Hello Bioinformatics lovers,

I was asked this question very often: “Tommy, what’s the p-value cutoff should I use to determine the differentially expressed genes; what log2 Fold change cutoff should I use too?”

For single-cell RNAseq quality control, what’s the cutoff for mitochondrial content?

My answer is always: it depends. I was joking: determining a cutoff is 90% of the work a bioinformatician does.

Why is that? read the full post here: https://divingintogeneticsandgenomics.com/post/bioinformatics-is-not-just-statistics/​

Other resources for this week:

1. I made a new video: How to choose a bioinformatics tool ​
2. ​How to Use Biomart to Find Mouse Orthologs for Human Genes​
3. ​RummaGEO: Automatic Mining of Human and Mouse Gene Sets from GEO​
4. ​Ten quick tips for avoiding pitfalls in multi-omics data integration analyses​
5. ​Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model​
6. Spatial transcriptomics reveals discrete tumour microenvironments and autocrine loops within ovarian cancer subclones https://www.nature.com/articles/s41467-024-47271-y​
7. ​MIT Deep Learning in Life Sciences course ​
8. ​a single line of unix code to merge RNAseq counts file: awk ‘NR == 1FNR > 1{print $0”\t”FILENAME}’ *tsvsed ‘1 s/sample1.tsv/sample/’sed ‘s/.tsv//’csvtk -t spread -k gene -v count
9. Genomics data visualization https://jef.works/genomic-data-visualization-2024/​

Happy Learning!

Tommy

DNA confesses Data speak

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Let's connect on twitter and Linkedin!

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