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COVID-19 

For large regions (Ile-de-France, Grand Est), the predictions are close to seological result, but the confidence intervals are very large. This isn't necessarily a problem, it just demonstrates that noise in the model is hard to control.

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COVID-19 

Empirical results from serological tests come from 3 studies ("EpiCoV study" [1] and "SpF study" [2], and Carrat et al. [3]) are the most important evidence for testing the model and are presented in Extended Data Figure 6.

[1] drees.solidarites-sante.gouv.f
[2] medrxiv.org/content/10.1101/20
[3] medrxiv.org/content/10.1101/20

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COVID-19 

The findings, if true, in this paper are important.

nature.com/articles/s41586-020

This paper suggests that between 60% and 90% of novel COVID-19 cases in France were not recorded by their healthcare system. This is a particular problem for the common strategy of test-trace-isolate that is used by many countries for handling the pandemic.

My main issue with this paper is that it's almost entirely model-based, with little empirical data or randomized experiments to support their findings.

Why does every new -cell paper "reveal" something?
There are thousands of articles on single-cell measurements that have "reveal" in their title.

pubmed.ncbi.nlm.nih.gov/?term=

This was already a meme a year or so after the first set of single-cell sequencing protocols were published. I'm surprised that it's still going on, years later, and that journals haven't come up with a better word

My PI is trying something new with the recent publications from our lab.

"Discovery Notes": a 5-10 min video explaining the major findings from a recent publication.

youtube.com/watch?v=eADV_NIWCE

Here's the most recent one for a publication on hematopoietic stem cells and how the structure of DNA relates to how quickly they can turn over.

Maybe someone will like this format!

I really like this.

tracy.posthaven.com/the-truth-

This is a thoughtful and honest discussion of what it's like to work hard for something and how life is always going to play a role

This is a good analysis of cancer deaths in Canada, and the trends over time.

erikdrysdale.com/cancer_cansta

Key takeaways:
1. Canada’s aging population accounts for the increase in aggregate and per capita cancer death rates
2. Age-specific cancer death rates are falling for all groups, except for the 85+ which are stable

I've been working on figures for my paper for a while now. I decided to collect some personal thoughts and tips from colleagues and others about use effective use of colour in scientific figures.

jrhawley.ca/2020/11/26/scienti

This was a long time coming from a postdoc in my lab, but his paper is finally out on quiescent stem cells!

cell.com/cell-stem-cell/fullte

In day-to-day , I've needed to modify my `PATH` environment variable. This can be a bit tricky and full of pitfalls, so I made a command line tool to modify it more safely and sanely

github.com/jrhawley/pad-path

I've also written a blog post about it, if you'd like to read that, too

jrhawley.github.io/2020/11/16/

If you find this tool useful, let me know! Feedback and pull requests are definitely welcome

Feedback, questions, and boosts always welcome for what I've described

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I work with data in my work. One part of analyzing Hi-C data that comes up often is aggregate peak analysis. Outside of the supplementary details of a few papers, I haven't seen a lot of discussion or code on how to do it or how to do it well.

I'm hoping to change that with this blog post

jrhawley.github.io/2020/10/29/

Academia is like "Be interdisciplinary and collaborate!"
And
"Present single-authored publications as proof of your worth!"

For a tl;dr, skip to the last section.

jrhawley.github.io/2020/10/15/

I often need to jump back to my notes when reading a new paper on this

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A big blog post about DNA sequencing analysis!

jrhawley.github.io/2020/10/15/

I often find papers describing "differential analysis" methods too complicated and don't talk about the forest from the trees.
I've tried to address the motivation and reasoning behind common statistical models for differential analysis and where some of the complication comes from.

Hopefully something like this will make it easier to see the forest.

Inspired by this earlier blog post^, I discuss more in depth a proof of the Central Limit Theorem. I also talk about something less often discussed, which is the convergence of the sample variance.

jrhawley.github.io/2020/10/08/

I found that using the notation I described earlier helped sort out some points of confusion that I and others have run into when trying to talk about just what the CLT is and does. Maybe this can help others understand it a bit better, too.

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I wrote some thoughts on mathematical notation and why I think certain concepts in stats are confusing as a result of bad notation.

jrhawley.github.io/2020/10/04/

I go through a couple examples that I've seen many people learning stats struggle with. Maybe this will be of use to somebody

My lab is looking for new postdocs and grad students!

If anyone is interested in functional epigenomics and cancer, consider applying!

twitter.com/MatLupien/status/1

Feel free to boost.

covid, canada 

This issue still remains, worse now than before.

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