Hello Bioinformatics lovers,

If you work on single-cell RNAseq data, do you know Seurat (in R) and scanpy (in python) had a discrepancy in calculating log2Fold change for marker genes per cluster?

Read more details in this blog post: https://divingintogeneticsandgenomics.com/post/do-you-really-understand-log2fold-change-in-single-cell-rnaseq-data/​

In this video, I show you how to use google gemini multi-modal to write math formula in Latex by feeding a screenshot to it:

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Other useful resources this week:

1. nf-core/fetchngs is a bioinformatics pipeline to fetch metadata and raw FastQ files from both public databases. At present, the pipeline supports SRA / ENA / DDBJ / GEO ids https://github.com/nf-core/fetchngs​
2. A different tool called fastq-dl https://github.com/rpetit3/fastq-dl​
3. In this interview, I shared my journey on learning bioinformatics
4. Supervised learning of high-confidence phenotypic subpopulations from single-cell data https://www.nature.com/articles/s42256-023-00656-y​
5. is single-cell RNAseq data 0 inflated or not? my old post negative bionomial distribution in (single-cell) RNAseq​
6. ​10 immunotherapy papers with scRNAseq datasets​
7. Biosyntax: Syntax Highlighting for Computational Biology. https://buff.ly/430TBcY​
8. ​The LINCS L1000 project has collected gene expression profiles for thousands of perturbagens at a variety of time points, doses, and cell line​
9. ​Imbalanced single-cell data integration leads to loss of biological information​
10. ​rMATS-turbo: an efficient and flexible computational tool for alternative splicing analysis of large-scale RNA-seq data​

Happy Learning!

Tommy

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DNA confesses Data speak

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Let's connect on twitter and Linkedin!

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