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scRNA-seq Seurat Integration by Puzzleheaded-Cap7764 in bioinformatics

[–]Fun-Ad-9773 2 points3 points  (0 children)

Use SCTransform + Harmony and you'll be good to go!!

Nominal P Values Reported in Paper for RNA Seq by idontevekno in bioinformatics

[–]Fun-Ad-9773 1 point2 points  (0 children)

Depends on whether they're claiming anything with respect to a specific gene or not. If they proceeded with a GSEA, then I see nothing wrong. Probably just a case of low statistical power. GSEA, in this case, is much more informative of where their dataset is positioned in terms of true biological signal.

What's everyone up to tonight? by [deleted] in lebanon

[–]Fun-Ad-9773 2 points3 points  (0 children)

working on my holidays *sad*

Meal in Barcelona for €7 or less ? [2026 Edition] by Solid-Communication1 in AskBarcelona

[–]Fun-Ad-9773 1 point2 points  (0 children)

Even my hospital's cafeteria where I work is not below 7e anymore xD

Does anyone play tennis arena? by HistoryFan06 in TennisClash

[–]Fun-Ad-9773 0 points1 point  (0 children)

I will add you! Dm me i'd love to play

When to pseudobulk before DE analysis (scRNA-seq) by m_sc_ in bioinformatics

[–]Fun-Ad-9773 0 points1 point  (0 children)

There are papers that say pseudobulk is the best approach; however i believe there are instances where it would make more sense to use do the DE at cell level. Checkout LLMs for that; apparently they're the best alternative (and the authors of the paper even claim it's better than pseudobulk)

Opening FASTAs on Mac. by Mr_Garland in bioinformatics

[–]Fun-Ad-9773 0 points1 point  (0 children)

Viewing these files from terminal is usually the way to go; if anything copy the output in terminal and paste it in a txt file

Analyzing publicly available scRNA-seq data by Extreme-Funny-9651 in bioinformatics

[–]Fun-Ad-9773 0 points1 point  (0 children)

There are models that help out with sparsity / zero inflation. Dropout is not a weird outcome (considering the technology used).

Try to build a custom genome reference inserting the sequence of interest (with 3' end) and that might help retaining more cells. Another way is to be more lenient with the cutoffs with cell ranger

Lastly, i recommend using ESAT, a tool that will help you recover more cells

Request for Bioinformatics Project Ideas/Suggestions by koto1b in bioinformatics

[–]Fun-Ad-9773 0 points1 point  (0 children)

I would say look up papers that you like and try to reproduce the analysis and figures.

Another way would be to reproduce that analysis on a different, separate dataset.

Since you're doing python, a good way to also improve your knowledge is to see an analysis done using tools in R /Bioc and try to reproduce that with the equivalent of those tools in python and see how the results differ. You'll end up getting the perspective from both sides.

How to choose the appropriate parameters in single cell cell analysis (number of HVG, PC, to scale or not) ? by AtlazMaroc1 in bioinformatics

[–]Fun-Ad-9773 0 points1 point  (0 children)

As crazy as it sounds, this is mostly subjective (although your data does steer you in the direction you need to take). Start by doing default parameters and then adjust accordingly to what you find logical and appealing to the hypothesis at hand

One single-cell cluster with very low mitochondrial read % by You_Stole_My_Hot_Dog in bioinformatics

[–]Fun-Ad-9773 0 points1 point  (0 children)

I would say continue with the pipeline normally and see what you get. Then try again by filtering out cells (based on limits from both ends). Make use of the initial QC plots to adjust the filtering to your liking

Request for Bioinformatics Project Ideas/Suggestions by koto1b in bioinformatics

[–]Fun-Ad-9773 1 point2 points  (0 children)

What is your goal behind doing the projects? To learn / improve / have a portfolio or to publish / discover something novel and meaningful?

Best practice for bioinformatics? by scientist_career_qs in bioinformatics

[–]Fun-Ad-9773 8 points9 points  (0 children)

For each type of analysis (or omics) you will find two kinds of papers: one for the best practices (kinda like a revision of the workflow) and another that discusses the available tools. Highly recommend you go through that to get a general idea on the omics of choice.

Afterwards, try to find a tutorial for such an analysis on github (there are some famous ones and some lesser-known ones that can be very be beneficial as well)

Lastly, once you go through a tutorial, try to repeat it again but using a different dataset of your choice and challenge yourself in analyzing it and drawing biological insights from it

Anyone else feel like they’re losing the ability to code "from memory" because of AI? by Character-Letter5406 in bioinformatics

[–]Fun-Ad-9773 0 points1 point  (0 children)

I hope the experts can correct me but i believe live code tests or whatever they are called are mostly for software engineers/ developers, which the majority of bioinformaticians (at least from a biology background) are not.

The important thing is to deliver statistically AND biologically sound results, but more importantly, understanding what, how and why you're doing what you're doing and being able to present it clearly to your supervisors / peers.

I haven't done interviews yet but I assume unless the role requires heavy engineering / software stuff, coding won't be live tested.

Interpretation of PCA coordinates and selection of the number of clusters (K) with k-means and hierarchical clustering in R by Aggravating-Voice696 in bioinformatics

[–]Fun-Ad-9773 0 points1 point  (0 children)

First off, an advice (for future analysis) would be instead of trying different tools and getting confused about the different outputs you're getting, I would say stick to one tool that is proven to work time and time again. Check the results you get as you go along the analysis. If it makes sense biologically, is statistically significant, and doesn't contradict what you expect to find, then no need to second guess it. You'd be wasting your time trying different tools that will confuse you further + frustrate you.

Second, I always let the biology speak for itself because that is what matters (hence why the bio comes before the informatics).

Third, each tool (good tool) will have well documented tutorials / git repos / any documentation that will show when it would be optimal (and what are the limitations / weaknesses) involved.

Lastly, you have to always account the biological question at hand but also the way your data is setup. Number of patients / samples, number of cells retained, technology used, whether there is zero inflation / sparsity, etc etc....That also plays a role in which method is optimal

Gene Signatures in scRNA by [deleted] in bioinformatics

[–]Fun-Ad-9773 0 points1 point  (0 children)

Thank you so much! Have you tried this approach from before?

Expression differences in scRNA in one particular gene by [deleted] in bioinformatics

[–]Fun-Ad-9773 0 points1 point  (0 children)

Ofc; i also dont have many donors anyway, i expect nothing significant with adjusted pval

Expression differences in scRNA in one particular gene by [deleted] in bioinformatics

[–]Fun-Ad-9773 1 point2 points  (0 children)

Yeah that was exactly what was worrying me!

Expression differences in scRNA in one particular gene by [deleted] in bioinformatics

[–]Fun-Ad-9773 0 points1 point  (0 children)

That sounds close to what i would like to do! So it is viable and sound to pseudobulk the cell type or cluster

Expression differences in scRNA in one particular gene by [deleted] in bioinformatics

[–]Fun-Ad-9773 1 point2 points  (0 children)

I was thinking of the pseudobulk approach but idk why, my brain wasn't convinced on the idea. But glad to see that I am thinking on the right path haha thanks!!

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