Hello Bioinformatics lovers,

First of all, some encouragement. I found this old tweet from me 11 years ago.

This is my audacious goal now: Ming Tommy Tang: On A Mission To Teach 1M People Bioinformatics​

The short answer is: yes, it is possible. You can achieve much more than you think you can!

When I started learning 12 years ago, I had little clue. Now at least you have me. I am here to help!

Let's dive into today's newsletter:)

One of the core questions in scRNAseq is to choose the right resolution to get the right number of clusters. There are statistical methods trying to address this problem. How do they perform?

New tutorial for you: Fine-tune the best clustering resolution for scRNAseq data: trying out callback https://divingintogeneticsandgenomics.com/post/fine-tune-the-best-clustering-resolution-for-scrnaseq-data-trying-out-callback/​

Other resources:

1. The goal of scLinear is to predict antibody derived tags (ADT) data from gene expression data in scRNA-seq data. https://github.com/DanHanh/scLinear​
2. Study 50K cancer transcriptomes through deep learning. data can be downloaded here.
   paper https://www.biorxiv.org/content/10.1101/2024.03.17.585426v1​
3. Batch correction methods used in single cell RNA-sequencing analyses are often poorly calibrated https://www.biorxiv.org/content/10.1101/2024.03.19.585562v1​
4. Best Bioinformatics book I have read https://www.oreilly.com/library/view/bioinformatics-data-skills/9781449367480/​
5. Multicenter integrated analysis of noncoding CRISPRi screens https://www.nature.com/articles/s41592-024-02216-7​
6. Proteome-scale discovery of protein degradation and stabilization effectors https://www.nature.com/articles/s41586-024-07224-3​
7. multi-sample multi-condition differential gene expression for single cell data: Latent Embedding Multivariate Regression (LEMUR) https://github.com/const-ae/lemur​
   python version: https://pylemur.readthedocs.io/en/latest/index.html​
8. sccomp tests differences in cell type proportions from single-cell data. https://github.com/stemangiola/sccomp​

Happy Learning!

Tommy

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DNA confesses Data speak

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