Utility to perform QA, data transformation and statistical analysis
(1) Read output from AB1700 software output; (2) Create raw data QA and associated plots including boxplot, control data signal plot; (3) Missing value calculation; (4) Create MA, scatter plot; (5) Perform quantile normalization; (6) Perform t test and fold change, or ANOVA (using separate function if more than 2 subgroups). (7) Create heatmap with hierarchical clustering. (8) The results are either in graphics or text files.
csv or tab delimit file contain expression measurement that are output from AB1700 software
Experiment design file, including information for sample type and additional phenotype information.
Specify which group statistical test will be performed on. The samples will be ordered according the group.
Specify whether to perform t test. By default, t test will be performed using specified group information.
Treat flagged value (above 5000) as missing value, and impute the missing value.
The method of normalizaiton. The default is "quantile". The following normMethods are supported: quantile, mean, median, trimMean, and trimAMean. If the parameter value is one of the supported normMethods, the analysis will be performed on the chosen method. If the parameter value is "all", the analysis will be performed on quantile only, but the normalization results will be produced for each of the normMethods.
Additional arguments. Use snThresh and/or detectSample to perform filtering. snThresh is the threshold of S/N value to be considered that the probe is detected (default value = 3, if snThresh is not specified). detectSample is used to determine if a probe should be included in statistical analysis (default value = 0.5, ie 50% of samples in any one subgroup).
The function works on AB1700 software export data file. It expects certain file format to work. The rows of the file represent probes. The columns should contain these headings: probeID, geneID, Signal, S/N, Flag, and optionally SDEV, CV, AssayNormSignal (these values will be ignored in the process).
It is optional to have control probes. If they are present, plots will be generated for the control probes and they will be removed for further analysis.
It is required to have an experiment design file in certain format. The rows of the file are samples or arrays. The first column should be sampleName. Perhaps, sampleName should be concise and no spaces between characters. Second and third columns maybe assayName and arrayName (arrayName is optional). Additional columns should specify what type of samples. Note: It is best to have assayName the same as in dataFile.
Group name should be the same as in designFile. The samples will be ordered according the group information. The samples within the same subgroup will be ordered together. Only one group is accepted.
If test is TRUE (default), t test and ANOVA (if applicable) results will be produced.
If impute is avg (default), the signal values of the flagged probes will be imputed from average of the subgroup only if there are 2 or more values remaining in the subgroup.
Even if snThresh is not specified in the argument, snThresh is set to 3 by default. If a value other than 3 is desired (e.g., 2), put 'snThresh = 2' in the argument.
detectSample is also preset to a value = 0.5. This means that if a probe is detected in 50% or more samples in any subgroup within the group, it is included in statistical analysis. For example, if the group is named 'tissue', and there are 2 subgroups named 'lung' and 'liver', then, if a probe is detected in 50% or more samples in 'lung', it is included in the statistical analysis regardless the detectability in the other subgroup ('liver').
ExpressionSet object. The
will be populated with normalized signals,
will be populated with S/N ratio values, and the
phenoData slot will
be populated with information from
designFile. Further analysis can
be performed on the
ExpressionSet object with various R and Bioconductor
Y Andrew Sun <firstname.lastname@example.org>
doPlotEset, doPlotFCT, doANOVA, matrixPlot, mvaPair2, doLPE, doVennDiagram, hclusterPlot
#- eset <- ABarray(dataFile, designFile, "sampleGroup") #- eset <- ABarray(dataFile, designFile, "group", detectSample = 0.8)
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