Version:	0.7
Date:	2021-08-08
Title:	Omics Data Analysis
Author:	Matthias Kohl [aut, cre]
Maintainer:	Matthias Kohl <Matthias.Kohl@stamats.de>
Depends:	R(≥ 3.5.0)
Imports:	stats, utils, graphics, grDevices, RColorBrewer, robustbase, limma, grid, circlize, ComplexHeatmap
Suggests:	knitr, rmarkdown
VignetteBuilder:	knitr
Description:	Similarity plots based on correlation and median absolute deviation (MAD); adjusting colors for heatmaps; aggregate technical replicates; calculate pairwise fold-changes and log fold-changes; compute one- and two-way ANOVA; simplified interface to package 'limma' (Ritchie et al. (2015), <doi:10.1093/nar/gkv007> ) for moderated t-test and one-way ANOVA; Hamming and Levenshtein (edit) distance of strings as well as optimal alignment scores for global (Needleman-Wunsch) and local (Smith-Waterman) alignments with constant gap penalties (Merkl and Waack (2009), ISBN:978-3-527-32594-8).
License:	LGPL-3
URL:	https://www.stamats.de/
NeedsCompilation:	no
Packaged:	2021-08-08 13:24:50 UTC; kohlm
Repository:	CRAN
Date/Publication:	2021-08-08 13:50:02 UTC

Omics Data Analysis.

Description

Similarity plots based on correlation and median absolute deviation (MAD); adjusting colors for heatmaps; aggregate technical replicates; calculate pairwise fold-changes and log fold-changes; computate one- and two-way ANOVA; simplified interface to package 'limma' (Ritchie et al. (2015), <doi:10.1093/nar/gkv007>) for moderated t-test and one-way ANOVA; Hamming and Levenshtein (edit) distance of strings as well as optimal alignment scores for global (Needleman-Wunsch) and local (Smith-Waterman) alignments with constant gap penalties (Merkl and Waack (2009), ISBN:978-3-527-32594-8).

Details

library(MKomics)

Author(s)

Matthias Kohl https://www.stamats.de

Maintainer: Matthias Kohl matthias.kohl@stamats.de

Correlation Distance Matrix Computation

Description

The function computes and returns the correlation and absolute correlation distance matrix computed by using the specified distance measure to compute the distances between the rows of a data matrix.

Usage

corDist(x, method = "pearson", diag = FALSE, upper = FALSE, abs = FALSE,
        use = "pairwise.complete.obs", ...)

Arguments

Details

The function computes the Pearson, Spearman, Kendall or Cosine sample correlation and absolute correlation; confer Section 12.2.2 of Gentleman et al (2005). For more details about the arguments we refer to functions dist and cor. Moreover, the function computes the minimum covariance determinant or the orthogonalized Gnanadesikan-Kettenring estimator. For more details we refer to functions covMcd and covOGK, respectively.

Value

corDist returns an object of class "dist"; cf. dist.

Note

A first version of this function appeared in package SLmisc.

Author(s)

Matthias Kohl Matthias.Kohl@stamats.de

References

Gentleman R. Ding B., Dudoit S. and Ibrahim J. (2005). Distance Measures in DNA Microarray Data Analysis. In: Gentleman R., Carey V.J., Huber W., Irizarry R.A. and Dudoit S. (editors) Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Springer.

P. J. Rousseeuw and A. M. Leroy (1987). Robust Regression and Outlier Detection. Wiley.

P. J. Rousseeuw and K. van Driessen (1999) A fast algorithm for the minimum covariance determinant estimator. Technometrics 41, 212-223.

Pison, G., Van Aelst, S., and Willems, G. (2002), Small Sample Corrections for LTS and MCD, Metrika, 55, 111-123.

Maronna, R.A. and Zamar, R.H. (2002). Robust estimates of location and dispersion of high-dimensional datasets; Technometrics 44(4), 307-317.

Gnanadesikan, R. and John R. Kettenring (1972). Robust estimates, residuals, and outlier detection with multiresponse data. Biometrics 28, 81-124.

Examples

## only a dummy example
M <- matrix(rnorm(1000), ncol = 20)
D <- corDist(M)

Plot of similarity matrix based on correlation

Description

Plot of similarity matrix. This function is a slight modification of function plot.cor of the archived package "sma".

Usage

corPlot(x, new = FALSE, col, minCor, 
        labels = FALSE, lab.both.axes = FALSE, labcols = "black", 
        title = "", cex.title = 1.2, 
        protocol = FALSE, cex.axis = 0.8, 
        cex.axis.bar = 1, signifBar = 2, ...)
        
corPlot2(x, new = FALSE, col, minCor = 0.5, labels = FALSE, 
         row.width = 6, column.height = 6, lab.both.axes = TRUE, 
         fontsize.axis = 12, title = "", fontsize.title = 16, 
         signifBar = 2)

Arguments

Details

This functions generates the so called similarity matrix (based on correlation) for a microarray experiment.

If min(x), respectively min(cor(x)) is smaller than minCor, the colors in col are adjusted such that the minimum correlation value which is color coded is equal to minCor.

Value

invisible()

Note

A first version of this function appeared in package SLmisc.

Author(s)

Matthias Kohl Matthias.Kohl@stamats.de

References

Sandrine Dudoit, Yee Hwa (Jean) Yang, Benjamin Milo Bolstad and with contributions from Natalie Thorne, Ingrid Loennstedt and Jessica Mar. sma: Statistical Microarray Analysis.
http://www.stat.berkeley.edu/users/terry/zarray/Software/smacode.html

Examples

## only a dummy example
M <- matrix(rnorm(1000), ncol = 20)
colnames(M) <- paste("Sample", 1:20)
M.cor <- cor(M)

corPlot(M.cor, minCor = min(M.cor))
corPlot(M.cor, minCor = min(M.cor), lab.both.axes = TRUE)
corPlot(M.cor, minCor = min(M.cor), protocol = TRUE)
corPlot(M.cor, minCor = min(M.cor), signifBar = 1)

corPlot2(M.cor, minCor = min(M.cor))
corPlot2(M.cor, minCor = min(M.cor), lab.both.axes = FALSE)
corPlot2(M.cor, minCor = min(M.cor), signifBar = 1)

Generate colors for heatmaps

Description

This function modifies a given color vector as used for heatmaps.

Usage

heatmapCol(data, col, lim, na.rm = TRUE)

Arguments

Details

Colors below and above a specified value are kept constant. In addition, the colors are symmetrizised.

Value

vector of colors

Note

A first version of this function appeared in package SLmisc.

Author(s)

Matthias Kohl Matthias.Kohl@stamats.de

Examples

data.plot <- matrix(rnorm(100*50, sd = 1), ncol = 50)
colnames(data.plot) <- paste("patient", 1:50)
rownames(data.plot) <- paste("gene", 1:100)
data.plot[1:70, 1:30] <- data.plot[1:70, 1:30] + 3
data.plot[71:100, 31:50] <- data.plot[71:100, 31:50] - 1.4
data.plot[1:70, 31:50] <- rnorm(1400, sd = 1.2)
data.plot[71:100, 1:30] <- rnorm(900, sd = 1.2)
nrcol <- 128

require(RColorBrewer)
myCol <- rev(colorRampPalette(brewer.pal(10, "RdBu"))(nrcol))
heatmap(data.plot, col =  myCol, main = "standard colors")
myCol2 <- heatmapCol(data = data.plot, col = myCol, 
                     lim = min(abs(range(data.plot)))-1)
heatmap(data.plot, col = myCol2, main = "heatmapCol colors")

Compute MAD between colums of a matrix or data.frame

Description

Compute MAD between colums of a matrix or data.frame. Can be used to create a similarity matrix for a microarray experiment.

Usage

madMatrix(x)

Arguments

Details

This functions computes the so called similarity matrix (based on MAD) for a microarray experiment; cf. Buness et. al. (2004).

Value

matrix of MAD values between colums of x

Note

A first version of this function appeared in package SLmisc.

Author(s)

Matthias Kohl Matthias.Kohl@stamats.de

References

Andreas Buness, Wolfgang Huber, Klaus Steiner, Holger Sueltmann, and Annemarie Poustka. arrayMagic: two-colour cDNA microarray quality control and preprocessing. Bioinformatics Advance Access published on September 28, 2004. doi:10.1093/bioinformatics/bti052

See Also

plotMAD

Examples

## only a dummy example
madMatrix(matrix(rnorm(1000), ncol = 10))

Plot of similarity matrix based on MAD

Description

Plot of similarity matrix based on MAD between microarrays.

Usage

madPlot(x, new = FALSE, col, maxMAD = 3, labels = FALSE, 
        labcols = "black", title = "", protocol = FALSE, ...)

madPlot2(x, new = FALSE, col, maxMAD = 3, labels = FALSE, 
         row.width = 6, column.height = 6, 
         lab.both.axes = TRUE, fontsize.axis = 12,
         title = "", fontsize.title = 16, signifBar = 2)

Arguments

Details

This functions generates the so called similarity matrix (based on MAD) for a microarray experiment; cf. Buness et. al. (2004). The function is similar to corPlot.

Note

A first version of this function appeared in package SLmisc.

Author(s)

Matthias Kohl Matthias.Kohl@stamats.de

References

Sandrine Dudoit, Yee Hwa (Jean) Yang, Benjamin Milo Bolstad and with contributions from Natalie Thorne, Ingrid Loennstedt and Jessica Mar. sma: Statistical Microarray Analysis.
http://www.stat.berkeley.edu/users/terry/zarray/Software/smacode.html

Andreas Buness, Wolfgang Huber, Klaus Steiner, Holger Sueltmann, and Annemarie Poustka. arrayMagic: two-colour cDNA microarray quality control and preprocessing. Bioinformatics Advance Access published on September 28, 2004. doi:10.1093/bioinformatics/bti052

See Also

corPlot

Examples

## only a dummy example
set.seed(13)
x <- matrix(rnorm(1000), ncol = 10)
x[1:20,5] <- x[1:20,5] + 10
madPlot(x, new = TRUE, maxMAD = 2.5)
madPlot2(x, new = TRUE, maxMAD = 2.5)
## in contrast
corPlot2(x, new = TRUE, minCor = -0.5)

Moderated 1-Way ANOVA

Description

Performs moderated 1-Way ANOVAs based on Bioconductor package limma.

Usage

mod.oneway.test(x, group, repeated = FALSE, subject, adjust.method = "BH", 
                sort.by = "none")

Arguments

, where "logFC" corresponds to difference in means.

Details

The function uses Bioconductor package limma to compute moderated 1-way ANOVAs. For more details we refer to ebayes.

Value

A data.frame with the results.

References

Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W., and Smyth, G.K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43(7), e47.

See Also

oneway.test, mod.t.test

Examples

set.seed(123)
X <- rbind(matrix(rnorm(5*20), nrow = 5, ncol = 20),
           matrix(rnorm(5*20, mean = 1), nrow = 5, ncol = 20))
gr <- factor(c(rep("A1", 5), rep("B2", 5), rep("C3", 5), rep("D4", 5)))
mod.oneway.test(X, gr)

## Welch 1-Way ANOVA (not moderated)
ow.test <- function(x, g){
  res <- oneway.test(x ~ g)
  c(res$statistic, res$p.value)
}
ow.res <- t(apply(X, 1, ow.test, g = gr))
colnames(ow.res) <- c("F", "p.value")
ow.res

## repeated measures
X <- rbind(matrix(rnorm(6*18), nrow = 6, ncol = 18),
           matrix(rnorm(6*18, mean = 1), nrow = 6, ncol = 18))
gr <- factor(c(rep("T1", 6), rep("T2", 6), rep("T3", 6)))
subjectID <- factor(c(rep(1:6, 3)))
mod.oneway.test(X, gr, repeated = TRUE, subject = subjectID)

Moderated t-Test

Description

Performs moderated t-tests based on Bioconductor package limma.

Usage

mod.t.test(x, group = NULL, paired = FALSE, subject, adjust.method = "BH",
           sort.by = "none", na.rm = TRUE)

Arguments

, where "logFC" corresponds to difference in means.

Details

The function uses Bioconductor package limma to compute moderated t-tests. For more details we refer to ebayes.

Value

A data.frame with the results.

References

Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W., and Smyth, G.K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43(7), e47.

See Also

t.test

Examples

## One-sample test
X <- matrix(rnorm(10*20, mean = 1), nrow = 10, ncol = 20)

mod.t.test(X)
## corresponds to
library(limma)
design <- matrix(1, nrow = ncol(X), ncol = 1)
colnames(design) <- "A"
fit1 <- lmFit(X, design)
fit2 <- eBayes(fit1)
topTable(fit2, coef = 1, number = Inf, confint = TRUE, sort.by = "none")[,-4]

## Two-sample test
set.seed(123)
X <- rbind(matrix(rnorm(5*20), nrow = 5, ncol = 20),
           matrix(rnorm(5*20, mean = 1), nrow = 5, ncol = 20))
g2 <- factor(c(rep("group 1", 10), rep("group 2", 10)))

mod.t.test(X, group = g2)
## corresponds to
design <- model.matrix(~ 0 + g2)
colnames(design) <- c("group1", "group2")
fit1 <- lmFit(X, design)
cont.matrix <- makeContrasts(group1vsgroup2="group1-group2", levels=design)
fit2 <- contrasts.fit(fit1, cont.matrix)
fit3 <- eBayes(fit2)
topTable(fit3, coef = 1, number = Inf, confint = TRUE, sort.by = "none")[,-4]

## Paired two-sample test
subjID <- factor(rep(1:10, 2))
mod.t.test(X, group = g2, paired = TRUE, subject = subjID)

A function for Analysis of Variance

Description

This function is a slight modification of function Anova of package genefilter.

Usage

oneWayAnova(cov, na.rm = TRUE, var.equal = FALSE)

Arguments

Details

The function returned by oneWayAnova uses oneway.test to perform a one-way ANOVA, where x is the set of gene expressions. The F statistic for an overall effect is computed and the corresponding p-value is returned.

The function Anova of package genefilter instead compares the computed p-value to a prespecified p-value and returns TRUE, if the computed p-value is smaller than the prespecified one.

Value

oneWayAnova returns a function with bindings for cov that will perform a one-way ANOVA.

The covariate can be continuous, in which case the test is for a linear effect for the covariate.

Note

A first version of this function appeared in package SLmisc.

Author(s)

Matthias Kohl Matthias.Kohl@stamats.de

References

R. Gentleman, V. Carey, W. Huber and F. Hahne (2006). genefilter: methods for filtering genes from microarray experiments. R package version 1.13.7.

See Also

oneway.test, twoWayAnova

Examples

set.seed(123)
af <- oneWayAnova(c(rep(1,5),rep(2,5)))
af(rnorm(10))

Compute pairwise fold changes

Description

This function computes pairwise fold changes. It also works for logarithmic data.

Usage

pairwise.fc(x, g, ave = mean, log = TRUE, base = 2, mod.fc = TRUE, ...)

Arguments

Details

The function computes pairwise fold changes between groups, where the group values are aggregated using the function which is given by the argument ave.

The fold changes are returned in a slightly modified form if mod.fc = TRUE. Fold changes FC which are smaller than 1 are reported as to -1/FC.

The implementation is in certain aspects analogously to pairwise.t.test.

Value

Vector with pairwise fold changes.

Author(s)

Matthias Kohl Matthias.Kohl@stamats.de

See Also

pairwise.t.test

Examples

set.seed(13)
x <- rnorm(100) ## assumed as log2-data
g <- factor(sample(1:4, 100, replace = TRUE))
levels(g) <- c("a", "b", "c", "d")
pairwise.fc(x, g)

## some small checks
res <- by(x, list(g), mean)
2^(res[[1]] - res[[2]]) # a vs. b
-1/2^(res[[1]] - res[[3]]) # a vs. c
2^(res[[1]] - res[[4]]) # a vs. d
-1/2^(res[[2]] - res[[3]]) # b vs. c
-1/2^(res[[2]] - res[[4]]) # b vs. d
2^(res[[3]] - res[[4]]) # c vs. d

Compute pairwise log-fold changes

Description

The function computes pairwise log-fold changes.

Usage

pairwise.logfc(x, g, ave = mean, log = TRUE, base = 2, ...)

Arguments

Details

The function computes pairwise log-fold changes between groups, where the group values are aggregated using the function which is given by the argument ave.

The implementation is in certain aspects analogously to pairwise.t.test.

Value

Vector with pairwise log-fold changes.

Author(s)

Matthias Kohl Matthias.Kohl@stamats.de

See Also

pairwise.t.test

Examples

set.seed(13)
x <- rnorm(100) ## assumed as log2-data
g <- factor(sample(1:4, 100, replace = TRUE))
levels(g) <- c("a", "b", "c", "d")
pairwise.logfc(x, g)

## some small checks
res <- by(x, list(g), mean)
res[[1]] - res[[2]] # a vs. b
res[[1]] - res[[3]] # a vs. c
res[[1]] - res[[4]] # a vs. d
res[[2]] - res[[3]] # b vs. c
res[[2]] - res[[4]] # b vs. d
res[[3]] - res[[4]] # c vs. d

Pairwise Moderated t-Tests

Description

Performs pairwise moderated t-tests (unpaired) based on Bioconductor package limma.

Usage

pairwise.mod.t.test(x, group, adjust.method = "BH", sort.by = "none")

Arguments

, where "logFC" corresponds to difference in means.

Details

The function uses Bioconductor package limma to compute pairwise moderated t-tests. For more details we refer to ebayes.

Value

A data.frame with the results.

References

Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W., and Smyth, G.K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43(7), e47.

See Also

oneway.test, mod.t.test

Examples

set.seed(123)
X <- rbind(matrix(rnorm(5*20), nrow = 5, ncol = 20),
           matrix(rnorm(5*20, mean = 1), nrow = 5, ncol = 20))
gr <- factor(c(rep("A1", 5), rep("B2", 5), rep("C3", 5), rep("D4", 5)))
mod.oneway.test(X, gr)
pairwise.mod.t.test(X, gr)

Compute mean of replicated spots

Description

Compute mean of replicated spots where additionally spot flags may incorporated.

Usage

repMeans(x, flags, use.flags = NULL, ndups, spacing, method, ...)

Arguments

Details

The incorporation of spot flags is controlled via argument use.flags.

NULL: flags are not used; minimum flag value of replicated spots is returned

"max": only spots with flag value equal to the maximum flag value of replicated spots are used

"median": only spots with flag values larger or equal to median of replicated spots are used

"mean": only spots with flag values larger or equal to mean of replicated spots are used

Value

LIST with components

Note

A first version of this function appeared in package SLmisc.

Author(s)

Matthias Kohl Matthias.Kohl@stamats.de

See Also

unwrapdups

Examples

## only a dummy example
M <- matrix(rnorm(1000), ncol = 10)
FL <- matrix(rpois(1000, lambda = 10), ncol = 10) # only for this example
res <- repMeans(x = M, flags = FL, use.flags = "max", ndups = 5, spacing = 20)

Plot of a similarity matrix.

Description

Plot of similarity matrix.

Usage

simPlot(x, col, minVal, labels = FALSE, lab.both.axes = FALSE, 
        labcols = "black", title = "", cex.title = 1.2, 
        protocol = FALSE, cex.axis = 0.8, 
        cex.axis.bar = 1, signifBar = 2, ...)
        
simPlot2(x, col, minVal, labels = FALSE, 
         row.width = 6, column.height = 6, lab.both.axes = TRUE, 
         fontsize.axis = 12, title = "", fontsize.title = 16, 
         signifBar = 2)

Arguments

Details

This functions generates a so called similarity matrix.

If min(x) is smaller than minVal, the colors in col are adjusted such that the minimum value which is color coded is equal to minVal.

Value

invisible()

Note

The function is a slight modification of function corPlot.

Author(s)

Matthias Kohl Matthias.Kohl@stamats.de

References

Sandrine Dudoit, Yee Hwa (Jean) Yang, Benjamin Milo Bolstad and with contributions from Natalie Thorne, Ingrid Loennstedt and Jessica Mar. sma: Statistical Microarray Analysis.
http://www.stat.berkeley.edu/users/terry/zarray/Software/smacode.html

Examples

## only a dummy example
M <- matrix(rnorm(1000), ncol = 20)
colnames(M) <- paste("Sample", 1:20)
M.cor <- cor(M)

simPlot(M.cor, minVal = min(M.cor))
simPlot(M.cor, minVal = min(M.cor), lab.both.axes = TRUE)
simPlot(M.cor, minVal = min(M.cor), protocol = TRUE)
simPlot(M.cor, minVal = min(M.cor), signifBar = 1)

simPlot2(M.cor, minVal = min(M.cor))
simPlot2(M.cor, minVal = min(M.cor), lab.both.axes = FALSE)
simPlot2(M.cor, minVal = min(M.cor), signifBar = 1)

Function to compute distances between strings

Description

The function can be used to compute distances between strings.

Usage

stringDist(x, y, method = "levenshtein", mismatch = 1, gap = 1)

Arguments

Details

The function computes the Hamming and the Levenshtein (edit) distance of two given strings (sequences).

In case of the Hamming distance the two strings must have the same length.

In case of the Levenshtein (edit) distance a scoring and a trace-back matrix are computed and are saved as attributes "ScoringMatrix" and "TraceBackMatrix". The characters in the trace-back matrix reflect insertion of a gap in string y (d: deletion), match (m), mismatch (mm), and insertion of a gap in string x (i).

Value

stringDist returns an object of S3 class "stringDist" inherited from class "dist"; cf. dist.

Note

The function is mainly for teaching purposes.

For distances between strings and string alignments see also Bioconductor package Biostrings.

Author(s)

Matthias Kohl Matthias.Kohl@stamats.de

References

R. Merkl and S. Waack (2009). Bioinformatik Interaktiv. Wiley.

See Also

dist, stringSim

Examples

x <- "GACGGATTATG"
y <- "GATCGGAATAG"
## Levenshtein distance
d <- stringDist(x, y)
d
attr(d, "ScoringMatrix")
attr(d, "TraceBackMatrix")

## Hamming distance
stringDist(x, y)

Function to compute similarity scores between strings

Description

The function can be used to compute similarity scores between strings.

Usage

stringSim(x, y, global = TRUE, match = 1, mismatch = -1, gap = -1, minSim = 0)

Arguments

Details

The function computes optimal alignment scores for global (Needleman-Wunsch) and local (Smith-Waterman) alignments with constant gap penalties.

Scoring and trace-back matrix are computed and saved in form of attributes "ScoringMatrix" and "TraceBackMatrix". The characters in the trace-back matrix reflect insertion of a gap in string y (d: deletion), match (m), mismatch (mm), and insertion of a gap in string x (i). In addition stop indicates that the minimum similarity score has been reached.

Value

stringSim returns an object of S3 class "stringSim" inherited from class "dist"; cf. dist.

Note

The function is mainly for teaching purposes.

For distances between strings and string alignments see also Bioconductor package Biostrings.

Author(s)

Matthias Kohl Matthias.Kohl@stamats.de

References

R. Merkl and S. Waack (2009). Bioinformatik Interaktiv. Wiley.

See Also

dist, stringDist

Examples

x <- "GACGGATTATG"
y <- "GATCGGAATAG"

## optimal global alignment score
d <- stringSim(x, y)
d
attr(d, "ScoringMatrix")
attr(d, "TraceBackMatrix")

## optimal local alignment score
d <- stringSim(x, y, global = FALSE)
d
attr(d, "ScoringMatrix")
attr(d, "TraceBackMatrix")

Function to trace back

Description

Function computes an optimal global or local alignment based on a trace back matrix as provided by function stringDist or stringSim.

Usage

traceBack(D, global = TRUE)

Arguments

Details

Computes one possible optimal global or local alignment based on the trace back matrix saved in an object of class "stringDist" or "stringSim".

Value

matrix: pairwise global/local alignment

Note

The function is mainly for teaching purposes.

For distances between strings and string alignments see Bioconductor package Biostrings.

Author(s)

Matthias Kohl Matthias.Kohl@stamats.de

References

R. Merkl and S. Waack (2009). Bioinformatik Interaktiv. Wiley.

See Also

stringDist

Examples

x <- "GACGGATTATG"
y <- "GATCGGAATAG"

## Levenshtein distance
d <- stringDist(x, y)
## optimal global alignment
traceBack(d)

## Optimal global alignment score
d <- stringSim(x, y)
## optimal global alignment
traceBack(d)

## Optimal local alignment score
d <- stringSim(x, y, global = FALSE)
## optimal local alignment
traceBack(d, global = FALSE)

A function for Analysis of Variance

Description

This function is a slight modification of function Anova of package genefilter.

Usage

twoWayAnova(cov1, cov2, interaction, na.rm = TRUE)

Arguments

Details

The function returned by twoWayAnova uses lm to fit a linear model of the form lm(x ~ cov1*cov2), where x is the set of gene expressions. The F statistics for the main effects and the interaction are computed and the corresponding p-values are returned.

Value

twoWayAnova returns a function with bindings for cov1 and cov2that will perform a two-way ANOVA.

Note

A first version of this function appeared in package SLmisc.

Author(s)

Matthias Kohl Matthias.Kohl@stamats.de

References

R. Gentleman, V. Carey, W. Huber and F. Hahne (2006). genefilter: methods for filtering genes from microarray experiments. R package version 1.13.7.

See Also

lm, oneWayAnova

Examples

set.seed(123)
af1 <- twoWayAnova(c(rep(1,6),rep(2,6)), rep(c(rep(1,3), rep(2,3)), 2))
af2 <- twoWayAnova(c(rep(1,6),rep(2,6)), rep(c(rep(1,3), rep(2,3)), 2), 
                   interaction = FALSE)
x <- matrix(rnorm(12*10), nrow = 10)
apply(x, 1, af1)
apply(x, 1, af2)