| Title: | ABSSeq: a new RNA-Seq analysis method based on modelling absolute expression differences |
|---|---|
| Description: | Inferring differential expression genes by absolute counts difference between two groups, utilizing Negative binomial distribution and moderating fold-change according to heterogeneity of dispersion across expression level. |
| Authors: | Wentao Yang |
| Maintainer: | Wentao Yang <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.61.0 |
| Built: | 2024-10-30 03:24:30 UTC |
| Source: | https://github.com/bioc/ABSSeq |
ABSDataSet object and constructors
ABSDataSet(counts, groups, normMethod = c("user", "qtotal", "total", "quartile", "geometric", "TMM"), sizeFactor = 0, paired = FALSE, minDispersion = NULL, minRates = 0.1, maxRates = 0.3, LevelstoNormFC = 100)ABSDataSet(counts, groups, normMethod = c("user", "qtotal", "total", "quartile", "geometric", "TMM"), sizeFactor = 0, paired = FALSE, minDispersion = NULL, minRates = 0.1, maxRates = 0.3, LevelstoNormFC = 100)
counts |
a matrix or table with at least two columns and one row, |
groups |
a factor with two groups, whose length should be equal with sample size |
normMethod |
method for estimating the size factors, should be one of 'user', 'qtotal', 'total', 'quartile', 'geometric' and 'TMM'. See |
sizeFactor |
size factors for 'user' method, self-defined size factors by user. |
paired |
switch for differential expression detection in paired samples. |
minDispersion |
a positive double for user-defined penalty of dispersion estimation |
minRates |
low bounder rate of baseline estimation for counts difference, default is 0.1 |
maxRates |
up bounder rate of baseline estimation for counts difference, default is 0.3. Setting minRates equal with maxRates will result in a testing on user-define rate, |
LevelstoNormFC |
maximal level of average standard deviation in fold-change normalization according to expression level, default is 100. |
The function contructs an ABSDataSet object with counts table and groups. It also checks the structure of counts and groups.The ABSDataSet is a class, used to store the input values, intermediate calculations and results of an analysis of differential expression. It also contains information for the running time of an analysis.
An ABSDataSet object.
counts <- matrix(1:4,ncol=2) groups <- factor(c("a","b")) obj <- ABSDataSet(counts, groups) obj <- ABSDataSet(counts, groups, paired=TRUE)counts <- matrix(1:4,ncol=2) groups <- factor(c("a","b")) obj <- ABSDataSet(counts, groups) obj <- ABSDataSet(counts, groups, paired=TRUE)
This function performs a default analysis by calling, in order, the functions:
normalFactors,
callParameter,
callDEs.
ABSSeq(object, adjmethod = "BH", replaceOutliers = TRUE, useaFold = FALSE, quiet = FALSE, ...)ABSSeq(object, adjmethod = "BH", replaceOutliers = TRUE, useaFold = FALSE, quiet = FALSE, ...)
object |
an |
adjmethod |
defualt is 'BH', method for p-value adjusted, see |
replaceOutliers |
default is TRUE, switch for outlier replacement. |
useaFold |
defualt is FALSE, switch for DE detection through fold-change, see |
quiet |
default is FALSE, whether to print messages at each step |
... |
parameters passed to |
The differential expression analysis models the total counts difference by a Negative binomal distribution
:
an ABSDataSet object with additional elements, which can be retrieved by results:
Amean and Bmean, mean of log2 normalized reads count for group A and B,
foldChange, shrinked (expression level and gene-specific) log2 of fold-change, B - A,
rawFC, raw log2 of fold-change, B-A (without shrinkage),
lowFC, expression level corrected log2 fold-change,
pvalue, pvalue from NB distribution model,
adj.pvalue, adjuested p-value used p.adjust method.
Wentao Yang
Wentao Yang, Philip Rosenstiel & Hinrich Schulenburg: ABSSeq: a new RNA-Seq analysis method based on modelling absolute expression differences
data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) obj <- ABSSeq(obj) res <- results(obj,c("Amean","Bmean","foldChange","pvalue","adj.pvalue")) head(res)data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) obj <- ABSSeq(obj) res <- results(obj,c("Amean","Bmean","foldChange","pvalue","adj.pvalue")) head(res)
This function performs a default analysis by calling, in order, the functions:
normalFactors,
aFoldcomplexDesign,
ABSSeqlm(object, design, condA, condB = NULL, lmodel = TRUE, preval = 0.05, qforkappa = 0, adjmethod = "BH", scale = FALSE, quiet = FALSE, ...)ABSSeqlm(object, design, condA, condB = NULL, lmodel = TRUE, preval = 0.05, qforkappa = 0, adjmethod = "BH", scale = FALSE, quiet = FALSE, ...)
object |
a |
design |
a numeric matrix for expriment, with samples and factors in rows and colnums, respectively. Design respresents the satuarated model. |
condA |
a vector of factors for DE analysis, which could be redundant, see |
condB |
a vector of factors for DE analysis, which could be redundant, default is null, if not provide, the DE analysis will switch to
assess difference across factors in condA (analysis of variance). If provide, DE analysis will focus on contrast between condB and condA (condB-condA).
See |
lmodel |
switch of fit linear model from limma-lmFit under design, default is TRUE. If TRUE, a gene-specific residual varaince will
be estimated from (satuarated model - reduced model). Satuarated model includes all factors in design matrix and reduced model includes factors in condA+condB.
if satuarated model == reduced model, the DE analysis performs pairwise comparison or one-way analysis of variance. See |
preval |
parameter for |
qforkappa |
parameter for |
adjmethod |
defualt is 'BH', method for p-value adjusted, see |
scale |
switch for scaling fold change according to common SD under log2 transformation, default is FALSE. |
quiet |
default is FALSE, whether to print messages at each step |
... |
parameters passed to lmFit in limma |
This function uses a linear model (limma-lmFit) to infer DE under complex design.
a result table with additional elements, including: basemean, log of basemean, foldChange, shrinked (expression level and gene-specific) log2 of fold-change, B - A, or (SDs under log2 for analysis of variance) pvalue, pvalue from NB distribution model, p.adj, adjuested p-value used p.adjust method. scaledlogFC, scaled logFC if scale=TRUE.
Wentao Yang
Wentao Yang, Philip Rosenstiel & Hinrich Schulenburg: ABSSeq: a new RNA-Seq analysis method based on modelling absolute expression differences
data(simuN5) groups=factor(simuN5$groups) obj <- ABSDataSet(counts=simuN5$counts) design <- model.matrix(~0+groups) res <- ABSSeqlm(obj,design,condA=c("groups0"),condB=c("groups1")) head(res)data(simuN5) groups=factor(simuN5$groups) obj <- ABSDataSet(counts=simuN5$counts) design <- model.matrix(~0+groups) res <- ABSSeqlm(obj,design,condA=c("groups0"),condB=c("groups1")) head(res)
Calculate aFold for each gene and general sd
aFoldcomplexDesign(nncounts, design, condA, condB = NULL, lmodel = TRUE, preval = 0.05, qforkappa = 0, priorgenesd, ...)aFoldcomplexDesign(nncounts, design, condA, condB = NULL, lmodel = TRUE, preval = 0.05, qforkappa = 0, priorgenesd, ...)
nncounts |
matrix for read count. |
design |
a numeric matrix for expriment, with samples and factors in rows and colnums, respectively. |
condA |
a vector of factors for DE analysis, which could be redundant. |
condB |
a vector of factors for DE analysis, which could be redundant, default is null. If not provide, the DE analysis will switch to assess difference across factors in condA (analysis of variance). If provide, DE analysis will focus on contrast between condB and condA (condB-condA). |
lmodel |
switch of fit linear model from limma-lmFit under design, default is TRUE. If TRUE, a gene-specific residual varaince will be estimated from (satuarated model - reduced model). Satuarated model includes all factors in design matrix and reduced model includes factors in condA+condB. |
preval |
pre-defined scale control for variance normalization, default is 0.05, a large value generally increases the fold-changes (decreases penalty of variances) under low expression. |
qforkappa |
quantile for estimating kappa(>=qforkappa), default is 0 (without trimming of data). Please set up a value in [0,1) if you want to trim the low expressed data. |
priorgenesd |
prior value for general SD of fold change, if provided, the estimation of general SD will be replaced by this value. |
... |
parameters passed to lmFit in limma |
shifted and calculate a set of parameters from normalized counts table
A list with log2 foldchange, general SD (gene-specific SD if lmodel is TRUE) for calculating pvalue, variance stablized counts and basemean
This function should run after normalFactors.
data(simuN5) groups=factor(simuN5$groups) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) mtx <- counts(obj,TRUE) design <- model.matrix(~0+groups) aFold <- aFoldcomplexDesign(mtx,design,condA=c("groups0"),condB=c("groups1")) hist(aFold[[1]])data(simuN5) groups=factor(simuN5$groups) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) mtx <- counts(obj,TRUE) design <- model.matrix(~0+groups) aFold <- aFoldcomplexDesign(mtx,design,condA=c("groups0"),condB=c("groups1")) hist(aFold[[1]])
Using NB distribution to calculate p-value for each gene as well as adjust p-value
callDEs(object, adjmethod = "BH", useaFold = FALSE)callDEs(object, adjmethod = "BH", useaFold = FALSE)
object |
an |
adjmethod |
the method for adjusting p-value, default is 'BH'. For details, see |
useaFold |
switch for DE detection through fold-change, which will use a normal distribution (N(0,sd)) to test the significance of log2 fold-change. The sd is estimated through a quantile function of gamma distribution at |
This function firstly calls p-value used pnbinom to call pvalue based on sum of counts difference between two
groups or used pnorm to call pvalue via log2 fold-change, then adjusts the pvalues via p.adjust method. In addition, it also shrink the log2 fold-change towards a common dispersion
after pvalue calling.
an ABSDataSet object with additional elements: shrinked log2 fold-change, pvalue and adjusted p-value,
denoted by foldChange pvalue and adj-pvalue, respectively. Use the results method to get access it.
this function should run after callParameter
data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) obj <- normalFactors(obj) obj <- callParameter(obj) obj <- callDEs(obj) head(results(obj))data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) obj <- normalFactors(obj) obj <- callParameter(obj) obj <- callDEs(obj) head(results(obj))
Calculate parameters for each gene (the moderating basemean, dispersions, moderated fold-change and general sd)
callParameter(object, replaceOutliers = TRUE, ...)callParameter(object, replaceOutliers = TRUE, ...)
object |
a |
replaceOutliers |
switch for outlier replacement, default is TRUE. |
... |
parameters past to |
shifted and calculate a set of parameters from normalized counts table before callDEs
A ABSDataSet object with absolute differences, basemean, mean of each group, variance,
log2 of foldchange, named as 'absD', 'baseMean', 'Amean', 'Bmean',
'Variance' and 'foldChange', respectively. Use the results to get access it and plotDifftoBase to plot it.
This function should run after normalFactors or providing size factors.
data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) obj <- normalFactors(obj) obj <- callParameter(obj) head(results(obj,c("foldChange","absD","baseMean"))) plotDifftoBase(obj)data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) obj <- normalFactors(obj) obj <- callParameter(obj) head(results(obj,c("foldChange","absD","baseMean"))) plotDifftoBase(obj)
Calculate parameters for each gene (the moderating basemean and dispersions), without replicates
callParameterwithoutReplicates(object)callParameterwithoutReplicates(object)
object |
a |
buliding a pseudo group to esitimate parameter by mean difference. shifted and calculate a set of parameters from normalized counts table before callDEs
A ABSDataSet object with absolute differences, basemean, mean of each group, variance,
log2 of foldchange, named as 'absD', 'baseMean', 'Amean', 'Bmean',
'Variance' and 'foldChange', respectively. Use the results to get access it
This function should run after normalFactors or providing size factors. This function firstly constructs an expression level depended fold-change cutoffs
and then separate the data into two groups. The group with fold-change less than cutoffs is used to training the dispersion. However, the cutoff might be too small when applied
on data set without or with less DEs. To avoid it, we set a prior value (0.5) to it.
data(simuN5) obj <- ABSDataSet(counts=(simuN5$counts)[,c(1,2)], groups=factor(c(1,2))) obj <- normalFactors(obj) obj <- callParameterwithoutReplicates(obj) obj <- callDEs(obj) head(results(obj))data(simuN5) obj <- ABSDataSet(counts=(simuN5$counts)[,c(1,2)], groups=factor(c(1,2))) obj <- normalFactors(obj) obj <- callParameterwithoutReplicates(obj) obj <- callDEs(obj) head(results(obj))
Accessors for the 'counts' slot of a ABSDataSet object, return a matrix
## S4 method for signature 'ABSDataSet' counts(object,norm=FALSE) ## S4 replacement method for signature 'ABSDataSet,matrix' counts(object)<-value## S4 method for signature 'ABSDataSet' counts(object,norm=FALSE) ## S4 replacement method for signature 'ABSDataSet,matrix' counts(object)<-value
object |
a |
norm |
logical indicating whether or not to normalize the counts before returning |
value |
an numeric matrix |
The counts slot holds the count data as a matrix of non-negative integer count values, rows and columns for genes and samples, respectively.
data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) head(counts(obj)) counts(obj) <- matrix(1:50,nrow=5,ncol=10) head(counts(obj))data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) head(counts(obj)) counts(obj) <- matrix(1:50,nrow=5,ncol=10) head(counts(obj))
This function is borrowed from DESeq.
estimateSizeFactorsForMatrix(counts, locfunc = median)estimateSizeFactorsForMatrix(counts, locfunc = median)
counts |
a matrix or data frame of counts, i.e., non-negative integer values |
locfunc |
a function to compute a location for a sample. By default, the median is used. |
Given a matrix or data frame of count data, this function estimates the size factors as follows: Each column is divided by the geometric means of the rows. The median (or, if requested, another location estimator) of these ratios (skipping the genes with a geometric mean of zero) is used as the size factor for this column. Typically, you will not call this function directly.
a vector with the estimates size factors, one element per column
Simon Anders
Simon Anders, Wolfgang Huber: Differential expression analysis for sequence count data. Genome Biology 11 (2010) R106, http://dx.doi.org/10.1186/gb-2010-11-10-r106
data(simuN5) dat <- simuN5 estimateSizeFactorsForMatrix(dat$counts)data(simuN5) dat <- simuN5 estimateSizeFactorsForMatrix(dat$counts)
Accessors for the 'excounts' slot of a ABSDataSet object, return a matrix
## S4 replacement method for signature 'ABSDataSet,matrix' excounts(object)<-value## S4 replacement method for signature 'ABSDataSet,matrix' excounts(object)<-value
object |
a |
value |
an numeric matrix |
The excounts slot holds the nomarlized (trimmed or not) count data as a matrix of non-negative integer count values, rows and columns for genes and samples, respectively.
ABSDataSet, ReplaceOutliersByMAD
data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) obj <- normalFactors(obj) obj <- ReplaceOutliersByMAD(obj) head(excounts(obj))data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) obj <- normalFactors(obj) obj <- ReplaceOutliersByMAD(obj) head(excounts(obj))
Calculate aFold for each gene and general sd
genAFold(nncounts, cond, preval = 0.05, qforkappa = 0, pair = FALSE, priorgenesd)genAFold(nncounts, cond, preval = 0.05, qforkappa = 0, pair = FALSE, priorgenesd)
nncounts |
matrix for read count. |
cond |
factor for conditions. If provide only one condition, fold-change estimation will be suppressed. |
preval |
pre-defined scale control for variance normalization, default is 0.05, a large value generally increases the fold-changes (decreases penalty of variances) under low expression. |
qforkappa |
quantile for estimating kappa(>=qforkappa), default is 0 (without trimming of data). Please set up a value in [0,1) if you want to trim the low expressed data. |
pair |
switch for paired samples, default is false |
priorgenesd |
prior value for general SD of fold change, if provided, the estimation of general SD will be replaced by this value. |
shifted and calculate a set of parameters from normalized counts table before callDEs
A list with log2 foldchange, general SD for calculating pvalue, variance stabilized counts and expression level adjusted counts (used for PCA analysis)
This function should run after normalFactors.
data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) mtx <- counts(obj,TRUE) aFold <- genAFold(mtx,factor(simuN5$groups)) hist(aFold[[1]])data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) mtx <- counts(obj,TRUE) aFold <- genAFold(mtx,factor(simuN5$groups)) hist(aFold[[1]])
Accessor functions for the 'groups' information in a ABSDataSet object.
## S4 method for signature 'ABSDataSet' groups(object) ## S4 replacement method for signature 'ABSDataSet,factor' groups(object)<-value## S4 method for signature 'ABSDataSet' groups(object) ## S4 replacement method for signature 'ABSDataSet,factor' groups(object)<-value
object |
an |
value |
a |
The 'groups' is a factor object, contains the experiment design for differential expression analysis. Its length should be equal with the sample size.
data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) groups(obj) groups(obj) <- factor(rep(c("A","B"),c(5,5))) groups(obj)data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) groups(obj) groups(obj) <- factor(rep(c("A","B"),c(5,5))) groups(obj)
Accessor functions for the 'LevelstoNormFC' slot of a ABSDataSet object.
## S4 method for signature 'ABSDataSet' LevelstoNormFC(object) ## S4 replacement method for signature 'ABSDataSet,numeric' LevelstoNormFC(object)<-value## S4 method for signature 'ABSDataSet' LevelstoNormFC(object) ## S4 replacement method for signature 'ABSDataSet,numeric' LevelstoNormFC(object)<-value
object |
an |
value |
a positive numeric object |
The 'LevelstoNormFC' is maximal level of average standard deviation in fold-change normalization according to expression level.
data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) LevelstoNormFC(obj) LevelstoNormFC(obj) <- 200 LevelstoNormFC(obj)data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) LevelstoNormFC(obj) LevelstoNormFC(obj) <- 200 LevelstoNormFC(obj)
Accessor functions for the 'maxRates' slot of a ABSDataSet object.
## S4 method for signature 'ABSDataSet' maxRates(object) ## S4 replacement method for signature 'ABSDataSet,numeric' maxRates(object)<-value## S4 method for signature 'ABSDataSet' maxRates(object) ## S4 replacement method for signature 'ABSDataSet,numeric' maxRates(object)<-value
object |
an |
value |
a positive numeric object |
The 'maxRates' is the upper bound of rate for baseline of counts difference esitimation.
data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) maxRates(obj) maxRates(obj) <- 0.4 maxRates(obj)data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) maxRates(obj) maxRates(obj) <- 0.4 maxRates(obj)
Accessor functions for the 'minDispersion' slot of a ABSDataSet object.
## S4 method for signature 'ABSDataSet' minimalDispersion(object) ## S4 replacement method for signature 'ABSDataSet,numeric' minimalDispersion(object)<-value## S4 method for signature 'ABSDataSet' minimalDispersion(object) ## S4 replacement method for signature 'ABSDataSet,numeric' minimalDispersion(object)<-value
object |
an |
value |
a positive numeric object |
The 'minimalDispersion' is the penalty of dispersion estimation. User can set the penalty of dispersion by this function
data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) minimalDispersion(obj) minimalDispersion(obj) <- 0.2 minimalDispersion(obj)data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) minimalDispersion(obj) minimalDispersion(obj) <- 0.2 minimalDispersion(obj)
Accessor functions for the 'minRates' slot of a ABSDataSet object.
## S4 method for signature 'ABSDataSet' minRates(object) ## S4 replacement method for signature 'ABSDataSet,numeric' minRates(object)<-value## S4 method for signature 'ABSDataSet' minRates(object) ## S4 replacement method for signature 'ABSDataSet,numeric' minRates(object)<-value
object |
an |
value |
a positive numeric object |
The 'minRates' is the lower bound of rate for baseline of counts difference esitimation.
data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) minRates(obj) minRates(obj) <- 0.3 minRates(obj)data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) minRates(obj) minRates(obj) <- 0.3 minRates(obj)
Function for esitmating size factors
normalFactors(object)normalFactors(object)
object |
a ABSSeq object with element of 'counts' and 'normMethod', see the constructor functions
|
Given a matrix of count data, this function esitmates the size factors by selected method. It aslo provides four different methods for normalizing according to user-defined size factors, total reads, up quantile (75
a ABSDataSet object with the estimates size factors, one element per column. Use the sFactors
to show it.
data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) obj <- normalFactors(obj) sFactors(obj)data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) obj <- normalFactors(obj) sFactors(obj)
Accessor functions for the 'normMethod' information in a ABSDataSet object.
## S4 method for signature 'ABSDataSet' normMethod(object) ## S4 replacement method for signature 'ABSDataSet,character' normMethod(object)<-value## S4 method for signature 'ABSDataSet' normMethod(object) ## S4 replacement method for signature 'ABSDataSet,character' normMethod(object)<-value
object |
an |
value |
a character object, should be one of 'user', 'qtoatl', 'total', 'quartile' and 'geometric'.
See |
The 'normMethod' is the method for calculating the size factors. Currently, Four methods: 'user', 'qtoatl', 'total', 'quartile' and 'DESeq' are available.
data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) normMethod(obj) normMethod(obj) <- "geometric" normMethod(obj)data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) normMethod(obj) normMethod(obj) <- "geometric" normMethod(obj)
Accessors for the 'paired' slot of a ABSDataSet object, return a logical value
## S4 method for signature 'ABSDataSet' paired(object) ## S4 replacement method for signature 'ABSDataSet,logical' paired(object)<-value## S4 method for signature 'ABSDataSet' paired(object) ## S4 replacement method for signature 'ABSDataSet,logical' paired(object)<-value
object |
a |
value |
value a boolean object, should be either TRUE or FALSE. |
The 'paired' is the switch for differential expression detection among paired samples, with a boolean value: TRUE or FALSE (default). When "paired" is TRUE, the replicates in each group should be equal.
data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) paired(obj) paired(obj) <- TRUE paired(obj)data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) paired(obj) paired(obj) <- TRUE paired(obj)
Plot absolute differencs against expression levels
plotDifftoBase(object, foldname = "foldChange", adj.pcut = 0.05, cols = c("black", "red"), pch = 16, xlab = "log2 of Expression level", ylab = "log2 fold-change", ...)plotDifftoBase(object, foldname = "foldChange", adj.pcut = 0.05, cols = c("black", "red"), pch = 16, xlab = "log2 of Expression level", ylab = "log2 fold-change", ...)
object |
a ABSDataSet |
foldname |
indicates kind of fold-change in plotting, default is 'foldChange', see |
adj.pcut |
cutoff for differential expressed genes, marked by different color, default is 0.05 |
cols |
the colors to mark the non-DE and DE genes, defualt is black and red, respectively |
pch |
pch, default is 16 |
xlab |
xlab, default is 'log2 of Expression level' |
ylab |
ylab, default is 'log2 fold-change' |
... |
further arguments to |
Plot absolute differencs against expression levels and mark the gene with a color at a given cutoff of fold-change
data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) obj <- ABSSeq(obj) plotDifftoBase(obj)data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) obj <- ABSSeq(obj) plotDifftoBase(obj)
Function of qtotal for esitmating size factors
qtotalNormalized(ma, qper = 0.95, qst = 0.1, qend = 0.95, qstep = 0.01, qbound = 0.05, mcut = 4, qcl = 1.5)qtotalNormalized(ma, qper = 0.95, qst = 0.1, qend = 0.95, qstep = 0.01, qbound = 0.05, mcut = 4, qcl = 1.5)
ma |
a count matrix |
qper |
quantile for assessing dispersion of data, default is 0.95, which serves to avoid outliers, should in (0,1] |
qst |
start of quantile for estimating cv ratio, should be in [0,1], default is 0.1 |
qend |
end of quantile for estimating cv ratio, should be in [qbound,1-qbound], default is .95 |
qstep |
step of quantile for estimating cv ratio (sliding window), should be in (0,1], default is 0.01 |
qbound |
window size for estimating cv and shifted size factor, default is 0.05, a smaller window size is suitable if number of genes is large |
mcut |
cutoff of mean from sliding window to avoid abnormal cv, should >=0, default is 4 |
qcl |
scale for outlier detection, should >=0, default is 1.5 |
Given a matrix of count data, this function esitmates the size factors by qtotal method, which is based on assessing DE (CV) and ranking. The CV is estimated via sliding window.
a vector with the estimates size factors, one element per column
data(simuN5) counts <- simuN5$counts qtotalNormalized(counts)data(simuN5) counts <- simuN5$counts qtotalNormalized(counts)
Function for replacing the outliers by MAD
ReplaceOutliersByMAD(object, replaceOutlier = TRUE, cutoff = 2, baseMean = 100, limitMad = 0.707, spriors = 2, Caseon = TRUE, ...)ReplaceOutliersByMAD(object, replaceOutlier = TRUE, cutoff = 2, baseMean = 100, limitMad = 0.707, spriors = 2, Caseon = TRUE, ...)
object |
a ABSSeq object with element of 'counts' and 'normMethod', see the constructor functions
|
replaceOutlier |
switch for replacing, default is TRUE. |
cutoff |
cutoff of moderating MAD for outliers, default is 2 |
baseMean |
parameter for limiting the trimming at low expression level by baseMean/(sample size), default is 100. |
limitMad |
the minimal prior for moderating MAD, default is set to 0.707, which is usually the highest standard deviation at expression level of 1 |
spriors |
prior weight size for prior MAD, default is 2 |
Caseon |
switch for dealing with outlier trimming at sample size of 2 |
... |
reserved parameters |
Given a matrix of count data, this function replacing the outliers by MAD. Noticely, this function also provides part of parameters for DEs calling.
It is called by callParameter
a ABSDataSet object with normalized counts after trimming (replaceOutlier=TRUE) or not (replaceOutlier=FALSE). Use the excounts
to show it. Use results with name 'trimmed' to view the trimming status.
data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) obj <- normalFactors(obj) obj <- ReplaceOutliersByMAD(obj) head(excounts(obj)) head(results(obj,c("trimmed")))data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) obj <- normalFactors(obj) obj <- ReplaceOutliersByMAD(obj) head(excounts(obj)) head(results(obj,c("trimmed")))
Accessor functions for the result from a ABSDataSet by given names
## S4 method for signature 'ABSDataSet' results(object, cnames = c("Amean", "Bmean", "baseMean", "absD", "Variance", "rawFC", "lowFC", "foldChange", "pvalue", "adj.pvalue", "trimmed"))## S4 method for signature 'ABSDataSet' results(object, cnames = c("Amean", "Bmean", "baseMean", "absD", "Variance", "rawFC", "lowFC", "foldChange", "pvalue", "adj.pvalue", "trimmed"))
object |
a ABSDataSet |
cnames |
a vecotr of names for output, which are among:
'Amean','Bmean', log2 of mean counts for group A and B,
"baseMean', estimated mean for absolute counts difference (absD), used for mu in |
This function returns the result of ABSSeq as a table or a vector depended on
the given names, see ABSSeq
a table according to canmes.
data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) obj <- normalFactors(obj) obj <- callParameter(obj) obj <- callDEs(obj) head(results(obj))data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) obj <- normalFactors(obj) obj <- callParameter(obj) obj <- callDEs(obj) head(results(obj))
Accessor functions for the 'sizeFactor' slot of a ABSDataSet object.
## S4 method for signature 'ABSDataSet' sFactors(object) ## S4 replacement method for signature 'ABSDataSet,numeric' sFactors(object)<-value## S4 method for signature 'ABSDataSet' sFactors(object) ## S4 replacement method for signature 'ABSDataSet,numeric' sFactors(object)<-value
object |
an |
value |
a numeric object, one for each sample |
The sizeFactors vector assigns to each sample a value, used to normalize the counts in each sample according to selected normMethod.
data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) obj <- normalFactors(obj) sFactors(obj) sFactors(obj) <- runif(10,1,2) sFactors(obj)data(simuN5) obj <- ABSDataSet(counts=simuN5$counts, groups=factor(simuN5$groups)) obj <- normalFactors(obj) sFactors(obj) sFactors(obj) <- runif(10,1,2) sFactors(obj)
Simulated study with random outliers, include five samples for two groups. It contains counts table, groups and defined differential expression genes.
data(simuN5)data(simuN5)
The format is: List of 3
$ counts: integer, reads count matrix
$ groups: two groups
$ DEs : differential expression genes
Multiple each gene with a value from 5-10 by chance at pvalue of 0.05.
http://bcf.isb-sib.ch/data/compcodeR/
Soneson C, Delorenzi M: A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinformatics 2013, 14(1):91.
data(simuN5)data(simuN5)