| Title: | Gene Expression Normalization Workflow |
|---|---|
| Description: | An extensive, customized expression normalization workflow incorporating Supervised Normalization of Microarryas(SNM), Surrogate Variable Analysis(SVA) and Principal Variance Component Analysis to identify batch effects and remove them from the expression data to enhance the ability to detect the underlying biological signals. |
| Authors: | Karthikeyan Murugesan [aut, cre], Greg Gibson [sad, ths] |
| Maintainer: | Karthikeyan Murugesan <[email protected]> |
| License: | GPL (>=3) |
| Version: | 1.38.0 |
| Built: | 2026-10-04 17:14:56 UTC |
| Source: | https://github.com/bioc/ExpressionNormalizationWorkflow |
An extensive, customized expression normalization workflow incorporating Supervised Normalization of Microarryas(SNM), Surrogate Variable Analysis(SVA) and Principal Variance Component Analysis to identify batch effects and remove them from the expression data to enhance the ability to detect the underlying biological signals.
| Package: | ExpressionNormalizationWorkflow |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2014-04-30 |
| License: | GPL (>=3) |
Karthikeyan Murugesan<karthikeyanm60 at yahoo.com>
~~ Literature or other references for background information ~~
~~ Optional links to other man pages, e.g. ~~
~~ <pkg> ~~
#~~ simple examples of the most important functions ~~#~~ simple examples of the most important functions ~~
This function calculates the quantiles for the list of input values and assigns a quantile group to every data point
conTocat(covariates, var_names)conTocat(covariates, var_names)
covariates |
samples x covariates matrix containing the continous covariates which need to be discretized |
var_names |
a list of the continuous variables that need to be discretized |
the function returns a modified samples x covariates matrix with the categorized variables appended to the end as another column
Karthikeyan Murugesan<karthikeyanm60 at yahoo.com> #@examples conTocat(covariates, var_names)
An expression set object can store the expression values and the meta data associated like the covariate information, annotations and experiment protocol data
expSetobj(expression, covariates, cov_desc = NULL)expSetobj(expression, covariates, cov_desc = NULL)
expression |
the gene expression value martix with features (probes) as rows and samples as columns |
covariates |
the phenotype (covariate) matrix with samples as rows and the covariates as columns |
cov_desc |
a list of descriptors for each of the covariates |
exp_datObj ExpressionSet Datatype Object
The return values of the function can be accessed as variable$parameter
Karthikeyan Murugesan <karthikeyanm60 at yahoo.com>
Gentleman RC, Carey VJ, Bates DM and others (2004). “Bioconductor: Open software development for computational biology and bioinformatics.” Genome Biology, 5, pp. R80. #@examples expSetobj(expression, covariates)
PVCA estimates the variance in the expression dataset due to each of the given covariates and attributes the remaining fraction to residual. It efficiently combines principal component analysis
pvcAnaly(exp_datObj, pct_threshold, batch_factors)pvcAnaly(exp_datObj, pct_threshold, batch_factors)
exp_datObj |
an expression set class object |
pct_threshold |
PVCA Threshold Value (a value between 0 and 1) is the percentile value of the minimum amount of the variabilities that the selected principal components need to explain |
batch_factors |
a list of covariates whose effect size on the expression data is to be determined |
dat A numerical vector that contains the percentile of sources of batch effect for each covariate term
label A character vector containing the name for each covariate term
The return values of the function can be accessed as <variable>$<parameter>
Karthikeyan Murugesan <karthikeyanm60 at yahoo.com>
Bushel P (2013). pvca: Principal Variance Component Analysis (PVCA).R package version 1.6.0.
pvcaBatchAssess
#@examples
pvcAnaly(exp_datObj, pct_threshold, batch_factors)
This function implements the Supervised Normalization of Microarrays on an expression matrix. The SNM technique tries to enhance our ability to detect the underlying biological signals by removing the effects of the adjustment variables which are usaully the causes of unwanted biases in the expression data #' It requires a set of biological covariates of interest and at least one probe-specific or intensity-dependent adjustment variable. Biological, Adjustment and the Intensity dependent adjustment variables are user defined
snmAnaly(exprs, cov, bv, av, iv, adj = TRUE)snmAnaly(exprs, cov, bv, av, iv, adj = TRUE)
exprs |
the gene expression value martix with features (probes) as rows and samples as columns |
cov |
the phenotype (covariate) matrix with samples as rows and the covariates as columns |
bv |
biological variable |
av |
adjustment variables whose effect on the expression has to be removed |
iv |
intenstity variables |
adj |
TRUE or FALSE value, default value is TRUE, If set to FALSE, then only the intensity dependent effects have been removed from the normalized data, implying the effects from the adjustment variables are still present. If TRUE, then the adjustment variables effects and the intensity dependent effects are both removed from the expression data |
norm.dat The matrix of normalized data
pvalues A vector of p-values testing the association of the biological variables with each probe. These p-values are obtained from an ANOVA comparing models where the full model contains both the probe-specific biological and adjustment variables versus a reduced model that just contains the probe-specific adjustment variables. The data used for this comparison has the intensity-dependent variables removed. These returned p-values are calculated after the final iteration of the algorithm.
pi0 The estimated proportion of true null probes pi_0, calculated after the final iteration of the algorithm
iter.pi0s A vector of length equal to num.iter containing the estimated pi_0 values at each iteration of the snm algorithm. These values should converge and any non-convergence suggests a problem with the data, the assumed model, or both
nulls A vector indexing the probes utilized in estimating the intensity-dependent effects on the final iteration.
bio.var The processed version of the same input variable
adj.var The processed version of the same input variable
int.var The processed version of the same input variable
df0 Degrees of freedom of the adjustment variable
df1 Degrees of freedom of the full model matrix, which includes the biological variables and the adjustment variables.
The return values of the function can be accessed as <variable>$<parameter>
Karthikeyan Murugesan <karthikeyanm60 at yahoo.com>
Mecham BH, Nelson PS, Storey JD (2010) Supervised normalization of microarrays.Bioinformatics, 26: 1308-1315, Brig Mecham and John D. Storey<jstorey at princeton.edu>
snm
#@examples snmAnaly(expression_matrix, covariate_matrix)
Surrogate variables are covariates constructed directly from high-dimensional data (like gene expression/RNA sequencing/methylation/brain imaging data) that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise.
surVarAnaly(expSetobj, blgy_var, n.sv = NULL)surVarAnaly(expSetobj, blgy_var, n.sv = NULL)
expSetobj |
Expression set object |
blgy_var |
biological variable of interest |
n.sv |
number of surrogate variables to be estimated (if no argument provided then the algorithm estimates n.sv for the user) |
sv A n by n.sv matrix where each column is a distinct surrogate variable (the main quantity of interest)
pprob.gam posterior probability that each gene is associated with one or more latent variables
pprob.b posterior probability that each gene is associated with the variables of interest b
n.sv The number of surrogate variables estimated, Enter if n.sv known or just dont enter the parameter, the tool will identify the number of surrogate variables on its own
expSetobject Modified expression set object with the surrogate variables added as additional columns to covariate matrix
The return values of the function can be accessed as <variable>$<parameter>
Karthikeyan Murugesan <karthikeyanm60 at yahoo.com>
Leek JT, Johnson WE, Parker HS, Fertig EJ, Jaffe AE and Storey JD. sva: Surrogate Variable Analysis. R package version 3.12.0.
sva
#@examples surVarAnaly(expSetobj, blgy_var, adj_var)