Quality Control for Spotted Arrays

April 15, 2008

Agnes Paquet¹, Andrea Barczak¹, (Jean) Yee Hwa Yang²

1. Department of Medicine, Functional Genomics Core Facility, University of California, San Francisco
paquetagnes@yahoo.com
2. School of Mathematics and Statistics, University of Sydney, Australia

 Content 

• two-color arrays processed using a GenePix scanner (gpr files)
• two-color arrays processed using Spot image analysis software (.spot files)
• two-color Agilent arrays (qualitative diagnostic plot only)

• Custom set of controls (to best fit the array design)
• Custom set of reference arrays for quantitative assessment of hybridization quality
• Integration of user defined quality metrics to be used in the comparative boxplot

• A plot specific to 9-mer hybridizations, to assess the performance of the print 
• A plot specific to  QC hybridization, to ensure reproducibility of the results between prints. 

1. Introduction to arrayQuality

ArrayQuality is a R package, available as part of Bioconductor, designed to help assessing quality of spotted array experiments at several stages of the microarray life cycle. It provides reports containing several plots and statistical measures that can help you determine if your hybridizations and slides are of good quality. More information about Bioconductor is available at http://www.bioconductor.org.

This guide provides an introduction to microarray quality and a description of the main functionalities of the package. A full description of the package is given by the individual function help documents available from the R online help system. To access the online help, type help(package=limma) at the R prompt or else start the html help system using help.start() or the Windows drop-down help menu.

2. Installing arrayQuality

2.1 Requirements

ArrayQuality is a library for the R project, part of Bioconductor. You will need to have R installed on your computer before installing arrayQuality. For more information about R, see the R project at http://www.r-project.org. ArrayQuality can work on different files at the same time, ONLY if they are from the SAME print-run (same GAL file). If you want to generate quality reports of slides from different print-runs, you need to place them in different folders, one for each print-run.

2.2 Installing arrayQuality

ArrayQuality can be installed from Bioconductor. The version from Bioconductor is updated every 6 months. 

• Automatic installation from Bioconductor
  This is the easiest way to install arrayQuality, as other Bioconductor packages required to use arrayQuality will be automatically installed. Start R on your computer and make sure you are connected to the internet. At the R prompt, type:
  source("http://www.bioconductor.org/biocLite.R")
  biocLite("arrayQuality")
  
  If you are using R GUI (Windows):
  In the R menu, click on "Packages". In the drop-down menu, select "Install package(s) from Bioconductor". Then, select arrayQuality in the list and click "OK".
  
  
• To load arrayQuality:
  Type at the R prompt:
  library(arrayQuality)
  
  
• Manual installation
  Download arrayQuality as a .zip file (Windows users) from Bioconductor. Start R on your computer and make sure you are connected to the internet.
  In the R console, click on "Packages".
  In the drop-down menu, select "Install package(s) from local zip file..."
  Browse to the zip file you would like to install, and click "Open".

3. Quick starting guide to arrayQuality

Here, we will discuss the components of the package aimed at verifying the performance of a hybridization, given the good quality of the slide, before any preprocessing steps or further quality assessment on individual spots are performed. If you are interested in the print-run or the MEEBO/HEEBO components, please refer to the appropriate help fike for these topics. 

Our package provides two kinds of quality control plots:

• A qualitative quality control measurement as a diagnostic plot. It is a quick visual way to determine hybridization quality gathering information from several statistical tools.


• A more quantitative comparison of slide quality as a comparative boxplot. We extract some statistical measures from the test slide and we compare them against results of “good quality” slides to assess the quality of the hybridization.

3.1 To generate quality plots from image processing output files

1. Create a directory and move the image processing output files (e.g. .gpr files) of the slides of interest to this directory. Make sure that all files in the directory come from the SAME print-run (same GAL file).


2. Start R, and change your R working directory to the one you have just created. In the R menu, select File, then click on “Change dir…”. Browse to your directory from the pop-up window, or enter it manually, and click OK. To double check that you are in the correct directory: in the File menu, click on “Display file(s)…”.


3. To load the package in your R session: type
   library(arrayQuality)
   
   If needed, you may have to install other required packages like marray, limma, convert and hexbin.


4. To generate both diagnostic plots and comparative boxplots on all files in the directory, type:
   result <- gpQuality(organism=”Mm”)


5. To generate diagnostic plots only, run:
   result <- gpQuality(organism="Mm", compBoxplot="FALSE")
   In this case, quantitative quality measures will not be calculated and the HTML report will not be generated.


6. To write down your quantitative quality measures and your normalized data to a file: set output = TRUE when calling gpQuality:
   result <- gpQuality(organism="Mm", output=TRUE)


7. By default, arrayQuality uses print-tip loess normalization. If you prefer to use another method, you can specify it in the norm argument:
   result <- gpQuality(norm="none")
   For more details about normalization methods, please refer to marray package help.

3.2 To generate quality plots from marrayRaw or RGList objects

• To generate diagnostic plots: if rawdata is a marrayRaw or RGList object, type in R:
  maQualityPlots(rawdata)

3.3 Results

• In the working directory: 
• Two plots for each test slide (a diagnostic plot and a comparative boxplot) 
• A HTML quality report


• In the R global environment, the function returns a list of two objects: 
• A marrayRaw object describing all tested slides 
• A quality measures matrix: this matrix contains all comparison measures values extracted for each test slide. Each column of the matrix represents a different slide.

4. Introduction to microarray quality

A microarray experiment is composed of several steps, including experimental design, sample preparation, and various statistical analyses (figure 1). They are represented in the microarray life cycle below. As microarray technology is complex and sensitive, it is important to assess the performance of each step before going to the next one. In addition, this is also a good way to trace back the cycle to understand potential causes for upstream problems.

Figure 1: Microarray experiment life cycle

1. Print quality
   
   • 9mer hybridization
   • Quality Control hybridization
2. mRNA quality
3. Array hybridization quality
4. Spot quality

Figure 2: Quality Control for spotted arrays experiment

1. Print quality
   This component is highly tailored to the Shared Genomics Core Facility at UCSF, but the framework can be adapted to other Core facilities or laboratories printing their arrays. It is an essential component of a printed array experiment, as any print pin, probe or slide surface defect will affect the quality of hybridization to the slide, and this can’t be fixed by statistics. Only prints that did pass the quality control check will be used for actual hybridization.


2. Hybridization quality
   This is a global assessment of the hybridization performance. It helps determine for example any problem with the dyes, or uneven hybridization. Then, once you have determined that your hybridization is good, you can look at each individual spot quality, remove bad spots, and perform statistical analysis.

4. General hybridization quality 

This component is aimed at verifying the performance of your hybridization, given the good quality of the slide, before any preprocessing steps or further quality assessment on individual spots. This is where you determine if your experiment quality is good enough for you array to enter your dataset. For example, you will need to remove any hybridization with very low SNR, or large spatial artifacts.

Our package provides two kinds of quality control plots. The first one is a qualitative quality control measurement as a diagnostic plot. It is a quick visual way to determine hybridization quality gathering information from several statistical tools. More details on individual diagnostic plots can be found in the vignette “marrayPlots” in the package marray. The second one is a more quantitative comparison of slide quality. We extract some statistical measures from the test slide and we compare them against results obtained for a collection of slides of “good quality” to assess the quality of the hybridization. This comparison is visualized through a comparative boxplot. Results are displayed in a HTML report. Figure 5 shows a screen shot of a typical HTML report. Users can click on each image to obtain a higher resolution plot.

Diagnostic plots can be generated  for different image processing software format: GenePix format files (.gpr files), Spot format files (.spot) and Agilent format files, or from marrayRaw or RGList objects. Most arguments can also be customized to match your own data: which probes are used as controls, which column of the image processing output file is used to define your spot types... You can also specify your own collection of good quality slides using the functions globalQuality and qualRefTable. For more details about these functions, please refer to the specific online help.

4.1 To generate quality plots from gpr files: gpQuality()

-          Copy the gpr files from the SAME print-run (same GAL file) in a directory.

-          Change R working directory to the one containing your gpr files as described in Section 3

-          To generate both diagnostic plots and comparative boxplots on all files in the directory, run:
         > result <- gpQuality(organism=”Mm”)

         -          To generate diagnostic plots only, run:
                   >  result <- gpQuality(organism="Mm", compBoxplot="FALSE")
                   In this case, quantitative quality measures will not be calculated and the HTML report will not be generated.

- To write down your quantitative quality measures and your normalized data to a file: set output = TRUE when calling gpQuality:
         > result <- gpQuality(organism="Mm", output=TRUE)

This command will create two files: quality.txt, which contains your quality measures, and NormalizedData.xls, which contains your normalized M values.  

If you have set compBoxplot = FALSE, quantitative quality measures are not calculated. Therefore, you will not generate the quality.txt file.

4.2 To generate quality plots from marrayRaw/RGList objects: maQualityPlots

-          To generate diagnostic plots: if rawdata is your marrayRaw/RGList object, type:
> maQualityPlots(rawdata) 

4.3 Results

gpQuality() outputs

• In the working directory:
•     two plots for each test slide (a diagnostic plot and a comparative boxplot)
• a HTML quality report
  

• In the R global environment, the function returns a list of two objects: 
• A marrayRaw object describing all tested slides
• A quality measures matrix: this matrix contains all comparison measures values extracted for each test slide. Each column of the matrix represents a different slide.
  

For each slide, you will find on the report how many of your slide’s results are below the recommended range. If you want to specify a directory to store the results, you can do it by modifying the argument resdir accordingly. For more details about gpQuality arguments, please refer to the online help for this function.

Figure 5: Example of HTML report generated by gpQuality

• In the working directory:
•  one diagnostic plot for each test slide
  

4.4 Restrictions

gpQuality calls two key functions, maQualityPlots and qualBoxplot. qualBoxplot supports Mouse (Mm) and Human (Hs) genomes only. To generate quality plots for other genomes, you need to set gpQuality argument compBoxplot = FALSE. In this case, only the diagnostic plots will be generated.

4.5 Description of the diagnostic plots:

Figure 6 represents an example of a good hybridization diagnostic plot.

1. MA-plot of raw M. No background subtraction is performed. The colored lines represent the loess curves for each print-tip group. The red dots highlight  any spot with corresponding weighted value  less than 0. Users can create their own weigthing scheme or function. Things to look for in a MA-plot are saturation of spots and the trend of loess curves, which is an indicator of the amount of normalization to be performed.

2. MA-plot of normalized data density. By default, print-tip loess normalization is used. Instead of the typical MA-plot, we have used the package "hexbin" to highlight density of dots on the MA-plot. A light yellow color indicates a high density of dots, whereas blue color represents a lower density. This plot gives you information on the bulk of your data intensity (low/high signal)

3. Spatial plot of rank of raw M values (no background subtraction): Each spot is ranked according to its M value. We use a blue to yellow color scale,where blue represents the higher rank (1), and yellow represents the lower one. Missing spots are represented as white squares. This is a quick way to visually detect uneven hybridization and missing spots.

4. Spatial plot of normalized M values ranks. By default, print-tip loess normalization is used. Each spot is ranked according to its M value. We use a blue to yellow color scale,where blue represents the higher rank (1), and yellow represents the lower one. Missing spots are represented as white squares. In addition, flagged spots are higllighted by a black square. This type of graphical representation helps verify that normalization removed any spatial effects.

5. Spatial plot of raw A values. The color indicates the strength of the signal intensity, i.e. the darker the color, the stronger the signal. Missing spots are represented in white.

6. Histogram of the signal-to-noise log-ratio (SNR) for Cy5 and Cy3 channels. The mean and the variance of the signal are printed on top of the histogram. In addition, overlay density of SNR stratified by different control types (status) are highlighted. Their color schemes are provided in Table 1. The SNR is a good indicator for dye problems. The negative and empty controls density lines should be closer, almost superimposed.

7. Dot plot of controls normalized M values. Controls with more than 3 replicates are represented on the Y-axis, the color scheme is represented in Table 1. Controls M values should be tight. and close to 0.
   

8. Dot plot of controls A values, without background subtraction. Controls with more than 3 replicates are represented on the Y-axis, the color scheme is represented in Table 1. Intensity of positive controls should be in the high-intensity region, negative and empty controls should be in the lower intensity region. Positive controls range and negative/empty controls range should be separated.
   

4.6 Description of the comparative boxplot:

Figure 7 shows an example of a comparative boxplot.

We have chosen a wide range of measures to quantify the quality of a typical hybridization: single channel measures (range of foreground signal, MAD of background, signal to noise ratio…), two channel measures (median A values for each type of controls, amount of normalization needed…), percentage of flagged spots... Some measures have been negated such that the quality scale had an increasing trend from problematic to good quality.

For each measure, we have represented the following on the graph :

-         Boxplot of the reference slides values.

-        1^st and 3^rd quantiles before scaling for each boxplot.

-         Y-axis on the right : for each measure, we have printed 2 values. The first one is the percentage of reference slides measures under your slide’s result. The second one is your slide value for this measure before scaling.

Unless otherwise specified:

4.7 Example

Data for this example was provided by the Functional Genomics Core Facility in UCSF. We have tested slide number "137" from print-run "9Mm". This array was fabricated using Operon Version 2 Mouse oligos and the hybridization measures differential gene expression in two RNA samples, Mouse Liver and Mouse Reference Pool. Results are represented Figure 5 and Figure 6.

To generate diagnostic plots, comparative boxplots, HTML report and to write your quality measure and normalized data to a file in a directory named "Results":

> library(arrayQuality)

> datadir <- system.file("gprQCData", package="arrayQuality")

> result <- gpQuality(fnames = "9Mm137.gpr", path = datadir,organism = ”Mm”, output = TRUE, resdir = "Results")

 
Figure 6: General hybridization quality diagnostic plot

Figure 7: Comparative boxplot