You have to hand it to Lex Fridman. His grift is not an amateur job. Take his Twitter photo. A professor standing in front of a blackboard with some math. Right?
This photo (see RHS of image below) is from what he calls his "MIT course" on Deep Learning for Self-Driving Cars. Sounds like good stuff. CS, math, self driving cars. #broheaven. So what is the problem? He is standing in front of the blackboard.
Well first of all, this was an MIT IAP class. IAP is a short period in January when students get to take fun classes on various topic that can be taught by anyone (many by students). I once sat in on a brain dissection. You can learn how to count cards. web.mit.edu/willma/www/mit…
The blackboard Lex is standing in front of has basic calculus left over from an actual real MIT calculus class. It has nothing to do with what he is "teaching". The stuff he is presenting is a joke. You can listen in to some of his rubbish here:
He clearly doesn't know what he's talking about. This explanation of L1 and L2 is 😭 He is standing in front of the formula 1-cos(2θ). I doubt he could tell you what the cosine of an angle is.
Lex doesn't quite lie, but obviously he is far from telling the whole truth. His "research position" is a whole other bunch of bull (for another time). And he uses this MIT mirage to great advantage, creating the perception that he is effectively a professor there.
By now people just take it for granted that he is an MIT prof. And he nurtures that myth. As I said, gotta hand it to him.
Full disclosure: Lex blocked me after I questioned why (with very few exceptions) he interviews only men.
The mantra that spatial transcriptomics is about location, location, location is catchy, but what does it really mean? We have just posted , work of Kayla Jackson et al., that describes the concordex method for identifying spatial homogeneous regions. 1/🧵biorxiv.org/content/10.110…
For years there's been a notion that spatial transcriptomics should allow for the definition of "spatial regions" or "tissue domains". In quotes because they're typically defined as "whatever the algorithm outputs". E.g. GASTON "spatial domains" vs. BANKSY "tissue domains". 2/
It's easy to go in circles. Should cell types be distinguished on the basis of spatial data? Or are cell types purely transcriptomic notions without regard to space? Do spatial regions depend on cell types? Or should they be identified together? We went in circles for a while. 3/
Aristotle was the first to notice honeybees dancing. In 1927 Karl von Frisch decoded the waggle. How it works was "explained" by MV Srinivasan AM FRS in the 1990s. Except @NeuroLuebbert found his papers are junk. A 🧵 about her discovery & our report: 1/arxiv.org/abs/2405.12998
First, if you're not familiar with the waggle, it's Nature magic! Watch this video for cool footage and an introduction.
Aristotle's observations in Historia animalium IX are arguably one of the first instances of observation driven inquiry and science. 2/
Karl von Frisch decoded the waggle, meaning he figured out how the number of waggles, and their direction, communicate information about the distance and direction of food sources. von Frisch won the Nobel Prize for his discovery. But exactly how it works remained a mystery. 3/
A lot of bioinformatics requires editing sequencing reads to facilitate QC and make them suitable for processing. To help with such tasks, @DelaneyKSull developed splitcode, now published at 1/ academic.oup.com/bioinformatics…
The input to splitcode are reads in FASTQ, along with a config file. The output can include edited reads or extracted subsequences, in FASTQ (including gzipped), BAM, or interleaved sequences to stdout. Regions can be identified using absolute location or relative anchors. 2/
The splitcode toolkit was motivated by our need for a versatile tool that can perform a range of tasks from adapter trimming to barcode extraction. Specialized tools exist for many tasks, e.g. fastp, UMI-tools,, etc. Splitcode is more general enabling a lot with one tool. 3/
It's been great to see the positive response of @satijalab & @fabian_theis to our preprint on Seurat & Scanpy, and their commitment to work to improve transparency of their tools. One immediate benefit will be better practice of PCA in genomics. 1/🧵biorxiv.org/content/10.110…
PCA became a mainstay in genomics after the papers of @soumya_boston, Josh Stuart & @Rbaltman () and @OrlyAlter () ca. 2000 demonstrated its power for studying gene expression. 2/worldscientific.com/doi/abs/10.114… pnas.org/doi/10.1073/pn…
Back then, having linear algebra on one's side was essential. A rich lab at that time might have something like a Sun Blade workstation clocking ~500MhZ w/ 2Gb RAM. So having fast SVD algorithms made PCA practical, when other methods based on more sophisticated models weren't. 3/
The difference in @10xGenomics' Cell Ranger's default between version 6 and 7 is discussed in this thread, but it's such a big deal that it's worth its own thread.
tl;dr: in v7 Cell Ranger changed how it produces the gene count matrix leading to a huge difference in results. 1/
The change was described in release notes on May 17, 2022, which via two clicks lead to a technical note with more detail: 2/ cdn.10xgenomics.com/image/upload/v…
To understand this technical note it is helpful to be familiar with the three types of reads that are produced in single-cell RNA-seq: spliced (M as a proxy for mature mRNAs), unspliced (N as a proxy for nascent RNAs), and ambiguous between both (labeled A). 3/