Science songs

I thought I’d compile a list of songs related to biomedical science. These were all found in my iTunes library. I’ve missed off multiple entries for the same kind of thing, as indicated.


  • Grand Mal -Elliott Smith from XO Sessions
  • She’s Lost Control – Joy Division from Unknown Pleasures (Epilepsy)
  • Aneuryism – Nirvana from Hormoaning EP
  • Serotonin – Mansun from Six
  • Serotonin Smile – Ooberman from Shorley Wall EP
  • Brain Damage – Pink Floyd from Dark Side of The Moon
  • Paranoid Schizophrenic – The Bats from How Pop Can You Get?
  • Headacher – Bear Quartet from Penny Century
  • Headache – Frank Black from Teenager of the Year
  • Manic Depression – Jimi Hendrix Experience and lots of other songs about depression
  • Paranoid – Black Sabbath from Paranoid (thanks to Joaquin for the suggestion!)


  • Cancer (interlude) – Mansun from Six
  • Hepatic Tissue Fermentation – Carcass or pretty much any song in this genre of Death Metal
  • Whiplash – Metallica from Kill ‘Em All
  • Another Invented Disease – Manic Street Preachers from Generation Terrorists
  • Broken Nose – Family from Bandstand
  • Bones – Radiohead from The Bends
  • Ana’s Song – Silverchair from Neon Ballroom (Anorexia Nervosa)
  • 4st 7lb – Manic Street Preachers from The Holy Bible (Anorexia Nervosa)
  • November Spawned A Monster – Morrissey from Bona Drag (disability)
  • Castles Made of Sand – Jimi Hendrix Experience from Axis: Bold As Love (disability)
  • Cardiac Arrest – Madness from 7
  • Blue Veins – The Raconteurs from Broken Boy Soldiers
  • Vein Melter – Herbie Hancock from Headhunters
  • Scoliosis – Pond from Rock Collection (curvature of the spine)
  • Taste the Blood – Mazzy Star… lots of songs with blood in the title.


  • Biotech is Godzilla – Sepultura from Chaos A.D.
  • Luminol – Ryan Adams from Rock N Roll
  • Feel Good Hit Of The Summer – Queens of The Stone Age from Rated R (prescription drugs of abuse)
  • Stars That Play with Laughing Sam’s Dice – Jimi Hendrix Experience (and hundreds of other songs about recreational drugs)
  • Tramazi Parti – Black Grape from It’s Great When You’re Straight…
  • Z is for Zofirax – Wingtip Sloat from If Only For The Hatchery
  • Goldfish and Paracetamol – Catatonia from International Velvet
  • L Dopa – Big Black from Songs About Fucking

Genetics and molecular biology

  • Genetic Reconstruction – Death from Spiritual Healing
  • Genetic – Sonic Youth from 100%
  • Hair and DNA – Hot Snakes from Audit in Progress
  • DNA – Circle from Meronia
  • Biological – Air from Talkie Walkie
  • Gene by Gene – Blur from Think Tank
  • My Selfish Gene – Catatonia from International Velvet
  • Sheer Heart Attack – Queen (“it was the DNA that made me this way”)
  • Mutantes – Os Mutantes
  • The Missing Link – Napalm Death from Mentally Murdered E.P.
  • Son of Mr. Green Genes – Frank Zappa from Hot Rats

Cell Biology

  • Sweet Oddysee Of A Cancer Cell T’ Th’ Center Of Yer Heart – Mercury Rev from Yerself Is Steam
  • Dead Embryonic Cells – Sepultura from Arise
  • Cells – They Might Be Giants from Here Comes Science (songs for kids about science)
  • White Blood Cells LP by The White Stripes
  • Anything by The Membranes
  • Soma – Smashing Pumpkins from Siamese Dream
  • Golgi Apparatus – Phish from Junta
  • Cell-scape LP by Melt Banana

Album covers with science images

Godflesh – Selfless. Scanning EM image of some cells growing on a microchip?



Circle – Meronia. Photograph of an ampuole?

Do you know any other science songs or album covers? Leave a comment!

Division Day: using PCA in cell biology

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.

Mitotic spindle in 3D. Kinetochores are green. Microtubules are red.
Mitotic spindle in 3D. Kinetochores are green. Microtubules are red.

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:

  1. PCA
  2. Point-to-plane
  3. 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.

PCAtestGIFNow, 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.

2. Point-to-plane

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.

Other notes

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.



If you want to use it, the code is available here (it seems I can only upload PDF at I used pygments for annotation.

The post title comes from “Division Day” a great single by Elliott Smith.