James Hawley @jrhawley@scholar.social

I'm a PhD candidate in Medical Biophysics at the University of Toronto studying epigenetics and cancer. Math and physics background taking a step into bioinformatics and evolution #introductions

And what's worse is that the samples were all paired, but the tables only contain the means across all the samples, so I can't look at any paired analyses.

This is why putting raw data where possible is useful

Just throw it in a tab-delimited text file and post it on GitHub or something, it's much easier for everyone

I spent my day extracting data from a paper's supplementary tables via OCR and manual checking because
1. the tables were embedded in the PDF
2. the columns were large and hidden, so copying the text led to a jumbled mess
3. they didn't include the tables in a standalone spreadsheet of any kind
4. the corresponding author didn't respond to my emails asking for the tables

I've found a number of other very interesting perspectives from Tukey in his 1961 publication "The Future of Data Analysis". I would highly recommend this as a read to scientists of all kinds

"Scientists know that they will sometimes be wrong; they try not to err too often, but they accept some insecurity as the price of wider scope. Data analysts must do the same" -John Tukey

Emacs users: what's your setup like? What are your favourite packages and configurations? How does using emacs simplify your life?

I've been a long-time #vim user, but I've seen a lot of interesting things about #emacs recently and I'm going to dive in and try it

I can't remember where I got this idea from, but I've been writing a couple sentences as an overview each paper I've read, recently. I have to say, it makes it much much easier to recall the content of a paper that way

Zotero has a really good system for this that makes it very easy to keep those notes appended to your papers, separate from the PDF

I've just discovered that Snakemake does HTML reports as of v5.1...this is amazing

https://snakemake.readthedocs.io/en/stable/snakefiles/reporting.html#

There's even a brand new Bayesian algorithm the authors developed to more accurately detect early and late RNA, which they also introduce with a single sentence.

Those two things alone must have taken an unbelievable amount of time and they don't even take up 2 sentences in the entire paper

To be clear, I don't blame the authors for this. It just amazes me that Nature does this and forces scientists to write such extremely succinct letters that obviously contain way more information than can reasonably be described in 5 pages

The authors are much more extensive in the supplementary information (20 pages long), but to shorten an entire method development process to not even half a sentence seems almost criminal

This has to be the most glossed over description of a methodological breakthrough I've ever seen.

https://www.nature.com/articles/s41586-019-1369-y

The entire paper is possible because of adapting a bulk sequencing experiment for single cell sequencing. This probably took months if not years of work, and the authors describe that process like this:

> After optimization for single-cell sequencing...

Kudos to the Anaconda team for conda v4.7. Solving dependencies and installing packages is now much _much_ faster than it was in v4.6

Here's a really good explanation of where PCR duplicates come from in the sequencing process.

http://www.cureffi.org/2012/12/11/how-pcr-duplicates-arise-in-next-generation-sequencing/

With probabilities, graphs, and a good explanation at the end of why PCR is even necessary