qaGeneST             package:ArrayTools             R Documentation

_C_r_e_a_t_i_n_g _Q_u_a_l_i_t_y _A_s_s_e_s_s_m_e_n_t _R_e_p_o_r_t _f_o_r _G_e_n_e _S_T _A_r_r_a_y

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

     Creating Quality Assessment Report for Gene ST Array in HTML file

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

     qaGeneST(object, parameters, QC, mydir = getwd(), outputFile = "QA.html")

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

  object: an 'ExpressionSet'

parameters: The names of the variables to be included in the report 

      QC: The QC report generated from Affymetrix Expression Console

   mydir: The name of the directory containing the report

outputFile: The name of the outputfile.  Make sure write ".html"

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

     This function creates quality control report in an HTML file that
     contains a  set of 8 assessment figures.  

     Figure1: The intensity distributio Plot. The raw intensity should
     be similar  across all chips

     Figure2: The Mean Signal Plot.  The mean signal of each group
     should be consistant across the samples.  The positive control
     should be higher than the negative controls.

     Figure3: BAC SPIKE plot. The mean signal of each group should be
     consistant across the samples. The signal for BioB should be the
     lowest, follows by BioC, BioD, and CreX (the highest).

     Figure4: POLYA SPIKE plot. The mean signal of each group should be
     consistant across the samples. The signal for Lys should be the
     lowest, follows by Thr, Phe, and Dap.

     Figure5: POS VS NEG AUC plot. Pos vs neg auc is the area under the
     curve (AUC) for a  receiver operating characteristic (ROC) plot
     comparing signal values for the positive  controls to the negative
     controls.  In practice the expected value for this metric is
     tissue type specific and may be sensitive to the quality of the
     RNA sample. Values between 0.80 and 0.90 are typical.

     Figure6: MAD RESIDUAL MEAN plot. A measure of how well or poor all
     of the probes on a  given chip fit the RMA or PLIER model. An
     unusually high mean absolute deviation of the residuals from the
     median suggests problematic data for that chip.

     Figure7: RLE MEAN plot. This metric is generated by taking the
     signal estimate for a given probeset on a given chip and
     calculating the difference in log base 2 from the median signal
     value of that probeset over all the chips. When just the
     replicates are analyzed together the mean absolute RLE should be
     consistently low, reflecting the low biological variability of the
     replicates.

     Figure8: Hierarchical Clustering of Samples .  Samples will be
     grouped using  hierarchical clustering and principal component
     analysis (PCA). If the sample  preparation steps introduced bigger
     variation than biological variation,  treatment groups will be
     mixed up in the plot. This could also happen when the  samples
     between groups were mixed up accidentally when the samples were
     prepared.  We acknowledge that clinical samples are harder to
     collect and sometimes impossible  to control. Therefore, sample QC
     criteria will be much looser when dealing with  clinical samples.

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

     no value is returned

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

     Xiwei Wu, Arthur Li

_R_e_f_e_r_e_n_c_e_s:


     '\url{http://www.affymetrix.com/support/technical/whitepapers/exon_gene_arrays_qa_whitepaper.pdf}'

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

     data(eSetExample)
     logdata <- preProcessGeneST(eSetExample)
     data(QC)
     ## Not run: qaGeneST(logdata,  c("Treatment", "Group"), QC)

