I have to say this is quite a brutal start with a README markdown as I have nothing prepared but this page just comes into my face. Anyway, let's give a flying-in introduction of me.

I'm not a "coder" for most Github users, but I look like a computing geek in the eyes of those conventional biologists. I have been trained as a "traditional" biologist with all my bachelor, master and PhD in biology. Even now, my main work as a scientist is still doing the "wet" experiments. Why "wet"? Because most of the biological experiments are made in solutions. For example, we need liquid media to culture cells and analyze them in liquid buffer and never dry them (of course they die when lacking water). But since my PhD training in 2009, I start analyzing complex biological data with "heavy" computing. The quotes means this sounds a little ridiculous today to say "heavy" for just launching a Significance Analysis of Microarrays with 1G RAM. But trust me, that was indeed "heavy" for most of biologists working only on "wet" benches, since we needed before that only Excel to calculate p-values for a low sample number (<20 in most cases). Today, we are working routinely with high-throuput sequencing data and single-cell sequencing data, even single-cell multiomics sequencing data, which requires much more competences of "real" informaticians. Most of time, a such experiment can give a big bite in lab's budget so requires a extremely carefull design before start.

When I go back to my bench, I'm an immunologist. My research interests are:

1. immune homostasis setup during a steady state (i.e. a normal no-infection, no-inflammation state)and;
2. immune responses when our bodies get attacked by pathogens (virus, bacteria, etc);
3. disordered of both points above. For example, a disordered immune homostasis --> auto-immune diseases; a disordered immune responses --> incompete immunity;
4. in the level of cell and molecular immunology, that means I study how cells work and what signals make them work and how.

Some useful links:

• My personal website: https://bai-qiang.com (everything built up by me or building me up)
• LinkedIn: https://www.linkedin.com/in/bai-qiang (my main networking place)
• Github page: https://github.com/BlanQwall (I just start using it, don't expect much)
• Lab's page: https://www.gigaimmunophysiology.uliege.be/cms/c_4673923/en/gigaimmunophysiology (what we are doing in lab?)
• ORCID ID: https://orcid.org/0000-0001-7423-0712 (my scientific records)

Sometimes I think I'm just distracted too much by too many things in this wonderful world... But I don't think life hobbies are something expected to show here. So, my interests related to the informatics are below:

• linux bash: mainly for working with analysis pipeline, like file handling, scripts for automatically running long analyses, sometimes working with the read sequences in NGS analysis.
• R: that's the language on which I spend most time every day. Most of my post-sequencing analyses, including almost single-cell analyses, are carried on with the uncountable R packages. I use always Rstudio in my work, so I'm also intested in:
• markdown/rmarkdown: that's a very beautiful part of Rstudio. I learn continuously from this site: https://bookdown.org/yihui/rmarkdown-cookbook/.
• Python: still learning but I don't work much with it. I found quite a lot of interesting Python packages that I like to play for data-mining. Yes, in most of time, that's only for fun.

Of course it's only the languages used in my working or daily life. In a higher level, my interest is to answer the sophisticated biological questions by deep analyzing the huge amount of data generated with the most advanced technologies. These technologies include but are not limited to:

• Next-generation sequencing: to distinguish from the next point, I would say all the sequence (not sequenc-ing) analyses, e.g. genomic variants/SNP, gene structure, TCR/BCR structure, etc.
• Single-cell analysis: here we can generalize the concept to all the techiques that mesure the biological responses of individual cells, e.g. flow cytometry, high-resolution imaging, digital PCR, etc. Most of these data were analzed in a 2-dimension way that might hide a big amount of useful information behind these precious data. I'm trying to re-visit these data and give them new values. However, now a big part of my daily work is to deal with the huge amount of sequencing data from single-cell RNA sequencing, single-cell multi-omics sequencing (transcriptmic + epigenomic). I'm quite interested in all the high-resolution spatial sequencing technologies even though I have not yet really touched any data from it.

Latex

I knew people several years ago writing their scientific paper and thesis with Latex. I thought we had to get really geek enough before writing papers using another language. Microsoft was my friend.

But recently I tried some well-made templates and rendered them neatly into reports. It was at that time I told myself I should also be geek on this...

Machine learning

I mean the real machine learning... Eventually machine learning is very often used every day in my data analysis but I just use it discretely. Now I'm learning how to really design and step-by-step learn the machine to understand data.

That really depends on wet bench work or dry screen work, on research fields. Contact me first.

Don't hesitate to contact me.

• LinkedIn: it's where I make networking. I publish news and comments (when I really have something to say) on it and I check it everytime when notified.
• GitHub: Just start, don't expect too much.
• Twitter: I check for news once per month.
• Email: I still prefer this.