I was excited to read this new preprint on #covid19 testing using NGS until I read the data and code availability section: "NGS data, as well as sample sheets and results are available under request."
Both were published with data as well as open source code and protocols making everything fully reproducible and usable.
Yet many groups continue to insist that it's ok to keep data and code locked away (and come up with all sorts of BS reasons to justify their stonewalling).
I get it. People want to make money from #covid19 testing. But right now this BS is slowing down the roll out of low-cost, scalable, technologies. There are many reasons why testing is broken in the US and this is not the only reason but it is a reason. liorpachter.wordpress.com/2020/07/31/how…
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So this plagiarism thing has happened to our lab.. again. This time it's plagiarism of our poseidon syringe pump paper @booeshaghi et al., 2019 in @SciReports:
Text has been plagiarized, as well as figures copied directly here: 1/🧵nature.com/articles/s4159… ijirset.com/upload/2024/ma…
Here is figure 1 from our paper (LHS) and figure 1 in the plagiarized paper (RHS) published in the "International Journal of Innovative Research" 2/ ijirset.com/upload/2024/ma…
The text seems to have been rewritten with an LLM. Our introduction (LHS) vs. the plagiarized version (RHS): 3/
I've checked this paper out, as instructed. I was also interested in the main result for personal reasons: I'm 51 years old. Is it true that I've just gone through a major change? And that another one awaits me in just a few years?
The main result about major changes in the mid 40s and 60s is shown in this plot (Fig. 4a). First, I redrew it with axes that start at 0, so the scale of change here was clearer. Not as impressive, but maybe it's a thing? 2/
The authors say that this finding is even corroborated in another study (ref 14). But that's not true. I looked it up, and it shows something totally different (see RHS Fig 3c from ref 14). No change in mid 40s, but a change in the mid 30s, and the real change in the 80s 😕 3/
I recently posted on @bound_to_love's work quantifying long-read RNA-seq. In response, a scientist acting in bad faith (Rob Patro @nomad421) trashed our work. This kind of mold in science's bathroom is extremely damaging so here's a bit of bleach. 1/🧵
At issue are benchmarking results we performed comparing our tool, lr-kallisto, to other programs including Patro's Oarfish. Shortly after we posted our preprint Patro started subtweeting our work, claiming we'd run an "appallingly wrong benchmark" and that we're "bullies". 2/
This was followed, within days, by Patro posting a hastily written preprint disguised as research work on benchmarking, but really just misusing @biorxivpreprint to broadcast the lie that our work "... may be repeatable, but it appears neither replicable nor reproducible." 3/
This recently published figure by @Sarah_E_Ancheta et al. is very disturbing and should lead to some deep introspection in the single-cell genomics community (I doubt it will).
It demonstrates complete disagreement among 5 widely used "RNA velocity" methods 1/
This is of course no surprise. In "RNA velocity unraveled" by @GorinGennady et al. in @PLOSCompBiol we wrote 55 page paper explaining the many ways in which RNA velocity makes no sense. 2/ journals.plos.org/ploscompbiol/a…
We're not the only ones to understand how flawed RNA velocity is. The paper from the groups of @KasperDHansen and @loyalgoff is titled "pumping the brakes on RNA velocity". The whole notion of putting arrows on UMAPs is ridiculous. 3/genomebiology.biomedcentral.com/articles/10.11…
Challenge accepted. Here are a few comments on the paper after starting to wade through its massive content. The paper in question is 1/🧵 nature.com/articles/s4158…
First, the claim that "lower OPC fraction across regions and, in particular, in non-neocortex regions was significantly associated with impaired cognition (Supplementary Fig. 37d)" is not true. Supp. Fig. 37d is below. I've boxed in red the panel the claim is based on. 2/
The R^2 value, i.e. proportion of variance explained is 0.0256. The "significance" claim is based on the reported p-value of 0.0071 which is less than 0.05. However significance vanishes once one corrects for the number of tests performed. 3/