Creating Quality Assessment Report for Gene ST Array
Creating Quality Assessment Report for Gene ST Array in HTML file
The names of the variables to be included in the report
The QC report generated from Affymetrix Expression Console
The name of the directory containing the report
The name of the outputfile. Make sure write ".html"
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.
no value is returned
Xiwei Wu, Arthur Li
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