Package 'iCheck'

Title:	QC Pipeline and Data Analysis Tools for High-Dimensional Illumina mRNA Expression Data
Description:	QC pipeline and data analysis tools for high-dimensional Illumina mRNA expression data.
Authors:	Weiliang Qiu [aut, cre], Brandon Guo [aut, ctb], Christopher Anderson [aut, ctb], Barbara Klanderman [aut, ctb], Vincent Carey [aut, ctb], Benjamin Raby [aut, ctb]
Maintainer:	Weiliang Qiu <[email protected]>
License:	GPL (>= 2)
Version:	1.37.0
Built:	2024-10-31 01:12:18 UTC
Source:	https://github.com/bioc/iCheck

Help Index

Draw parallel plots for top results in whole-genome-wide analysis
Draw estimated density plots for all arrays
Generate an ExpressionSet object
Generating simulated data set from conditional normal distributions
Get principal components of arrays
Perform glm test for all gene probes
Perform glm test for all gene probes
A wrapper function for the function 'lmFit' of the R Bioconductor package 'limma' for paired data
A wrapper function for the function 'lmFit' of the R Bioconductor package 'limma'
Output slots (exprs, pData, fData) of an LumiBatch object into 3 text files
Scatter plot of first 2 principal components
Scatter plot of 3 specified principal components
Plot trajectories of probe profiles across arrays
Plot trajectories of specific QC probes (e.g., biotin, cy3_hyb, housekeeping gene probes, low stringency probes, etc.) across arrays
Plot trajectories of the ratio of 95th percentile to 5th percentile of sample probe profiles across arrays
Plot trajectories of quantiles across arrays
Draw heatmap of square of correlations among arrays
Draw scatter plots for top results in whole-genome-wide analysis
Sort the order of samples for an ExpressionSet object

Draw parallel plots for top results in whole-genome-wide analysis

Description

Draw scatter plots for top results in whole-genome-wide analysis to test for the association of probes to a continuous-type phenotype variable.

Usage

boxPlots(
  resFrame, 
  es, 
  col.resFrame = c("probeIDs", "stats", "pval", "p.adj"), 
  var.pheno = "sex", 
  var.probe = "TargetID", 
  var.gene = "Symbol", 
  var.chr = "Chr", 
  nTop = 20, 
  myylab = "expression level", 
  datExtrFunc = exprs, 
  fileFlag = FALSE, 
  fileFormat = "ps", 
  fileName = "boxPlots.ps")
boxPlots(
  resFrame, 
  es, 
  col.resFrame = c("probeIDs", "stats", "pval", "p.adj"), 
  var.pheno = "sex", 
  var.probe = "TargetID", 
  var.gene = "Symbol", 
  var.chr = "Chr", 
  nTop = 20, 
  myylab = "expression level", 
  datExtrFunc = exprs, 
  fileFlag = FALSE, 
  fileFormat = "ps", 
  fileName = "boxPlots.ps")

Arguments

`resFrame`	A data frame stores testing results, which must contain columns that indicate probe id, test statistic, p-value and optionally adjusted p-value.
`es`	An `ExpressionSet` object that used to run the whole genome-wide tests.
`col.resFrame`	A vector of characters indicating column names of `resFrame` corresponding to probe id, test statistic, p-value and optionally adjusted p-value.
`var.pheno`	character. the name of continuous-type phenotype variable that is used to test the association of this variable to probes.
`var.probe`	character. the name of feature variable indicating probe id.
`var.gene`	character. the name of feature variable indicating gene symbol.
`var.chr`	character. the name of feature variable indicating chromosome number.
`nTop`	integer. indicating how many top tests will be used to draw the scatter plot.
`myylab`	character. indicating y-axis label.
`datExtrFunc`	name of the function to extract genomic data. For an `ExpressionSet` object, you should set `datExtrFunc=exprs`; for a `MethyLumiSet` object, you should set `datExtrFunc=betas`.
`fileFlag`	logic. indicating if plot should be saved to an external figure file.
`fileFormat`	character. indicating the figure file type. Possible values are “ps”, “pdf”, or “jpeg”. All other values will produce “png” file.
`fileName`	character. indicating figure file name (file extension should be specified). For example, you set `fileFormat="pdf"`, then you can set `fileName="test.pdf"`, but not `fileName="test"`.

Value

Value 0 will be returned if no error occurs.

Author(s)

Weiliang Qiu <[email protected]>, Brandon Guo <[email protected]>, Christopher Anderson <[email protected]>, Barbara Klanderman <[email protected]>, Vincent Carey <[email protected]>, Benjamin Raby <[email protected]>

Examples

  # generate simulated data set from conditional normal distribution
  set.seed(1234567)
  es.sim = genSimData.BayesNormal(nCpGs = 100, 
    nCases = 20, nControls = 20,
    mu.n = -2, mu.c = 2,
    d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
    outlierFlag = FALSE, 
    eps = 1.0e-3, applier = lapply) 
  print(es.sim)

  res.limma = lmFitWrapper(
    es = es.sim, 
    formula = ~as.factor(memSubj), 
    pos.var.interest = 1,
    pvalAdjMethod = "fdr", 
    alpha = 0.05, 
    probeID.var = "probe", 
    gene.var = "gene", 
    chr.var = "chr", 
    verbose = TRUE)

  boxPlots(
    resFrame=res.limma$frame, 
    es=es.sim, 
    col.resFrame = c("probeIDs", "stats", "pval"), 
    var.pheno = "memSubj", 
    var.probe = "probe", 
    var.gene = "gene", 
    var.chr = "chr", 
    nTop = 20, 
    myylab = "expression level", 
    datExtrFunc = exprs, 
    fileFlag = FALSE, 
    fileFormat = "ps", 
    fileName = "boxPlots.ps")

# generate simulated data set from conditional normal distribution
  set.seed(1234567)
  es.sim = genSimData.BayesNormal(nCpGs = 100, 
    nCases = 20, nControls = 20,
    mu.n = -2, mu.c = 2,
    d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
    outlierFlag = FALSE, 
    eps = 1.0e-3, applier = lapply) 
  print(es.sim)

  res.limma = lmFitWrapper(
    es = es.sim, 
    formula = ~as.factor(memSubj), 
    pos.var.interest = 1,
    pvalAdjMethod = "fdr", 
    alpha = 0.05, 
    probeID.var = "probe", 
    gene.var = "gene", 
    chr.var = "chr", 
    verbose = TRUE)

  boxPlots(
    resFrame=res.limma$frame, 
    es=es.sim, 
    col.resFrame = c("probeIDs", "stats", "pval"), 
    var.pheno = "memSubj", 
    var.probe = "probe", 
    var.gene = "gene", 
    var.chr = "chr", 
    nTop = 20, 
    myylab = "expression level", 
    datExtrFunc = exprs, 
    fileFlag = FALSE, 
    fileFormat = "ps", 
    fileName = "boxPlots.ps")

Draw estimated density plots for all arrays

Description

Draw estimated density plots for all arrays.

Usage

densityPlots(
  es, 
  requireLog2 = TRUE, 
  myxlab = "expression level", 
  mymain = "density plots", 
  datExtrFunc = exprs, 
  fileFlag = FALSE, 
  fileFormat = "ps", 
  fileName = "densityPlots.ps")
densityPlots(
  es, 
  requireLog2 = TRUE, 
  myxlab = "expression level", 
  mymain = "density plots", 
  datExtrFunc = exprs, 
  fileFlag = FALSE, 
  fileFormat = "ps", 
  fileName = "densityPlots.ps")

Arguments

`es`	An `ExpressionSet` object that used to run the whole genome-wide tests.
`requireLog2`	logic. indicating if log2 transformation is required before estimating densities.
`myxlab`	character. indicating x-axis label.
`mymain`	character. indicating title of the plot.
`datExtrFunc`	name of the function to extract genomic data. For an `ExpressionSet` object, you should set `datExtrFunc=exprs`; for a `MethyLumiSet` object, you should set `datExtrFunc=betas`.
`fileFlag`	logic. indicating if plot should be saved to an external figure file.
`fileFormat`	character. indicating the figure file type. Possible values are “ps”, “pdf”, or “jpeg”. All other values will produce “png” file.
`fileName`	character. indicating figure file name (file extension should be specified). For example, you set `fileFormat="pdf"`, then you can set `fileName="test.pdf"`, but not `fileName="test"`.

Value

A list object, the $i$ -th element is the object returned by function density for the $i$ -th array.

Author(s)

Examples

  # generate simulated data set from conditional normal distribution
  set.seed(1234567)
  es.sim = genSimData.BayesNormal(nCpGs = 100, 
    nCases = 20, nControls = 20,
    mu.n = -2, mu.c = 2,
    d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
    outlierFlag = FALSE, 
    eps = 1.0e-3, applier = lapply) 
  print(es.sim)

  densityPlots(
    es = es.sim, 
    requireLog2 = FALSE, 
    myxlab = "expression level", 
    mymain = "density plots", 
    datExtrFunc = exprs, 
    fileFlag = FALSE, 
    fileFormat = "ps", 
    fileName = "densityPlots.ps")
  
# generate simulated data set from conditional normal distribution
  set.seed(1234567)
  es.sim = genSimData.BayesNormal(nCpGs = 100, 
    nCases = 20, nControls = 20,
    mu.n = -2, mu.c = 2,
    d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
    outlierFlag = FALSE, 
    eps = 1.0e-3, applier = lapply) 
  print(es.sim)

  densityPlots(
    es = es.sim, 
    requireLog2 = FALSE, 
    myxlab = "expression level", 
    mymain = "density plots", 
    datExtrFunc = exprs, 
    fileFlag = FALSE, 
    fileFormat = "ps", 
    fileName = "densityPlots.ps")

Generate an ExpressionSet object

Description

Generate a simple ExpressionSet object.

Usage

genExprSet(
  ex, 
  pDat, 
  fDat = NULL, 
  annotation = "lumiHumanAll.db")
genExprSet(
  ex, 
  pDat, 
  fDat = NULL, 
  annotation = "lumiHumanAll.db")

Arguments

`ex`	A matrix of expression levels. Rows are gene probes and columns are arrays.
`pDat`	A data frame describing arrays. Rows are arrays and columns are variables describing arrays. The row names of `pDat` must be the same as the column of `ex`.
`fDat`	A data frame describing gene probes. Rows are gene probes and columns are variables describing gene probes. The rownames of `fDat` must be the same as that of `ex`.
`annotation`	character string. Indicating the annotation library (e.g. `lumiHumanAll.db` for the gene probes.

Value

an ExpressionSet object.

Author(s)

Generating simulated data set from conditional normal distributions

Description

Generating simulated data set from conditional normal distributions.

Usage

genSimData.BayesNormal(
  nCpGs, 
  nCases, 
  nControls,
  mu.n = -2,
  mu.c = 2,
  d0 = 20, 
  s02 = 0.64,
  s02.c = 1.5,
  testPara = "var", 
  outlierFlag = FALSE,
  eps = 0.001, 
  applier = lapply)
genSimData.BayesNormal(
  nCpGs, 
  nCases, 
  nControls,
  mu.n = -2,
  mu.c = 2,
  d0 = 20, 
  s02 = 0.64,
  s02.c = 1.5,
  testPara = "var", 
  outlierFlag = FALSE,
  eps = 0.001, 
  applier = lapply)

Arguments

`nCpGs`	integer. Number of genes.
`nCases`	integer. Number of cases.
`nControls`	integer. Number of controls.
`mu.n`	numeric. mean of the conditional normal distribution for controls. See details.
`mu.c`	numeric. mean of the conditional normal distribution for cases. See details.
`d0`	integer. degree of freedom for scale-inverse chi squared distribution. See details.
`s02`	numeric. scaling parameter for scale-inverse chi squared distribution for controls. See details.
`s02.c`	numeric. scaling parameter for scale-inverse chi squared distribution for cases. See details.
`testPara`	character string. indicating if the test is for testing equal mean, equal variance, or both.
`outlierFlag`	logical. indicating if outliers would be generated. If `outlierFlag=TRUE`, then we followed Phipson and Oshlack's (2014) simulation studies to generate one outlier for each CpG site by replacing the DNA methylation level of one diseased subject by the maximum of the DNA methylation levels of all CpG sites.
`eps`	numeric. if $\|mean0-mean1\|<eps$ then we regard $mean0=mean1$ . Similarly, if $\|var0-var1\|<eps$ then we regard $var0=var1$ . $mean0$ and $var0$ are the mean and variance of the chi squared distribution for controls. $mean1$ and $var1$ are the mean and variance of the chi squared distribution for cases.
`applier`	function name to do `apply` operation.

Details

Based on Phipson and Oshlack's (2014) simulation algorithm. For each CpG site, variance of the DNA methylation was first sampled from an scaled inverse chi-squared distribution with degree of freedom $d_0$ and scaling parameter $s_0^2$ : $\sigma^2_i ~ scale-inv \chi^2(d_0, s_0^2)$ . M value for each CpG was then sampled from a normal distribution with mean $\mu_n$ and variance equal to the simulated variance $\sigma^2_i$ . For cases, the variance was first generated from $\sigma^2_{i,c} ~ scale-inv \chi^2(d_0, s_{0,c}^2)$ . M value for each CpG was then sampled from a normal distribution with mean $\mu_c$ and variance equal to the simulated variance $\sigma^2_{i,c}$ .

Value

An ExpressionSet object. The phenotype data of the ExpressionSet object contains 2 columns: arrayID (array id) and memSubj (subject membership, i.e., case (memSubj=1) or control (memSubj=0)). The feature data of the ExpressionSet object contains 4 elements: probe (probe id), gene (psuedo gene symbol), chr (psuedo chromosome number), and memGenes (indicating if a gene is differentially expressed (when testPara="mean") or indicating if a gene is differentially variable (when testPara="var") ).

Author(s)

References

Phipson B, Oshlack A. DiffVar: A new method for detecting differential variability with application to methylation in cancer and aging. Genome Biol 2014; 15:465

Examples

    # generate simulated data set from conditional normal distribution
    set.seed(1234567)
    es.sim = genSimData.BayesNormal(nCpGs = 100, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)

# generate simulated data set from conditional normal distribution
    set.seed(1234567)
    es.sim = genSimData.BayesNormal(nCpGs = 100, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)

Get principal components of arrays

Description

Get principal components of arrays.

Usage

getPCAFunc(es, 
           labelVariable = "subjID", 
            hybName = "Hybridization_Name",
           requireLog2 = TRUE,
           corFlag = FALSE
)
getPCAFunc(es, 
           labelVariable = "subjID", 
            hybName = "Hybridization_Name",
           requireLog2 = TRUE,
           corFlag = FALSE
)

Arguments

`es`	An `ExpressionSet` object
`labelVariable`	A character string. The name of a phenotype data variable use to label the arrays in the boxplots. By default, `labelVariable = "subjID"` which is equivalent to `labelVariable = "Hybridization_Name"`.
`hybName`	character string. indicating the phenotype variable `Hybridization_Name`.
`requireLog2`	logical. `requiredLog2=TRUE` indicates probe expression levels will be log2 transformed. Otherwise, no transformation will be performed.
`corFlag`	logical. Indicating if correlation matrix (`corFlag=TRUE`) or covariance (`corFlag=FALSE`) is used to obtain principal components.

Value

A list with 3 elements:

`es.s`	An `ExpressionSet` object with the arrays sorted according to Batch_Run_Date, Chip_Barcode, and Chip_Address
`pcs`	An object returned by the function `prcomp` of the R package `stats`. It contans the following components. `sdev` (the square roots of the eigenvalues of the covariance/correlation matrix); `rotation` (a matrix whose columns contain the eigenvectors); `x` (a matrix whose columns contain principal components); `center` (the centering used or `FALSE`); `scale` (the scale used or `FALSE`)
`requireLog2`	logical. The same value as the input `requireLog2`.

Author(s)

Examples

    # generate simulated data set from conditional normal distribution
    set.seed(1234567)
    es.sim = genSimData.BayesNormal(nCpGs = 100, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)

    pca.obj = getPCAFunc(es = es.sim, 
               labelVariable = "subjID", 
               hybName = "memSubj",
               requireLog2 = FALSE,
               corFlag = FALSE
    )
# generate simulated data set from conditional normal distribution
    set.seed(1234567)
    es.sim = genSimData.BayesNormal(nCpGs = 100, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)

    pca.obj = getPCAFunc(es = es.sim, 
               labelVariable = "subjID", 
               hybName = "memSubj",
               requireLog2 = FALSE,
               corFlag = FALSE
    )

Perform glm test for all gene probes

Description

Perform glm test for all gene probes.

Usage

glmWrapper(es, 
           formula = FEV1 ~ xi + age + gender, 
           pos.var.interest = 1,
           family = gaussian, 
           logit = FALSE, 
           pvalAdjMethod = "fdr", 
           alpha = 0.05, 
           probeID.var = "ProbeID", 
           gene.var = "Symbol", 
           chr.var = "Chromosome", 
           applier = lapply,
           verbose = TRUE) 
glmWrapper(es, 
           formula = FEV1 ~ xi + age + gender, 
           pos.var.interest = 1,
           family = gaussian, 
           logit = FALSE, 
           pvalAdjMethod = "fdr", 
           alpha = 0.05, 
           probeID.var = "ProbeID", 
           gene.var = "Symbol", 
           chr.var = "Chromosome", 
           applier = lapply,
           verbose = TRUE)

Arguments

`es`	An LumiBatch object. `fData(es)` should contains information about probe ID, chromosome number and gene symbol.
`formula`	An object of class `formula`. The left handside of `~` is the response variable. Gene probe must be represented by the variable `xi`. For example, `xi~age+gender` (gene probe is the response variable); Or `FEV1~xi+age+gender` (gene probe is the predictor).
`pos.var.interest`	integer. Indicates which covariate in the right-hand-size of `~` of `formula` is of the interest. pos.var.interest $= 0$ means the intercept is of the interest. If the covariate of the interest is an factor or interaction term with more than 2 levels, the smallest p-value will represent the pvalue for the covariate of the interest.
`family`	By default is gaussian. refer to `glm`.
`logit`	logical. Indicate if the gene probes will be logit transformed. For example, for DNA methylation data, one might want to logit transformation for the beta-value ( $methylated/(methylated+unmethylated)$ ).
`pvalAdjMethod`	One of p-value adjustment methods provided by the R function `p.adjust` in R package `stats`: “holm”, “hochberg”, “hommel”, “bonferroni”, “BH”, “BY”, “fdr”, “none”.
`alpha`	Significance level. A test is claimed to be significant if the adjusted p-value $<$ `alpha`.
`probeID.var`	character string. Name of the variable indicating probe ID in feature data set.
`gene.var`	character string. Name of the variable indicating gene symbol in feature data set.
`chr.var`	character string. Name of the variable indicating chromosome number in feature data set.
`applier`	By default, it is lapply. If the library multicore is available, can use mclapply to replace lappy.
`verbose`	logical. Determine if intermediate output need to be suppressed. By default `verbose=TRUE`, intermediate output will be printed.

Details

This function applies R function glm for each gene probe.

Value

A list with the following elements:

`n.sig`	Number of significant tests after p-value adjustment.
`frame`	A data frame containing test results sorted according to the ascending order of unadjusted p-values for the covariate of the interest. The data frame contains 7 columns: `probeIDs`, `geneSymbols` (gene symbols of the genes where the probes come from), `chr` (numbers of chromosomes where the probes locate), `stats` (z-value), `pval` (p-values of the tests for the covariate of the interest), `p.adj` (adjusted p-values), `pos` (row numbers of the probes in the expression data matrix).
`statMat`	A matrix containing test statistics for all covariates and for all probes. Rows are probes and columns are covariates. The rows are ordered according to the ascending order of unadjusted p-values for the covariate of the interest.
`pvalMat`	A matrix containing pvalues for all covariates and for all probes. Rows are probes and columns are covariates. The rows are ordered according to the ascending order of unadjusted p-values for the covariate of the interest.
`pval.quantile`	Quantiles (minimum, 25 for each covariate including intercept provided in the input argument `formula`.
`frame.unsorted`	A data frame containing test results. The data frame contains 7 columns: `probeIDs`, `geneSymbols` (gene symbols of the genes where the probes come from), `chr` (numbers of chromosomes where the probes locate), `stats` (z-value for the covariate of the interest), `pval` (p-values of the tests for the covariate of the interest), `p.adj` (adjusted p-values), `pos` (row numbers of the probes in the expression data matrix).
`statMat.unsorted`	A matrix containing test statistics for all covariates and for all probes. Rows are probes and columns are covariates.
`pvalMat.unsorted`	A matrix containing pvalues for all covariates and for all probes. Rows are probes and columns are covariates.
`memGenes`	A numeric vector indicating the cluster membership of probes (unsorted). `memGenes[i]=1` if the $i$ -th probe is significant (adjusted pvalue $<$ `alpha`) with positive z-value for the covariate of the interest; `memGenes[i]=2` if the $i$ -th probe is nonsignificant ; `memGenes[i]=3` if the $i$ -th probe is significant with negative z-value for the covariate of the interest;
`memGenes2`	A numeric vector indicating the cluster membership of probes (unsorted). `memGenes2[i]=1` if the $i$ -th probe is significant (adjusted pvalue $<$ `alpha`). `memGenes2[i]=0` if the $i$ -th probe is nonsignificant.
`mu1`	Mean expression levels for arrays for probe cluster 1 (average taking across all probes with `memGenes` value equal to 1.
`mu2`	Mean expression levels for arrays for probe cluster 2 (average taking across all probes with `memGenes` value equal to 2.
`mu3`	Mean expression levels for arrays for probe cluster 3 (average taking across all probes with `memGenes` value equal to 3.
`resMat`	A matrix with $2p$ columns, where $p$ is the number of covariates (including intercept; for a nominal variable with 3 levels say, there were 2 dummy covariates). The first $p$ columns are p-values. The remaining $p$ columns are test statistics.

Note

If the covariate of the interest is a factor or interaction term with more than 2 levels, then the p-value of the likelihood ratio test might be more appropriate than the smallest p-value for the covariate of the interest.

Author(s)

Examples

    # generate simulated data set from conditional normal distribution
    set.seed(1234567)
    es.sim = genSimData.BayesNormal(nCpGs = 100, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)

   res.glm = glmWrapper(
  es = es.sim, 
  formula = xi~as.factor(memSubj), 
  pos.var.interest = 1,
  family = gaussian, 
  logit = FALSE, 
  pvalAdjMethod = "fdr", 
  alpha = 0.05, 
  probeID.var = "probe", 
  gene.var = "gene", 
  chr.var = "chr", 
  applier = lapply,
  verbose = TRUE) 


# generate simulated data set from conditional normal distribution
    set.seed(1234567)
    es.sim = genSimData.BayesNormal(nCpGs = 100, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)

   res.glm = glmWrapper(
  es = es.sim, 
  formula = xi~as.factor(memSubj), 
  pos.var.interest = 1,
  family = gaussian, 
  logit = FALSE, 
  pvalAdjMethod = "fdr", 
  alpha = 0.05, 
  probeID.var = "probe", 
  gene.var = "gene", 
  chr.var = "chr", 
  applier = lapply,
  verbose = TRUE)

Perform glm test for all gene probes

Description

Perform glm test for all gene probes.

Usage

lkhrWrapper(es, 
           formulaReduced = FEV1 ~ xi + gender,
           formulaFull =    FEV1 ~ xi + age + gender,
           family = gaussian, 
           logit = FALSE, 
           pvalAdjMethod = "fdr", 
           alpha = 0.05, 
           probeID.var = "ProbeID", 
           gene.var = "Symbol", 
           chr.var = "Chromosome", 
           applier = lapply,
           verbose = TRUE) 
lkhrWrapper(es, 
           formulaReduced = FEV1 ~ xi + gender,
           formulaFull =    FEV1 ~ xi + age + gender,
           family = gaussian, 
           logit = FALSE, 
           pvalAdjMethod = "fdr", 
           alpha = 0.05, 
           probeID.var = "ProbeID", 
           gene.var = "Symbol", 
           chr.var = "Chromosome", 
           applier = lapply,
           verbose = TRUE)

Arguments

`es`	An LumiBatch object. `fData(es)` should contains information about probe ID, chromosome number and gene symbol.
`formulaReduced`	An object of class `formula`. Formula for reduced model. The left handside of `~` is the response variable. Gene probe must be represented by the variable `xi`. For example, `xi~gender` (gene probe is the response variable); Or `FEV1~xi+gender` (gene probe is the predictor).
`formulaFull`	An object of class `formula`. Formula for Full model. The left handside of `~` is the response variable. Gene probe must be represented by the variable `xi`. For example, `xi~age+gender` (gene probe is the response variable); Or `FEV1~xi+age+gender` (gene probe is the predictor).
`family`	By default is gaussian. refer to `glm`.
`logit`	logical. Indicate if the gene probes will be logit transformed. For example, for DNA methylation data, one might want to logit transformation for the beta-value ( $methylated/(methylated+unmethylated)$ ).
`pvalAdjMethod`	One of p-value adjustment methods provided by the R function `p.adjust` in R package `stats`: “holm”, “hochberg”, “hommel”, “bonferroni”, “BH”, “BY”, “fdr”, “none”.
`alpha`	Significance level. A test is claimed to be significant if the adjusted p-value $<$ `alpha`.
`probeID.var`	character string. Name of the variable indicating probe ID in feature data set.
`gene.var`	character string. Name of the variable indicating gene symbol in feature data set.
`chr.var`	character string. Name of the variable indicating chromosome number in feature data set.
`applier`	By default, it is lapply. If the library multicore is available, can use mclapply to replace lappy.
`verbose`	logical. Determine if intermediate output need to be suppressed. By default `verbose=TRUE`, intermediate output will be printed.

Details

This function applies R functions lrtest in R package lmtest and glm for each gene probe.

Value

A list with the following elements:

frame

A data frame containing test results sorted according to the ascending order of unadjusted p-values for the covariate of the interest. The data frame contains 8 columns: probeIDs, geneSymbols (gene symbols of the genes where the probes come from), chr (numbers of chromosomes where the probes locate), Chisq (chi square test statistic), Df (degree of freedom of the chisquare test statistic), pval (p-values of the tests for the covariate of the interest), p.adj (adjusted p-values), pos (row numbers of the probes in the expression data matrix). The rows are ordered based on the descending order of chisquare test statistic.

frame.unsorted

A data frame containing test results. unordered frame.

Author(s)

Examples

    # generate simulated data set from conditional normal distribution
    set.seed(1234567)
    es.sim = genSimData.BayesNormal(nCpGs = 100, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)

set.seed(1234567)
es.sim$age = rnorm(ncol(es.sim), mean=50, sd=5)
res.lkh = lkhrWrapper(
  es = es.sim, 
  formulaReduced = xi ~ memSubj,
  formulaFull =    xi ~ memSubj + age,
  family = gaussian(), 
  logit = FALSE, 
  pvalAdjMethod = "fdr", 
  alpha = 0.05, 
  probeID.var = "probe", 
  gene.var = "gene", 
  chr.var = "chr", 
  applier = lapply,
  verbose = TRUE) 

# generate simulated data set from conditional normal distribution
    set.seed(1234567)
    es.sim = genSimData.BayesNormal(nCpGs = 100, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)

set.seed(1234567)
es.sim$age = rnorm(ncol(es.sim), mean=50, sd=5)
res.lkh = lkhrWrapper(
  es = es.sim, 
  formulaReduced = xi ~ memSubj,
  formulaFull =    xi ~ memSubj + age,
  family = gaussian(), 
  logit = FALSE, 
  pvalAdjMethod = "fdr", 
  alpha = 0.05, 
  probeID.var = "probe", 
  gene.var = "gene", 
  chr.var = "chr", 
  applier = lapply,
  verbose = TRUE)

A wrapper function for the function 'lmFit' of the R Bioconductor package 'limma' for paired data

Description

A wrapper function for the function 'lmFit' of the R Bioconductor package 'limma' for paired data.

Usage

lmFitPaired(
    esDiff, 
    formula = ~1, 
    pos.var.interest = 0,
    pvalAdjMethod = "fdr", 
    alpha = 0.05, 
    probeID.var="ProbeID", 
    gene.var = "Symbol", 
    chr.var = "Chromosome", 
    verbose = TRUE)
lmFitPaired(
    esDiff, 
    formula = ~1, 
    pos.var.interest = 0,
    pvalAdjMethod = "fdr", 
    alpha = 0.05, 
    probeID.var="ProbeID", 
    gene.var = "Symbol", 
    chr.var = "Chromosome", 
    verbose = TRUE)

Arguments

`esDiff`	An LumiBatch object containing log2 difference between cases and controls. `fData(esDiff)` should contains information about probe ID, chromosome number and gene symbol.
`formula`	An object of class `formula`. The intercept measures the effect of treatment. Other covariates measure the effects of their interaction and treatment. The p-values for the intercept will be output. No left handside of `~` should be specified since the response variable will be the expression level.
`pos.var.interest`	integer. Indicates which covariate on the right-hand-side of `~` in `formula` is the covariate of the interest. By default, it is the intercept `pos.var.interest=0`.
`pvalAdjMethod`	One of p-value adjustment methods provided by the R function `p.adjust` in R package `stats`: “holm”, “hochberg”, “hommel”, “bonferroni”, “BH”, “BY”, “fdr”, “none”.
`alpha`	Significance level. A test is claimed to be significant if the adjusted p-value $<$ `alpha`.
`probeID.var`	character string. Name of the variable indicating probe ID in feature data set.
`gene.var`	character string. Name of the variable indicating gene symbol in feature data set.
`chr.var`	character string. Name of the variable indicating chromosome number in feature data set.
`verbose`	logical. Determine if intermediate output need to be suppressed. By default `verbose=TRUE`, intermediate output will be printed.

Details

This is a wrapper function of R Bioconductor functions lmFit and eBayes for paired data to make it easier to input design and output list of significant results.

Value

A list with the following elements:

`n.sig`	Number of significant tests after p-value adjustment.
`frame`	A data frame containing test results sorted according to the ascending order of unadjusted p-values for the intercept. The data frame contains 7 columns: `probeIDs`, `geneSymbols` (gene symbols of the genes where the probes come from), `chr` (numbers of chromosomes where the probes locate), `stats` (moderated t-statistics for the intercept), `pval` (p-values of the tests for the intercept), `p.adj` (adjusted p-values), `pos` (row numbers of the probes in the expression data matrix).
`statMat`	A matrix containing test statistics for all covariates and for all probes. Rows are probes and columns are covariates. The rows are ordered according to the ascending order of unadjusted p-values for the intercept.
`pvalMat`	A matrix containing pvalues for all covariates and for all probes. Rows are probes and columns are covariates. The rows are ordered according to the ascending order of unadjusted p-values for the intercept.
`pval.quantile`	Quantiles (minimum, 25 for all covariates including intercept provided in the input argument `formula`.
`frame.unsorted`	A data frame containing test results. The data frame contains 7 columns: `probeIDs`, `geneSymbols` (gene symbols of the genes where the probes come from), `chr` (numbers of chromosomes where the probes locate), `stats` (moderated t-statistics for the intercept), `pval` (p-values of the tests for the intercept), `p.adj` (adjusted p-values), `pos` (row numbers of the probes in the expression data matrix).
`statMat.unsorted`	A matrix containing test statistics for all covariates and for all probes. Rows are probes and columns are covariates.
`pvalMat.unsorted`	A matrix containing pvalues for all covariates and for all probes. Rows are probes and columns are covariates.
`memGenes`	A numeric vector indicating the cluster membership of probes (unsorted). `memGenes[i]=1` if the $i$ -th probe is significant (adjusted pvalue $<$ `alpha`) with positive moderated t-statistic; `memGenes[i]=2` if the $i$ -th probe is nonsignificant ; `memGenes[i]=3` if the $i$ -th probe is significant with negative moderated t-statistic;
`memGenes2`	A numeric vector indicating the cluster membership of probes (unsorted). `memGenes2[i]=1` if the $i$ -th probe is significant (adjusted pvalue $<$ `alpha`). `memGenes2[i]=0` if the $i$ -th probe is nonsignificant.
`mu1`	Mean expression levels for arrays for probe cluster 1 (average taking across all probes with `memGenes` value equal to 1.
`mu2`	Mean expression levels for arrays for probe cluster 2 (average taking across all probes with `memGenes` value equal to 2.
`mu3`	Mean expression levels for arrays for probe cluster 3 (average taking across all probes with `memGenes` value equal to 3.
`ebFit`	object returned by R Bioconductor function `eBayes`.

Author(s)

Examples

    # generate simulated data set from conditional normal distribution
    set.seed(1234567)
    es.sim = genSimData.BayesNormal(nCpGs = 100, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)

  # although the generated data is not from 
  # paired design, we use it to illusrate the
  # usage of the function lmFitPaired 


res.limma = lmFitPaired(
  es = es.sim, 
  formula = ~as.factor(memSubj), 
  pos.var.interest = 0, # the intercept is what we are interested
  pvalAdjMethod = "fdr", 
  alpha = 0.05, 
  probeID.var = "probe", 
  gene.var = "gene", 
  chr.var = "chr", 
  verbose = TRUE)
# generate simulated data set from conditional normal distribution
    set.seed(1234567)
    es.sim = genSimData.BayesNormal(nCpGs = 100, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)

  # although the generated data is not from 
  # paired design, we use it to illusrate the
  # usage of the function lmFitPaired 


res.limma = lmFitPaired(
  es = es.sim, 
  formula = ~as.factor(memSubj), 
  pos.var.interest = 0, # the intercept is what we are interested
  pvalAdjMethod = "fdr", 
  alpha = 0.05, 
  probeID.var = "probe", 
  gene.var = "gene", 
  chr.var = "chr", 
  verbose = TRUE)

A wrapper function for the function 'lmFit' of the R Bioconductor package 'limma'

Description

A wrapper function for the function 'lmFit' of the R Bioconductor package 'limma'.

Usage

lmFitWrapper(
    es, 
    formula = ~as.factor(gender), 
    pos.var.interest = 1,
    pvalAdjMethod = "fdr", 
    alpha = 0.05, 
    probeID.var = "ProbeID", 
    gene.var = "Symbol", 
    chr.var = "Chromosome", 
    verbose = TRUE)
lmFitWrapper(
    es, 
    formula = ~as.factor(gender), 
    pos.var.interest = 1,
    pvalAdjMethod = "fdr", 
    alpha = 0.05, 
    probeID.var = "ProbeID", 
    gene.var = "Symbol", 
    chr.var = "Chromosome", 
    verbose = TRUE)

Arguments

`es`	An LumiBatch object. `fData(es)` should contains information about chromosome number and gene symbol.
`formula`	An object of class `formula`. No left handside of `~` should be specified since the response variable will be the expression level.
`pos.var.interest`	integer. Indicates which covariate on the right-hand-side of `~` in `formula` is the covariate of the interest. By default, it is the first covariate `pos.var.interest=1`.
`pvalAdjMethod`	One of p-value adjustment methods provided by the R function `p.adjust` in R package `stats`: “holm”, “hochberg”, “hommel”, “bonferroni”, “BH”, “BY”, “fdr”, “none”.
`alpha`	Significance level. A test is claimed to be significant if the adjusted p-value $<$ `alpha`.
`probeID.var`	character string. Name of the variable indicating probe ID in feature data set.
`gene.var`	character string. Name of the variable indicating gene symbol in feature data set.
`chr.var`	character string. Name of the variable indicating chromosome number in feature data set.
`verbose`	logical. Determine if intermediate output need to be suppressed. By default `verbose=TRUE`, intermediate output will be printed.

Details

This is a wrapper function of R Bioconductor functions lmFit and eBayes to make it easier to input design and output list of significant results.

Value

A list with the following elements:

`n.sig`	Number of significant tests after p-value adjustment.
`frame`	A data frame containing test results sorted according to the ascending order of unadjusted p-values for the covariate of the interest. The data frame contains 7 columns: `probeIDs`, `geneSymbols` (gene symbols of the genes where the probes come from), `chr` (numbers of chromosomes where the probes locate), `stats` (moderated t-statistics for the covariate of interest, i.e. the first covariate), \ codepval (p-values of the tests for the covariate of interest, i.e. the first covariate), `p.adj` (adjusted p-values), `pos` (row numbers of the probes in the expression data matrix).
`statMat`	A matrix containing test statistics for all covariates and for all probes. Rows are probes and columns are covariates. The rows are ordered according to the ascending order of unadjusted p-values for the covariate of the interest.
`pvalMat`	A matrix containing pvalues for all covariates and for all probes. Rows are probes and columns are covariates. The rows are ordered according to the ascending order of unadjusted p-values for the covariate of the interest.
`pval.quantile`	Quantiles (minimum, 25 for all covariates including intercept provided in the input argument `formula`.
`frame.unsorted`	A data frame containing test results. The data frame contains 7 columns: `probeIDs`, `geneSymbols` (gene symbols of the genes where the probes come from), `chr` (numbers of chromosomes where the probes locate), `stats` (moderated t-statistics for the covariate of the interest), `pval` (p-values of the tests for the covariate of the interest), `p.adj` (adjusted p-values), `pos` (row numbers of the probes in the expression data matrix).
`statMat.unsorted`	A matrix containing test statistics for all covariates and for all probes. Rows are probes and columns are covariates.
`pvalMat.unsorted`	A matrix containing pvalues for all covariates and for all probes. Rows are probes and columns are covariates.
`memGenes`	A numeric vector indicating the cluster membership of probes (unsorted). `memGenes[i]=1` if the $i$ -th probe is significant (adjusted pvalue $<$ `alpha`) with positive moderated t-statistic; `memGenes[i]=2` if the $i$ -th probe is nonsignificant ; `memGenes[i]=3` if the $i$ -th probe is significant with negative moderated t-statistic;
`memGenes2`	A numeric vector indicating the cluster membership of probes (unsorted). `memGenes2[i]=1` if the $i$ -th probe is significant (adjusted pvalue $<$ `alpha`). `memGenes2[i]=0` if the $i$ -th probe is nonsignificant.
`mu1`	Mean expression levels for arrays for probe cluster 1 (average taking across all probes with `memGenes` value equal to 1.
`mu2`	Mean expression levels for arrays for probe cluster 2 (average taking across all probes with `memGenes` value equal to 2.
`mu3`	Mean expression levels for arrays for probe cluster 3 (average taking across all probes with `memGenes` value equal to 3.
`ebFit`	object returned by R Bioconductor function `eBayes`.

Author(s)

Examples

    # generate simulated data set from conditional normal distribution
    set.seed(1234567)
    es.sim = genSimData.BayesNormal(nCpGs = 100, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)

   
res.limma = lmFitWrapper(
  es = es.sim, 
  formula = ~as.factor(memSubj), 
  pos.var.interest = 1,
  pvalAdjMethod = "fdr", 
  alpha = 0.05, 
  probeID.var = "probe", 
  gene.var = "gene", 
  chr.var = "chr", 
  verbose = TRUE)
# generate simulated data set from conditional normal distribution
    set.seed(1234567)
    es.sim = genSimData.BayesNormal(nCpGs = 100, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)

   
res.limma = lmFitWrapper(
  es = es.sim, 
  formula = ~as.factor(memSubj), 
  pos.var.interest = 1,
  pvalAdjMethod = "fdr", 
  alpha = 0.05, 
  probeID.var = "probe", 
  gene.var = "gene", 
  chr.var = "chr", 
  verbose = TRUE)

Output slots (exprs, pData, fData) of an LumiBatch object into 3 text files

Description

Output slots (exprs, pData, fData) of an LumiBatch object into 3 text files.

Usage

LumiBatch2Table(
  es, 
  probeID.var="ProbeID",
  gene.var="Symbol",
  chr.var="Chromosome",
  sep = ",", 
  quote = FALSE,
  filePrefix = "test", 
  fileExt = "csv")
LumiBatch2Table(
  es, 
  probeID.var="ProbeID",
  gene.var="Symbol",
  chr.var="Chromosome",
  sep = ",", 
  quote = FALSE,
  filePrefix = "test", 
  fileExt = "csv")

Arguments

`es`	An LumiBatch object
`probeID.var`	character string. Name of the variable indicating probe ID in feature data set.
`gene.var`	character string. Name of the variable indicating gene symbol in feature data set.
`chr.var`	character string. Name of the variable indicating chromosome number in feature data set.
`sep`	Field delimiter for the output text files
`quote`	logical. Indicating if any character or factor. See also `write.table`.
`filePrefix`	Prefix of the names of the output files.
`fileExt`	File extension of the names of the output files.

Details

Suppose filePrefix="test" and fileExt=".csv". Then, the file names of the 3 output files are: “test_exprs.csv”, “test_pDat.csv”, and “test_fDat.csv”, respectively.

Value

None.

Author(s)

Examples

    # generate simulated data set from conditional normal distribution
    set.seed(1234567)
    es.sim = genSimData.BayesNormal(nCpGs = 100, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)

   LumiBatch2Table(
    es = es.sim, 
    probeID.var="probe",
    gene.var="gene",
    chr.var="chr",
    sep = ",", 
    quote = FALSE,
    filePrefix = "test", 
    fileExt = "csv")

# generate simulated data set from conditional normal distribution
    set.seed(1234567)
    es.sim = genSimData.BayesNormal(nCpGs = 100, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)

   LumiBatch2Table(
    es = es.sim, 
    probeID.var="probe",
    gene.var="gene",
    chr.var="chr",
    sep = ",", 
    quote = FALSE,
    filePrefix = "test", 
    fileExt = "csv")

Scatter plot of first 2 principal components

Description

Scatter plot of first 2 principal components.

Usage

pca2DPlot(pcaObj, 
          plot.dim = c(1,2),
          labelVariable = "subjID", 
          hybName = "Hybridization_Name",
          outFileName = "test_pca_raw.pdf", 
          title = "Scatter plot of pcas", 
          plotOutPutFlag = FALSE, 
          mar = c(5, 4, 4, 2) + 0.1, 
          lwd = 1.5, 
          equalRange = TRUE, 
          xlab = NULL, 
          ylab = NULL, 
          xlim = NULL, 
          ylim = NULL, 
          cex.legend = 1.5, 
          cex = 1.5, 
          cex.lab = 1.5, 
          cex.axis = 1.5, 
          legendPosition = "topright", 
          ...)
pca2DPlot(pcaObj, 
          plot.dim = c(1,2),
          labelVariable = "subjID", 
          hybName = "Hybridization_Name",
          outFileName = "test_pca_raw.pdf", 
          title = "Scatter plot of pcas", 
          plotOutPutFlag = FALSE, 
          mar = c(5, 4, 4, 2) + 0.1, 
          lwd = 1.5, 
          equalRange = TRUE, 
          xlab = NULL, 
          ylab = NULL, 
          xlim = NULL, 
          ylim = NULL, 
          cex.legend = 1.5, 
          cex = 1.5, 
          cex.lab = 1.5, 
          cex.axis = 1.5, 
          legendPosition = "topright", 
          ...)

Arguments

`pcaObj`	An object returned by the function `pca` of the R package `pcaMethods`.
`plot.dim`	A vector of 2 positive-integer-value integer specifying which 2 pcas will be plot.
`labelVariable`	The name of a column of the phenotype data matrix. The elements of the column will replace the column names of the expression data matrix.
`hybName`	character string. indicating the phenotype variable `Hybridization_Name`.
`outFileName`	Name of the figure file to be created.
`title`	Title of the scatter plot.
`plotOutPutFlag`	logical. `plotOutPutFlag=TRUE` indicates the plots will be output to pdf format files. Otherwise, the plots will not be output to external files.
`mar`	A numerical vector of the form 'c(bottom, left, top, right)' which gives the number of lines of margin to be specified on the four sides of the plot. The default is 'c(5, 4, 4, 2) + 0.1'. see `par`.
`lwd`	The line width, a _positive_ number, defaulting to '1'. see `par`.
`equalRange`	logical. Indicating if the x-axis and y-axis have the same range.
`xlab`	Label of x axis.
`ylab`	Label of y axis.
`xlim`	Range of x axis.
`ylim`	Range of y axis.
`cex.legend`	Font size for legend.
`cex`	numerical value giving the amount by which plotting text and symbols should be magnified relative to the default. see `par`.
`cex.lab`	The magnification to be used for x and y labels relative to the current setting of cex.
`cex.axis`	The magnification to be used for axis annotation relative to the current setting of cex. see `par`.
`legendPosition`	Position of legend. Possible values are “bottomright”, “bottom”, “bottomleft”, “left”, “topleft”, “top”, “topright”, “right” and “center”.
`...`	Arguments to be passed to `plot`.

Value

A matrix of PCA scores. Each column corresponds to a principal component.

Author(s)

Examples

    # generate simulated data set from conditional normal distribution
    set.seed(1234567)
    es.sim = genSimData.BayesNormal(nCpGs = 100, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)

    pca.obj = getPCAFunc(es = es.sim, 
                     labelVariable = "subjID", 
                     hybName = "memSubj",
                     requireLog2 = FALSE,
                     corFlag = FALSE
)

pca2DPlot(pcaObj = pca.obj, 
          plot.dim = c(1,2),
          labelVariable = "subjID", 
          hybName = "memSubj",
          plotOutPutFlag = FALSE, 
          cex.legend = 0.5, 
          legendPosition = "topright") 
    
# generate simulated data set from conditional normal distribution
    set.seed(1234567)
    es.sim = genSimData.BayesNormal(nCpGs = 100, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)

    pca.obj = getPCAFunc(es = es.sim, 
                     labelVariable = "subjID", 
                     hybName = "memSubj",
                     requireLog2 = FALSE,
                     corFlag = FALSE
)

pca2DPlot(pcaObj = pca.obj, 
          plot.dim = c(1,2),
          labelVariable = "subjID", 
          hybName = "memSubj",
          plotOutPutFlag = FALSE, 
          cex.legend = 0.5, 
          legendPosition = "topright")

Scatter plot of 3 specified principal components

Description

Scatter plot of 3 specified principal components.

Usage

pca3DPlot(pcaObj, 
          plot.dim = c(1,2, 3),
          labelVariable = "subjID", 
           hybName = "Hybridization_Name",
          outFileName = "test_pca_raw.pdf", 
          title = "Scatter plot of pcas", 
          plotOutPutFlag = FALSE, 
          mar = c(5, 4, 4, 2) + 0.1, 
          lwd = 1.5, 
          equalRange = TRUE, 
          xlab = NULL, 
          ylab = NULL, 
          zlab = NULL, 
          xlim = NULL, 
          ylim = NULL, 
          zlim = NULL, 
          cex.legend = 1.5, 
          cex = 1.5, 
          cex.lab = 1.5, 
          cex.axis = 1.5, 
          legendPosition = "topright", 
          ...)
pca3DPlot(pcaObj, 
          plot.dim = c(1,2, 3),
          labelVariable = "subjID", 
           hybName = "Hybridization_Name",
          outFileName = "test_pca_raw.pdf", 
          title = "Scatter plot of pcas", 
          plotOutPutFlag = FALSE, 
          mar = c(5, 4, 4, 2) + 0.1, 
          lwd = 1.5, 
          equalRange = TRUE, 
          xlab = NULL, 
          ylab = NULL, 
          zlab = NULL, 
          xlim = NULL, 
          ylim = NULL, 
          zlim = NULL, 
          cex.legend = 1.5, 
          cex = 1.5, 
          cex.lab = 1.5, 
          cex.axis = 1.5, 
          legendPosition = "topright", 
          ...)

Arguments

`pcaObj`	An object returned by the function `pca` of the R package `pcaMethods`.
`plot.dim`	A vector of 3 positive-integer-value integer specifying which 3 pcas will be plot.
`labelVariable`	The name of a column of the phenotype data matrix. The elements of the column will replace the column names of the expression data matrix.
`hybName`	character string. indicating the phenotype variable `Hybridization_Name`.
`outFileName`	Name of the figure file to be created.
`title`	Title of the scatter plot.
`plotOutPutFlag`	logical. `plotOutPutFlag=TRUE` indicates the plots will be output to pdf format files. Otherwise, the plots will not be output to external files.
`mar`	A numerical vector of the form 'c(bottom, left, top, right)' which gives the number of lines of margin to be specified on the four sides of the plot. The default is 'c(5, 4, 4, 2) + 0.1'. see `par`.
`lwd`	The line width, a _positive_ number, defaulting to '1'. see `par`.
`equalRange`	logical. Indicating if the x-axis and y-axis have the same range.
`xlab`	Label of x axis.
`ylab`	Label of y axis.
`zlab`	Label of z axis.
`xlim`	Range of x axis.
`ylim`	Range of y axis.
`zlim`	Range of z axis.
`cex.legend`	Font size for legend.
`cex`	numerical value giving the amount by which plotting text and symbols should be magnified relative to the default. see `par`.
`cex.lab`	The magnification to be used for x and y labels relative to the current setting of cex.
`cex.axis`	The magnification to be used for axis annotation relative to the current setting of cex. see `par`.
`legendPosition`	Position of legend. Possible values are “bottomright”, “bottom”, “bottomleft”, “left”, “topleft”, “top”, “topright”, “right” and “center”.
`...`	Arguments to be passed to `plot`.

Value

A matrix of PCA scores. Each column corresponds to a principal component.

Author(s)

Examples

    # generate simulated data set from conditional normal distribution
    set.seed(1234567)
    es.sim = genSimData.BayesNormal(nCpGs = 100, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)

    pca.obj = getPCAFunc(es = es.sim, 
                     labelVariable = "subjID", 
                     hybName = "memSubj",
                     requireLog2 = FALSE,
                     corFlag = FALSE
)


pca3DPlot(pcaObj = pca.obj, 
          plot.dim = c(1,2,3),
          labelVariable = "subjID", 
          hybName = "memSubj",
          plotOutPutFlag = FALSE, 
          cex.legend = 0.5, 
          legendPosition = "topright") 
    

# generate simulated data set from conditional normal distribution
    set.seed(1234567)
    es.sim = genSimData.BayesNormal(nCpGs = 100, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)

    pca.obj = getPCAFunc(es = es.sim, 
                     labelVariable = "subjID", 
                     hybName = "memSubj",
                     requireLog2 = FALSE,
                     corFlag = FALSE
)


pca3DPlot(pcaObj = pca.obj, 
          plot.dim = c(1,2,3),
          labelVariable = "subjID", 
          hybName = "memSubj",
          plotOutPutFlag = FALSE, 
          cex.legend = 0.5, 
          legendPosition = "topright")

Plot trajectories of probe profiles across arrays

Description

Plot trajectories of probe profiles across arrays

Usage

plotCurves(
    dat, 
    curveNames, 
    fileName,
    plotOutPutFlag=FALSE,
    requireLog2 = FALSE, 
    cex = 1, 
    ylim = NULL, 
    xlab = "", 
    ylab = "intensity", 
    lwd = 3, 
    main = "Trajectory plot", 
    mar = c(10, 4, 4, 2) + 0.1,
    las = 2,
    cex.axis=1,
    ...)
plotCurves(
    dat, 
    curveNames, 
    fileName,
    plotOutPutFlag=FALSE,
    requireLog2 = FALSE, 
    cex = 1, 
    ylim = NULL, 
    xlab = "", 
    ylab = "intensity", 
    lwd = 3, 
    main = "Trajectory plot", 
    mar = c(10, 4, 4, 2) + 0.1,
    las = 2,
    cex.axis=1,
    ...)

Arguments

`dat`	Numeric data matrix. Rows are probes; columns are arrays.
`curveNames`	Probe names.
`fileName`	file name of output figure.
`plotOutPutFlag`	logical. `plotOutPutFlag=TRUE` indicates the plots will be output to pdf format files. Otherwise, the plots will not be output to external files.
`requireLog2`	logical. `requiredLog2=TRUE` indicates probe expression levels will be log2 transformed. Otherwise, no transformation will be performed.
`cex`	numerical value giving the amount by which plotting text and symbols should be magnified relative to the default. see `par`.
`ylim`	Range of y axis.
`xlab`	Label of x axis.
`ylab`	Label of y axis.
`lwd`	The line width, a _positive_ number, defaulting to '1'. see `par`.
`main`	Main title of the plot.
`mar`	A numerical vector of the form 'c(bottom, left, top, right)' which gives the number of lines of margin to be specified on the four sides of the plot. The default is 'c(5, 4, 4, 2) + 0.1'. see `par`.
`las`	'las' numeric in 0,1,2,3; the style of axis labels. 0 - always parallel to the axis, 1 - always horizontal, 2 - always perpendicular to the axis, or 3 - always vertical. see `par`.
`cex.axis`	The magnification to be used for axis annotation relative to the current setting of cex. see `par`.
`...`	Arguments to be passed to `plot`.

Value

no return value.

Author(s)

Examples

    # generate simulated data set from conditional normal distribution
    set.seed(1234567)
    es.sim = genSimData.BayesNormal(nCpGs = 100, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)
    
  # plot trajectories of the first 5 genes
  plotCurves(
  dat = exprs(es.sim)[1:5,], 
  curveNames = featureNames(es.sim)[1:5], 
  plotOutPutFlag=FALSE,
  cex = 0.5,
  requireLog2 = FALSE) 

# generate simulated data set from conditional normal distribution
    set.seed(1234567)
    es.sim = genSimData.BayesNormal(nCpGs = 100, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)
    
  # plot trajectories of the first 5 genes
  plotCurves(
  dat = exprs(es.sim)[1:5,], 
  curveNames = featureNames(es.sim)[1:5], 
  plotOutPutFlag=FALSE,
  cex = 0.5,
  requireLog2 = FALSE)

Plot trajectories of specific QC probes (e.g., biotin, cy3_hyb, housekeeping gene probes, low stringency probes, etc.) across arrays

Description

Plot trajectories of specific QC probes (e.g., biotin, cy3_hyb, housekeeping gene probes, low stringency probes, etc.) across arrays

Usage

plotQCCurves(
    esQC, 
    probes = c("biotin", "cy3_hyb", "housekeeping", 
      "low_stringency_hyb", "signal", "p95p05"), 
    labelVariable = "subjID",
    hybName = "Hybridization_Name",
    reporterGroupName = "Reporter_Group_Name",
    requireLog2 = TRUE, 
    projectName = "test", 
    plotOutPutFlag = FALSE, 
    cex = 1, 
    ylim = NULL, 
    xlab = "", 
    ylab = "intensity", 
    lwd = 3, 
    mar = c(10, 4, 4, 2) + 0.1,
    las = 2,
    cex.axis = 1,
    sortFlag = TRUE,
    varSort = c("Batch_Run_Date", "Chip_Barcode", "Chip_Address"), 
    timeFormat = c("%m/%d/%Y", NA, NA),
    ...)
plotQCCurves(
    esQC, 
    probes = c("biotin", "cy3_hyb", "housekeeping", 
      "low_stringency_hyb", "signal", "p95p05"), 
    labelVariable = "subjID",
    hybName = "Hybridization_Name",
    reporterGroupName = "Reporter_Group_Name",
    requireLog2 = TRUE, 
    projectName = "test", 
    plotOutPutFlag = FALSE, 
    cex = 1, 
    ylim = NULL, 
    xlab = "", 
    ylab = "intensity", 
    lwd = 3, 
    mar = c(10, 4, 4, 2) + 0.1,
    las = 2,
    cex.axis = 1,
    sortFlag = TRUE,
    varSort = c("Batch_Run_Date", "Chip_Barcode", "Chip_Address"), 
    timeFormat = c("%m/%d/%Y", NA, NA),
    ...)

Arguments

`esQC`	ExpressionSet object of QC probe profiles. `fData(esQC)` should contains the variable `Reporter_Group_Name`.
`probes`	A character vectors of QC probe names. By default, it includes the following probe names “biotin”, “cy3_hyb”, “housekeeping”, “low_stringency_hyb”, “signal”, “p95p05”. For “signal”, trajectories of 5th, 25th, 50th, 75th, and 95th percentiles of the expression levels of all QC probes will be ploted. For “p95p05”, the trajectory of the ratio of 95th percentile to 5th percentile of the expression levels of all QC probes will be ploted.
`labelVariable`	A character string. The name of a phenotype data variable use to label the arrays in the boxplots. By default, `labelVariable = "subjID"` which is equivalent to `labelVariable = "Hybridization_Name"`.
`hybName`	character string. indicating the phenotype variable `Hybridization_Name`.
`reporterGroupName`	character string. indicating feature variable `Reporter_Group_Name` (QC probe's name).
`requireLog2`	logical. `requiredLog2=TRUE` indicates probe expression levels will be log2 transformed. Otherwise, no transformation will be performed.
`projectName`	A character string. Name of the project. The plots will be saved as pdf format files, the names of which have the format `projectName_probeName_traj_plot.pdf`.
`plotOutPutFlag`	logical. `plotOutPutFlag=TRUE` indicates the plots will be output to pdf format files. Otherwise, the plots will not be output to external files.
`cex`	numerical value giving the amount by which plotting text and symbols should be magnified relative to the default. see `par`.
`ylim`	Range of y axis.
`xlab`	Label of x axis.
`ylab`	Label of y axis.
`lwd`	The line width, a _positive_ number, defaulting to '1'. see `par`.
`mar`	A numerical vector of the form 'c(bottom, left, top, right)' which gives the number of lines of margin to be specified on the four sides of the plot. The default is 'c(5, 4, 4, 2) + 0.1'. see `par`.
`las`	'las' numeric in 0,1,2,3; the style of axis labels. 0 - always parallel to the axis, 1 - always horizontal, 2 - always perpendicular to the axis, or 3 - always vertical. see `par`.
`cex.axis`	The magnification to be used for axis annotation relative to the current setting of cex. see `par`.
`sortFlag`	logical. Indicates if arrays need to be sorted according to `Batch_Run_Date`, `Chip_Barcode`, and `Chip_Address`.
`varSort`	A vector of phenotype variable names to be used to sort the samples of `es`.
`timeFormat`	A vector of time format for the possible time variables in `varSort`. The length of `timeFormat` should be the same as that of `varSort`. For non-time variable, the corresponding time format should be set to be equal to `NA`.
`...`	Arguments to be passed to `plot`.

Value

no return value.

Author(s)

Examples

    # generate simulated data set from conditional normal distribution
    set.seed(1234567)
    esQC.sim = genSimData.BayesNormal(nCpGs = 10, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 

    print(esQC.sim)

    fDat = fData(esQC.sim)
    esQC.sim$Hybridization_Name = sampleNames(esQC.sim)
    fDat$Reporter_Group_Name = c( rep("biotin", 5),
      rep("housekeeping", 5))
    fData(esQC.sim)=fDat

    # plot trajectories of the QC probes
    plotQCCurves(
      esQC = esQC.sim, 
      probes = c("biotin", "housekeeping"), 
      labelVariable = "subjID",
      hybName = "Hybridization_Name",
      reporterGroupName = "Reporter_Group_Name",
      requireLog2 = FALSE, 
      plotOutPutFlag = FALSE, 
      sortFlag = FALSE)

# generate simulated data set from conditional normal distribution
    set.seed(1234567)
    esQC.sim = genSimData.BayesNormal(nCpGs = 10, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 

    print(esQC.sim)

    fDat = fData(esQC.sim)
    esQC.sim$Hybridization_Name = sampleNames(esQC.sim)
    fDat$Reporter_Group_Name = c( rep("biotin", 5),
      rep("housekeeping", 5))
    fData(esQC.sim)=fDat

    # plot trajectories of the QC probes
    plotQCCurves(
      esQC = esQC.sim, 
      probes = c("biotin", "housekeeping"), 
      labelVariable = "subjID",
      hybName = "Hybridization_Name",
      reporterGroupName = "Reporter_Group_Name",
      requireLog2 = FALSE, 
      plotOutPutFlag = FALSE, 
      sortFlag = FALSE)

Plot trajectories of the ratio of 95th percentile to 5th percentile of sample probe profiles across arrays

Description

Plot trajectories of the ratio of 95th percentile to 5th percentile of sample probe profiles across arrays.

Usage

plotSamplep95p05(
    es, 
    labelVariable = "subjID", 
     hybName = "Hybridization_Name",
    requireLog2 = FALSE, 
    projectName = "test", 
    plotOutPutFlag = FALSE, 
    cex = 1, 
    ylim = NULL, 
    xlab = "", 
    ylab = "", 
    lwd = 1.5, 
    mar = c(10, 4, 4, 2) + 0.1, 
    las = 2, 
    cex.axis=1.5,
    title = "Trajectory of p95/p05",
    cex.legend = 1.5,
    cex.lab = 1.5,
    legendPosition = "topright",
    cut1 = 10,
    cut2 = 6,
    sortFlag = TRUE,
    varSort = c("Batch_Run_Date", "Chip_Barcode", "Chip_Address"), 
    timeFormat = c("%m/%d/%Y", NA, NA),
    verbose = FALSE,
    ...)
plotSamplep95p05(
    es, 
    labelVariable = "subjID", 
     hybName = "Hybridization_Name",
    requireLog2 = FALSE, 
    projectName = "test", 
    plotOutPutFlag = FALSE, 
    cex = 1, 
    ylim = NULL, 
    xlab = "", 
    ylab = "", 
    lwd = 1.5, 
    mar = c(10, 4, 4, 2) + 0.1, 
    las = 2, 
    cex.axis=1.5,
    title = "Trajectory of p95/p05",
    cex.legend = 1.5,
    cex.lab = 1.5,
    legendPosition = "topright",
    cut1 = 10,
    cut2 = 6,
    sortFlag = TRUE,
    varSort = c("Batch_Run_Date", "Chip_Barcode", "Chip_Address"), 
    timeFormat = c("%m/%d/%Y", NA, NA),
    verbose = FALSE,
    ...)

Arguments

`es`	ExpressionSet object of Sample probe profiles.
`labelVariable`	A character string. The name of a phenotype data variable use to label the arrays in the boxplots. By default, `labelVariable = "subjID"` which is equivalent to `labelVariable = "Hybridization_Name"`.
`hybName`	character string. indicating the phenotype variable `Hybridization_Name`.
`requireLog2`	logical. `requiredLog2=TRUE` indicates probe expression levels will be log2 transformed. Otherwise, no transformation will be performed.
`projectName`	A character string. Name of the project. The plots will be saved as pdf format files, the names of which have the format `projectName_probeName_traj_plot.pdf`.
`plotOutPutFlag`	logical. `plotOutPutFlag=TRUE` indicates the plots will be output to pdf format files. Otherwise, the plots will not be output to external files.
`cex`	numerical value giving the amount by which plotting text and symbols should be magnified relative to the default. see `par`.
`ylim`	Range of y axis.
`xlab`	Label of x axis.
`ylab`	Label of y axis.
`lwd`	The line width, a _positive_ number, defaulting to '1'. see `par`.
`mar`	A numerical vector of the form 'c(bottom, left, top, right)' which gives the number of lines of margin to be specified on the four sides of the plot. The default is 'c(5, 4, 4, 2) + 0.1'. see `par`.
`las`	'las' numeric in 0,1,2,3; the style of axis labels. 0 - always parallel to the axis, 1 - always horizontal, 2 - always perpendicular to the axis, or 3 - always vertical. see `par`.
`cex.axis`	The magnification to be used for axis annotation relative to the current setting of cex. see `par`.
`title`	Figure title.
`cex.legend`	Font size of legend text.
`cex.lab`	The magnification to be used for x and y labels relative to the current setting of cex.
`legendPosition`	Position of legend. Possible values are “bottomright”, “bottom”, “bottomleft”, “left”, “topleft”, “top”, “topright”, “right” and “center”.
`cut1`	second horiztonal line setting the cutoff for the ratio `p95/p05`. A ratio above this line indicates the corresponding array is good.
`cut2`	second horiztonal line setting the cutoff for the ratio `p95/p05`. A ratio below this line indicates the corresponding array is bad.
`sortFlag`	logical. Indicates if arrays need to be sorted according to `Batch_Run_Date`, `Chip_Barcode`, and `Chip_Address`.
`varSort`	A vector of phenotype variable names to be used to sort the samples of `es`.
`timeFormat`	A vector of time format for the possible time variables in `varSort`. The length of `timeFormat` should be the same as that of `varSort`. For non-time variable, the corresponding time format should be set to be equal to `NA`. The details of the time format for time variable can be found in the R function `strptime`.
`verbose`	logical. Determine if intermediate output need to be suppressed. By default `verbose=FALSE`, intermediate output will not be printed.
`...`	Arguments to be passed to `plot`.

Details

The trajectory of the ratio of 95 to 5

Value

A list of 2 elements. The first element is the 2 x n matrix, where n is the number of arrays. The first row of the matrix is the 5-th percentile and the second row of the matrix is the 95-th percentile.

The second element is the ratio of the 95-th percentile to the 5-th percentile.

Author(s)

Examples

    # generate simulated data set from conditional normal distribution
    set.seed(1234567)
    es.sim = genSimData.BayesNormal(nCpGs = 100, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)

  es.sim$Batch_Run_Date = 1:ncol(es.sim)
  es.sim$Chip_Barcode = 1:ncol(es.sim)
  es.sim$Chip_Address = 1:ncol(es.sim)
  

 plotSamplep95p05(
  es = es.sim, 
  labelVariable = "subjID", 
  hybName = "memSubj",
  requireLog2 = FALSE, 
  projectName = "test", 
  plotOutPutFlag = FALSE, 
  title = "Trajectory of p95/p05",
  cex.legend = 0.5,
  legendPosition = "topright",
  sortFlag = TRUE,
  varSort = c("Batch_Run_Date", "Chip_Barcode", "Chip_Address"), 
  timeFormat = c("%m/%d/%Y", NA, NA),
  verbose = FALSE)

# generate simulated data set from conditional normal distribution
    set.seed(1234567)
    es.sim = genSimData.BayesNormal(nCpGs = 100, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)

  es.sim$Batch_Run_Date = 1:ncol(es.sim)
  es.sim$Chip_Barcode = 1:ncol(es.sim)
  es.sim$Chip_Address = 1:ncol(es.sim)
  

 plotSamplep95p05(
  es = es.sim, 
  labelVariable = "subjID", 
  hybName = "memSubj",
  requireLog2 = FALSE, 
  projectName = "test", 
  plotOutPutFlag = FALSE, 
  title = "Trajectory of p95/p05",
  cex.legend = 0.5,
  legendPosition = "topright",
  sortFlag = TRUE,
  varSort = c("Batch_Run_Date", "Chip_Barcode", "Chip_Address"), 
  timeFormat = c("%m/%d/%Y", NA, NA),
  verbose = FALSE)

Plot trajectories of quantiles across arrays

Description

Plot trajectories of quantiles across arrays.

Usage

quantilePlot(
    dat, 
    fileName, 
    probs = c(0, 0.05, 0.25, 0.5, 0.75, 0.95, 1), 
    plotOutPutFlag = FALSE, 
    requireLog2 = FALSE, 
    sortFlag = TRUE,
    cex = 1, 
    ylim = NULL, 
    xlab = "", 
    ylab = "intensity", 
    lwd = 3, 
    main = "Trajectory plot of quantiles", 
    mar = c(15, 4, 4, 2) + 0.1, 
    las = 2, 
    cex.axis = 1)
quantilePlot(
    dat, 
    fileName, 
    probs = c(0, 0.05, 0.25, 0.5, 0.75, 0.95, 1), 
    plotOutPutFlag = FALSE, 
    requireLog2 = FALSE, 
    sortFlag = TRUE,
    cex = 1, 
    ylim = NULL, 
    xlab = "", 
    ylab = "intensity", 
    lwd = 3, 
    main = "Trajectory plot of quantiles", 
    mar = c(15, 4, 4, 2) + 0.1, 
    las = 2, 
    cex.axis = 1)

Arguments

`dat`	Expression data. Rows are gene probes; columns are arrays.
`fileName`	File name of output figure.
`probs`	quantiles (any real values between the interval $[0, 1]$ ).
`plotOutPutFlag`	logical. `plotOutPutFlag=TRUE` indicates the plots will be output to pdf format files. Otherwise, the plots will not be output to external files.
`requireLog2`	logical. `requiredLog2=TRUE` indicates probe expression levels will be log2 transformed. Otherwise, no transformation will be performed.
`sortFlag`	logical. `sortFlag=TRUE` indicates arrays will be sorted by the ascending order of MAD (median absolute deviation)
`cex`	numerical value giving the amount by which plotting text and symbols should be magnified relative to the default. see `par`.
`ylim`	Range of y axis.
`xlab`	Label of x axis.
`ylab`	Label of y axis.
`lwd`	The line width, a _positive_ number, defaulting to '1'. see `par`.
`main`	Charater string. main title of the plot.
`mar`	A numerical vector of the form 'c(bottom, left, top, right)' which gives the number of lines of margin to be specified on the four sides of the plot. The default is 'c(5, 4, 4, 2) + 0.1'. see `par`.
`las`	'las' numeric in 0,1,2,3; the style of axis labels. 0 - always parallel to the axis, 1 - always horizontal, 2 - always perpendicular to the axis, or 3 - always vertical. see `par`.
`cex.axis`	The magnification to be used for axis annotation relative to the current setting of cex. see `par`.

Value

The quantile matrix with row quantiles and column array.

Author(s)

Examples

    # generate simulated data set from conditional normal distribution
    set.seed(1234567)
    es.sim = genSimData.BayesNormal(nCpGs = 100, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)


   png(file="qplot.png")

     quantilePlot(
       dat = exprs(es.sim), 
       probs = c(0, 0.05, 0.25, 0.5, 0.75, 0.95, 1), 
       plotOutPutFlag = FALSE, 
       requireLog2 = FALSE, 
       sortFlag = TRUE)

   dev.off()
  
# generate simulated data set from conditional normal distribution
    set.seed(1234567)
    es.sim = genSimData.BayesNormal(nCpGs = 100, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)


   png(file="qplot.png")

     quantilePlot(
       dat = exprs(es.sim), 
       probs = c(0, 0.05, 0.25, 0.5, 0.75, 0.95, 1), 
       plotOutPutFlag = FALSE, 
       requireLog2 = FALSE, 
       sortFlag = TRUE)

   dev.off()

Draw heatmap of square of correlations among arrays

Description

Draw heatmap of square of correlations among arrays.

Usage

R2PlotFunc(
    es, 
    hybName = "Hybridization_Name",
    arrayType = c("all", "replicates", "GC"), 
    GCid = c("128115", "Hela", "Brain"),
    probs = seq(0, 1, 0.25), 
    col = gplots::greenred(75), 
    labelVariable = "subjID", 
    outFileName = "test_R2_raw.pdf", 
    title = "Raw Data R^2 Plot", 
    requireLog2 = FALSE, 
    plotOutPutFlag = FALSE, 
    las = 2, 
    keysize = 1, 
    margins = c(10, 10), 
    sortFlag = TRUE,
    varSort=c("Batch_Run_Date", "Chip_Barcode", "Chip_Address"), 
    timeFormat=c("%m/%d/%Y", NA, NA),
    ...)
R2PlotFunc(
    es, 
    hybName = "Hybridization_Name",
    arrayType = c("all", "replicates", "GC"), 
    GCid = c("128115", "Hela", "Brain"),
    probs = seq(0, 1, 0.25), 
    col = gplots::greenred(75), 
    labelVariable = "subjID", 
    outFileName = "test_R2_raw.pdf", 
    title = "Raw Data R^2 Plot", 
    requireLog2 = FALSE, 
    plotOutPutFlag = FALSE, 
    las = 2, 
    keysize = 1, 
    margins = c(10, 10), 
    sortFlag = TRUE,
    varSort=c("Batch_Run_Date", "Chip_Barcode", "Chip_Address"), 
    timeFormat=c("%m/%d/%Y", NA, NA),
    ...)

Arguments

`es`	ExpressionSet object of QC probe profiles.
`hybName`	character string. indicating the phenotype variable `Hybridization_Name`.
`arrayType`	A character string indicating if the correlations are calculated based on all arrays, arrays with replicates, or genetic control arrays.
`GCid`	A vector of character string. symbols for genetic control samples. The symbols can be more than one.
`probs`	A vector of probabilities specify the quantiles of correlations to be output.
`col`	colors used for the image. see the function `heatmap.2` in R package `gplots`.
`labelVariable`	A character string indicating how to label the arrays.
`outFileName`	A character string. The name of output file.
`title`	Title of the plot.
`requireLog2`	logical. `requiredLog2=TRUE` indicates probe expression levels will be log2 transformed. Otherwise, no transformation will be performed.
`plotOutPutFlag`	logical. `plotOutPutFlag=TRUE` indicates the plots will be output to pdf format files. Otherwise, the plots will not be output to external files.
`las`	'las' numeric in 0,1,2,3; the style of axis labels. 0 - always parallel to the axis, 1 - always horizontal, 2 - always perpendicular to the axis, or 3 - always vertical. see `par`.
`keysize`	numeric value indicating the size of the key. see the function `heatmap.2` in R package `gplots`.
`margins`	numeric vector of length 2 containing the margins. see the function `heatmap.2` in R package `gplots`.
`sortFlag`	logical. Indicates if arrays need to be sorted according to `Batch_Run_Date`, `Chip_Barcode`, and `Chip_Address`.
`varSort`	A vector of phenotype variable names to be used to sort the samples of `es`.
`timeFormat`	A vector of time format for the possible time variables in `varSort`. The length of `timeFormat` should be the same as that of `varSort`. For non-time variable, the corresponding time format should be set to be equal to `NA`. The details of the time format for time variable can be found in the R function `strptime`.
`...`	Arguments to be passed to `heatmap.2`.

Value

A list with 3 elments. The first element R2Mat is the matrix of squared correlation. The second element R2vec is the vector of the upper triangle of the matrix of squared correlation (diagnoal elements are excluded). The third element R2vec.within.req is the vector of within-replicate $R^2$ , that is, any element in R2vec.within.req is the squared correlation coefficient between two arrays/replicates for a subject.

Author(s)

Examples

    # generate simulated data set from conditional normal distribution
    set.seed(1234567)
    es.sim = genSimData.BayesNormal(nCpGs = 100, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)

    es.sim$Batch_Run_Date = 1:ncol(es.sim)
    es.sim$Chip_Barcode = 1:ncol(es.sim)
    es.sim$Chip_Address = 1:ncol(es.sim)
  
    # draw heatmap for the first 5 subjects
    png(file="r2plot.png")
    R2PlotFunc(
      es = es.sim[, 1:5], 
      hybName = "memSubj",
      arrayType = c("all", "replicates", "GC"), 
      GCid = c("128115", "Hela", "Brain"),
      probs = seq(0, 1, 0.25), 
      col = gplots::greenred(75), 
      labelVariable = "subjID", 
      outFileName = "test_R2_raw.pdf", 
      title = "Raw Data R^2 Plot", 
      requireLog2 = FALSE, 
      plotOutPutFlag = FALSE, 
      las = 2, 
      keysize = 1, 
      margins = c(10, 10), 
      sortFlag = TRUE,
      varSort=c("Batch_Run_Date", "Chip_Barcode", "Chip_Address"), 
      timeFormat=c("%m/%d/%Y", NA, NA))
    dev.off()
        
# generate simulated data set from conditional normal distribution
    set.seed(1234567)
    es.sim = genSimData.BayesNormal(nCpGs = 100, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)

    es.sim$Batch_Run_Date = 1:ncol(es.sim)
    es.sim$Chip_Barcode = 1:ncol(es.sim)
    es.sim$Chip_Address = 1:ncol(es.sim)
  
    # draw heatmap for the first 5 subjects
    png(file="r2plot.png")
    R2PlotFunc(
      es = es.sim[, 1:5], 
      hybName = "memSubj",
      arrayType = c("all", "replicates", "GC"), 
      GCid = c("128115", "Hela", "Brain"),
      probs = seq(0, 1, 0.25), 
      col = gplots::greenred(75), 
      labelVariable = "subjID", 
      outFileName = "test_R2_raw.pdf", 
      title = "Raw Data R^2 Plot", 
      requireLog2 = FALSE, 
      plotOutPutFlag = FALSE, 
      las = 2, 
      keysize = 1, 
      margins = c(10, 10), 
      sortFlag = TRUE,
      varSort=c("Batch_Run_Date", "Chip_Barcode", "Chip_Address"), 
      timeFormat=c("%m/%d/%Y", NA, NA))
    dev.off()

Draw scatter plots for top results in whole-genome-wide analysis

Description

Draw scatter plots for top results in whole-genome-wide analysis to test for the association of probes to a continuous-type phenotype variable.

Usage

scatterPlots(
  resFrame, 
  es, 
  col.resFrame = c("probeIDs", "stats", "pval", "p.adj"), 
  var.pheno = "bmi", 
  outcomeFlag = FALSE,
  fitLineFlag = TRUE,
  var.probe = "TargetID", 
  var.gene = "Symbol", 
  var.chr = "Chr", 
  nTop = 20, 
  myylab = "expression level", 
  datExtrFunc = exprs, 
  fileFlag = FALSE, 
  fileFormat = "ps", 
  fileName = "scatterPlots.ps")
scatterPlots(
  resFrame, 
  es, 
  col.resFrame = c("probeIDs", "stats", "pval", "p.adj"), 
  var.pheno = "bmi", 
  outcomeFlag = FALSE,
  fitLineFlag = TRUE,
  var.probe = "TargetID", 
  var.gene = "Symbol", 
  var.chr = "Chr", 
  nTop = 20, 
  myylab = "expression level", 
  datExtrFunc = exprs, 
  fileFlag = FALSE, 
  fileFormat = "ps", 
  fileName = "scatterPlots.ps")

Arguments

`resFrame`	A data frame stores testing results, which must contain columns that indicate probe id, test statistic, p-value and optionally adjusted p-value.
`es`	An `ExpressionSet` object that used to run the whole genome-wide tests.
`col.resFrame`	A vector of characters indicating column names of `resFrame` corresponding to probe id, test statistic, p-value and optionally adjusted p-value.
`var.pheno`	character. the name of continuous-type phenotype variable that is used to test the association of this variable to probes.
`outcomeFlag`	logic. indicating if `var.pheno` is the outcome variable in regression analysis.
`fitLineFlag`	logic. indicating if a fitted line $y=a+bx$ should be plotted. If `outcomeFlag=TRUE`, then $y$ is `var.pheno` and $x$ is the top probe. If `outcomeFlag=FALSE`, then $y$ is the top probe and $x$ is `var.pheno`.
`var.probe`	character. the name of feature variable indicating probe id.
`var.gene`	character. the name of feature variable indicating gene symbol.
`var.chr`	character. the name of feature variable indicating chromosome number.
`nTop`	integer. indicating how many top tests will be used to draw the scatter plot.
`myylab`	character. indicating y-axis label.
`datExtrFunc`	name of the function to extract genomic data. For an `ExpressionSet` object, you should set `datExtrFunc=exprs`; for a `MethyLumiSet` object, you should set `datExtrFunc=betas`.
`fileFlag`	logic. indicating if plot should be saved to an external figure file.
`fileFormat`	character. indicating the figure file type. Possible values are “ps”, “pdf”, or “jpeg”. All other values will produce “png” file.
`fileName`	character. indicating figure file name (file extension should be specified). For example, you set `fileFormat="pdf"`, then you can set `fileName="test.pdf"`, but not `fileName="test"`.

Value

Value 0 will be returned if no error occurs.

Author(s)

Examples

  # generate simulated data set from conditional normal distribution
  set.seed(1234567)
  es.sim = genSimData.BayesNormal(nCpGs = 100, 
    nCases = 20, nControls = 20,
    mu.n = -2, mu.c = 2,
    d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
    outlierFlag = FALSE, 
    eps = 1.0e-3, applier = lapply) 
  print(es.sim)

  # generate phenotype age
  es.sim$age = rnorm(ncol(es.sim), mean=50, sd=5)

  res.limma = lmFitWrapper(
    es = es.sim, 
    formula = ~age, 
    pos.var.interest = 1,
    pvalAdjMethod = "fdr", 
    alpha = 0.05, 
    probeID.var = "probe", 
    gene.var = "gene", 
    chr.var = "chr", 
    verbose = TRUE)

  scatterPlots(
    resFrame=res.limma$frame, 
    es=es.sim, 
    col.resFrame = c("probeIDs", "stats", "pval"), 
    var.pheno = "age", 
    outcomeFlag = FALSE,
    fitLineFlag = TRUE,
    var.probe = "probe", 
    var.gene = "gene", 
    var.chr = "chr", 
    nTop = 20, 
    myylab = "expression level", 
    datExtrFunc = exprs, 
    fileFlag = FALSE, 
    fileFormat = "ps", 
    fileName = "scatterPlots.ps")
  
# generate simulated data set from conditional normal distribution
  set.seed(1234567)
  es.sim = genSimData.BayesNormal(nCpGs = 100, 
    nCases = 20, nControls = 20,
    mu.n = -2, mu.c = 2,
    d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
    outlierFlag = FALSE, 
    eps = 1.0e-3, applier = lapply) 
  print(es.sim)

  # generate phenotype age
  es.sim$age = rnorm(ncol(es.sim), mean=50, sd=5)

  res.limma = lmFitWrapper(
    es = es.sim, 
    formula = ~age, 
    pos.var.interest = 1,
    pvalAdjMethod = "fdr", 
    alpha = 0.05, 
    probeID.var = "probe", 
    gene.var = "gene", 
    chr.var = "chr", 
    verbose = TRUE)

  scatterPlots(
    resFrame=res.limma$frame, 
    es=es.sim, 
    col.resFrame = c("probeIDs", "stats", "pval"), 
    var.pheno = "age", 
    outcomeFlag = FALSE,
    fitLineFlag = TRUE,
    var.probe = "probe", 
    var.gene = "gene", 
    var.chr = "chr", 
    nTop = 20, 
    myylab = "expression level", 
    datExtrFunc = exprs, 
    fileFlag = FALSE, 
    fileFormat = "ps", 
    fileName = "scatterPlots.ps")

Sort the order of samples for an ExpressionSet object

Description

Sort the order of samples for an ExpressionSet object.

Usage

sortExpressionSet(
    es, 
    varSort = c("Batch_Run_Date", "Chip_Barcode", "Chip_Address"), 
    timeFormat = c("%m/%d/%Y", NA, NA)
)
sortExpressionSet(
    es, 
    varSort = c("Batch_Run_Date", "Chip_Barcode", "Chip_Address"), 
    timeFormat = c("%m/%d/%Y", NA, NA)
)

Arguments

`es`	An ExpressionSet.
`varSort`	A vector of phenotype variable names to be used to sort the samples of `es`.
`timeFormat`	A vector of time format for the possible time variables in `varSort`. The length of `timeFormat` should be the same as that of `varSort`. For non-time variable, the corresponding time format should be set to be equal to `NA`. Please refer to function `strptime` of the `base` package.

Value

An ExpressionSet object with samples sorted based on the variables indicated in varSort.

Author(s)

Examples

    # generate simulated data set from conditional normal distribution
    set.seed(1234567)
    es.sim = genSimData.BayesNormal(nCpGs = 100, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)

  es.sim$Batch_Run_Date = 1:ncol(es.sim)
  es.sim$Chip_Barcode = 1:ncol(es.sim)
  es.sim$Chip_Address = 1:ncol(es.sim)
  

  es.sim2 = sortExpressionSet(
    es = es.sim, 
    varSort = c("Batch_Run_Date", "Chip_Barcode", "Chip_Address"), 
    timeFormat = c("%m/%d/%Y", NA, NA)
  )
  
# generate simulated data set from conditional normal distribution
    set.seed(1234567)
    es.sim = genSimData.BayesNormal(nCpGs = 100, 
      nCases = 20, nControls = 20,
      mu.n = -2, mu.c = 2,
      d0 = 20, s02 = 0.64, s02.c = 1.5, testPara = "var",
      outlierFlag = FALSE, 
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)

  es.sim$Batch_Run_Date = 1:ncol(es.sim)
  es.sim$Chip_Barcode = 1:ncol(es.sim)
  es.sim$Chip_Address = 1:ncol(es.sim)
  

  es.sim2 = sortExpressionSet(
    es = es.sim, 
    varSort = c("Batch_Run_Date", "Chip_Barcode", "Chip_Address"), 
    timeFormat = c("%m/%d/%Y", NA, NA)
  )