Hello Bioinformatics lovers,

I can not believe it is already the second quarter of 2024. Are you learning new things about computational biology?

If you master these 6 types of plots: bar plots, scatter plots, line plots, histograms, boxplots/violin plots, and heat maps, you will be able to reproduce 90% of the figures in the genomics paper.

Watch my video here. I will prove you at the end of the video that most of the figures are of those six basic types.

The dotplot commonly used in scRNAseq papers are actually just heatmaps adding size of the circle to represent % of positive cells. see more details in this blog post:https://divingintogeneticsandgenomics.com/post/clustered-dotplot-for-single-cell-rnaseq/​

Other resources in this past week.

1. Minipileup is a simple pileup-based variant caller. It takes a reference FASTA and one or multiple alignment BAM as input, and outputs a multi-sample VCF along with allele counts:https://github.com/lh3/minipileup​
2. Built on top of SpatialData, Sopa enables processing and analyses of image-based spatial-omics using a standard data structure and output. We currently support the following technologies: Xenium, MERSCOPE, CosMX, PhenoCycler, MACSima, Hyperion. https://github.com/gustaveroussy/sopa​
3. ​meet list column and purrr for repetitive work in R​
4. ​Extracting Tables from PDFs using Tabulizer R package​
5. figeno: Tool for making genomics figures in python https://github.com/CompEpigen/figeno​
6. in R, checkout plotgardener for muti-omics datahttps://phanstiellab.github.io/plotgardener/articles/guides/plotting_multiomic_data.html​
7. Package installation can be a big hurdle. How Difficult Is It To Start Your Single-cell Analysis As A Beginner https://notarocketscientist.xyz/posts/2024-01-18-how-difficult-is-it-to-start-your-single-cell-analysis-as-a-beginner/​
8. Navigating the pitfalls of mapping DNA and RNA modifications https://www.nature.com/articles/s41576-022-00559-5​

Happy Learning!

Tommy

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DNA confesses Data speak

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