🖥️🧬🧪 Publication Alert!

I’m very excited to share our latest paper: “NovumRNA: Accurate prediction of non-canonical #tumor #antigens from RNA #sequencing data”

And... I had a lot of fun with my 5yo who put together a tentative cover for iHet

🖌️ This artwork represents how RNA-seq data generated from different tumors carry information on the capability of immune cells (red dots in the upper part) to infiltrate tumors...

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We demonstrated that iHet has predictive value for #ICB #immunotherapy, which can be further improved by disentangling three major determinants of anticancer immune responses:
- activity of immune cells
- immune infiltration vs. exclusion
- tumor foreignness

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We demonstrated that the iHet signature, despite being derived with an unsupervised approach, is conserved in different cancers and associated with antitumor immunity

Through analysis of single-cell and spatial transcriptomics, we traced back the cellular origin of iHet

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The cellular and molecular heterogeneity of tumors is a major obstacle to cancer immunotherapy
We used a systems biology approach to derive a signature (iHet) capturing the main sources of #TME #heterogeneity from lung cancer transcriptomic data

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🧬 🖥️ Thrilled to share our recent work on TME heterogeneity, published on iScience:

t.co/whajXd0SjU

A joint effort with Federica Eduati's lab
Follow the thread for more🧵👇

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🧬 🖥️ If you are looking for more tools, resources, and insights about next-generation #deconvolution, have a look at our #omnideconv website: omnideconv.org

We provide the scientific community with an ecosystem of tools and resources, #omnideconv, that strongly simplifies the application, benchmarking, and optimization of deconvolution methods: omnideconv.org

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… and systematically assess how deconvolution performance is affected by different sources of variation and bias

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We put together a unique set of real and simulated datasets to comprehensively benchmark second-generation deconvolution tools in different scenarios...

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spacedeconv is a unified interface to different #deconvolution methods and additionally streamlines data preprocessing, visualization, and analysis of cell communication and multimodal data.

🧬 🖥️ Need help for investigating tissue complexity from #spatial #transcriptomics data? Try out our #spacedeconv R package!

💻 Code: github.com/omnideconv/s...
📝 Preprint: www.researchsquare.com/article/rs-5...