Science Advances Cover
6 January 2023, Issue 9, Volume 1
contents
Kijima, Y. et al. A universal sequencing read interpreter (2023) Science Advances 9.
The cover depicts three sets of 672 bases of barcode sequences, which are encoded onto 7-dimensional cubes. Three overlapping cubes are shown, one for each of the three sequencing platforms: 10X Chromium v3 scRNA-seq, Quartz-seq2, and Drop-seq.
Individual bases are encoded by oriented triangles on each of the 2-dimensional faces of the cube (of which there are 672). I created various designs and in the one chosen by Science Advances, the triangles are 7.5% of their full size (more details).
Science Advances caption: DNA sequencing read translation in high-dimensional space. The cover image was created when 672 bases of sequencing barcodes generated by three different single-cell RNA sequencing platforms were encoded as oriented triangles on the faces of three 7-dimensional cubes. Kijima et al. have developed a software tool that interprets DNA sequences to extract encoded information for additional biological analysis. The tool called, INTERSTELLAR, will facilitate development of sequencing-based experiments and sharing of data analysis pipelines.
We wanted to create some kind of high-dimensional encoding of sequences. For this, I looked back to my collaboration with Max Cooper's for his Ascent video from his Unspoken Words album.
In ascent, we animated multiple 5-dimensional cubes to create a flurries of lines and shapes — a promising direction for encoding sequences.
Browse my gallery of cover designs.
Symmetric alternatives to the ordinary least squares regression
What immortal hand or eye, could frame thy fearful symmetry? — William Blake, "The Tyger"
This month, we look at symmetric regression, which, unlike simple linear regression, it is reversible — remaining unaltered when the variables are swapped.
Simple linear regression can summarize the linear relationship between two variables `X` and `Y` — for example, when `Y` is considered the response (dependent) and `X` the predictor (independent) variable.
However, there are times when we are not interested (or able) to distinguish between dependent and independent variables — either because they have the same importance or the same role. This is where symmetric regression can help.
Luca Greco, George Luta, Martin Krzywinski & Naomi Altman (2025) Points of significance: Symmetric alternatives to the ordinary least squares regression. Nat. Methods 22:1610–1612.
Beyond Belief Campaign BRCA Art
Fuelled by philanthropy, findings into the workings of BRCA1 and BRCA2 genes have led to groundbreaking research and lifesaving innovations to care for families facing cancer.
This set of 100 one-of-a-kind prints explore the structure of these genes. Each artwork is unique — if you put them all together, you get the full sequence of the BRCA1 and BRCA2 proteins.
Propensity score weighting
The needs of the many outweigh the needs of the few. —Mr. Spock (Star Trek II)
This month, we explore a related and powerful technique to address bias: propensity score weighting (PSW), which applies weights to each subject instead of matching (or discarding) them.
Kurz, C.F., Krzywinski, M. & Altman, N. (2025) Points of significance: Propensity score weighting. Nat. Methods 22:638–640.
Happy 2025 π Day—
TTCAGT: a sequence of digits
Celebrate π Day (March 14th) and sequence digits like its 1999. Let's call some peaks.
Crafting 10 Years of Statistics Explanations: Points of Significance
I don’t have good luck in the match points. —Rafael Nadal, Spanish tennis player
Points of Significance is an ongoing series of short articles about statistics in Nature Methods that started in 2013. Its aim is to provide clear explanations of essential concepts in statistics for a nonspecialist audience. The articles favor heuristic explanations and make extensive use of simulated examples and graphical explanations, while maintaining mathematical rigor.
Topics range from basic, but often misunderstood, such as uncertainty and P-values, to relatively advanced, but often neglected, such as the error-in-variables problem and the curse of dimensionality. More recent articles have focused on timely topics such as modeling of epidemics, machine learning, and neural networks.
In this article, we discuss the evolution of topics and details behind some of the story arcs, our approach to crafting statistical explanations and narratives, and our use of figures and numerical simulations as props for building understanding.
Altman, N. & Krzywinski, M. (2025) Crafting 10 Years of Statistics Explanations: Points of Significance. Annual Review of Statistics and Its Application 12:69–87.