The cover of the album is based on my information graphic of gene expression designed for Scientific American Graphic Science.
bb 3000
Curious machines explore the mysteries of a tumor cell. If you listen carefully, you can hear them hum to themselves and each other. It’s their way of talking.
micro centrifuge
A compact and low profile centrifuge with whisper-quiet operation and extremely fast acceleration and braking. The motorized latch secures the lid and rotary knobs allow fast parameter selection.
Relative centrifugal field (RCF) is the centrifugal force generated by a rotor of radius `R` (cm) spinning at a speed `S` (rpm). It is expressed in units of the Earth’s acceleration due to gravity, `g = 9.8 \, \text{m} / \text{s}^2` and given by `\text{RCF} = 1.118\times10^{–5}RS^2`
MAXIMUM RCF
16,000 g
CAPACITY
24 × 1.5/2.0 mL tubes
SPEED
800–13,200 rpm
automation
Robots automate liquid handling with interchangeable single or eight channel pipettes that can accommodate various tips. They are used to normalize samples, set up chemical reactions, or SPRI bead-clean samples prior to loading sequencers.
CAPACITY
30 tracks
WASH TIME
45 sec/cycle
TIP VOLUME
10, 50, 300, 1,000 μL
PRECISION
1%, 0.75%, 0.75%, 0.75%
server room
Computational pipelines and databases process more than 100 terabases of sequence per month.
BASES SEQUENCED
2,825,069,728,668,255
CORES
30,000
Dell Intel E5
HPE E7 2.5Tb high-memory servers
STORAGE
24 PB
EMC Isilon X410
EMC A2000
NetApp 9000
DDN GS12K + GridScalar
NETWORK
40 Gbps
Brocade MLX Layer 2
Ruckus ICX Layer3
mini centrifuge
This compact centrifuge provide a quick method to spin down samples from the walls and caps of microcentrifuge tubes.
MAXIMUM RCF
2,000 g
CAPACITY
6 × 1.5/2.0 mL tubes
SPEED
6,000 rpm
thermal cycler
The thermal cycler was instrumental to the Human Genome Project.It is used to amplify DNA libraries prior to loading onto DNA sequencers.
RANGE
–5°C – 105°C
ACCURACY
±0.3°C
DENATURE
5' 95°C
CUT & KILL
60' 37°C
5' 70°C
Nasa to send our human genome discs to the Moon
We'd like to say a ‘cosmic hello’: mathematics, culture, palaeontology, art and science, and ... human genomes.
Comparing classifier performance with baselines
All animals are equal, but some animals are more equal than others. —George Orwell
This month, we will illustrate the importance of establishing a baseline performance level.
Baselines are typically generated independently for each dataset using very simple models. Their role is to set the minimum level of acceptable performance and help with comparing relative improvements in performance of other models.
Unfortunately, baselines are often overlooked and, in the presence of a class imbalance5, must be established with care.
Megahed, F.M, Chen, Y-J., Jones-Farmer, A., Rigdon, S.E., Krzywinski, M. & Altman, N. (2024) Points of significance: Comparing classifier performance with baselines. Nat. Methods 20.
Happy 2024 π Day—
sunflowers ho!
Celebrate π Day (March 14th) and dig into the digit garden. Let's grow something.
How Analyzing Cosmic Nothing Might Explain Everything
Huge empty areas of the universe called voids could help solve the greatest mysteries in the cosmos.
My graphic accompanying How Analyzing Cosmic Nothing Might Explain Everything in the January 2024 issue of Scientific American depicts the entire Universe in a two-page spread — full of nothing.
The graphic uses the latest data from SDSS 12 and is an update to my Superclusters and Voids poster.
Michael Lemonick (editor) explains on the graphic:
“Regions of relatively empty space called cosmic voids are everywhere in the universe, and scientists believe studying their size, shape and spread across the cosmos could help them understand dark matter, dark energy and other big mysteries.
To use voids in this way, astronomers must map these regions in detail—a project that is just beginning.
Shown here are voids discovered by the Sloan Digital Sky Survey (SDSS), along with a selection of 16 previously named voids. Scientists expect voids to be evenly distributed throughout space—the lack of voids in some regions on the globe simply reflects SDSS’s sky coverage.”
voids
Sofia Contarini, Alice Pisani, Nico Hamaus, Federico Marulli Lauro Moscardini & Marco Baldi (2023) Cosmological Constraints from the BOSS DR12 Void Size Function Astrophysical Journal 953:46.
Nico Hamaus, Alice Pisani, Jin-Ah Choi, Guilhem Lavaux, Benjamin D. Wandelt & Jochen Weller (2020) Journal of Cosmology and Astroparticle Physics 2020:023.
Sloan Digital Sky Survey Data Release 12
Alan MacRobert (Sky & Telescope), Paulina Rowicka/Martin Krzywinski (revisions & Microscopium)
Hoffleit & Warren Jr. (1991) The Bright Star Catalog, 5th Revised Edition (Preliminary Version).
H0 = 67.4 km/(Mpc·s), Ωm = 0.315, Ωv = 0.685. Planck collaboration Planck 2018 results. VI. Cosmological parameters (2018).
constellation figures
stars
cosmology
Error in predictor variables
It is the mark of an educated mind to rest satisfied with the degree of precision that the nature of the subject admits and not to seek exactness where only an approximation is possible. —Aristotle
In regression, the predictors are (typically) assumed to have known values that are measured without error.
Practically, however, predictors are often measured with error. This has a profound (but predictable) effect on the estimates of relationships among variables – the so-called “error in variables” problem.
Error in measuring the predictors is often ignored. In this column, we discuss when ignoring this error is harmless and when it can lead to large bias that can leads us to miss important effects.
Altman, N. & Krzywinski, M. (2024) Points of significance: Error in predictor variables. Nat. Methods 20.
Background reading
Altman, N. & Krzywinski, M. (2015) Points of significance: Simple linear regression. Nat. Methods 12:999–1000.
Lever, J., Krzywinski, M. & Altman, N. (2016) Points of significance: Logistic regression. Nat. Methods 13:541–542 (2016).
Das, K., Krzywinski, M. & Altman, N. (2019) Points of significance: Quantile regression. Nat. Methods 16:451–452.