Peisen Sun (孙培森)

STMiner, an algorithm specifically designed for complex #spatialtranscriptomic data can remove false-positive genes from the analysis results. We are deeply appreciative of @CellGenomics for affording us the platform to present this significant research outcome.

ANNEVO uses a deep learning approach to advance accurate and scalable ab initio gene annotation of evolutionarily diverse genomes. nature.com/articles/s4159…

🚀 New in Communications Biology: Generative models of cell dynamics - from Neural ODEs to Flow Matching nature.com/articles/s4200… We discuss modeling single-cell dynamics beyond snapshots: from cont-time Neural ODEs to simulation-free flow matching for scalable pop modeling.

@qoder_ai_ide I gave QoderWork a try and found it excellent. However, the trial version's credit of 300 feels quite limited. Would it be possible to increase the credit a bit?

Try QoderWork on anything: coding, research, writing, planning — whatever you’re doing already. Share what you attempted, what changed, and what you learned — even if it’s small. That’s how you become a QoFounder — through real work, not perfect demos.

@boxiangliu @NatureComms Page not found😂

Clever idea: embedding single-cell RNA-seq data on hyperspheres and hyperbolic spaces rather than Euclidean. The geometry better captures cell type hierarchies. Now published in @NatureComms. nature.com/articles/s4146…

Principles for Dealing with the Changing World Order by Ray Dalio youtu.be/xguam0TKMw8?si… 来自 @YouTube

@fabian_theis This is indeed important

Excited to share new preprint led by Sergio Salas on “unassigned RNAs” in imaging-based spatial transcriptomics. Across technologies, 30-40% of transcripts fall outside segmentation, & many show structured, biologically meaningful extrasomatic patterns. 📄 biorxiv.org/content/10.648…

The model of gene expression taught in school is highly misleading! Transcription factors are proteins that bind to DNA and then help repress, or activate, the expression of genes. Cells have hundreds of different types of transcription factors, each tuned to regulate different genes based on short snippets of DNA located near those genes. The basic model, taught in school, says that these transcription factor proteins float around the cell and, when they bump into a DNA sequence, either latch onto it strongly (CORRECT SITE!) or fall off quickly (WRONG SITE) and keep searching. All the other DNA in a cell is basically abstracted away as unimportant or irrelevant; mere background noise. But again, this model is naive! And a new paper, published in Cell, beautifully shows how the sequences SURROUNDING a transcription factor's binding site also matter a great deal. This won't be surprising to many biologists, as "cracks" in the standard two-state model began emerging decades(?) ago. Biologists have tagged transcription factors with fluorescent tags and then watched them move around living cells. And they have noticed that when transcription factors land in a "wrong" location in the genome, they skip or hop to a nearby location and repeat this until finally connecting with the "correct" sequence. So in other words, there are actually three states that a transcription factor can exist in: free-floating, "searching", or "bound." (More technically, transcription factors first do a 3D search, then latch onto DNA and do a 1D search to find the correct location.) For this new paper, though, scientists exhaustively quantified *how* the sequences flanking a transcription factor binding site influence the search of the protein. They did a huge in vitro experiment, wherein they placed a specific transcription factor with a known binding site, called KLF1, in a huge library of 11,812 different DNA sequences. These sequences had mutated "core" binding sites and variations in the flanking sequences. They also prepared negative controls. Then, these researchers measured the binding kinetics of KLF1 with each sequence to understand which bases in the flanking sites impact the 1D search. What they found is that KLF1 has a basically flat disocciation rate from its core sequence, but that the PROBABILITY that it finds this sequence depends a lot on the surrounding context. Even mutations located dozens of bases away from the core site matter a lot, either pushing KLF1 to "hop" faster to find the site, or "trapping" KLF1 and slowing down its search. These flanking sequences can cause up to a 40-fold variation in the affinity of a transcription factor for its target site! This is just one small part of the paper, though, so I encourage anyone interested to read the whole thing. It is challenging throughout.

1989年的杨乐昆在他的电脑上演示了卷神经网络的demo。

Nice city🤩 #Liverpool

please check github.com/xjtu-omics/STM… for detail~

My poster for #ISMB is ready! 🙌🙌🙌 Analyzing Complex Spatial Omics Data Based on Optimal Transport💪💪💪 #ISCB #ECCB

STMiner Github Page: github.com/xjtu-omics/STM…

Utilizing STMiner to analyze complex spatial transcriptomics data, including identifying SVG and their spatial patterns. STMiner also enables to specify gene sets of interest and evaluate which genes share identical spatial expression patterns with them. star-protocols.cell.com/protocols/4224

Mystery Solved!

#scRNA #SpatialTranscriptomcis #Genomics #STMiner

@Jianfeng_Sunny Most studies have focused on downstream analyses, but addressing the biases introduced during the UMI step is indeed noteworthy. Congratulations!😀

🌟A big thanks to Prof. Adam Cribbs for his very important guidance and support throughout this project series

The Tang Dynasty (618–907 AD) female figurines in the Xi'an Museum feature plump figures and serene expressions, embodying the mainstream aesthetic ideals of that historical period. #AncientChina #TangDynasty

Popular methods like UMAP & t-SNE are stochastic and distort data structure. Bonsai - a novel method - builds trees to relate high-dimensional objects, accounting for measurement noise. biorxiv.org/content/10.110…