| Title: | Integrated Analysis on two human genomic datasets |
|---|---|
| Description: | Finds associations between two human genomic datasets. |
| Authors: | Renee X. de Menezes and Judith M. Boer |
| Maintainer: | Renee X. de Menezes <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.75.0 |
| Built: | 2024-07-18 05:13:29 UTC |
| Source: | https://github.com/bioc/SIM |
SIM is a statistical model to identify associations between two genomic datasets. Where one is assigned as dependent variable and the other as independent e.g. copy number measurements on several samples versus expression measurements on the same samples. A region of interest can be chosen to run the integrated analysis on either the same region for both dependent and independent datasets or different regions. For each dependent feature a P-value measures the association with the independent data, the contribution of each independent feature is given as Z-scores. The integrated analysis is based on the random-effect model for gene-sets as implemented in gt.
maybe something about annotation?
By default we use method.adjust = "BY" (Benjamini-Yekutieli) for multiple testing correction.
This method accounts for dependence between measurements and is more conservative than "BH" (Benjamini-Hochberg).
For details on the multiple testing correction methods see p.adjust.
We have experienced that a rather low stringency cut-off on the BY-values of
20% allows the detection of associations for data with a low number of samples or a low
frequency of abberations. False positives are rarely observed.
Make sure that the array probes are mapped to the same builds of the genome, and that the chrom.table used by the integrated.analysis is from the same build as well. See sim.update.chrom.table.
| Package: | SIM |
| Type: | Package |
| Version: | 1.7.1 |
| Date: | 2010-09-14 |
| License: | Open |
Marten Boetzer, Melle Sieswerda, Renee X. de Menezes [email protected]
Menezes RX, Boetzer M, Sieswerda M, van Ommen GJ, Boer JM (2009). Integrated analysis of DNA copy number and gene expression microarray data using gene sets. BMC Bioinformatics, 10, 203-.
Goeman JJ, van de Geer SA, de Kort F, van Houwelingen HC (2004). A global test for groups of genes: testing association with a clinical outcome. Bioinformatics, 20, 93-109.
assemble.data, integrated.analysis, sim.plot.zscore.heatmap, sim.plot.pvals.on.region, sim.plot.pvals.on.genome, tabulate.pvals, tabulate.top.dep.features, tabulate.top.indep.features, impute.nas.by.surrounding, sim.update.chrom.table, sim.plot.overlapping.indep.dep.features, getoverlappingregions
#load the datasets and the samples to run the integrated analysis data(expr.data) data(acgh.data) data(samples) #assemble the data assemble.data(dep.data = acgh.data, indep.data = expr.data, dep.ann = colnames(acgh.data)[1:4], indep.ann = colnames(expr.data)[1:4], dep.id="ID", dep.chr = "CHROMOSOME", dep.pos = "STARTPOS", dep.symb="Symbol", indep.id="ID", indep.chr = "CHROMOSOME", indep.pos = "STARTPOS", indep.symb="Symbol", overwrite = TRUE, run.name = "chr8q") #run the integrated analysis integrated.analysis(samples = samples, input.regions ="8q", zscores=TRUE, run.name = "chr8q") # use functions to plot the results of the integrated analysis #plot the P-values along the genome sim.plot.pvals.on.genome(input.regions = "8q", significance = c(0.2, 0.05), adjust.method = "BY", pdf = FALSE, run.name = "chr8q") #plot the P-values along the regions sim.plot.pvals.on.region(input.regions = "8q", adjust.method="BY", run.name = "chr8q") #plot the z-scores in an association heatmap #plot the zscores in a heatmap sim.plot.zscore.heatmap(input.regions = "8q", method="full", significance=0.2, z.threshold=3, show.names.indep=TRUE, show.names.dep=TRUE, adjust.method = "BY", add.plot = "smooth", smooth.lambda = 2, pdf = FALSE, run.name = "chr8q") sim.plot.zscore.heatmap(input.regions = "8q", method="full", significance = 0.05, z.threshold = 1, show.names.indep=TRUE, show.names.dep=FALSE, adjust.method = "BY", add.plot = "heatmap", smooth.lambda = 2, pdf = FALSE, run.name = "chr8q") sim.plot.zscore.heatmap(input.regions = "8q", method="full", significance = 0.05, z.threshold = 1, show.names.indep=TRUE, show.names.dep=TRUE, adjust.method = "BY", add.plot = "none", pdf = FALSE, run.name = "chr8q") #tabulate the P-values per region (prints to screen) tabulate.pvals(input.regions = "8q", adjust.method="BY", bins=c(0.001,0.005,0.01,0.025,0.05,0.075,0.10,0.20,1.0), run.name = "chr8q") table.dep <- tabulate.top.dep.features(input.regions="8q", adjust.method="BY", method="full", significance=0.05, run.name="chr8q") head(table.dep[["8q"]]) table.indep <- tabulate.top.indep.features(input.regions="8q", adjust.method="BY", method="full", significance= 0.05, z.threshold=c(-1, 1), run.name="chr8q") head(table.indep[["8q"]]) sim.plot.overlapping.indep.dep.features(input.regions="8q", adjust.method="BY", significance=0.1, z.threshold= c(-1,1), log=TRUE, summarize="consecutive", pdf=FALSE, method="full", run.name="chr8q")#load the datasets and the samples to run the integrated analysis data(expr.data) data(acgh.data) data(samples) #assemble the data assemble.data(dep.data = acgh.data, indep.data = expr.data, dep.ann = colnames(acgh.data)[1:4], indep.ann = colnames(expr.data)[1:4], dep.id="ID", dep.chr = "CHROMOSOME", dep.pos = "STARTPOS", dep.symb="Symbol", indep.id="ID", indep.chr = "CHROMOSOME", indep.pos = "STARTPOS", indep.symb="Symbol", overwrite = TRUE, run.name = "chr8q") #run the integrated analysis integrated.analysis(samples = samples, input.regions ="8q", zscores=TRUE, run.name = "chr8q") # use functions to plot the results of the integrated analysis #plot the P-values along the genome sim.plot.pvals.on.genome(input.regions = "8q", significance = c(0.2, 0.05), adjust.method = "BY", pdf = FALSE, run.name = "chr8q") #plot the P-values along the regions sim.plot.pvals.on.region(input.regions = "8q", adjust.method="BY", run.name = "chr8q") #plot the z-scores in an association heatmap #plot the zscores in a heatmap sim.plot.zscore.heatmap(input.regions = "8q", method="full", significance=0.2, z.threshold=3, show.names.indep=TRUE, show.names.dep=TRUE, adjust.method = "BY", add.plot = "smooth", smooth.lambda = 2, pdf = FALSE, run.name = "chr8q") sim.plot.zscore.heatmap(input.regions = "8q", method="full", significance = 0.05, z.threshold = 1, show.names.indep=TRUE, show.names.dep=FALSE, adjust.method = "BY", add.plot = "heatmap", smooth.lambda = 2, pdf = FALSE, run.name = "chr8q") sim.plot.zscore.heatmap(input.regions = "8q", method="full", significance = 0.05, z.threshold = 1, show.names.indep=TRUE, show.names.dep=TRUE, adjust.method = "BY", add.plot = "none", pdf = FALSE, run.name = "chr8q") #tabulate the P-values per region (prints to screen) tabulate.pvals(input.regions = "8q", adjust.method="BY", bins=c(0.001,0.005,0.01,0.025,0.05,0.075,0.10,0.20,1.0), run.name = "chr8q") table.dep <- tabulate.top.dep.features(input.regions="8q", adjust.method="BY", method="full", significance=0.05, run.name="chr8q") head(table.dep[["8q"]]) table.indep <- tabulate.top.indep.features(input.regions="8q", adjust.method="BY", method="full", significance= 0.05, z.threshold=c(-1, 1), run.name="chr8q") head(table.indep[["8q"]]) sim.plot.overlapping.indep.dep.features(input.regions="8q", adjust.method="BY", significance=0.1, z.threshold= c(-1,1), log=TRUE, summarize="consecutive", pdf=FALSE, method="full", run.name="chr8q")
Copy number data taken from Pollack et al. PNAS. 2002, 99(20): 12963-8.
data(acgh.data)data(acgh.data)
A data frame with 99 observations on 45 variables. The first 4 columns are the unique identifier, symbol for the chromosome and start position of the probe the next 41 columns are the copy number measurements of 41 samples.
A subset of the original data is taken namely all the probes on the long arm of chromosome 8.
http://genome-www.stanford.edu/aCGH_breast/data.shtml
Pollack JR, Sorlie T, Perou CM, Rees CA, Jeffrey SS, Lonning PE, Tibshirani R, Botstein D, Borresen-Dale AL, Brown PO (2002). Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors. Proc Natl Acad Sci USA. 1, 99(20), 12963-8.
data(acgh.data)data(acgh.data)
Assembles the dependent and independent data and annotation of the both data sets.
assemble.data(dep.data, indep.data, dep.id = "ID", dep.chr = "CHROMOSOME", dep.pos = "STARTPOS", dep.ann = NULL, dep.symb, indep.id = "ID", indep.chr = "CHROMOSOME", indep.pos = "STARTPOS", indep.ann = NULL, indep.symb, overwrite = FALSE, run.name = "analysis_results")assemble.data(dep.data, indep.data, dep.id = "ID", dep.chr = "CHROMOSOME", dep.pos = "STARTPOS", dep.ann = NULL, dep.symb, indep.id = "ID", indep.chr = "CHROMOSOME", indep.pos = "STARTPOS", indep.ann = NULL, indep.symb, overwrite = FALSE, run.name = "analysis_results")
dep.data |
The dependent data ( |
indep.data |
|
dep.ann |
|
indep.ann |
|
dep.id |
|
dep.chr |
|
dep.pos |
|
dep.symb |
Optional, either missing or a single vector with the column name in the dependent data that contains the symbols. Will be used in sim.plot.zscore.heatmap as label. |
indep.id |
|
indep.chr |
|
indep.pos |
|
indep.symb |
Optional, either missing or a vector with the column name in the dependent data that contains the Symbols. Will be used in sim.plot.zscore.heatmap as label. |
overwrite |
|
run.name |
Name of the analysis. The results will be
stored in a folder with this name in the current working directory
(use |
Based on the chromosome and probe position an absolute position is calculated according to
. Chromosome column is converted to factor and releveled according to
the levels of the chrom.table, so the only levels allowed are c(1:22, "X", "Y").
Currently only human genome support without mitochondrial DNA.
No values are returned. Instead, the datasets and annotation columns are stored in
separate files in the data folder in the directory specified in run.name.
If assemble.data has run succesfully, the integrated.analysis function can be performed.
Marten Boetzer, Melle Sieswerda, Renee X. de Menezes [email protected]
# Generate datasets and the samples to run the integrated analysis set.seed(53245) ngenes <- 100 nsamples <- 100 # generate copy number measurements x <- matrix(rnorm(n = ngenes*nsamples), nrow = ngenes, ncol = nsamples) # add mean shift effect for half of the samples, copy gain for 2nd half of the genes x[ seq_len(ngenes/2), seq_len(nsamples/2)] <- x[ seq_len(ngenes/2), seq_len(nsamples/2)] + 2 # generate gene expression with normal distribution and mean equal to gene copy number y <- rnorm(n = ngenes*nsamples, mean = matrix(x, nrow = ngenes*nsamples, ncol = 1), sd = 0.8) y <- matrix(y, nrow = ngenes, ncol = nsamples) samples <- paste0("S", seq_len(nsamples)) colnames(x) <- colnames(y) <- samples # Making data objects acgh.data <- data.frame(ID = paste0("G", seq_len(ngenes)), CHROMOSOME = rep(1, ngenes), STARTPOS = seq_len(ngenes)*12*10^5, Symbol = paste0("Gene", seq_len(ngenes)), x) expr.data <- data.frame(ID = paste0("G", seq_len(ngenes)), CHROMOSOME = rep(1, ngenes), STARTPOS = seq_len(ngenes)*12*10^5, Symbol = paste0("Gene", seq_len(ngenes)), y) #assemble the data assemble.data(dep.data = acgh.data, indep.data = expr.data, dep.ann = colnames(acgh.data)[1:4], indep.ann = colnames(expr.data)[1:4], dep.id="ID", dep.chr = "CHROMOSOME", dep.pos = "STARTPOS", dep.symb="Symbol", indep.id="ID", indep.chr = "CHROMOSOME", indep.pos = "STARTPOS", indep.symb="Symbol", overwrite = TRUE, run.name = "chr1p")# Generate datasets and the samples to run the integrated analysis set.seed(53245) ngenes <- 100 nsamples <- 100 # generate copy number measurements x <- matrix(rnorm(n = ngenes*nsamples), nrow = ngenes, ncol = nsamples) # add mean shift effect for half of the samples, copy gain for 2nd half of the genes x[ seq_len(ngenes/2), seq_len(nsamples/2)] <- x[ seq_len(ngenes/2), seq_len(nsamples/2)] + 2 # generate gene expression with normal distribution and mean equal to gene copy number y <- rnorm(n = ngenes*nsamples, mean = matrix(x, nrow = ngenes*nsamples, ncol = 1), sd = 0.8) y <- matrix(y, nrow = ngenes, ncol = nsamples) samples <- paste0("S", seq_len(nsamples)) colnames(x) <- colnames(y) <- samples # Making data objects acgh.data <- data.frame(ID = paste0("G", seq_len(ngenes)), CHROMOSOME = rep(1, ngenes), STARTPOS = seq_len(ngenes)*12*10^5, Symbol = paste0("Gene", seq_len(ngenes)), x) expr.data <- data.frame(ID = paste0("G", seq_len(ngenes)), CHROMOSOME = rep(1, ngenes), STARTPOS = seq_len(ngenes)*12*10^5, Symbol = paste0("Gene", seq_len(ngenes)), y) #assemble the data assemble.data(dep.data = acgh.data, indep.data = expr.data, dep.ann = colnames(acgh.data)[1:4], indep.ann = colnames(expr.data)[1:4], dep.id="ID", dep.chr = "CHROMOSOME", dep.pos = "STARTPOS", dep.symb="Symbol", indep.id="ID", indep.chr = "CHROMOSOME", indep.pos = "STARTPOS", indep.symb="Symbol", overwrite = TRUE, run.name = "chr1p")
A table indicating the base positions of the beginning and end of chromosome arms and bands. Currently based on the UCSC March 2006/NCBI 36 build of the human genome.
data(chrom.table)data(chrom.table)
A data frame with 862 observations on the following 6 variables.
chr, arm, band, start, end, stain
Possibly the chrom.table can be update by sim.update.chrom.table.
Currently only human genome support without mitochondrial DNA.
data(chrom.table)data(chrom.table)
Expression data taken from Pollack et al. PNAS. 2002, 99(20): 12963-8.
data(expr.data)data(expr.data)
A data frame with 99 observations on 45 variables. The first 4 columns are the unique identifier, symbol for the chromosome and start position of the probe the next 41 columns are the expression log-ratios of 41 samples.
A subset of the original data is taken namely all the probes on the long arm of chromosome 8.
http://genome-www.stanford.edu/aCGH_breast/data.shtml
Pollack JR, Sorlie T, Perou CM, Rees CA, Jeffrey SS, Lonning PE, Tibshirani R, Botstein D, Borresen-Dale AL, Brown PO (2002). Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors. Proc Natl Acad Sci USA. 1, 99(20), 12963-8.
data(expr.data)data(expr.data)
Generates a table with overlapping regions.
getoverlappingregions(independent_regions, dependent_regions, method = c("union", "overlapping"))getoverlappingregions(independent_regions, dependent_regions, method = c("union", "overlapping"))
independent_regions |
data.frame().Independent regions found with tab tabulate.top.indep.features. |
dependent_regions |
data.frame().Independent regions found with tab tabulate.top.dep.features. |
method |
method to estimate the overlapping regions, either "union" or "overlapping".
|
Calculates the overlap between two tables.
Marten Boetzer, Melle Sieswerda, Renee X. de Menezes [email protected]
SIM, tabulate.top.dep.features, tabulate.top.indep.features, sim.plot.overlapping.indep.dep.features
#no examples yet!#no examples yet!
Replace an NA by the median of the surrounding features in the same sample.
impute.nas.by.surrounding(dataset, window.size = 5)impute.nas.by.surrounding(dataset, window.size = 5)
dataset |
data.frame with the dataset to replace the NA's by the medians of the surrounding features. |
window.size |
numeric value, specifying of how many features around the NA the median should be taken. |
This function can be used when the dependent dataset in the integrated.analysis function is array-CGH data and contains probes that have an NA. To avoid loosing data by throwing away the probes with NA's, the impute.nas.by.surrounding function can be used which simply takes the median of the probes around an NA. The number of probes used for the imputatin is chosen by giving a value for window.size. This script takes quite long to run!
A data.frame is returned, containing the inserted “dataset” all NA replaced with median of the window of
size “window.size” around the NA.
Marten Boetzer, Melle Sieswerda, Renee X. de Menezes [email protected]
SIM, assemble.data, integrated.analysis
#no examples yet!#no examples yet!
Runs the Integrated Analysis to test for associations between dependent and independent microarray data on the same set of samples.
integrated.analysis(samples, input.regions = "all chrs", input.region.indep = NULL, zscores = FALSE, method = c("full", "smooth", "window", "overlap"), dep.end = 1e5, window = c(1e6, 1e6), smooth.lambda=2, adjust = ~1, run.name = "analysis_results", ...)integrated.analysis(samples, input.regions = "all chrs", input.region.indep = NULL, zscores = FALSE, method = c("full", "smooth", "window", "overlap"), dep.end = 1e5, window = c(1e6, 1e6), smooth.lambda=2, adjust = ~1, run.name = "analysis_results", ...)
samples |
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input.regions |
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input.region.indep |
indicating the independent region which will be analysed in combination of the dependent region. Only one input region can given using the same format as the dependent input region. |
zscores |
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method |
either one of “full”, “window”, “overlap” or “smooth”. This defines how the data is used for the |
dep.end |
|
window |
numeric values. Window to search for overlapping independent features per dependent probe.
First value is the number of positions to the left from the middle of the probe, the second value is the number
of positions to the right from the middle of the probe. Only needed when |
smooth.lambda |
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adjust |
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run.name |
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... |
additional arguments for gt e.g. |
The Integrated Analysis is a regression of the independent data on the dependent features. The regression itself is done using the
gt, which means that the genes in a region (e.g. a chromosome arm) are tested as a gene set.
The individual associations between each dependent and each independent feature are calculated as Z-scores (standardized influences, see ?gt).
This function splits the datasets into separate sets for each region (as specified by the input.regions) and runs the analysis for each region
separately.
When running the Integrated Analysis for a predefined input region, like “all arms”
and “all chrs”, output can be obtained for all input regions, as well as
subsets of it. But note that the genomic unit must be the same: if integrated.analysis
was run using chromosomes as units, any of the functions and plots must also use chromosomes
as units, and not chromosome arms. Similarly, if integrated analysis was run using
chromosome arms as units, these units must also be used to produce plots and outputs.
For example if the input.regions = "all arms" was used, P-value plots
(see sim.plot.pvals.on.region can be produced by inserting the input.regions = "all arms",
but also for instance “1p” or “20q”. However, to produce a plot of the whole
chromosome, for example chromosome 1, the integrated should be re-run with input.region=1.
The same goes for “all chrs”: P-value plots etc. can be produced for chromosome 1,2 and so on...
but to produce plots for an arm, the integrated.analysis should be re-run for that region.
This also goes for subregions of the chromosome like "chr1:1-1000000".
By default the gt uses a “linear” model, only when the dependent data is a logical matrix containing
TRUE and FALSE a “logistic” model is selected. All other models need model = "", see gt
for available models.
No values are returned. Instead, the results of the analysis are stored
in the subdirectories of the directory specified in run.name. E.g. the z-score matrices
are saved in subfolder method.
The following functions can be used to visualize the data:
1) |
sim.plot.zscore.heatmap (only possible when |
2) |
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3) |
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4) |
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Other functions can be used to tabulate the results: |
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1) |
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2) |
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3) |
tabulate.top.indep.features (only possible when |
4) |
getoverlappingregions (only possible when tablulate.top.dep.features and tabulate.top.indep.features were run. |
Marten Boetzer, Melle Sieswerda, Renee X. de Menezes [email protected]
Menezes RX, Boetzer M, Sieswerda M, van Ommen GJ, Boer JM (2009). Integrated analysis of DNA copy number and gene expression microarray data using gene sets. BMC Bioinformatics, 10, 203-.
Goeman JJ, van de Geer SA, de Kort F, van Houwelingen HC (2004). A global test for groups of genes: testing association with a clinical outcome. Bioinformatics, 20, 93-109.
SIM, sim.plot.zscore.heatmap, sim.plot.pvals.on.region, sim.plot.pvals.on.genome, tabulate.pvals, tabulate.top.dep.features, tabulate.top.indep.features, getoverlappingregions, sim.plot.overlapping.indep.dep.features, gt
#first run example(assemble.data) data(samples) #perform integrated analysis without Z-scores using the method = "full" integrated.analysis(samples=samples, input.regions="8q", zscores=FALSE, method="full", run.name="chr8q")#first run example(assemble.data) data(samples) #perform integrated analysis without Z-scores using the method = "full" integrated.analysis(samples=samples, input.regions="8q", zscores=FALSE, method="full", run.name="chr8q")
Get annotation out of a AnnotationData package and link them to the expression data using the expression probe ID's
link.metadata(data = expr.data, col.ID.link = 1, chr = as.list(hgu133plus2CHR), chrloc = as.list(hgu133plus2CHRLOC), symbol = as.list(hgu133plus2SYMBOL))link.metadata(data = expr.data, col.ID.link = 1, chr = as.list(hgu133plus2CHR), chrloc = as.list(hgu133plus2CHRLOC), symbol = as.list(hgu133plus2SYMBOL))
data |
|
col.ID.link |
numeric value, specifying the column of |
chr |
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chrloc |
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symbol |
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Often, the annotation for expression array probes lack chromosome position information. Therefore, this function adds the two required columns to run the integrated.analysis: "CHROMOSOME" and "STARTPOS". In addition, the optional column, "Symbol" is added.
A data.frame is returned, containing a dataset with annotation columns
which can be used forintegrated.analysis.
Marten Boetzer, Melle Sieswerda, Renee x Menezes [email protected]
# first download and install the AnnotationData package for your expression array platform # for example ## Not run: library(hgu133plus2) ## Not run: expr.data <- link.metadata(data, col.ID.link = 1, chr = as.list(hgu133plus2CHR), chrloc = as.list(hgu133plus2CHRLOC), symbol = as.list(hgu133plus2SYMBOL)) ## End(Not run)# first download and install the AnnotationData package for your expression array platform # for example ## Not run: library(hgu133plus2) ## Not run: expr.data <- link.metadata(data, col.ID.link = 1, chr = as.list(hgu133plus2CHR), chrloc = as.list(hgu133plus2CHRLOC), symbol = as.list(hgu133plus2SYMBOL)) ## End(Not run)
Get annotation out of the RESOURCERER annotation file and link them to expression data with help of expression ID's
RESOURCERER.annotation.to.ID(data, poslist, col.ID.link = 1, col.poslist.link = 1)RESOURCERER.annotation.to.ID(data, poslist, col.ID.link = 1, col.poslist.link = 1)
data |
data.frame with expression data including an expression ID column. |
poslist |
data.frame containing the RESOURCERER annotation file |
col.ID.link |
numeric value, specifying the column of |
col.poslist.link |
numeric value, specifying the column of |
This function will output the inserted dataset, including the necessary, for integrated.analysis, a
nnotation columns: "CHROMOSOME", "STARTPOS"and "Symbol" out of the inserted RESOURCE
RER annotation file poslist.
A data.frame is returned, containing a dataset with annotation columns which can be used for
integrated.analysis
Marten Boetzer, Melle Sieswerda, Renee x Menezes [email protected]
# download expression array annotation from RESOURCERER # ftp://occams.dfci.harvard.edu/pub/bio/tgi/data/Resourcerer # it may be necessary to remove the first row, which states the genome build used for mapping ## Not run: read.an <- read.delim("affy_U133Plus2.txt", sep="\t", header=T) # get physical mapping columns ## Not run: expr.data <- RESOURCERER_annotation_to_ID(data = read.expr, poslist = read.an, col.ID.link = 1, col.poslist.link = 1)# download expression array annotation from RESOURCERER # ftp://occams.dfci.harvard.edu/pub/bio/tgi/data/Resourcerer # it may be necessary to remove the first row, which states the genome build used for mapping ## Not run: read.an <- read.delim("affy_U133Plus2.txt", sep="\t", header=T) # get physical mapping columns ## Not run: expr.data <- RESOURCERER_annotation_to_ID(data = read.expr, poslist = read.an, col.ID.link = 1, col.poslist.link = 1)
Vector of sample names corresponding to the column headers containing the
data in both the copy number (acgh.data) and expression (expr.data) example datasets.
data(samples)data(samples)
A character vector.
http://genome-www.stanford.edu/aCGH_breast/data.shtml
Pollack JR, Sorlie T, Perou CM, Rees CA, Jeffrey SS, Lonning PE, Tibshirani R, Botstein D, Borresen-Dale AL, Brown PO (2002). Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors. Proc Natl Acad Sci USA. 1, 99(20), 12963-8.
data(samples)data(samples)
Generates three plots: The first plot contains the P-values along the region, with the cut-off displayed. The second plot contains the mean-zscores along the region, with the cut-offs displayed. The third plots generates the cytobands of the region.
sim.plot.overlapping.indep.dep.features(input.regions, input.region.indep = NULL, adjust.method = "BY", log = FALSE, significance = 0.2, max.pow = 5, z.threshold = c(-3,3), summarize = c("consecutive", "stretch", "window"), stretch = 10, window = 1e6, percentage = 0.5, xlim=NULL, pdf = FALSE, method = c("full", "smooth", "window", "overlap"), run.name = "analysis_results", ...)sim.plot.overlapping.indep.dep.features(input.regions, input.region.indep = NULL, adjust.method = "BY", log = FALSE, significance = 0.2, max.pow = 5, z.threshold = c(-3,3), summarize = c("consecutive", "stretch", "window"), stretch = 10, window = 1e6, percentage = 0.5, xlim=NULL, pdf = FALSE, method = c("full", "smooth", "window", "overlap"), run.name = "analysis_results", ...)
input.regions |
|
input.region.indep |
indicating the independent region which will be analysed in combination of the dependent region. Only one input region can given using the same format as the dependent input region. |
adjust.method |
Method used to adjust the P-values for multiple testing, see p.adjust. Default is “BY” recommended when copy number is used as dependent data. See SIM for more information about adjusting P-values. |
log |
|
significance |
The threshold for selecting significant P-values. |
max.pow |
|
z.threshold |
Threshold to display a green or red bar in the color bar on top of
the heatmap for independent features with mean z-scores above |
summarize |
either one of “consecutive”, “stretch”, “window” which visualizes the subregions according to
the selected summarization a) “consecutive” shows the consecutive significant regions b) “stretch” shows
a percentage |
and c) “window” a percentage percentage
significant window of length window
stretch |
|
window |
|
percentage |
|
xlim |
|
pdf |
|
method |
this must be the either full, window, overlap or smooth but the data should generated by the
same method in |
run.name |
This must be the same a given to |
... |
not used in this version |
details: Cytobands plot adapted from SNPChip
No values are returned.The results are stored in a subdirectory of run.name as pdf.
Marten Boetzer, Melle Sieswerda, Renee X. de Menezes [email protected]
SIM, tabulate.top.dep.features, tabulate.top.indep.features, getoverlappingregions
#first run example(assemble.data) #and example(integrated.analysis) #overview plot of the dependent and independent features sim.plot.overlapping.indep.dep.features(input.regions="8q", adjust.method="BY", significance=0.1, z.threshold= c(-1,1), log=TRUE, summarize="consecutive", pdf=FALSE, method="full", run.name="chr8q")#first run example(assemble.data) #and example(integrated.analysis) #overview plot of the dependent and independent features sim.plot.overlapping.indep.dep.features(input.regions="8q", adjust.method="BY", significance=0.1, z.threshold= c(-1,1), log=TRUE, summarize="consecutive", pdf=FALSE, method="full", run.name="chr8q")
Generates a plot of the analyzed dependent data probe positions and their significance on all chromosomes.
sim.plot.pvals.on.genome(input.regions = "all chrs", significance = c(0.05, 0.20), adjust.method = "BY", method = c("full", "smooth", "window", "overlap"), run.name = "analysis_results", pdf = TRUE, main = "Significantly associated features", ylab = "Chromosomes", ann=par("ann"), ...)sim.plot.pvals.on.genome(input.regions = "all chrs", significance = c(0.05, 0.20), adjust.method = "BY", method = c("full", "smooth", "window", "overlap"), run.name = "analysis_results", pdf = TRUE, main = "Significantly associated features", ylab = "Chromosomes", ann=par("ann"), ...)
input.regions |
|
significance |
Two values that categorize the P-values on the selected chromosomes.
These margins are indicated by different colors shown in the legend.
These values can be defined, e.g. |
adjust.method |
Method used to adjust the P-values for multiple testing. see p.adjust Default is “BY” recommended when copy number is used as dependent data. See SIM for more information about adjusting P-values. |
method |
this must be the either full, window, overlap or smooth but the data should generated by the
same method in |
pdf |
Boolean. Indicate whether to generate a pdf of the plots in the current working directory or not. |
run.name |
This must be the same a given to |
main |
the usual graphical parameter for the caption of the plot. |
ylab |
the usual graphical parameter for the y-axis label of the plot. |
ann |
the usual graphical parameter for annotation of the plot. |
... |
Arguments to be passed to |
Grey vertical lines indicate unsignificant probes on top the significant ones are plotted. A purple dot indicates the centromere and a organe line the input region.
Sometimes it is useful to make the genome-plot as A4 landscape-format, add the following parameters to the
sim.plot.pvals.on.genome(..., paper='a4r', width=0, height=0)
No values are returned. The results are stored in the folder “pvalue.plots” in directory run.name as pdf.
Marten Boetzer, Melle Sieswerda, Renee X. de Menezes [email protected]
SIM, sim.plot.zscore.heatmap, sim.plot.pvals.on.region
#first run example(assemble.data) #and example(integrated.analysis) #plot the p-values along the genome sim.plot.pvals.on.genome(input.regions="8q", significance=c(0.05, 0.005), adjust.method="BY", method="full", pdf=FALSE, run.name="chr8q")#first run example(assemble.data) #and example(integrated.analysis) #plot the p-values along the genome sim.plot.pvals.on.genome(input.regions="8q", significance=c(0.05, 0.005), adjust.method="BY", method="full", pdf=FALSE, run.name="chr8q")
Generates two plots of the P-values for an analyzed region. The first plot contains the distribution of the raw P-values and ranked plots of the raw and adjusted P-values. The second plot contains the P-values along the genome of analyzed input regions.
sim.plot.pvals.on.region(input.regions = c("all chrs"), significance = 0.2, adjust.method = "BY", method = c("full", "smooth", "window", "overlap"), run.name = "analysis_results", ...)sim.plot.pvals.on.region(input.regions = c("all chrs"), significance = 0.2, adjust.method = "BY", method = c("full", "smooth", "window", "overlap"), run.name = "analysis_results", ...)
input.regions |
|
significance |
The threshold for selecting significant P-values. |
adjust.method |
Method used to adjust the P-values for multiple testing. see p.adjust Default is “BY” recommended when copy number is used as dependent data. See SIM for more information about adjusting P-values. |
method |
this must be the either full, window, overlap or smooth but the data should generated by the
same method in |
run.name |
This must be the same a given to |
... |
Arguments to be passed to |
This function returns a pdf containing the P-value plots. The second plot contains the multiple testing corrected P-values plotted along the chromosome (arm). On the x-axis, the start positions of the dependent features are displayed. On the y-axis, the P-value levels are displayed. Two dotted lines indicate P-value levels 0.2 and 0.1. In general, P-values below 0.2 are said to be “significant”.
No values are returned. The results are stored in a subdirectory of run.name as pdf.
Marten Boetzer, Melle Sieswerda, Renee X. de Menezes [email protected]
#first run example(assemble.data) #and example(integrated.analysis) #plot the p-values along the regions sim.plot.pvals.on.region(input.regions="8q", adjust.method="BY", method="full", run.name="chr8q")#first run example(assemble.data) #and example(integrated.analysis) #plot the p-values along the regions sim.plot.pvals.on.region(input.regions="8q", adjust.method="BY", method="full", run.name="chr8q")
Zoom in on pervious produced heatmap by sim.plot.zscore.heatmap
sim.plot.zoom.in(call)sim.plot.zoom.in(call)
call |
|
sim.plot.zscore.heatmap returns (invisible) the function call and as attribute the local function environment. So by adjusting the the xlim and ylim a zoom in is created.
an additional plot is created, on the previous plot a rectangle indicating the zoomed region.
Marten Boetzer, Melle Sieswerda, Renee X. de Menezes [email protected]
#first run example(assemble.data) #and example(integrated.analysis) #plot the zscores in a heatmap sim.plot <- sim.plot.zscore.heatmap(input.regions = "8q", adjust.method = "BY", run.name = "chr8q", pdf = FALSE) #only when runned interactive if(interactive()) sim.plot.zoom.in(sim.plot)#first run example(assemble.data) #and example(integrated.analysis) #plot the zscores in a heatmap sim.plot <- sim.plot.zscore.heatmap(input.regions = "8q", adjust.method = "BY", run.name = "chr8q", pdf = FALSE) #only when runned interactive if(interactive()) sim.plot.zoom.in(sim.plot)
Produces an association heatmap that shows the association (standardized influence) of each independent feature (expression measurement) with each dependent feature (copy number measurement). A P-value bar on the left indicates test signficance. A color bar on top indicates genes with mean z-scores across the signficant copy number probes above a set threshold. A summary of the copy number data helps to identify what copy number alterations are present in a region of association with expression. Positive association can mean copy number gain and increased expression, or deletion and decreased expression. The heatmaps can also be used in an exploratory analysis, looking for very local effects of copy number changes (usually small amplifications) on gene expression, that do not lead to a significant test result.
sim.plot.zscore.heatmap(input.regions = "all chrs", input.region.indep = NULL, method = c("full", "smooth", "window", "overlap"), adjust = ~1, significance = 0.2, z.threshold = 3, colRamp = colorRampPalette(c("red", "black", "green")), add.colRamp = colorRampPalette(c("blue", "black", "yellow"))(7), show.names.indep = FALSE, show.names.dep = FALSE, adjust.method = "BY", scale, add.scale, add.plot = c("smooth", "none", "heatmap"), smooth.lambda = 2, pdf = TRUE, run.name = "analysis_results",...)sim.plot.zscore.heatmap(input.regions = "all chrs", input.region.indep = NULL, method = c("full", "smooth", "window", "overlap"), adjust = ~1, significance = 0.2, z.threshold = 3, colRamp = colorRampPalette(c("red", "black", "green")), add.colRamp = colorRampPalette(c("blue", "black", "yellow"))(7), show.names.indep = FALSE, show.names.dep = FALSE, adjust.method = "BY", scale, add.scale, add.plot = c("smooth", "none", "heatmap"), smooth.lambda = 2, pdf = TRUE, run.name = "analysis_results",...)
input.regions |
|
input.region.indep |
indicating the independent region which will be analysed in combination of the dependent region. Only one input region can given using the same format as the dependent input region. |
method |
this must be the either full, window, overlap or smooth but the data should generated by the
same method in |
adjust |
This variable must be a vector with the same length as |
significance |
The threshold for selecting significant P-values. |
z.threshold |
Threshold to display a green or red bar in the color bar on top of
the heatmap for independent features with mean z-scores above |
colRamp |
Palette of colors to be used in the heatmap. |
add.colRamp |
Palette of colors to be used in the added plot. |
show.names.indep |
|
show.names.dep |
|
adjust.method |
Method used to adjust the P-values for multiple testing, see p.adjust. Default is "BY" recommended when copy number is used as dependent data. See SIM for more information about adjusting P-values. |
scale |
Vector specifying the color scale in the heatmap. If scale="auto", the maximum and minimum value of all z-scores will be calculated and set as the limits for all analyzed regions. Another option is to define a custom scale, e.g. scale = c(-5,5). |
add.scale |
Vector specifying the color scale in the left plot near the heatmap. If scale="auto", the maximum and minimum value of all the values will be calculated and set as the limits for all analyzed regions. Another option is to define a custom scale, e.g. scale = c(-5,5). |
add.plot |
Summary plot of copy number data in left panel. Either |
smooth.lambda |
Numeric value, specifying the quantile smoothing parameter for
|
pdf |
|
run.name |
This must be the same a given to |
... |
not used in this version |
The sim.plot.zscore.heatmap function can only run after the integrated.analysis
is run with zscores = TRUE.
The results are returned as a single-page pdf containing an association heatmap of the regions
listed in input.regions. For high-density arrays large files will be produced, both
demanding more memory available from your computer to produce them as well as being heavier to
open on screen. To avoid this, analyze chromosome arms as units instead of chromosomes, both
here and in input.regions = "all arms".
The heatmap contains the z-scores generated by the function integrated.analysis with
zscores=TRUE. The dependent features are plotted from bottom to top, the independent
features from left to right. Positive associations are shown in green, negative associations in red
(color scale on the right). At the left side of the heatmap a color bar represents the
multiple testing corrected P-values of the probes in the dependent data (copy number), also
with a color legend. Dependening on which plot.method is used, a summary of copy number
changes is shown on the left. At the top of the heatmap is a color bar corresponding to
the mean z-scores of the independent features (expression data) that are above or below
the z.threshold. If show.names.indep is set to TRUE, labels will be drawn for
the probes with mean z-scores greater than z.threshold or lower than -z.threshold
at the bottom of the heatmap. If show.names.dep is set to TRUE, labels will be drawn for
the significant dependent probes lower than significance to the right of the heatmap.
No values are returned. The results are stored in a subdirectory of run.name as pdf.
Marten Boetzer, Melle Sieswerda, Renee X. de Menezes [email protected]
Eilers PH, de Menezes RX. 2005 Apr 1, Quantile smoothing of array CGH data. Bioinformatics, 21(7):1146-53.
Wang P, Kim Y, Pollack J, Narasimhan B, Tibshirani R. 2005, A method for calling gains and losses in array CGH data. Biostatistics, 6 :45-58.
SIM, tabulate.pvals, tabulate.top.dep.features, tabulate.top.indep.features, sim.plot.overlapping.indep.dep.features
#first run example(assemble.data) #and example(integrated.analysis) #plot the zscores in a heatmap sim.plot.zscore.heatmap(input.regions = "8q", adjust.method = "BY", run.name = "chr8q", pdf = FALSE) sim.plot.zscore.heatmap(input.regions = "8q", method="full", significance = 0.05, z.threshold = 1, colRamp = colorRampPalette(c("red", "black", "green"))(15), show.names.indep=TRUE, show.names.dep=TRUE, adjust.method = "holm", add.plot = "heatmap", smooth.lambda = 2, pdf = FALSE, run.name = "chr8q") sim.plot.zscore.heatmap(input.regions = "8q", method="full", significance = 0.05, z.threshold = 1, show.names.indep = TRUE, show.names.dep = TRUE, add.plot = "none", smooth.lambda = 2, scale = c(-2, 2), pdf = FALSE, run.name = "chr8q")#first run example(assemble.data) #and example(integrated.analysis) #plot the zscores in a heatmap sim.plot.zscore.heatmap(input.regions = "8q", adjust.method = "BY", run.name = "chr8q", pdf = FALSE) sim.plot.zscore.heatmap(input.regions = "8q", method="full", significance = 0.05, z.threshold = 1, colRamp = colorRampPalette(c("red", "black", "green"))(15), show.names.indep=TRUE, show.names.dep=TRUE, adjust.method = "holm", add.plot = "heatmap", smooth.lambda = 2, pdf = FALSE, run.name = "chr8q") sim.plot.zscore.heatmap(input.regions = "8q", method="full", significance = 0.05, z.threshold = 1, show.names.indep = TRUE, show.names.dep = TRUE, add.plot = "none", smooth.lambda = 2, scale = c(-2, 2), pdf = FALSE, run.name = "chr8q")
A function to update the genomic positions of chromosome arms. Base locations of the start and end of chromosome arms should be used from the same organism and build of genome as the location provided as annotation with the datasets.
sim.update.chrom.table(db = "homo_sapiens_core_40_36b")sim.update.chrom.table(db = "homo_sapiens_core_40_36b")
db |
database name |
This functions requires library RMySQL. Currently SIM only supports integrated analysis on the human genome without mitochondrial DNA.
Chromosome table chrom.table.
Marten Boetzer, Renee X. de Menezes [email protected]
http://www.ensembl.org/info/data/mysql.html
#youn need internet connection for this! #sim.update.chrom.table(db = "homo_sapiens_core_40_36b")#youn need internet connection for this! #sim.update.chrom.table(db = "homo_sapiens_core_40_36b")
Generates a data.frame with the significance of P-values in the analyzed regions, dividing them into bins.
tabulate.pvals(input.regions = "all chrs", adjust.method = "BY", bins = c(0.001, 0.005, 0.01, 0.025, 0.05, 0.075, 0.1, 0.2, 1), significance.idx = 8, order.by, decreasing = TRUE, method = c("full", "smooth", "window", "overlap"), run.name = "analysis_results")tabulate.pvals(input.regions = "all chrs", adjust.method = "BY", bins = c(0.001, 0.005, 0.01, 0.025, 0.05, 0.075, 0.1, 0.2, 1), significance.idx = 8, order.by, decreasing = TRUE, method = c("full", "smooth", "window", "overlap"), run.name = "analysis_results")
input.regions |
|
adjust.method |
Method used to adjust the P-values for multiple testing, see p.adjust. Default is “BY” recommended when copy number is used as dependent data. See SIM for more information about adjusting P-values. |
bins |
|
significance.idx |
Index of “bins” to use when computing the percentage of significant P-values. Defaults to 8 (i.e. the first entry in “bins”), in this case 0.20. |
order.by |
Column used for sorting the table. Defaults to "%" (i.e. the percentage of significant p-values). |
decreasing |
Direction used for sorting. Defaults to TRUE (i.e. highest values on top). |
method |
this must be the either full, window, overlap or smooth but the data should generated by the
same method in |
run.name |
This must be the same a given to |
Returns a data.frame. Each row corresponds to a chromosome and has
as many entries as entries in bins, plus 1. Each entry contains the
number of P-values that is smaller or equal to the corresponding entry
in bins.
The last entry holds the percentage of P-values that is smaller than or
equal to the bin identified by significance.idx.
Marten Boetzer, Melle Sieswerda, Renee X. de Menezes [email protected]
SIM, tabulate.top.dep.features, tabulate.top.indep.features
#first run example(assemble.data) #and example(integrated.analysis) tabulate.pvals(input.regions="8q", adjust.method="BY", bins=c(0.001,0.005,0.01,0.025,0.05,0.075,0.10,0.20,1.0), run.name="chr8q")#first run example(assemble.data) #and example(integrated.analysis) tabulate.pvals(input.regions="8q", adjust.method="BY", bins=c(0.001,0.005,0.01,0.025,0.05,0.075,0.10,0.20,1.0), run.name="chr8q")
Lists the integrated analysis P-values for the dependent features in the analyzed regions, together with the available annotation.
tabulate.top.dep.features(input.regions = "all chrs", adjust.method="BY", method = c("full", "smooth", "window", "overlap"), significance = 1, run.name = "analysis_results")tabulate.top.dep.features(input.regions = "all chrs", adjust.method="BY", method = c("full", "smooth", "window", "overlap"), significance = 1, run.name = "analysis_results")
input.regions |
|
adjust.method |
Method used to adjust the P-values for multiple testing, see p.adjust. Default is “BY” recommended when copy number is used as dependent data. See SIM for more information about adjusting P-values. |
method |
this must be the either one of “full”, “window”, “overlap” or “smooth” but the data should generated by the
same method in |
significance |
threshold used to select the significant dependent features. Pvalues below this threshold will be used to estimate regions. |
run.name |
This must be the same a given to |
Output is a .txt file containing a table with sorted integrated analysis P-values of the
dependent features. It includes the ann.dep columns that were read in the assemble.data function.
Additionally it returns a .txt file containing the significant P-value rich regions. No P-value rich regions are returned when zscores.diag = "all".
Returns a list of data.frame's for each input region.
Significant P-value rich regions are returned as a data.frame.
This data.frame can be used as an input for getoverlappingregions.
Additionally, the results are stored in a subdirectory of run.name as txt.
Marten Boetzer, Melle Sieswerda, Renee X. de Menezes [email protected]
SIM, tabulate.pvals, tabulate.top.indep.features
#first run example(assemble.data) #and example(integrated.analysis) #get the top dependent features sorted by p-value table.dep <- tabulate.top.dep.features(input.regions="8q", adjust.method="BY", method="full", significance=0.05, run.name="chr8q") head(table.dep[["8q"]])#first run example(assemble.data) #and example(integrated.analysis) #get the top dependent features sorted by p-value table.dep <- tabulate.top.dep.features(input.regions="8q", adjust.method="BY", method="full", significance=0.05, run.name="chr8q") head(table.dep[["8q"]])
Lists the mean z-scores for independent features in the analyzed regions, calculated across the significant dependent features. Gives insight in the expression levels most strongly associated with copy number changes.
tabulate.top.indep.features(input.regions = "all chrs", input.region.indep = NULL, method = c("full", "smooth", "window", "overlap"), adjust.method = "BY", significance = 1, decreasing=TRUE, z.threshold = c(0, 0), run.name = "analysis_results")tabulate.top.indep.features(input.regions = "all chrs", input.region.indep = NULL, method = c("full", "smooth", "window", "overlap"), adjust.method = "BY", significance = 1, decreasing=TRUE, z.threshold = c(0, 0), run.name = "analysis_results")
input.regions |
|
input.region.indep |
fill in |
method |
this must be the either one of “full”, “window”, “overlap” or “smooth” but the data should generated by the
same method in |
adjust.method |
Method used to adjust the P-values for multiple testing, see p.adjust. Default is "BY" recommended when copy number is used as dependent data. See SIM for more information about adjusting P-values. |
significance |
threshold used to select the significant dependent features. Only the z-scores with these features are used to calculate the mean z-scores across the independent features. |
decreasing |
fill in |
z.threshold |
fill in |
run.name |
This must be the same a given to |
tabulate.top.indep.features can only be run after integrated.analysis
with zscores = TRUE.
Output is a .txt file containing a table with the mean z-scores of all independent features
per analyzed region. It includes the ann.indep columns that were read in the
assemble.data function.
Additionally it returns a .txt file containing the significant zscores rich regions.
Depending on the argument "adjust.method", the P-values are first corrected for multiple testing. Next, th e z-scores are filtered to include only those entries that correspond to significant (P-value < "significa nce") dependent features to calculate the mean z-scores.
The dependent table can not be generated for diagonal integrated runs.
Returns a list of data.frame's for each input region.
Significant P-value rich regions are returned as a data.frame.
This data.frame can be used as an input for getoverlappingregions.
Additionally, the results are stored in a subdirectory of run.name as txt.
Marten Boetzer, Melle Sieswerda, Renee X. de Menezes [email protected]
SIM, tabulate.pvals, tabulate.top.dep.features
#first run example(assemble.data) #and example(integrated.analysis) table.indep <- tabulate.top.indep.features(input.regions="8q", adjust.method="BY", method="full", significance= 0.05, z.threshold=c(-1, 1), run.name="chr8q") head(table.indep[["8q"]])#first run example(assemble.data) #and example(integrated.analysis) table.indep <- tabulate.top.indep.features(input.regions="8q", adjust.method="BY", method="full", significance= 0.05, z.threshold=c(-1, 1), run.name="chr8q") head(table.indep[["8q"]])