A curated list of Quarto talks, tools, examples & articles! Contributions welcome! - mcanouil/awesome-quarto

New study dates the Petralona skull up to 539,000 years old, offering fresh insight into Europe’s human evolution mystery.

YouTube video by Center for Physical Genomics and Engineering

Scientists are seeking to decipher the role of non-coding DNA in the human genome, helped by a suite of artificial-intelligence tools.

The identification of structural variants (SVs) in genomic data represents an ongoing challenge because of difficulties in reliable SV calling leading to reduced sensitivity and specificity. We…

Building LLMs in Julia? This post walks through creating a PyTorch-style DataLoader that handles batching, shuffling, and next-token prediction. Efficient, clean, and built with MLUtils.jl. https://ln...

Probabilistic cell segmentation for in situ spatial transcriptomics - dcjones/proseg

A comprehensive toolkit for analyzing microscopy data output from 'QuPath' software. Provides functionality for automated data processing, metadata extraction, and statistical analysis of imaging results. The methodology implemented in this package is based on Labrosse et al. (2024) &lt;<a href="https://doi.org/10.1016%2Fj.xpro.2024.103274" target="_top">doi:10.1016/j.xpro.2024.103274</a>&gt; "Protocol for quantifying drug sensitivity in 3D patient-derived ovarian cancer models", which describes the complete workflow for drug sensitivity analysis in patient-derived cancer models.

Genomic analyses suggest that ~15% of transcript molecules are spliced into unproductive transcripts targeted by nonsense-mediated decay, which have a larger effect on gene expression than previously ...

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Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc. - seandavi/awesome-single-cell

Clonal tree inference brings crucial insights to the analysis of tumor heterogeneity and cancer evolution. Recent progress in single cell sequencing has prompted a demand for more advanced probabilistic models of copy number evolution, coupled with inference methods which can account for the noisy nature of the data along with dependencies between adjacent sites in copy number profiles. We present VICTree, a variational inference based algorithm for joint Bayesian inference of clonal trees, together with a novel Tree-structured Mixture Hidden Markov Model (TSMHMM) which combines HMMs related through a tree with a mixture model. For the tree inference, we introduce a new algorithm, LARS, for sampling directed labeled multifurcating trees. To evaluate our proposed method, we conduct experiments on simulated data and on samples of multiple myeloma and breast cancer. We demonstrate VICTree’s capacity for reliable clustering, clonal tree reconstruction, copy number evolution and the utility of the ELBO for model selection. Lastly, VICTree’s results are compared in terms of quality and speed of inference to other state-of-the-art methods. The code for VICTree is available on GitHub: [github.com/Lagergren-Lab/victree][1]. ### Competing Interest Statement The authors have declared no competing interest. [1]: http://github.com/Lagergren-Lab/victree

Chan Zuckerberg CELL by GENE Discover aims to be a one-stop shop for single-cell RNA sequencing data storage, access and analysis. Chan Zuckerberg CELL by GENE Discover aims to be a one-stop shop for ...