ABarray               package:ABarray               R Documentation

_U_t_i_l_i_t_y _t_o _p_e_r_f_o_r_m _Q_A, _d_a_t_a _t_r_a_n_s_f_o_r_m_a_t_i_o_n _a_n_d _s_t_a_t_i_s_t_i_c_a_l _a_n_a_l_y_s_i_s

_D_e_s_c_r_i_p_t_i_o_n:

     (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 graphcis or text files.

_U_s_a_g_e:

     ABarray(dataFile, designFile, group, test = TRUE, impute = "avg", normMethod = "quantile", ...)

_A_r_g_u_m_e_n_t_s:

dataFile: csv or tab delimit file contain expression measurement that
          are output from AB1700 software 

designFile: Experiment design file, including information for sample
          type and additional phenotype information 

   group: specify which group statistical test will be performed on.
          The samples will be ordered according the group 

    test: specify whether to perform t test. By default, t test will be
          performed using specified group information 

  impute: Treat flagged value (above 5000) as missing value, and impute
          the missing value 

normMethod: 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). 

_D_e_t_a_i_l_s:

     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').

_V_a_l_u_e:

     An 'exprSet' object. The 'exprs' slot will be populated with
     normalized signals, 'se.exprs' slot 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 'exprSet' object with various R and Bioconductor packages.

_A_u_t_h_o_r(_s):

     Y Andrew Sun <sunya@appliedbiosystems.com>

_S_e_e _A_l_s_o:

     doPlotEset, doPlotFCT, doANOVA, matrixPlot, mvaPair2, doLPE,
     doVennDiagram, hclusterPlot

_E_x_a_m_p_l_e_s:

     #- eset <- ABarray(dataFile, designFile, "sampleGroup")
     #- eset <- ABarray(dataFile, designFile, "group", detectSample = 0.8)

