Nicola Romanò @nicolaromano@social.cleverlibre.org

Wow, this paper on #scRNAseq and differential expression methods is an eye opener. https://www.nature.com/articles/s41467-021-25960-2

Nice overview of methods and relatively easy to understand explanation of what is wrong with certain, especially single-cell-specific, methods.

Of great help in my own scRNA-seq efforts. Should probably have read this earlier. Now, back to R I go. 😅

#statistics #rnaseq #limma #edgeR #DESeq2

As a blind individual, I have to say that #Caturday on #Mastodon is far far more enjoyable then at #Twitter, where the vast majority of cat photos are not described.

It is also been fascinating for me to hear descriptions written by those who sent the image, immediately followed by Apple image recognition’s attempts to describe the same image. Only rarely does that add anything to the ALT, & often directly contradicts it.

There is no auto magical solution to image description. It is something best done by a human, for only that human can explain the “why” of the image: the reason that image was chosen to speak so eloquently without words for those who can see it. ALT allows you to provide those words.

Pride in history can be detrimental to progress. It can make people delusional that they’re special relating to times they didn’t exist in. The reality of those times is often translated to them as one-sided stories where they imagine themselves to be the heroes. They get stuck.

"More than our rank".
This is an initiative that every university would do well to join, regardless of its ranking.
Indeed, each institution, for various reasons, many of which are its own, is worth much more than the rank assigned to it by a limited, indigent and ideologically connoted system of indicators.
https://inorms.net/more-than-our-rank/

It only takes a few lines of #python code to gain insights from scientific images.

They are just a click away if you are not into coding - ask me about it.
#deeplearning #bioimageanalysis #microscopy

Why? I really started believing in federated platforms (again). That’s what the internet was and should be. I must take the sacrifice for this belief, despite loving the Twitter community.

The slides for my recent intro to open science (OS) talk, including 5 things about OS that everyone should know

➡️ https://osf.io/zy2pc ⬅️

1. OS practices accelerate scientific discovery
2. Adopting OS practices can make you a more competitive job/grant applicant
3. Data sharing is on a continuum (it doesn’t have to be either fully open or fully closed)
4. Take it one step at a time, you don’t have to learn every skill at once
5. Your future self will thank you for adopting OS practices

Move to #Mastodon is going well, but raises an important question: how and when do I get rid of #Twitter altogether?!?
Whats a reasonable timeline and strategy?!? 6 month? When 50% of my contacts moved? Opinions???

🔖 Statistical code in a high-impact medical journal

A journal started asking authors to submit code with their manuscripts. They then analysed the next 314 papers accepted

87% denied using code, even when publishing substantial statistical analysis

10% used code but refused to share it with the journal

For the few that provided code, none scored even moderately on basic quality criteria

Assel & Vickers
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6705117/

#MedMastodon
#OpenScience
#ResearchWaste
#doi:10.7326/M17-2863

Thanks to a toot from @DavidKnuffke that I saw this morning, I tried another #AI tool - Elicit https://elicit.org/
"This workflow tries to answer your research question with information from published papers."
My example is shown here with my initial question in the red box. Wow.
#EdTech #EduTooter #Education #GoogleEDU

Just making sure that the Mastodon #rstats community is aware of the {dadjoke} package. And if you need it, add this to your .Rprofile for a dadjoke on startup:

# Dadjoke on start up
if (interactive())
dadjokeapi::groan(sting = FALSE)

🔖 Do cancer researchers make their data and code available?

306 cancer studies:

16% shared data

4% shared analysis code

1% of data were FAIR (posted to a recognised repository, in a non-proprietary format, with an identifier and a license)

Cancer research transparency was this bad despite many journals having policies mandating openness.

Daniel Hamilton et al
https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-022-02644-2

#MedMastodon
#OpenScience
#ResearchIntegrity
#ResearchWaste
#doi:10.1186/s12916-022-02644-2