prob")data(f2cross, expr1, genepos, pmap) library(qtl) # calc QTL genotype probabilities f2cross <- calc.genoprob(f2cross, step=1) # find nearest pseudomarkers pmark <- find.gene.pseudomarker(f2cross, pmap, genepos, "prob")
Identify which individuals are in common between a QTL mapping data set and a matrix of phenotypes, series of genes.
findCommonID(id1, id2)findCommonID(id1, id2)
id1 |
A character vector of individual IDs. This can also be a QTL
cross object (see |
id2 |
Like |
A list with three components:
First, a data frame with rows corresponding to all individuals (across the
two sets of individual IDs) and three columns: indexInFirst and
indexInSecond contain numeric indices to the locations of the
individuals within cross and pheno, and inBoth is a
logical vector to indicate which individuals appear in both crosses. The
row names are the individual identifiers.
The second and third components are vectors of indices in id1 and
id2, respectively, indicating the paired locations of the individuals
that are in common.
Karl W Broman, [email protected]
calc.locallod(), corbetw2mat()
data(f2cross, expr1) # align IDs id <- findCommonID(f2cross, expr1) # aligned data f2cross_aligned <- f2cross[,id$first] expr1_aligned <- expr1[id$second,]data(f2cross, expr1) # align IDs id <- findCommonID(f2cross, expr1) # aligned data f2cross_aligned <- f2cross[,id$first] expr1_aligned <- expr1[id$second,]
Standardize each column in a matrix, so that the columns have mean 0 and SD 1.
fscale(x)fscale(x)
x |
A numeric matrix. |
Missing values (NA) are ignored and left as is.
If there is just 1 non-missing value in a column, it is left as is.
This function uses a one-pass algorithm to calculate the mean and SD, which is fast but can show a bit of round-off error.
A matrix of the same form as the input, but with columns transformed to have mean 0 and SD 1.
Karl W Broman, [email protected]
x <- matrix(1:10, ncol=2) y <- fscale(x)x <- matrix(1:10, ncol=2) y <- fscale(x)
A table with the genomic positions of genes in the simulated
expression data, expr1() and expr2().
data(genepos)data(genepos)
A data frame with two columns, chromosome and physical position (in Mbp).
expr1(), expr2(), f2cross(), pmap()
data(genepos) # interplot genetic positions library(qtl) data(pmap) data(f2cross) genepos_interp <- interpPositions(genepos, pmap, pull.map(f2cross)) genepos[1:5,] # 'newpos' column is the interpolated cM positiondata(genepos) # interplot genetic positions library(qtl) data(pmap) data(f2cross) genepos_interp <- interpPositions(genepos, pmap, pull.map(f2cross)) genepos[1:5,] # 'newpos' column is the interpolated cM position
Print the version number of the currently installed version of R/lineup.
lineupversion()lineupversion()
A character string with the version number of the currently installed version of R/lineup.
Karl W Broman, [email protected]
lineupversion()lineupversion()
Replace the diagonal (that is, self-self distances) from a distance matrix
calculated by distee() or disteg() with missing
values (so that only self-nonself distances are left).
omitdiag(d)omitdiag(d)
d |
We use the row and column names to identify which entries are self-self.
A matrix of the same form as the input, but with self-self distances
replaced with NA.
Karl W Broman, [email protected]
pulldiag(), distee(), disteg(),
summary.lineupdist(), plot2dist(),
plot.lineupdist()
data(expr1, expr2) # distance as RMS difference d <- distee(expr1, expr2) # focus on the self-nonself distances # (replace self-self distances with NA) d_selfnonself <- omitdiag(d)data(expr1, expr2) # distance as RMS difference d <- distee(expr1, expr2) # focus on the self-nonself distances # (replace self-self distances with NA) d_selfnonself <- omitdiag(d)
Plot histograms of self-self and self-nonself distances from a distance
matrix calculated by distee() or disteg().
## S3 method for class 'lineupdist' plot(x, breaks = NULL, add.rug = TRUE, what = c("both", "ss", "sn"), ...)## S3 method for class 'lineupdist' plot(x, breaks = NULL, add.rug = TRUE, what = c("both", "ss", "sn"), ...)
x |
|
breaks |
Optional vector of breaks, passed to
|
add.rug |
If true, also include |
what |
Indicates whether to plot both self-self and self-nonself
distances (or correlations) or just one or the other. ( |
... |
Ignored at this point. |
We call pulldiag() and omitdiag() to get the
self-self and self-nonself distances.
If all of the self-self distances are missing, we plot just the self-nonself distances.
None.
Karl W Broman, [email protected]
pulldiag(), distee(),
plot2dist()
data(expr1, expr2) # distance as correlation d <- distee(expr1, expr2, "cor") # plot histograms of self-self and self-nonself correlations plot(d)data(expr1, expr2) # distance as correlation d <- distee(expr1, expr2, "cor") # plot histograms of self-self and self-nonself correlations plot(d)
Plot two sets of inter-individual distances against one another, colored by self and non-self distances.
plot2dist( d1, d2, hirow = NULL, hicol = NULL, xlab = NULL, ylab = NULL, smoothScatter = FALSE, colself = "black", colnonself = "gray", colhirow = "green", colhicol = "orange", ... )plot2dist( d1, d2, hirow = NULL, hicol = NULL, xlab = NULL, ylab = NULL, smoothScatter = FALSE, colself = "black", colnonself = "gray", colhirow = "green", colhicol = "orange", ... )
d1 |
Output of |
d2 |
Output of |
hirow |
Names of rows to highlight in green. |
hicol |
Names of columns to highlight in orange. |
xlab |
X-axis label (optional) |
ylab |
Y-axis label (optional) |
smoothScatter |
If TRUE, plot non-self distances with
|
colself |
Color to use for the self-self points. If NULL, these aren't plotted. |
colnonself |
Color to use for the non-self points. If NULL, these aren't plotted. |
colhirow |
Color to use for the |
colhicol |
Color to use for the |
... |
Passed to |
None.
Karl W Broman, [email protected]
pulldiag(), distee(),
summary.lineupdist()
data(expr1, expr2) # distances as RMS difference and correlation d_rmsd <- distee(expr1, expr2, "rmsd") d_cor <- distee(expr1, expr2, "cor") # plot distances against one another plot2dist(d_rmsd, d_cor)data(expr1, expr2) # distances as RMS difference and correlation d_rmsd <- distee(expr1, expr2, "rmsd") d_cor <- distee(expr1, expr2, "cor") # plot distances against one another plot2dist(d_rmsd, d_cor)
Diagnostic plot of one of the eQTL classifiers from the results of
disteg(): generally expression phenotype against observed eQTL
genotype, colored by inferred eQTL genotype.
plotEGclass( d, eqtl = 1, outercol = "inferred", innercol = "observed", thecolors = c("#7B68ED", "#1B9E78", "#CA3767", "#E59E00"), ... )plotEGclass( d, eqtl = 1, outercol = "inferred", innercol = "observed", thecolors = c("#7B68ED", "#1B9E78", "#CA3767", "#E59E00"), ... )
d |
Output of |
eqtl |
Numeric index or a character vector (of the form "[email protected]") indicating the eQTL to consider. |
outercol |
Indicates how to color the outer edge of the points:
|
innercol |
Like |
thecolors |
The colors to use in the plot. The last element (after the number of genotypes) indicates the color to use for missing values. |
... |
Passed to |
The function produces a diagnostic plot for studying one of the k-nearest
neighbor classifiers underlying the output from disteg().
In the case of one expression phenotype attached to the selected eQTL, the plot is a dot plot of gene expression against observed eQTL genotype.
In the case of two expression phenotypes, the plot is a scatterplot of the two expression phenotypes against each other.
In the case of more than two expression phenotypes, we use
graphics::pairs() to produce a matrix of scatterplots.
None.
Karl W Broman, [email protected]
disteg(), plot.lineupdist(),
plot2dist(), class::knn()
library(qtl) # load example data data(f2cross, expr1, pmap, genepos) # calculate QTL genotype probabilities f2cross <- calc.genoprob(f2cross, step=1) # find nearest pseudomarkers pmark <- find.gene.pseudomarker(f2cross, pmap, genepos) # line up individuals id <- findCommonID(f2cross, expr1) # calculate LOD score for local eQTL locallod <- calc.locallod(f2cross[,id$first], expr1[id$second,], pmark) # take those with LOD > 25 expr1s <- expr1[,locallod>25,drop=FALSE] # calculate distance between individuals # (prop'n mismatches between obs and inferred eQTL geno) d <- disteg(f2cross, expr1s, pmark) # plot of classifier for and second eQTL par(mfrow=c(2,1), las=1) plotEGclass(d) plotEGclass(d, 2)library(qtl) # load example data data(f2cross, expr1, pmap, genepos) # calculate QTL genotype probabilities f2cross <- calc.genoprob(f2cross, step=1) # find nearest pseudomarkers pmark <- find.gene.pseudomarker(f2cross, pmap, genepos) # line up individuals id <- findCommonID(f2cross, expr1) # calculate LOD score for local eQTL locallod <- calc.locallod(f2cross[,id$first], expr1[id$second,], pmark) # take those with LOD > 25 expr1s <- expr1[,locallod>25,drop=FALSE] # calculate distance between individuals # (prop'n mismatches between obs and inferred eQTL geno) d <- disteg(f2cross, expr1s, pmark) # plot of classifier for and second eQTL par(mfrow=c(2,1), las=1) plotEGclass(d) plotEGclass(d, 2)
Physical map (Mbp positions) of the markers in f2cross()
data(pmap)data(pmap)
A list of vectors, each containing the locations of markers in Mbp.
(Technically, an object of class "map".)
expr1(), expr2(), f2cross(), genepos()
data(pmap) summary(pmap) plot(pmap)data(pmap) summary(pmap) plot(pmap)
Pull out the diagonal from a distance matrix calculated by
distee() (that is, self-self distances).
pulldiag(d)pulldiag(d)
d |
A distance matrix calculated by |
We use the row and column names to identify which entries are self-self.
A vector with the self-self distances.
Karl W Broman, [email protected]
omitdiag(), distee(), disteg(),
summary.lineupdist(), plot2dist(),
plot.lineupdist()
data(expr1, expr2) # distance as RMS difference d <- distee(expr1, expr2) # pull out the self-self distances d_selfself <- pulldiag(d) # samples with smallest self-self correlation sort(d_selfself)[1:10]data(expr1, expr2) # distance as RMS difference d <- distee(expr1, expr2) # pull out the self-self distances d_selfself <- pulldiag(d) # samples with smallest self-self correlation sort(d_selfself)[1:10]
Pull out a specified set of rows and columns from a distance matrix
calculated by distee() or disteg().
## S3 method for class 'lineupdist' subset(x, rows = NULL, cols = NULL, ...) ## S3 method for class 'lineupdist' x[rows = NULL, cols = NULL]## S3 method for class 'lineupdist' subset(x, rows = NULL, cols = NULL, ...) ## S3 method for class 'lineupdist' x[rows = NULL, cols = NULL]
x |
A distance matrix object as obtained from |
rows |
Optional vector of selected rows. |
cols |
Optional vector of selected columns. |
... |
Ignored at this point. |
The input distance matrix object, but with only the specified subset of the data.
Karl W Broman, [email protected]
disteg(), distee(), pulldiag()
data(expr1, expr2) # find samples in common id <- findCommonID(expr1, expr2) # calculate correlations between cols of x and cols of y thecor <- corbetw2mat(expr1[id$first,], expr2[id$second,]) expr1s <- expr1[,thecor > 0.8]/1000 expr2s <- expr2[,thecor > 0.8]/1000 # calculate correlations among samples d <- distee(expr1s, expr2s, d.method="cor") # pull out distances for samples 24, 92, 44, 66 samp <- c("24", "92", "44", "66") d[samp, samp]data(expr1, expr2) # find samples in common id <- findCommonID(expr1, expr2) # calculate correlations between cols of x and cols of y thecor <- corbetw2mat(expr1[id$first,], expr2[id$second,]) expr1s <- expr1[,thecor > 0.8]/1000 expr2s <- expr2[,thecor > 0.8]/1000 # calculate correlations among samples d <- distee(expr1s, expr2s, d.method="cor") # pull out distances for samples 24, 92, 44, 66 samp <- c("24", "92", "44", "66") d[samp, samp]
Summarize the results of distee() or disteg(), with
inter-individual distances between two sets of gene expression data.
## S3 method for class 'lineupdist' summary( object, cutoff = NULL, dropmatches = TRUE, reorder = c("alignmatches", "bydistance", "no"), ... )## S3 method for class 'lineupdist' summary( object, cutoff = NULL, dropmatches = TRUE, reorder = c("alignmatches", "bydistance", "no"), ... )
object |
|
cutoff |
(Optional) Cutoff on correlation/distance, with rows in the results only being kept if the best distance/correlation is above this cutoff or the self-self result is not missing and is above this cutoff. |
dropmatches |
If TRUE, omit rows for which an individual's best match is itself. |
reorder |
If |
... |
Passed to |
A list with two components: the distances summarized by row and the distances summarized by column.
For each individual, we calculate the minimum distance to others, next-smallest distance, the self-self distance, the mean and SD of the distances to others, and finally indicate the individual (or individuals) that is closest.
Karl W Broman, [email protected]
pulldiag(), omitdiag(),
distee(), disteg(), plot2dist(),
plot.lineupdist()
data(expr1, expr2) # distance as correlation d <- distee(expr1, expr2, "cor") # summary of potential problems summary(d)data(expr1, expr2) # distance as correlation d <- distee(expr1, expr2, "cor") # summary of potential problems summary(d)