Coffalyser v8

This quick analysis manual explains how to perform a simple analysis using the Coffalyser VBA analysis

software V8. This manual is an extension on the normal complete manual from v7 and only explains how

to perform a quick analysis. A complete overview on support documents can be found at

http://www.mlpa.com/coffalyser and in the document available online

How_to_use_the_Coffalyser_Support_Page_v1.

CONTENT

1. Downloading and installing the Coffalyser

2. Starting the Coffalyser and updating a MLPA mix

3. Loading your MLPA mix

4. Changing the Actual length of the control fragments for automated quality control

5. Importing and visualizing your fragment data files

a. Supported sample types

b. Adjusting File names

6. Adjusting the actual probes lengths for automated data filtering

7. Changing the metric for normalization

8. Filtering my imported data files.

9. Filtering data for a methylations status analysis

10. Checking my imported signals.

11. Settings your reference data.

12. Setting analysis method.

13. Exploring results (sample reports enabled)

a. Bar charts

b. Technical data figures

c. MLPA Results Reports

14. Exploring results 2 (No sample reports enabled)

a. Ratio result sheet

b. PPMC

c. MAD VALUE

d. Reference results sheet

e. Statistics sheet

f. Predictions sheet

g. Saving Results Summaries

h. HEATMAP images

1. Downloading and installing the Coffalyser

First download the latest install version from our website (http://www.mlpa.com/coffalyser).

Run the Coffalyser v8.exe file and install the program to an easy to find location (e.g. directly on

C: or D:). After installation, you first need to make a few minor adjustments to prepare Excel for

running the Coffalyser VBA program. These changes can be found in the Excel Preparation

manual for 2003 or 2007 available online. The Coffalyser V8 may work in older version of Excel

although this is untested and may give errors.

2. Starting the Coffalyser and updating a MLPA mix.

The Coffalyser comes prepared for all available MLPA mixes. The contents and sheets of all

MLPA mixes added can be found in the folder called MLPA mixes. These sheets are needed to

run the Coffalyser. If your MLPA mix is not added to the program you may find your sheets

online or it can be requested through email (coffalyser@mlpa.com). Copy the sheet into the

folder MLPA mixes and the program will automatically update after restarting.

Double click on the Coffalyser.xls file to start the program. In office 2003 you will receive the

question to enable or disable Macro usage (if security has been set to medium), choose to

enable the Macros. In Office 2007, you need to click on the button Options next to the security

warning. A popup window will appear next click on Enable this content (figure 1)

Figure 1 Enabling of macros in Office 2007

3. Loading your MLPA mix

After enabling the macros a window will pop-up where you can load your MLPA mix, enter your

name, kit number and project name. First select your MLPA mix and lot number from the box

underneath Load your MLPA mix and click on Load mix. When you MLPA mix has been

loaded a message will appear.

Now you may fill in your user name and kit number (not required). You may furthermore create

a new database. Click on new, to create a new database, or select a database from the box

underneath User Database. Raw data will be stored in a folder named after the database in

the folder called Raw data database while results will be stored in the in a folder named after

the database in the folder called MLPA results.

Change the project name if you start a new project. Your results summary files will be names

after your project name, so create logical names such as: P002 breast cancer samples 0409 or

p301 lot 0207 QT-A.

By selecting Search Saved Project you may reopen an old project containing the raw data files

and results (if stored during analysis).

Click on Start MLPA analysis to open the Coffalyser userform.

Figure 2 Coffalyser mix loading screen.

4. Changing the Actual length of the control fragments for automated quality control

A deviation between the cloned real fragment lengths and the actually detected fragment

lengths always exist. To enable the automated quality control of the Coffalyser the actual

lengths of the control fragments need to be adapted before data filtering. After opening the

Coffalyser user form, click in the All MLPA mixes screen on control and mut probes on the left

side of the screen. (You may navigate between the different screens by clicking on the tabs on

the topside of the program. The tab surrounded with the pink edge is the active screen. ) A list

with all control fragments will appear as well as (if present) mutation specific probes (figure 4).

You need to note the actual lengths of the control fragments and mutation specific probes in the

size called electropherogram (figure 3) and then adjust these lengths in the coffalyser before

data filtering. The concentration control fragments (Q-fragments) are expected at 61, 68, 74

and 80 bp and may only be visible in a NO-DNA run. Double click on each of the listed lengths

and change these to the actually detected length. Repeat this procedure for the denaturation

fragements (D-fragments) which are expected at 88 and 96 bp and for the Y and X specific

fragments which are expected at resp. 100 and 105 bp. After finishing click on file and select

Save all mix changes, overwrite the old file.

If these fragments are not in your kit, then you dont have to change the lengths and you may

ignore the possible warnings (e.g. incomplete denaturation).

Figure 3 MLPA run with DQX fragments.

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

50 100 150 200 250 300 350 400 450

P275 new DQX 20 ng male.C04_08031113LE

Size (nt)

Dye

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163. 87

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193.05

201.60209. 72

221.23

229.02

245. 29

253.82

264.28

282.57

291. 34

301. 47

310.89

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Figure 4 MLPA mixes screens at the control/mutation fragments page.

5. Importing and visualizing your fragment data files.

Size called fragment data files as well as fsa files from the ABI-310/ABI-3100/ABI-3700 series can

be imported in the Coffalyser for data normalization or visualization. Please note that all

visualization of electropherograms are rebuilds and that it is always recommended to use the

software you used for size calling for proper troubleshooting.

Go to the Raw MLPA data screen and click on file. Next select the proper data format that

matches your raw files. If you are unsure if you file format is correct, navigate to the folder

called Manuals and open the folder called Possible file formats. This folder contains example

files of all useable files; open them in Excel to explore the content.

After importing the separated run names will appear in the left column. You may double click on

them to visualize the run. By changing the combobox on the bottom left side to show/print or

show/save image, you may choose not only to show the run in the window but also print or

save an image of the run (figure 5). Using the Play button will start a slideshow, showing all

images one by one.

Supported sample types (example files can be found in the folder called manuals > possible file formats)

"Import CEQ CSV FILE (single file)"

Single sample files created in the CEQ fragment analysis software by the export files option.

"Import CEQ CSV FILE (fragments genotypes)"

Multiple runs file created in the CEQ fragment analysis software by the option export fragments

/ genotypes

"Import GENEMAPPER TXT FILE (single)"

Single sample files created in Genemapper software by export of a single size called sample file

"Import GENEMAPPER TXT FILE (multiple runs)"

Mulitple runs files created in genemapper software exported in the plot view when displaying

multiple runs

"Import PEAK SCANNER FILE (combined table)"

Multiple runs file created in Peak Scanner Software after size calling

"Import SPECTROPHOTOMETRIX FILE (combined table)"

Multiple runs file created using the export txt file option the spectrometrix software data

"Import Megabace file (combined table)"

Multiple runs file created using the export txt file option after size calling in the Megabace

software

"Import LICOR file (GeneTools)"

Multiple runs file created using by Genetools scanning a slabgel image of a licor device

"Import GENESCAN TXT FILE (single)"

Single sample files created in Genescan software by export of a single size called sample file

"Import GENESCAN PROJECT FILE (multiple runs)"

Mulitple runs files created in Genescan software exported in the plot view when displaying

multiple runs

"Import ABI -310(0)/3700 FSA FILES"

Size caling of raw fsa files coming from the older series of ABI may be performed directly. These

files will be size called and transformed to genescan txt files during import.

Figure 5 Raw data datascreen.

Adjusting File names.

In some cases you may want to adjust the filenames your import, this may for instance be

practical with a methylation status analysis, where cut and uncut run need to have the same

names. The Coffalyser allows a few different possibilities to do this.

Click on Settings and then go to the tab called Sample Names. Cut Sample Names is activated

on default and cuts each file name down to a maximum of 31 characters (no more is allowed for

import). Left and right thus indicate the position of characters counting from the left side of the

file name where the name that will be imported from the filename will start (left) and where it

will be cut down (Right).

Alternatively you may enable the options to resize the filenames using SIGNS. Setting a sign will

cut the filename down to everything before the set sign. For instance if you filename is called

Sample1_CUT_1909234_A09_Row8, then set your sign to be: CUT. After importing your sample

name will be Sample1_CUT.

Finally you may enable to remove track ends; this will cut everything in your file name after the

_ underscore sign. This may not always be recommended because you may cut too much!

Figure 6 Sample names setting tab

6. Adjusting the actual probes lengths for automated data filtering.

A deviation between the cloned probe lengths and the actually detected probe lengths also

exists. To filter the correct probe signals we first need to adapt the actual lengths as found after

size calling. The easiest way to do this may be by using the autobin function. Navigate to the

data filtering page (figure 7). Next select a number of runs (preferably runs performed on

normal DNA, with MS-MLPA UNCUT runs) with overall good signals with equal intensities and a

low background. Next click on AddA (>>) while the combo box on the left side is set to

add/remove reference runs. The names will move underneath the header Reference Runs,

now change the combo box on the left side to perform autobin on references (P) and click on

the left play button.

The program will now try to find the actual length belonging to the probes in the mix. After

finishing the actual lengths will be displayed underneath SET BIN while the cloned lengths of

the probes can be seen underneath CLONED LENGTH. Check the estimated lengths underneath

SET BIN with for instance an image of an size called electropherogram and make sure that they

all contain valid values. Alternatively you may double click on a run name in the left column. All

fragments lengths and intensities of the set metric will appear in the most right columns. You

can use this list to define the actual length if they were not estimated correct. Probe signals will

always be close to the cloned length and will have high signals intensities as compared to

background signals. Each set bin value can be adapted manually by clicking on the fragment, and

adjusting the value using the pop up box.

When you are sure all actual set lengths in the SET BIN list are correct, change the left combo

box to Save all mix changes and click on the left Play button. Overwrite the old files. The actual

lengths are now set and saved and can be used every time you use this MLPA mix as long as the

capillary device, size calling method, gel, size standard etc remain the same.

To remove imported runs, change the combo box on the left side to Delete imported Files (P)

and click on the left play button.

Figure 7 Data filtering page

7. Changing the metric for normalization.

The Coffalyser on default imports the peak heights as the basis for normalization. This is set so,

because the estimation of the peak area is often determined wrong when peaks are quite wide.

You may however choose to change the metric to the total peak area found within a bin or the

highest peak area signal in a bin. A bin is made up out of the actual set probe length in the set

bin +/- 2 bp (on default).

Click on Setting and navigate to the Filter options tab (Figure 8). To change the metric to peak

height, select Use peak areas next to Peak areas or Peak heights. To use the summation of all

signals in a bin (not recommended, because background will be taken into the probe

fluorescence) enable the option box Use summation of peaks next to the text Multiple signals

in a bin.

This page will also allow you to change the minimal set signal intensity (arbitrary number) and

the width of each bin.

Figure 8 Filter options tab

8. Filtering my imported data files.

Now the Coffalyser is prepared for automated filtering, you can start filtering and checking your

probe signals.

Navigate to the data filtering page (figure 7). Next select the runs which you want to use as

reference runs. In most cases this will be MLPA runs performed on normal DNA. Note that

reference and sample runs which are isolated in the same way and are from the same tissue

type will be easier to compare and provide better results.

Next click on AddA (>>) while the combobox on the left side is set to add/remove reference

runs. The names will move underneath the header Reference Runs. Next change the

combobox on the left side to add/remove sample runs. Select the runs which you want to use

as sample runs and click on click on AddA (>>). Your reference runs and sample runs should now

be right underneath the headers REFERENCE RUNS and resp. SAMPLE RUNS.

Click on the Play button on the right side to start data filtering. The program will work of both

lists, when data filtering is finished the quality overview screen should appear.

9. Filtering data for a methylations status analysis

Separate rules apply when you want to perform a methylation status analysis. First of all, you

need to make sure that your cut and uncut runs have equal names to make automatic

comparison possible. A cut run may for instance be named: Sample1-cut, while the uncut run

should then be named: Sample1-uncut. Also see the section on Adjusting File names for this.

Navigate to the data filtering page (figure 7). Next select the all uncut runs in the left column

and click on AddA (>>) while the combobox on the left side is set to add/remove reference

runs. The names will move underneath the header Reference Runs. Next change the

combobox on the left side to add/remove sample runs. Select the CUT runs belonging to your

previously imported uncut runs and click on click on AddA (>>). Your reference and sample data

should now be right underneath the headers REFERENCE RUNS and resp. SAMPLE RUNS. Click

on the play button on the right side to start filtering your data.

10. Checking my imported signals.

Direct after analysis the quality control is opened giving an overview of the found signals and

results of the quality control steps of all imported runs (figure 9). If this menu doesnt open, you

may click on All run QC in the left window. The displayed parameters are: Run name, DNA

concentration estimation, Number of probe signals found, Number of reference probes found,

Ligation fragment found, Male/Female determination (if Y fragment present), Denaturation

check on fragments 88 bp and denaturation check on fragments 96 bp. Please note that these

tests are dependent on the correct settings of the actual lengths before data filtering! When the

probe or control fragments lengths are not set correct, probe signals may be missing or runs

may seem to be denatured incomplete while this is not the case. In doubt always check your

runs on the RAW electropherogram.

To separately remove bad reference or sample runs, first click on Reference runs QC or Sample

runs QC. The same check appears but now only for the reference or sample runs. To prevent

errors during analysis, remove reference runs missing any probe signal and remove sample runs

missing reference probe signals. You may do this by double clicking on the run name in the

center windows while the Reference runs QC or Sample runs QC is displayed.

If you think that some probes were not filtered correct, possible because they are missing in

several runs, click on Reference runs or Sample runs in the left column in the bottom. This will

display the imported signals of each reference or sample run, allowing you to recognize the

probes which has been filtered wrong. You need to go one step back and manually adjust the

set bin for this fragment by double clicking on it.

Figure 9 Data analysis page, all runs quality check point.

11. Settings your reference data.

Depending on the MLPA mix you used and the samples you have used a normalization method

needs to be chosen. The coffalyser allows many different normalization methods to fit many

different situations. All settings can be found by clicking on settings. For you convenience a

number of presets has been created which can be chosen from the combo box on the right side.

First you need to consider your reference runs. If you have performed a number of reference

runs, this data will be used to correct and normalize your sample runs and it will be used to

calculate the reproducibility of the MLPA probes in this experiment.

If you do not have reference runs you may set a median of all samples as reference data. Please

note that this is only recommended if the coincidence of aberrations for each probe is less than

30% over all samples. Estimated standard deviations and predictions are unreliable when you

are comparing your samples against themselves and these results should thus be ignored. Click

on settings, to change your reference data type and navigate to the tab called Analysis Options

(figure 10). Change the option box at Reference data TYPE to median of all imported samples

to normalize samples without reference runs. Alternative method allow the average or

minimum of all samples, these are only to be used by advanced users in special situations.

Figure 10 Basic analysis settings.

12. Setting analysis method.

Now that you reference data is set you need to choose an analysis method. To best meet the

requirements of the context of your experimental design a number of pre-made settings are

available. Each of these methods may work well in some circumstances but may before poorer

in other. Depending on expected aberrations and availability of reference probes and reference

runs and quality of the runs a method needs to be chosen by the user. The section here under

will discuss each of the pre-defined methods; also see a quick overview in table 1.

After choosing a method in the Combo box, click on the right play button to start the analysis. In

case missing signals were found in reference runs or reference probes were missing in sample

runs, you may get warning messages. Always delete the runs that are noted as not good, unless

you are sure that the missing signals are really in your sample and thus not caused by bad

filtering or other artifacts.

Table 1 Quick overview of the pre-set analysis methods and features

"Directanalysis"

This method is the most straight forward method to normalize your data. It doesnt correct for

sloping effects. This method should thus only be used when size to signal sloping is more or less

the same between your reference and sample runs. You can always check if there is a sloping

artifact in your results by ordering your ratio results on probe length (after analysis, using the

normal XLS order function) and this may thus be the best method to start with. When the

average ratio of the shorter probes is higher or lower than the average ratio of the longer probes

(not taking into account ratio caused by aberrant genes), you may need to re-perform the

analysis using an alternative analysis method.

This method assumes that the set reference probes remain normal between reference and

sample runs. Each reference probes will be used to separately determine the ratio of a target

probe. The median of all these calculated ratios will estimate the final ratio. The median is the

center value, and this method is thus robust for aberrations in the reference probes as long as

more than 51% of all reference probes remain normal.

"Directanalysis (all probes)"

This is almost equal to the directanalysis method in that it doesnt correct for sloping effects. This

method assumes the number of changes in the target sequences of the probes is minimal. Each

target probe will be used to separately determine the ratio of each target probe. The median of

all these calculated ratios will estimate the final ratio. The median is the center value, and this

method is thus robust as long as more than 51% of all probes remain normal.

This method will usually be used for MLPA kits where no reference probes are present and the

number of aberrant probes is limited to no more than 5 probes although this method may also

provide good (or sometimes even better) results with MLPA kits having only a few reference

probes (5 or less).

"Directmethylation status"

This is almost equal to the directanalysis method in that it doesnt correct for sloping effects. This

will usually not be necessary to perform the methylation status analysis because the same DNA is

being compared to each other (Cut & Uncut). This method assumes the number of changes in the

target sequences of the reference probes is minimal. Each reference probe will be used to

separately determine the ratio of each target probe. The median of all these calculated ratios will

estimate the final ratio. The median is the center value, and this method is thus robust for

aberrations in the reference probes as long as more than 51% of these probes remain normal.

Please note that in many cases some reference probes may give higher or lower signals in the cut

sample. This is not due to aberrations but rather because of the structural changes present after

cutting the DNA or because of the reduced presence of amplifiable targets during the PCR

reaction.

This method calculates the relative ratio of each probe signal between the cut and uncut

reaction. When a target is completely methylated, no target is cut and the signal of this probe is

expected to be ratio 1 or 100%. When a target is hemi-methylated, a single allele is expected to

be completely cut and the expected ratio between the cut and uncut single is 0.5 or 50%.

"Control Probe Analysis (LS)"

The control probe analysis (LS) method is only recommended for MLPA kits with at least 7-8

reference probes which are targeted to areas of which the user is sure that no changes occur in

these regions in the used samples (please check the probe mix description for the targets of the

reference probes in your kit).

The control probes method (LS) first defines if there are outliers between the set reference

probes in each sample assuming that a median of all reference probes is normal. The reference

probes signals that are not outliers are then used to calculate the amount of regression in that

run using an adapted the least of squares regression method. The size to signal drop is thus

expected to be more or less linear, if this is not the case then use one of the LMS methods. Next

to this, the least of squares method is sensitive with a low number of signals. It may thus be

recommended to use the Tumor analysis (LS) method instead which is more robust and also

normalizes against the reference probes. After regression correction the method assumes that

the set reference probes remain normal between reference and sample runs. Each reference

probes will be used to separately determine the ratio of a target probe. The median of all these

calculated ratios will estimate the final ratio. The median is the center value, and this method is

thus robust for aberrations in the reference probes as long as more than 51% of all reference

probes remain normal.

"Tumor analysis (LS)"

The tumor analysis (LS) method is the recommended method for users who wish to normalized

their data against their reference probes, but the number of available reference probes is either

too low, or some samples may have aberrations in the regions to which the reference probes are

targeted.

The tumor analysis (LS) first defines if there are outliers over all probes in each sample assuming

that a median of all probes is the normal status. It should be noted that it doesnt matter if a

median of all signals falls on an aberrant probe. If most probes are targeted to a region, or

regions that have the same copy number status, size to signal drop is still estimated correct. The

probes signals that are not defined as outliers are then used to calculate the amount of

regression in that run using an adapted the least of squares regression method. The size to signal

drop is thus expected to be more or less linear, if this is not the case then use one of the LMS

methods.

After regression correction the method assumes that the set reference probes remain normal

between reference and sample runs. Each reference probes will be used to separately determine

the ratio of a target probe. The median of all these calculated ratios will estimate the final ratio.

The median is the center value, and this method is thus robust for aberrations in the reference

probes as long as more than 51% of all reference probes remain normal.

"Population Analysis (LS)"

The population analysis (LS) method is recommended for MLPA kits without any reference

probes and the number of expected changed in the target regions of the probes is less than 40%.

This method has also been proven to be useful for MLPA kits having a limited number of

reference probes and the number of expected changed in the target regions of all probes in the

samples is less than 40%.

The population analysis (LS) first defines if there are outliers over all probes in each sample

assuming that a median of all probes is the normal status. The probes signals that are not defined

as outliers are then used to calculate the amount of regression in that run using an adapted the

least of squares regression method. The size to signal drop is thus expected to be more or less

linear, if this is not the case then use one of the LMS methods.

Each target probe will be used to separately determine the ratio of each target probe. The

median of all these calculated ratios will estimate the final ratio. The median is the center value,

and this method is thus robust as long as more than 51% of all probes remain normal.

"Control Probe Analysis (LMS)"

The control probe analysis (LMS) method is equal to the reference probe (LS) method except that

the regression is determined directly on all control probe signals (without outlier detection) using

a least of median squares method. This method is very robust in the presence of outliers,

although it is less precise. This is also the recommended method when size to drop is non-linear,

this problem usually has to be solved in the experimental procedure itself although sometimes

LMS correction may give good results.

After regression correction the method assumes that the set reference probes remain normal

between reference and sample runs. Each reference probes will be used to separately determine

the ratio of a target probe. The median of all these calculated ratios will estimate the final ratio.

The median is the center value, and this method is thus robust for aberrations in the reference

probes as long as more than 51% of all reference probes remain normal.

"Tumor analysis (LMS)"

The Tumor analysis (LMS) method is equal to the Tumor analysis (LS) method except that the

regression is determined directly on all probe signals (without outlier detection) using a least of

median squares method. This method is very robust in the presence of outliers, although it may

be less precise. This is also the recommended method when size to drop is non-linear; this

problem usually has to be solved in the experimental procedure itself although sometimes LMS

correction may give good results.

After regression correction the method assumes that the set reference probes remain normal

between reference and sample runs. Each reference probes will be used to separately determine

the ratio of a target probe. The median of all these calculated ratios will estimate the final ratio.

The median is the center value, and this method is thus robust for aberrations in the reference

probes as long as more than 51% of all reference probes remain normal.

"Population Analysis (LMS)"

The Population Analysis (LMS) method is equal to the Population Analysis (LS) method except

that the regression is determined directly on all probe signals (without outlier detection) using a

least of median squares method. This method is very robust in the presence of outliers, although

it is less precise. This is also the recommended method when size to drop is non-linear, this

problem usually has to be solved in the experimental procedure itself although sometimes LMS

correction may give good results.

Each target probe will be used to separately determine the ratio of each target probe. The

median of all these calculated ratios will estimate the final ratio. The median is the center value,

and this method is thus robust as long as more than 51% of all probes remain normal.

"Methylation Status Analysis (P)"

Please note that it is recommended to use the direct methylations status analysis method, unless

there seems to be a sloping difference between your cut and uncut samples. This method

assumes the number of changes in the target sequences of the reference probes is minimal.

The regression is determined directly on all reference probe signals (without outlier detection)

using a least of median squares method. This method is very robust in the presence of outliers,

After correction each reference probe will then be used to separately determine the ratio of each

target probe. The median of all these calculated ratios will estimate the final ratio. The median is

the center value, and this method is thus robust for aberrations in the reference probes as long

as more than 51% of these probes remain normal. Please note that in many cases some

reference probes may give higher or lower signals in the cut sample. This is not due to

aberrations but rather because of the structural changes present after cutting the DNA or

because of the reduced presence of amplifiable targets during the PCR reaction.

This method calculates the relative ratio of each probe signal between the cut and uncut

reaction. When a target is completely methylated, no target is cut and the signal of this probe is

expected to be ratio 1 or 100%. When a target is hemi-methylated, a single allele is expected to

be completely cut and the expected ratio between the cut and uncut single is 0.5 or 50%.

"RNA Intra Normalization"

The RNA intra normalization method is different from all methods in that it doesnt compare

reference and sample run but only normalized the Sample run signals to the internal reference

probe signals. This is thus only recommended if you are in the internal target concentration in a

sample as compared to an internal control (such as B2M). If you want to compare reference and

sample run performed with RNA MLPA kits, you may also use the directanalysis based on the

control probes.

"USER DEFINED"

Putting the combobox on user defined means that you may define all steps yourself. You can do

this by clicking on settings and making adjustments at the different tabs. This is only

recommended for the advanced user. More info on advanced settings can be found in the

complete coffalyser manual, appendix Advanced analysis settings.

13. Exploring results (sample reports enabled)

After the analysis is finished, the results page will automatically open. If you have selected to create separate

reports (enable option: create and save all sample reports), you can explore chart and summaries from this

page. When this option is enabled for each samples a separate results report will be generated. This costs

more time and analyzing in this way is thus much slower. In practice it is more useful to first empirically define

the optimal analysis method by performing your normalization multiple times using different normalization

methods. By evaluating the different results an optimal analysis method can be defined with which you may

want to create the separate results report sheets and charts.

On the MLPA results page you have a number of options to continue.

- Save your result sheets / results

- Explore results reports

- Explore project results.

- Open the quick explorer

- Open the advanced explorer

Bar charts

Click on Result Reports to view the bar charts of your sample runs. In the MLPA results window use the

right combobox to choose the way you want to view your sample results. This is either in a bar ratio chart,

result report method or technical chart. Double click in the left column on a sample name to view the bar

chart. The probes in this chart are ordered in the recommended order, showing the Map view locations

on the X-axis and the found probe ratio on the Y-axis. The reference probes are usually ordered to the

most right position. The error bars coming with the chart are the calculated standard deviations (1x)

found for each probes, assumed you have used reference runs which were performed on normal human

DNA. These standard deviations are calculated from the reproducibility of reference runs and the

confidence of the normalization factor. High standard deviations are usually found when the used

reference samples were not reproducible as compared to each other.

Figure 11 Results screen

Technical data figures

The technical figures can be visualized by changing the right combo box to choose technical chart and

double clicking on a sample name to open it. The figure that appears contains the relative MLPA signals

after correction for probe specific bias and log transformation. In the default LS methods outliers are first

determined where after a sloping correction is performed on the signals that were determined as not

being outliers. Depending on the method a normal status will be declined on the reference probe signals

(control probe method) or on all probe signals (tumor analysis method / population method). Please note

that the normal status during outlier detection may also fall on aberrant probes, e.g. when applying the

tumor analysis method, sloping may be determined on aberrant probes even though the normalization

may normalize the data afterwards to the reference probes.

In figure 12 a sample is visualized analyzed in the Population method. Most probes, including the

reference probes (green dots) give equal signal intensities except for 9 aberrant probes. These 9 aberrant

probes were defined as outliers by dynamic correlation checks (see four outlier lines) and will not be used

for slope correction. The remaining signals will be used for regression correction by calculating a expected

regression line through the remaining point and using the distance of each point to this line for the

coming normalization.

This figure should thus only be used to determine if your sloping correction was ok, and how your

reference probes behave according to each other (in figure 12 they all have the same chromosomal

status, and thus give equal signal intensities, although more or less effected by the sloping artifact).

Figure 12 Technical MLPA run figure

MLPA Results Reports

Results reports images (figure 13) can be generated for each run separately by changing the right combo

box to show results report and the left combobox to save as image and then double clicking on a

sample name to open it. The practical information will also be displayed in the results window but doesnt

have any color casing, which is available in the report.

The report contains all important run information and analysis settings in the top. Next to this it contains

all information on the probes of the used mix and results are ordered in the MRC recommended ordering,

placing the important genes in the top and reference probes more to the bottom.

The calculated ratios are those of the sample as compared to the noted Reference runs using the set

normalization method. The added standard deviations are calculated from a combination of the

reproducibility of the reference runs and the confidence of the normalization factor, giving an indication

how much variation this probe had in the used experimental setting. The 95% confidence range is then

furthermore basically the range in which the ratio may have fallen assuming two standard deviations.

Finally the probability calculation can aid in results interpretation, it uses the estimated ratio and standard

deviation to questimate if the estimated ratio is more probably to be a gain (ratio 1.5) or a loss (ratio 0.5)

assuming that the variation of each probes follows a Gauss distribution. Ratio that are red are confident

to be gained or lost for more than 95%. Please note that these are questimations and that for real statistic

calculation on losses and gains several positive control samples are necessary. Questimation also assumes

that the variation distribution of a gain is equal to that of a normal probe results.

Figure 13 MLPA results report image, created by saving result report as image.

sample namesample namesample namesample name D1.03.01831DMD__MLPA-P034^GSsTXT.txt OperatorOperatorOperatorOperator User1

Analysis Date 5/2/2008 17:16 Kit numberKit numberKit numberKit number

normalisat ion methodnormalisat ion methodnormalisat ion methodnormalisat ion method Tumor analysis (LS)

Average / Median Median on Normalisation Factor Refe rence runs (

14. Exploring results 2 (No sample reports enabled)

After the analysis is finished, the results page will automatically open (also when no reports are generated). Click

in the right column on Open the quick explorer, the main user form will minimize and the quick results explorer

will open together with the sheets containing the results. You can now use the results explorer to guide you to the

calculated ratio results or just navigate to the different sheets. You can also use the minimize button in the right

top, the coffalyser user form will then close and you can explore your results on the sheets.

To minimize the number of available sheets, most calculation sheet will be hidden if you use the minimize button

or quick explorer option. To view all sheets you can use the right mouse click button and click on Show all Sheets,

you can also use the right mouse button to reopen the coffalyser user form by clicking on Open coffalyser.

Figure 14 Quick results explorer form. Change the combo box to the results type you wish to view and

then select the sample name in the main window. By clicking on Save results summary you can save a

file containing all relevant result sheets.

Ratio result sheet

The most important sheet is probably the ratio results sheets or RESULTS (1), this sheet contains the calculated

ratio results of all analyzed sample runs. Above the calculated ratios sample information and quality check points

are displayed, in order they are: Sample name, analysis data, chosen normalization method, if an average or

median over the reference probes was used as a normalization factor, the number of found probes, the number of

found reference probes, if the ligation control peak (92 bp was found), if the sample was male or female (if a Y

probe was present), the PPMC and MAD values (which will be explained more down, if there was enough DNA

during the MLPA reaction (by estimation of the relative signal of the Q-fragments (60, 68, 74, 80 bp)), if the DNA

was denatured completely (by estimation of the relative signals of the DD fragments (88, 96 bp)).

PPMC

The PPMC value, or Pearson product moment correlation value is the correlation value found when slope

correction is applied. For every MLPA run, first the probe specific biases are corrected; hereafter the signals which

originate from targets with a normal status are expected to have a linear relationship with each other. These

signals either originate from the reference probes (control probe methods) or all probes (tumor analysis /

population method). Whether the signal are normal or not is defined by a outlier detection system which follows a

Monte Carlo like simulation assuming that the median of all signals is the normal status, or can be used to find the

sloping relation. Sloping correction can thus also be done on signals that are actually aberrant, but do show a linear

relationship with all, cause the majority contains the same chromosomal status. In most cases correction of sloping

can best be performed using all signals, this being much more robust that correcting on the reference probes only.

Correction on the reference probes (control probes methods) is thus only recommended when the number of

reference probes is 10 or more.

MAD VALUE

The MAD value or median of absolute deviation is the value which arises when you subtract the final estimated

ratio (displayed in RESULTS (1)) from each separately calculated probe ratio (using each reference probes

separately) and then take the median value of these. This value is thus very small (or zero) if each reference probes

creates the same result, if this is not the case the target sequences to which the reference probes hybridize may be

aberrant. The median estimator is however quite robust though a higher MAD value may sincerely compromise

the confidence of the calculated ratios. In normal cases the color on the MAD value will be green or orange (with

more aberrations), when the MAD value becomes red you may need to try a different normalization factor or

adjust the reference probes for a more optimized normalization.

Figure 15 Sample result information displayed above each sample ratios

Figure 16 Different results sheets can also be used to explore your results. The colored sheets contain all important

results and data.

The sheet called RESULT (2) basically contains the same results but now with the 95% confidence range followed

after the estimated ratio. This 95% confidence range is the same as discussed earlier at the results reports. The

sheets called Reference runs and Sample runs contain the filtered raw peak height or areas (this can be set at the

settings option, in the tab called data filtering). Since 2008 on default the Coffalyser uses peak height even though

peak areas are theoretically more correct. This is done so, because several program often calculate the areas

underneath the peak incorrect, especially when the peak has a large base. Peak areas are thus only recommended

to use when the peaks contain little or no shoulders and when background signals are low.

Reference results sheet

Another important sheet is the reference results sheet. During the normalization a single reference signal is

created for each probe from all your reference runs. Before actual normalization of the samples, each separate

reference runs is also normalized against this signal. This not only allows the program to calculate the

reproducibility of each probe but can also inform you which reference runs failed. The results are displayed as

normal sample runs and each run should be completely blue in case your reference runs are performed on normal

DNA. The standard deviation over the reference runs should be low (it will be bold if the value is too high). If there

are reference runs that seem to contain aberrant probes, it is good to try to reanalyze your samples are deleting

the aberrant reference runs to see if results improve. This may also work when you set a median of your samples

runs as reference data (settings Tab, reference data). After the initial analysis where all your samples were set as

reference runs, you can explore your reference run results, recognize the samples that are most aberrant, then

reopen the coffalyser and delete these samples from the reference list. In a reanalysis the most normal looking

samples are now that as reference data which can improve your results.

Statistics sheet

The statistics sheet contains basic statistical information calculated over your reference runs. This sheet thus

doesnt give information about the reproducibility of your probes (unless all samples were normal runs), but rather

informs you on the probes that are changed most or least in the sample group used.

Predictions sheet

The predictions sheet can aid in results interpretation, although it should be used solely. The predictions are

furthermore only useful when at least 3 reference samples are used which were performed on normal human

DNA. The predictions are based on the same calculations as discussed earlier in the results report section. Each

prediction checks if the calculated ratio is more likely to be a gain (ratio 1,5) or loss (ratio 0.5). Signals that were

not found are automatically set as a homozygous deletion and ratios that were estimated higher than ratio 2.5 are

set as amplifications. Please note that most MLPA probes will not reach a ratio 1.5 in the case of a gain but rather

have a ratio between the 1.3-1.5. This makes the prediction less accurate and a gain may thus seem to be

ambiguous (or undetermined) while it is truly gained. True statistics are thus dependent on positive controls and

information about the MLPA probes signals in the case of a gain and also the reproducibility and spread of the

MLPA probes in the case of a gain (or loss).

A basic rule for MLPA probes ratios is that each aberrant probe signal should be confirmed by a second probe. This

can be a probe within the same sample results which has its target sequence close to that of the first probes or in a

second run or duplo.

Saving Results Summaries

To save a results summary of your current analyzed results you can either click on the save results summary

button in the quick explorer or click on File at the results page and then click on File > Save last analyzed results

summary. Both options will create a new Excel file containing data of the sheets: reference runs, sample runs,

results (1), results (2), predictions, reference runs and statistics.

HEATMAP images

A heatmap image can also be generated from the last analyzed results by clicking on Create heatmap image

under File at the results page. Heatmap images have the advantage that the set color borders are more gradual

which may make data interpretation easier, especially in the case of heterogenous cell populations (figure 17)

when ratios of gained sequences tend to be a bit lower.

Ratio result sheet

PPMC

MAD VALUE

Reference results sheet

Statistics sheet

Predictions sheet

Saving Results Summaries

HEATMAP images

Figure 17 Heatmap image of some samples with the P034 MLPA mix.

P034 MLPA probemix lot 1105, 0505,

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DMD probe 1353-L1001 1.01 1.03 1.03 1.09 1.04 1.08 1.14 1.04 1.08DMD probe 1357-L1005 1.45 1.02 1.05 1.00 1.02 1.01 1.01 1.03 0.99DMD probe 1361-L1009 1.45 1.05 1.06 1.03 0.99 1.07 1.04 1.02 1.02DMD probe 1365-L1013 1.34 0.95 0.99 0.99 0.93 1.05 0.96 1.02 1.00DMD probe 1954-L1574 1.42 0.89 0.94 0.93 0.88 0.92 0.93 0.94 0.90DMD probe 1373-L1021 1.41 0.99 1.04 1.03 1.03 1.06 1.00 1.00 1.00DMD probe 1713-L1281 1.50 1.05 0.97 1.04 1.04 0.99 0.95 1.02 0.99DMD probe 1715-L1283 1.41 0.47 0.95 0.98 0.95 0.94 1.04 1.04 0.94DMD probe 1385-L1033 1.45 0.51 1.03 1.00 1.04 1.03 0.99 1.00 1.00DMD probe 1718-L1286 1.45 0.44 0.89 0.90 0.86 0.92 1.06 0.97 0.91DMD probe 1355-L1615 0.97 0.99 1.12 1.09 1.03 1.06 1.06 1.01 1.05DMD probe 1359-L1007 0.97 1.03 1.04 1.08 1.03 1.04 1.08 1.05 1.06DMD probe 1363-L1011 0.90 0.93 0.99 1.01 0.94 1.04 1.04 1.02 1.00DMD probe 1958-L1518 0.93 0.98 1.04 1.09 1.01 1.03 0.99 0.96 1.06DMD probe 1371-L1019 0.96 1.06 0.99 1.06 1.01 1.05 1.03 0.99 0.99DMD probe 1375-L1023 1.00 1.00 1.06 1.05 1.02 1.03 1.04 0.96 0.98DMD probe 1379-L1616 0.96 0.98 0.99 1.02 0.96 1.06 0.99 1.02 0.99DMD probe 1716-L1284 0.96 0.99 0.99 1.07 0.95 0.90 0.96 0.93 1.01DMD probe 1387-L1035 1.04 1.11 1.07 1.06 1.05 1.00 1.02 0.99 1.05DMD probe 1391-L1039 0.97 1.05 0.96 0.94 0.89 0.95 0.86 0.95 0.90DMD probe 1354-L1002 0.96 0.96 1.00 0.98 0.97 0.98 1.00 0.97 1.00DMD probe 1711-L1279 0.96 1.04 1.02 1.12 1.03 1.08 1.07 1.01 1.03DMD probe 1362-L1010 0.98 0.97 0.99 0.99 1.01 0.98 0.98 1.02 0.99DMD probe 1366-L1014 1.00 1.02 1.01 1.03 0.97 1.02 1.00 1.03 1.03DMD probe 1370-L1287 1.01 0.96 0.00 0.51 0.97 0.00 1.00 0.97 0.97DMD probe 1374-L1288 1.01 1.11 0.00 0.52 1.01 0.00 1.06 1.01 1.02DMD probe 1378-L1026 1.00 1.00 0.00 0.49 0.96 0.00 1.00 1.01 1.00DMD probe 1382-L1030 0.98 1.11 1.03 0.53 1.03 0.00 0.00 1.01 1.06DMD probe 1717-L1285 1.01 0.92 0.98 0.50 0.85 0.00 0.00 0.95 0.93DMD probe 1390-L1038 1.06 0.96 0.97 0.47 0.91 0.00 0.00 0.90 0.97DMD probe 1356-L1004 0.98 0.97 1.06 0.97 1.00 1.04 1.04 1.03 1.02DMD probe 1897-L1008 1.03 1.02 1.06 1.04 1.00 1.06 1.01 1.01 1.03DMD probe 1364-L1012 0.98 1.02 1.07 1.00 1.03 1.06 1.03 1.04 1.07DMD probe 1368-L1016 0.99 1.00 1.00 0.96 1.00 0.97 0.97 1.00 0.98DMD probe 1372-L1020 1.00 1.05 1.01 1.01 1.02 1.03 1.01 1.00 1.03DMD probe 1376-L1024 1.03 1.03 1.08 0.90 0.97 1.03 0.95 0.99 1.04DMD probe 1960-L1520 1.02 1.05 1.00 1.03 1.06 1.01 0.98 0.99 1.03DMD probe 2482-L2711 1.04 1.06 1.02 1.08 1.04 1.00 1.03 1.00 1.05DMD probe 1388-L1036 0.95 1.03 0.98 0.97 0.98 0.99 0.99 0.98 0.98DMD probe 1392-L1040 0.99 1.04 1.01 0.96 0.95 0.97 0.95 0.99 1.04Control probe 1691-L0465 0.98 0.92 1.05 0.97 0.99 0.99 1.02 0.99 1.00Control probe 1690-L0423 1.03 0.90 0.92 0.96 1.02 1.01 0.93 0.95 0.94Control probe 1692-L1531 1.03 1.06 0.88 0.97 0.95 1.00 0.92 0.94 0.48Control probe 1768-L1617 0.99 0.95 1.01 1.02 0.98 0.98 1.01 1.02 0.46Control probe 1770-L1334 0.99 0.94 0.89 0.95 0.93 0.93 0.82 0.96 0.44