The Daily Dongle

Comp Matrix file tips

This post deals with compensation matrix file issues for our Mac version and covers:

• Comp matrix file format
• Loading a comp matrix file 
• Comp parameter default naming

 

File Format:

If you save a compensation matrix from flowjo and open the file In a text editor (say TextWrangler), you can see that the values are tab separated with end-of-line CR  (Mac classic). The Parameter names in the text file have to match the parameter names in your FCS data file. (click image below to enlarge)

 

Loading a comp matrix file

From the "Platform : Compensate : Load Matrix Form File" menu item in Flowjo v9, pick your matrix file.

(If someone sent you the matrix file and it is greyed out, the file type may not be TEXT. Some servers will change the HFS filetype. No worries. Open the file in TextWrangler and resave, or use the Terminal unix command SetFile. i.e. SetFile -t TEXT filename

If your matrix list gets long, one can remove an unused matrix from the matrix list in V9.

Comp parameter default naming

By default, FlowJo v9 will name compensated parameters by enclosing the original parameter name with < >. If you want to change the default for a matrix, edit the matrix file changing the " <  >" to what you like, maybe "Comp_"  like in our Java version v7.6.5. 

One can also load a Flowjo v9 matrix file into our Java version, v7.6.5, if it is named with the .mtx extension. 

Three Rules for Compensation Controls

First and foremost, there must be a single stained control for every parameter in the experiment!

In addition, there are three rules for “good” compensation controls:

1) Controls need to be at least as bright or brighter than any sample the          compensation will be applied to

2) Background fluorescence should be the same for the positive and negative control

3) Compensation controls MUST match the exact experimental fluorochrome

 

1) Controls need to be at least as bright or brighter than any sample the compensation will be applied to

An important consideration is to select the sample with the brightest fluorescence of the experiment. “Dimness” is relatively irrelevant. Only brightest matters, and that is so that low spillovers can be accurately estimated. For example, if a spillover is so low that a MFI of 10,000 doesn't cause enough spillover to be above autofluorescence, then the system assumes no compensation is necessary. At a MFI of 100,000, the spillover becomes apparent and then compensation value can be accurately assessed. Compensation is only about estimating the slope. The bottom line is that because the compensation coefficients are computed based on the RATIO of the DIFFERENCE in MFI's (of the spillover channel and the primary channel), so small absolute errors in the position of the negative control become irrelevant as the positive controls become brighter. The error in the compensation coefficient is the sum of the absolute errors in the MFI's of both the negative and the positive control; the latter has an inherently much larger absolute error than the former.

2) Background fluorescence should be the same for the positive and negative control

Any carrier for binding fluorochromes can be used for single stain compensation controls, such as cells or particles. However, the positive and negative carrier of a parameter must have the same autofluorescence. This is because compensation is a subtraction algorithm. It is imperative to NOT include autofluorescence in the compensation calculation, so if the positive and negative have the same autofluorescence, then the autofluorescence contribution to the compensation spillover calculation will be zero. If this is met, one can apply the compensation matrix to any population. For example, one can compensate on particles and apply that to cells.

3) Compensation control fluorochromes MUST match the exact experimental fluorochrome

Each flurochrome has a unique emission profile.  Therefore, the amount of spillover will be different, even for fluorochromes that emit light at about the same wavelength (e.g. FITC and Alexa Fluor 488) 

This rule is even more restrictive when applied to tandem dyes.  Each lot of tandem dye (PE-TR, PE-Cy5, PerCP-Cy5.5, APC-Cy7, etc.) should be considered unique and require its own single stain control. If a user is using two different lots of PE-Cy7 in an experiment, then they need to have two PE-Cy7 compensation single stain controls, one from each lot. Different lots will have different conjugation ratios, i.e. more Cy7 conjugates to PE or less.

One final note

Finally, compensation controls must be treated in the same manner as experimental samples. This is because exposure to light and treatments like fixation/permeabilization may alter the fluorochrome, particularly the tandem conjugation ratio, i.e. lose some Cy7 on each PE molecule.

Compensation particles versus cells for single stain controls

There is no difference in the accuracy of the two approaches for compensation. However, compensation particles do have numerous benefits over using cells. First and foremost, precious sample does not need to be wasted on single stain controls. All the cells can then be used for the experimental samples. In addition, compensation particles typically provide the brightest signal possible for any given parameter. Compensation particles are also more precise. The reason that particles are more precise for compensation is fairly straightforward. Cells have a large variance in background fluorescence (a high CV), higher than particles. This means that the spillover computation has significant error for compensation coefficients where the measurement of spillover fluorescence on cells is dominated by the error in the autofluorescence measurement. On the other hand, the particles have a much smaller error in the distribution of background fluorescence, meaning that the spillover computation is far more precise.

Of course, particles have some limitations.   

1) Compensation particles cannot be used for dyes like PI, DAPI, or EMA -- but they can be used them with amine-reactive viability dyes.  (Also, some manufacturers are now providing specific dyes preloaded into particles to use as single stain compensation controls).

2)  Particles do not bind all antibody reagents and in some cases they simply are not bright enough.

3) For some experimental conditions using tandems (e.g. permeabilization/fixation), one must ensure that the fluorescence spectrum of the experiment does not alter the emission spectrum of the tandems attached to particles in a different manner than it would the tandems attached to cells.

So, in fact, in many experiments, a user may have one or two cell-based compensation controls for some parameters used together with bead based compensation controls for the other parameters.

- Question of comments can be sent to nick@treestar.com

 

Transformation Differences

One of the most frequently questions I'm asked is, "Why does the data look different in FlowJo as compared to DIVA?"

It is a result of the biexponential transformation.

DIVA calculates the "best" transform and typically uses a width basis around -100 by default.  Therefore, the first two decades are compressed into the linear space around 0. 

Since the fcs3.0 standard does not include a keyword for exporting the transform used, it is up to the analysis program to apply the transform to the data.

FlowJo's default is set at -10 by default.  Hence, only the first decade is compressed into the linear space.  This leads to a difference in the visualization of data between DIVA and FlowJo.

We encourage users to start with a width basis of -10 and increase it if necessary.  Too much initial compression can lead to the accidental loss of dim events because they are compressed into the negative population.  While a width basis of -100 often makes the data appear 'cleaner', we recommend users start with a minor transform and scale it up as necessary.  If you prefer to start with a default width basis of -100 in FlowJo, you can set it in the preferences.

For Mac: FlowJo>preferences>workspace tab>digital data

For PC: Edit>preferences>fcs3.0 scaling preferences

Other aquistion programs use different default transforms or no transformation.  You can also turn the transform off in FlowJo by default if you prefer.

- Question or comments can be sent to nick@treestar.com

 

 

FMO vs. Isotype Controls

In addition to compensation controls, there are several other controls that can, and in most cases, should be used to help resolve issue in staining. Fluorescence Minus One (FMO) controls can help identify gating boundaries, isotype controls can help identify staining issues and unstained controls show you the background or autofluorescence of the system.

FMO controls are ideal for showing gating boundaries, even with compensation issues. FMO controls contain every stain in the panel except the one you are controlling for in that sample. For example, a FITC FMO control would contain all fluorochromes except FITC. (You can include isotype controls in FMO controls if you would like, but isotype controls DO NOT accurately show staining boundaries.) The following figure shows how a gate on an unstained or isotype would be set incorrectly as compared to an FMO control. Furthermore, the second pane demonstrates how FMO controls can resolve gating boundaries even with improper compensation:

Isotype controls do not provide gating controls. Every antibody has specific and nonspecific binding properties. Isotype controls are different antibodies than antigen specific antibodies and will have different activity. Isotype controls are good staining controls to identify potential problems in staining, particularly if a primary and secondary antibody are used. However, isotypes do not reliably identify what is negative and what is positive. If users insist on using isotype
controls, makes sure they also titrate them as well, since nonspecific activity at super saturating levels will increase total measured binding and skew the “negative” population more positive.

- Questions or comments can be directed to nick@treestar.com

 

*Images courtesy of Mario Roederer

Overton or SED, which to use for determining percent positive?

There are a lot of ways to decide what comprises "positive" in flow data, statistically.  All available methods begin by binning the data - counting the number of events that fall into discreet ranges. This can be visualized in one dimension as a histogram.  The X-axis units are bins, and the Y-axis are the number of cells in each bin.   FlowJo divides the data into 1024 bins of equal size by default.  To compare whether two samples have a statistically different distribution, or figure out where "negative" ends and positive begins using a control for reference, one must eliminate differences caused simply by having more events in one tube than the other.  This can be achieved though "normalization".

The Overton method is one of the original methods applied to cytometric data for calculating "positive" in unimodal distributions.  It is popular because it is easy to understand and works reasonably well.  The process for normalization in the  Overton method is to find the mode (the bin that has the most cells in it) of each tube and divide the data from that tube by that value.  This puts the data on roughly the same scale (0 - 100%) while preserving features.  The Overton method then subtracts the control data from the comparison tube and counts the number of events that remain per bin, labeling these "positive".

The SED method has never been published.  There is a link for a paper written by Bruce Bagwell (the person who created the SED method, among other things) at the bottom of this article that sums up the algorithms that lead to the SED method...but he never actually says specifically what the SED does differently.  What we've put in FlowJo is essentially the Enhanced Normalization Subtraction Method (ENS) which is very similar to the SED (it lacks some correction factor).  The difference between ENS and Overton is that the normalization is better - the control and test algorithms are normalized so that they have the same area.   This protects the user from bad normalization due to one outlier bin having a huge number of events in it, usually due to a data artifact.  One bin with a huge number of cells would cause normalize one of the tube to a huge number and would then make the scales very different, resulting in a huge number of events to be labelled positive.  The ENS also estimates the probability distribution function of the positive population, and aligns it to the data using the point of maximum difference between samples.  By estimating the shape of the positive population, the algorithm is less likely to create a poor fit because of noisy data.

So overall what is called the SED method in FlowJo is superior to the Overton method because it factors in some safety precautions for data artifacts.  For really nice clean data, there should be almost no difference between the two methods.

Download SED paper by Bagwell.

- Bad Mo'Flow

Send comments and questions to John@treestar.com

Patient-Day Tables (Keyword Iteration)

Many Datasets can benefit from tables created using keyword iteration. A couple users have asked us about this lately. Setting up keyword iterated tables is not so intuitive the first time, so lets walk through it together using a dataset containing tubes from multiple patients, collected on multiple days. (Our Advanced Tutorial makes use of this dataset type).

The default in flowjo table batching creates a row for each tube. For a Patient-Day dataset this may look like the graph below. Admittedly, not the most desireable table, with summary stats not applicable.

 

 

More desireable is a table with different rows for each Day, different columns for each Patient, and useful table summary statistics in the last 2 rows. The table below is created by batching with Keyword Iteration.

If your tube files do not have appropriate keyword values, first create a keyword value series in flowjo. With appropriate Day and Patient keyword values for the tubes, open the Table Editor, choose Tables menu > Iteration Options, then set the Iterator to the Day keyword, and the Descriminator to the Patient keyword.

Now, set up the table by dragging nodes from the Workspace into the Table Editor from tubes that have the same Iterator Keyword Value (same Day in this case). Each dragged in node will create a column in a batched table, each change in the Iterator will create a new row in a batched table. (screengrab below, click on to open a larger image)

 

 

Note:

-The table columns use conditionally formatted numbers, in this case set to be red if the number is more than 2 standard deviations from the mean of the numbers in the column. (Conditional formatting gets set up before batching by selecting a row in the Table Editor and clicking the "i" button).

-Layout Editor batching using keyword iteration works in an analogous manner.

-The screengrabs are from our Mac version 9.3.3, but everything works similar in our Java version 7.6.x that runs on both Windows and MacOSX.