In a new preprint, @sinabooeshaghi et al. present deep SMART-Seq, @10xGenomics and MERFISH #scRNAseq (37,925,526,323 reads, 344,256 cells) from the mouse primary motor cortex, demonstrating the benefits of cross-platform isoform-level analysis. biorxiv.org/content/10.110… 1/15
We produce an isoform atlas and identify isoform markers for classes, subclasses and clusters of cells across all layers of the primary motor cortex. 2/15
Isoform-level results are facilitated by kallisto isoform-level quantification of the SMART-seq data. We show that such EM-based isoform quantification is essential not just for isoform but for gene-level results. #methodsmatter 3/15
Using the 10xv3 data, we also present the first cross-platform validation of SMART-seq isoform quantification (possible by analyzing transcripts with unique sequence near the 3’ end). 4/15
A key result is that many cell types have strong isoform markers that cannot be detected at the gene level. See the example below: 5/15
One exciting application of isoform quantification is spatial extrapolation. We show that in some cases isoform resolution can be achieved spatially even with gene probes. This is a powerful way to leverage SMART-seq for MERFISH and SEQFISH. 6/15
These results argue for a rethinking of current #scRNAseq best practices. We find that SMART-seq complements droplet-based methods and spatial RNA-seq, adding layers of important isoform resolution to cell atlases. 7/15
We recommend droplet-based high-throughput methods for cell type identification, SMART-seq for isoform resolution, and spatial RNA-seq for location information. The whole is great than the sum of the parts. 8/15
The beautiful t-SNE above is a lot cleaner than is usually the case. This is thanks to an idea of @sinabooeshaghi who made it w/ t-SNE of neighborhood component analysis (NCA) dimensionality reduction (@geoffreyhinton, Roweis et al. cs.toronto.edu/~hinton/absps/…) rather than PCA. 9/15
NCA finds a projection that maximizes a stochastic variant of the leave-one-out kNN score given cluster assignments. Intuitively, it projects so as to keep cells from the same cluster near each other, which is exactly what we want. 10/15
You might worry that NCA overfits. It doesn't. We ran a permutation test to confirm that we are seeing real structure in the data. 11/15
As an aside, we found that the t-SNE of the NCA projection preserved global structure better than t-SNE of the PCA projection (e.g., in terms of inhibitory / excitatory neuron classes). It seems that people have been confounding the performance of t-SNE (& UMAP) with PCA. 12/15
All this based on amazing publicly available data from the BICCN, and this is just a preview of the whole mouse brain which is on its way. 14/15
All of this is a result of incredible work by @sinabooeshaghi who did all the analysis single-handedly. Follow him for more interesting #scRNAseq in the near future. 15/15
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My blog passed 3 million views today from more than 1.8 million visitors. There have been a total of 119 posts in just over 10 years.
I'm one of those visitors. The blog is an idea repository and I go back sometimes for recall. Some highlights 1/🧵 liorpachter.wordpress.com
Just today I revisited the PCA post to recall some of the properties of the transform. A student, Nick Markarian, taught me the Borel-Kolmogorov paradox today (topic for a future post) and the post was helpful in thinking about some things. 2/ liorpachter.wordpress.com/2014/05/26/wha…
This year I had the privilege of enjoying in-person conferences again, and in April I met @dvir_a & Dan Gorbonos, from whom I learned a bunch of interesting science. Here we are having burgers at Hans im Glück in Bonn.
And now, a 🧵about genocide.. 1/
The topic came up at dinner. History presents a heavy burden for Jews in Bonn.. even 78 years after WWII. The "Hans in luck" restaurant we were dining at is just a few meters from where the local synagogue was burned down during "Kirstallnacht" in 1938. 2/
Although decades have passed since the holocaust, in Bonn the events felt closer in time. We were attending the Bonn Conference on Mathematical Life Sciences, which held a moment of silence for Holocaust Remembrance Day while we were there. 3/
The virial theorem is a 150-year old tool in (astro)physics. First described by Rudolf Clausius in 1870 in connection with studies of heat transfer, it gained prominence after it was used by Fred Zwicky in 1933 to posit the existence of dark matter. 2/
The virial theorem is elementary calculus. For objects w/ mass m_1,..,m_n at positions z_1,..,z_n, velocities v_1,..,v_n, & acted on by forces F_1,...F_n, the virial "theorem" is the identity shown below. S = \sum_i p_iz_i (p_i is momentum), U is potential energy; T, kinetic.3/
The speech begins with the claim that the public has been losing faith in universities because universities are pushing political agendas instead of catering to excellence. But he provides no evidence of a link.
He says that in 2016 70% headed for college vs 62% now. But provides no evidence that it's because of "American universities abandoning focus on excellence... to pursue... DEI".
We have molecule counts X_{ig} where i ranges over cells & g over genes, and we consider two groups of cells G_1 and G_2 containing n_1 and n_2 cells respectively. Let's start with Seurat which calculates LFC according to the formula below. But what is Y_{ig}? 3/
The "performance" in this analysis boils down to checking consistency of the kNN graph after transformation. That's certainly a property one can optimize for, but it's by no means the only one. In fact, if it was the only property of interest, one could just not transform. 2/
Of course that is trivial and uninteresting. The purpose of normalization is to remove technical noise and stabilize variance. But then one should check how well that is done. And as it turns out, log(y/s+1) actually removes too much "noise". 3/