To validate our analyses, I’ve been using randomisation to show that the results we see would not arise due to chance. For example, the location of pixels in an image can be randomised and the analysis rerun to see if – for example – there is still colocalisation. A recent task meant randomising live cell movies in the time dimension, where two channels were being correlated with one another. In exploring how to do this automatically, I learned a few new things about permutations.
Here is the problem: If we have two channels (fluorophores), we can test for colocalisation or cross-correlation and get a result. Now, how likely is it that this was due to chance? So we want to re-arrange the frames of one channel relative to the other such that frame i of channel 1 is never paired with frame i of channel 2. This is because we want all pairs to be different to the original pairing. It was straightforward to program this, but I became interested in the maths behind it.
The maths: Rearranging n objects is known as permutation, but the problem described above is known as Derangement. The number of permutations of n frames is n!, but we need to exclude cases where the ith member stays in the ith position. It turns out that to do this, you need to use the principle of inclusion and exclusion. If you are interested, the solution boils down to
Which basically means: for n frames, there are n! number of permutations, but you need to subtract and add diminishing numbers of different permutations to get to the result. Full description is given in the wikipedia link. Details of inclusion and exclusion are here.
I had got as far as figuring out that the ratio of permutations to derangements converges to e. However, you can tell that I am not a mathematician as I used brute force calculation to get there rather than write out the solution. Anyway, what this means in a computing sense, is that if you do one permutation, you might get a unique combination, with two you’re very likely to get it, and by three you’ll certainly have it.
Back to the problem at hand. It occurred to me that not only do we not want frame i of channel 1 paired with frame i of channel 2 but actually it would be preferable to exclude frames i ± 2, let’s say. Because if two vesicles are in the same location at frame i they may also be colocalised at frame i-1 for example. This is more complex to write down because for frames 1 and 2 and frames n and n-1, there are fewer possibilities for exclusion than for all other frames. For all other frames there are n-5 legal positions. This obviously sets a lower limit for the number of frames capable of being permuted.
The answer to this problem is solved by rook polynomials. You can think of the original positions of frames as columns on a n x n chess board. The rows are the frames that need rearranging, excluded positions are coloured in. Now the permutations can be thought of as Rooks in a chess game (they can move horizontally or vertically but not diagonally). We need to work out how many arrangements of Rooks are possible such that there is one rook per row and such that no Rook can take another.
If we have an 7 frame movie, we have a 7 x 7 board looking like this (left). The “illegal” squares are coloured in. Frame 1 must go in position D,E,F or G, but then frame 2 can only go in E, F or G. If a rook is at E1, then we cannot have a rook at E2. And so on.
To calculate the derangements:
This is a polynomial expansion of this expression:
where is an associated Laguerre polynomial. The solution in this case is 8 possibilities. From 7! = 5040 permutations. Of course our movies have many more frames and so the randomisation is not so limited. In this example, frame 4 can only either go in position A or G.
Why is this important? The way that the randomisation is done is: the frames get randomised and then checked to see if any “illegal” positions have been detected. If so, do it again. When no illegal positions are detected, shuffle the movie accordingly. In the first case, the computation time per frame is constant, whereas in the second case it could take much longer (because there will be more rejections). In the case of 7 frames, with the restriction of no frames at i ±2, then the failure rate is 5032/5040 = 99.8%. Depending on how the code is written, this can cause some (potentially lengthy) wait time. Luckily, the failure rate comes down with more frames.
What about it practice? The numbers involved in directly calculating the permutations and exclusions quickly becomes too big using non-optimised code on a simple desktop setup (a 12 x 12 board exceeds 20 GB). The numbers and rates don’t mean much, what I wanted to know was whether this slows down my code in a real test. To look at this I ran 100 repetitions of permutations of movies with 10-1000 frames. Whereas with the simple derangement problem permutations needed to be run once or twice, with greater restrictions, this means eight or nine times before a “correct” solution is found. The code can be written in a way that means that this calculation is done on a placeholder wave rather than the real data and then applied to the data afterwards. This reduces computation time. For movies of around 300 frames, the total run time of my code (which does quite a few things besides this) is around 3 minutes, and I can live with that.
So, applying this more stringent exclusion will work for long movies and the wait times are not too bad. I learned something about combinatorics along the way. Thanks for reading!
The first derangement issue I mentioned is also referred to as the hat-check problem. Which refers to people (numbered 1,2,3 … n) with corresponding hats (labelled 1,2,3 … n). How many ways can they be given the hats at random such that they do not get their own hat?
Adding i+1 as an illegal position is known as problème des ménages. This is a problem of how to seat married couples so that they sit in a man-woman arrangement without being seated next to their partner. Perhaps i ±2 should be known as the vesicle problem?
The post title comes from “The Second Arrangement” by Steely Dan. An unreleased track recorded for the Gaucho sessions.
Caution: this post is for nerds only.
I watched this numberphile video last night and was fascinated by the point pattern that was created in it. I thought I would quickly program my own version to recreate it and then look at patterns made by more points.
I didn’t realise until afterwards that there is actually a web version of the program used in the video here. It is a bit limited though so my code was still worthwhile.
A fractal triangular pattern can be created by:
- Setting three points
- Picking a randomly placed seed point
- Rolling a die and going halfway towards the result
- Repeat last step
If the first three points are randomly placed the pattern is skewed, so I added the ability to generate an equilateral triangle. Here is the result.
and here are the results of a triangle through to a decagon.
All of these are generated with one million points using alpha=0.25. The triangle, pentagon and hexagon make nice patterns but the square and polygons with more than six points make pretty uninteresting patterns.
Watching the creation of the point pattern from a triangular set is quite fun. This is 30000 points with a frame every 10 points.
Here is the code.
Some other notes: this version runs in IgorPro. In my version, the seed is set at the centre of the image rather than a random location. I used the random allocation of points rather than a six-sided dice.
The post title is taken from the title track from Bolt Thrower’s “Realm of Chaos”.
Statistical hypothesis testing, commonly referred to as “statistics”, is a topic of consternation among cell biologists.
This is a short practical guide I put together for my lab. Hopefully it will be useful to others. Note that statistical hypothesis testing is a huge topic and one post cannot hope to cover everything that you need to know.
What statistical test should I do?
To figure out what statistical test you need to do, look at the table below. But before that, you need to ask yourself a few things.
- What are you comparing?
- What is n?
- What will the test tell you? What is your hypothesis?
- What will the p value (or other summary statistic) mean?
If you are not sure about any of these things, whichever test you do is unlikely to tell you much.
The most important question is: what type of data do you have? This will help you pick the right test.
- Measurement – most data you analyse in cell biology will be in this category. Examples are: number of spots per cell, mean GFP intensity per cell, diameter of nucleus, speed of cell migration…
- Normally-distributed – this means it follows a “bell-shaped curve” otherwise called “Gaussian distribution”.
- Not normally-distributed – data that doesn’t fit a normal distribution: skewed data, or better described by other types of curve.
- Binomial – this is data where there are two possible outcomes. A good example here in cell biology would be a mitotic index measurement (the proportion of cells in mitosis). A cell is either in mitosis or it is not.
- Other – maybe you have ranked or scored data. This is not very common in cell biology. A typical example here would be a scoring chart for a behavioural effect with agreed criteria (0 = normal, 5 = epileptic seizures). For a cell biology experiment, you might have a scoring system for a phenotype, e.g. fragmented Golgi (0 = is not fragmented, 5 = is totally dispersed). These arbitrary systems are a not a good idea. Especially, if the person scoring is unblinded to the experimental procedure. Try to come up with an unbiased measurement procedure.
|What do you want to do?||Measurement
|Describe one group||Mean, SD||Median, IQR||Proportion|
|Compare one group to a value||One-sample t-test||Wilcoxon test||Chi-square|
|Compare two unpaired groups||Unpaired t-test||Wilcoxon-Mann-Whitney two-sample rank test||Fisher’s exact test
|Compare two paired groups||Paired t-test||Wilcoxon signed rank test||McNemar’s test|
|Compare three or more unmatched groups||One-way ANOVA||Kruskal-Wallis test||Chi-square test|
|Compare three or more matched groups||Repeated-measures ANOVA||Friedman test||Cochran’s Q test|
|Quantify association between two variables||Pearson correlation||Spearman correlation|
|Predict value from another measured variable||Simple linear regression||Nonparametric regression||Simple logistic regression|
|Predict value from several measured or binomial variables||Multiple linear (or nonlinear) regression||Multiple logistic regression|
Modified from Table 37.1 (p. 298) in Intuitive Biostatistics by Harvey Motulsky, 1995 OUP.
What do “paired/unpaired” and “matched/unmatched” mean?
Most of the data you will get in cell biology is unpaired or unmatched. Individual cells are measured and you have say, 20 cells in the control group and 18 different cells in the test group. These are unpaired (or unmatched in the case of more than one test group) because the cells are different in each group. If you had the same cell in two (or more) groups, the data would be paired (or matched). An example of a paired dataset would be where you have 10 cells that you treat with a drug. You take a measurement from each of them before treatment and a measurement after. So you have paired measurements: one for cell A before treatment, one after; one for cell B before and after, and so on.
How to do some of these tests in IgorPRO
The examples below assume that you have values in waves called data0, data1, data2,… substitute the wavenames for your actual wave names.
Is it normally distributed?
The simplest way is to plot them and see. You can plot out your data using Analysis>Histogram… or Analysis>Packages>Percentiles and BoxPlot… Another possibility is to look at skewness or kurtosis of the dataset (you can do this with WaveStats, see below)
However, if you only have a small number of measurements, or you want to be sure, you can do a test. There are several tests you can do (Kolmogorov-Smirnoff, Jarque-Bera, Shapiro-Wilk). The easiest to do and most intuitive (in Igor) is Shapiro-Wilk.
If p < 0.05 then the data are not normally distributed. Statistical tests on normally distributed data are called parametric, while those on non-normally distributed data are non-parametric.
Describe one group
To get the mean and SD (and lots of other statistics from your data):
To get the median and IQR:
The mean and sd are also stored as variables (V_avg, V_sdev). StatsQuantiles calculates V_median, V_Q25, V_Q75, V_IQR, etc. Note that you can just get the median by typing Print StatsMedian(data0) or – in Igor7 – Print median(data0). There is often more than one way to do something in Igor.
Compare one group to a value
It is unlikely that you will need to do this. In cell biology, most of the time we do not have hypothetical values for comparison, we have experimental values from appropriate controls. If you need to do this:
Compare two unpaired groups
Use this for normally distributed data where you have test versus control, with no other groups. For paired data, use the additional flag /PAIR.
For the non-parametric equivalent, if n is large computation takes a long time. Use additional flag /APRX=2. If the data are paired, use the additional flag /WSRT.
For binomial data, your waves will have 2 points. Where point 0 corresponds to one outcome and point 1, the other. Note that you can compare to expected values here, for example a genetic cross experiment can be compared to expected Mendelian frequencies. To do Fisher’s exact test, you need a 2D wave representing a contingency table. McNemar’s test for paired binomial data is not available in Igor
If you have more than two groups, do not do multiple versions of these tests, use the correct method from the table.
Compare three or more unmatched groups
For normally-distributed data, you need to do a 1-way ANOVA followed by a post-hoc test. The ANOVA will tell you if there are any differences among the groups and if it is possible to investigate further with a post-hoc test. You can discern which groups are different using a post-hoc test. There are several tests available, e.g. Dunnet’s is useful where you have one control value and a bunch of test conditions. We tend to use Tukey’s post-hoc comparison (the /NK flag also does Newman-Keuls test).
StatsAnova1Test/T=1/Q/W/BF data0,data1,data2,data3 StatsTukeyTest/T=1/Q/NK data0,data1,data2,data3
The non-parametric equivalent is Kruskal-Wallis followed by a multiple comparison test. Dunn-Holland-Wolfe method is used.
StatsKSTest/T=1/Q data0,data1,data2,data3 StatsNPMCTest/T=1/DHW/Q data0,data1,data2,data3
Compare three or more matched groups
It’s unlikely that this kind of data will be obtained in a typical cell biology experiment.
There are also operations for StatsFriedmanTest and StatsCochranTest.
Straightforward command for two waves or one 2D wave. Waves (or columns) must be of the same length
At this point, you probably want to plot out the data and use Igor’s fitting functions. The best way to get started is with the example experiment, or just display your data and Analysis>Curve Fitting…
Hazard and survival data
In the lab we have, in the past, done survival/hazard analysis. This is a bit more complex and we used SPSS and would do so again as Igor does not provide these functions.
Notes for use
The good news is that all of this is a lot more intuitive in Igor 7! There is a new Menu item called Statistics, where most of these functions have a dialog with more information. In Igor 6.3 you are stuck with the command line. Igor 7 will be out soon (July 2016).
- Note that there are further options to most of these commands, if you need to see them
- check the manual or Igor Help
- or type ShowHelpTopic “StatsMedian” in the Command Window (put whatever command you want help with between the quotes).
- Extra options are specified by “flags”, these are things like “/Q” that come after the command. For example, /Q means “quiet” i.e. don’t print the output into the history window.
- You should always either print the results to the history or put them into a table so that we can check them. Note that the table gets over written if you do the same test with different data, so printing in this case is a good idea.
- The defaults in Igor are setup OK for our needs. For example, Igor does two-tailed comparison, alpha = 0.05, Welch’s correction, etc.
- Most operations can handle waves of different length (or have flags set to handle this case).
- If you are used to doing statistical tests in Excel, you might be wondering about tails and equal variances. The flags are set in the examples to do two-tailed analysis and unequal variances are handled by Welch’s correction.
- There’s a school of thought that says that using non-parametric tests is best to be cautious. These tests are not as powerful and so it is best to use parametric tests (t test, ANOVA) when you can.
Part of a series on the future of cell biology in quantitative terms.
In this post I’ll describe a computational method for splitting two sides of a cell biological structure. It’s a simple method that relies on principal component analysis, otherwise known as PCA. Like all things mathematical there are some great resources on the web, if you want to understand this operation in more detail (for example, this great post by Lior Pachter). PCA can applied to many biological problems, you’ve probably seen it used to find patterns in large data sets, e.g. from proteomic studies. It can also be useful for analysing microscopy data. Since our analysis using this method is unlikely to make it into print any time soon, I thought I’d put it up on Quantixed.
During mitosis, a cell forms a mitotic spindle to share copied chromosomes equally to the two new cells. Our lab is working on how this process works and how it goes wrong in cancer. The chromosomes attach to the spindle via kinetochores and during prometaphase they are moved to the middle of the cell. Here, the chromosomes are organised into a disc-like structure called the metaphase plate. The disc is thin in the direction of the spindle axis, but much larger in width and height. To examine the spatial distribution of kinetochores on the plate we wanted a way to approximately separate kinetochores on one side if the plate from the other.
Kinetochores can be easily detected in 3D confocal images of mitotic cells by particle analysis. Kinetochores are easily stained and appear as bright spots that a computer can pick out (we use Imaris for this). The cartesian coordinates of each detected kinetochore were saved as csv and fed into IgorPro. A procedure could then be run which works in three steps. The code is shown at the bottom, it is wrapped in further code that deals with multiple datasets from many cells/experiments etc. The three steps are:
- Analysis on each subset
I’ll describe each step and how it works.
1. Principal component analysis
This is used to find the 3rd eigenvector, which can be used to define a plane passing through the centre of the plate. This plane is used for division.
Now, because the metaphase plate is a disc it has three dimensions, the third of which – “thickness” – is the smallest. PCA will find the principal component, i.e. the direction in which there is most variance. Orthogonal to that is the second biggest variance and orthogonal to that direction is the smallest. These directions are called eigenvectors and their magnitude is the eigenvalue. As there are three dimensions to the data we can get all three eigenvectors out and the 3rd eigenvector corresponds to thickness of the metaphase plate. Metaphase plates in cells grown on coverslips are orientated similarly, but the cells themselves are at random orientations. PCA takes no notice of this and can simply reveal the direction of the smallest dimension of a 3D structure. The movie shows this in action for a simulated data set. The black spots are arranged in a disk shape about the origin. They are rotated about x by 45° (the blue spots). We then run PCA and show the eigenvectors as unit vectors (red lines). The 3rd eigenvector is normal to the plane of division, i.e. the 1st and 2nd eigenvectors lie on the plane of division.
Also, the centroid needs to be defined. This is simply the cartesian coordinates for the average of each dimension. It is sometimes referred to as the mean vector. In the example this was the origin, in reality this will depend on the position and the overall height of the cell.
A much longer method to get the eigenvectors is to define the variance-covariance matrix (sometimes called the dispersion matrix) for each dimension, for all kinetochores and then do an eigenvector decomposition on the matrix. PCA is one command, whereas the matrix calculation would be an extra loop followed by an additional command.
The distance of each kinetochore to the plane that we defined is calculated. If it is a positive value then the kinetochore lies on the same side as the normal vector (defined above). If it is negative then it is on the other side. The maths behind how to do this are in section 10.3.1 of Geometric Tools for Computer Graphics by Schneider & Eberly (starting on p. 374). Google it, there is a PDF version on the web. I’ll save you some time, you just need one equation that defines a plane,
Where the unit normal vector is [a b c] and a point on the plane is [x y z]. We’ll use the coordinates of the centroid as a point on the plane to find d. Now that we know this, we can use a similar equation to find the distance of any point to the plane,
Results for each kinetochore are used to sort each side of the plane into separate waves for further calculation. In the movie below, the red dots and blue dots show the positions of the kinetochores on either side of the division plane. It’s a bit of an optical illusion, but the cube is turning in a right hand fashion.
3. Analysis on each subset
Now that the data have been sorted, separate calculations can be carried out on each. In the example, we were interested in how the kinetochores were organised spatially and so we looked at the distance to nearest neighbour. This is done by finding the Euclidean distance from each kinetochore to every other kinetochore and putting the lowest value for each kinetochore into a new wave. However, this calculation can be anything you want. If there are further waves that specify other properties of the kinetochores, e.g. brightness, then these can be similarly processed here.
The code in its present form (not very streamlined) was fast and could be run on every cell from a number of experiments, reading out positional data for 10,000 kinetochores in ~2 s. For QC it is possible to display the two separated coordinated sets to check that the division worked fine (see above). The power of this method is that it doesn’t rely on imaging spindle poles or anything else to work out the orientation of the metaphase plate. It works well for metaphase cells, but cells with any misaligned chromosomes ruin the calculation. It is possible to remove these and still fit the plane, but for our analysis we focused on cells at metaphase with a defined plate.
What else can it be used for?
Other structures in the cell can be segregated in a similar way. For example, the Golgi apparatus has a trans and a cis side, which could be similarly divided (although using the 2nd eigenvector as normal to the plane, rather than the 3rd).
Acknowledgements: I’d like to thank A.G. at WaveMetrics Inc. for encouraging me to try PCA rather than my dispersion matrix approach.
The post title comes from “Division Day” a great single by Elliott Smith.