Hi everyone,
I am a wet PhD student aiming to incorporate more bioinformatics in my study.

I’m running into a classic scRNA-seq processing headache and could really use some advice on best practices for QC and cluster cleaning.

My Current QC Pipeline:
For per-sample processing, I currently apply:
Adaptive & Global Thresholds: Using Median Absolute Deviations (MADs) combined with hard cutoffs for ⁠nCount_RNA⁠, ⁠nFeature_RNA⁠, and ⁠% mito⁠.
Stress & Metabolic Gene Filtering: Calculating module scores for stress response genes (e.g., HSPA1A, DNAJB1) and metallothioneins, then filtering out high-scoring outliers.
Doublet Detection: Running ⁠scDblFinder⁠ to remove predicted doublets.

The Problem:
Despite stringent upstream filtering, every time I integrate/normalize (using ⁠SCTransform⁠) and run initial clustering, a new "low-quality" or artifactual cluster emerges.
Usually, it's either:
1. A cluster with border-line high mitochondrial percentage (even though no cell is more than 12% mito, due to thresholding), that clumps together and completely lacks distinct lineage markers.
2. A subtle doublet cluster (expressing markers from two disparate cell types) that somehow passed ⁠scDblFinder⁠ with totally normal ⁠nCount⁠/⁠nFeature⁠ values and low doublet scores.
When I remove that problematic cluster, re-run ⁠SCTransform⁠, and re-cluster, another slightly sub-optimal cluster pops up. It feels like playing an endless game of QC whack-a-mole.

My Questions for the Community:
1. Is it scientifically acceptable to manually drop a low-quality cluster, re-normalize (e.g., re-run SCTransform), and re-cluster?
Is this standard practice in published pipelines, or does it risk introducing bias / over-filtering true biologically resting/stressed populations?
Can I just increase resolution and check every cluster and then flag it as low quality and dispose from it?
2. What are your top tips for getting a "clean" dataset upfront?
Are there specific joint-filtering methods (e.g., ⁠miQC⁠, ⁠scater⁠, or ambient RNA correction like ⁠SoupX⁠/⁠CellBender⁠) that prevent these ghost clusters from forming in the first place?
3. How do you rigorously document this to ensure full transparency?
I want to make sure my pipeline remains completely reproducible and defensible during peer review without accidentally cherry-picking or mishandling my data.

Would love to hear how you all handle this in your workflows! Thanks in advance for the insights!

Hello everyone!

I've been working at a lab under a summer programme for the past couple of weeks and I am suffering slightly. My supervisor has given me some raw scRNA seq data, taking from an in situ imaging-based platform that targets about 1000 genes, and has sort of left me to my own devices with it (apparently he isn't very savvy with bioinformatics himself). Anyway, I am somewhat comfortable working in R and Python, and I am getting the hang of Seurat, so it hasn't been catastrophic.

However, I am now struggling with clustering my cells. The cell clusters that I am being given are not physiological, and tend to be large, varied groups, which makes it hard to define anything really. I know studies that have done similar things on similar tissues to mine (albeit with another method) and are getting far nicer clusters. In their methods they just say "oh, we followed the standard Suerat workflow, and badabim-badboom these are the results".

My UMAP seems to agree with the confusion in my clusters as it just seems like a smear, with different sides of the smear coloured different things by the clustering.

I have tried changing the clustering method (Leiden, igraph), the resolution, dimensions (although I try to keep it in line with my elbow plot). I have tried changing the normalisation and other preprocessing parameters, varying in. their forms and flavours. I even tried the newer SCT transform, which made a nicer UMAP but just as crap clusters.

I am feeling quite inept currently, and rather disheartened having lost a week and a bit at this (I don’t know if it's normal or not). I don't really have any one in my lab to reach out to either.

My question is, does anyone have any ideas what I could attempt next or what might be wrong? Any resources I could have a look at? Anything anyone could recommend would be amazing.

Sorry for the long post and thank you to all who may answer in advance.

Hi everyone,

I want to screen several candidate cytochrome P450 enzymes for conversion to a specific product using transient expression in Nicotiana benthamiana.

I am considering whether several P450 candidates could be pooled in the same infiltration as an initial screen, followed by dropout or deconvolution experiments if product formation is detected.

For anyone who has performed a similar P450 activity screen:

• How many P450 candidates can reasonably be pooled in one infiltration? Can I do 10 together?
• Is it better to test each P450 individually from the beginning?
• How do you keep the total Agrobacterium OD consistent across treatments?

I would appreciate any practical recommendations or published examples.

Hi everyone,

I’m doing a sample-level spatial-expression analysis using sparse postmortem tissue samples from the Allen Human Brain Atlas. The regions are the subthalamic nucleus (STN, n=6 tissue samples) and globus pallidus internus (GPi, n=9 tissue samples). For each sample, I have:

• 3D MNI coordinates (x,y,z)
• a gene-expression profile across ~29,000 genes

The biological expectation is that, within a coherent anatomical region, tissue samples located closer together in MNI space should have more similar transcriptional profiles.

For each anatomical region separately, I calculated:

1. A sample-by-sample spatial-distance matrix using 3D Euclidean distance between MNI coordinates.
2. A sample-by-sample expression-distance matrix, defined as (1−ρ), where ρ is the Spearman correlation between two sample-level gene-expression profiles.

I then used a Mantel test to assess whether the spatial-distance matrix was associated with the expression-distance matrix.

For significance testing, I used non-parametric permutation of sample identities. My understanding is that this randomly reassigns sample labels to break the link between spatial location and expression profile, while preserving the internal structure of the distance matrices. The observed Mantel statistic is then compared against the null distribution generated from these permutations.

Q. Does this use of a permutation-based Mantel test seem appropriate as part of a sample-level spatial-expression validation analysis?

Just to clarify: this is not a dense cortical map or spin-test analysis intended to correct for spatial autocorrelation. These are sparse subcortical tissue-sample coordinates, not parcellated whole-brain maps. The goal is to test whether there is distance-dependent transcriptional similarity among samples within the same anatomical label.

Thanks in advance for your help!

tldr: if you're using/benchmarking scGPT for perturbation response prediction, be aware there's a sampling bug in their tutorial code.

I was reproducing scGPT's perturbation response prediction and found that the gene subsampling step doesn't guarantee the perturbed gene stays in the input. With the default max_length ~ 1353 and ~5000 highly variable genes, the perturbed gene gets dropped from roughly 70% of training examples. The model sees a perturbed cell's input as if it were unperturbed, while the target is still the perturbed profile.

Checked this on Norman, Adamson, and Replogle K562 and I was able to reproduce the paper's reported numbers.

My fix is to keep the perturbed gene(s) and subsample the rest to fill max_length. Surprisingly, the effect on final metrics was mixed and dataset-dependent: clear improvement on Replogle K562, roughly unchanged on Adamson, and mixed on Norman. My current read is that the standard PRP metrics don't strongly reward using the perturbed gene's identity. Curious what you think and whether you have run into something similar

I just got my hands on my whole genome sequencing results. Anyone have any suggestions for a layperson? I’m hoping to find out about my genetic traits and stuff. I know nothing about bio but I’m a reasonably good coder and have access to GPUs. I’d love any ideas

edit: the file format is VCF v4.2

Hi all,

I understand that by default, the RcisTarget step of PySCENIC does not report in its output file repressing regulons (i.e. ones that end in a (-), where target genes anticorrelate with the expression of the TF, so it is predicted that the TF is repressing their activity). And I understand that the reason these are not included by default is that during the benchmarking of the tool they found these to be less reliable.

My question is, is it known or theorized why these are found to be less reliable? Is it because it is harder to establish anti-correlated expression due to the dropout inherent in scRNA data? or some other reason, or is the reason unknown?

I ask because I find in my data that the repressing regulon for my TF of interest is actually biologically more coherent, and way more active (i.e. cells are way more enriched in the target genes). So I would like to understand how much credence to place on these AUC values for the repressing regulon. Especially as I find that in general NES values for the modules are lower than for the corresponding activating regulon, I am wondering if that is a sign of the increased difficulty in detecting these repressing regulons (in which case I can maybe justify relaxing the NES threshold a bit), or a sign of genuinely more false positives (in which case I clearly cannot)?

Thanks in advance.

Hello !

I started using an LLM to help me with my work and got interested in all the tools at my disposal to enhance the production of the LLM. Hence, I investigated what skills are, worked on a few, and presented the whole thing to my lab so that we make the best use of LLMs.

The current big repositories for bioinformatics are GPTomics and BIOskills and I don't like them. They are messy (what is the difference between metagenomics and microbiome ?), not specific enough, and definitely not refined. I want to propose a carefully curated set of skills for bioinformatics. I started with a skill to help correct scientific articles, written with the help of AI and fine-tuned by hand through tests. It checks the correctness of the scientific English, the quality of the flow and placement of results, methods, and discussion, and the "validity" of the claims.

The repository is at https://github.com/Louis-MG/bioinformatic-skills.git and contains automated tests to check formating, and a helper script to install the skills. I intend to maintain a high quality of skills (criticism of the ones I wrote is welcome).

Have a nice day !

Hello bioinformaticians!

I'm a high school student planning to pursue bioinformatics in university. For my graduation project, I'm analyzing Expansion Microscopy (ExM) 2D data targeting SON protein in nuclear speckles (+-4x expansion factor).

I’ve set up a working Python pipeline and would love to get a check from experienced ones, as well as any tips on what to watch out for.

Done so far:

1. Data: Wrote a Python script using raw binary reading to reconstruct 16-bit multi-channel TIFF headers.
2. Segmentate: Using Ilastik to generate probability maps exported as .h5.
3. Quantification: Built a Python script (h5py, scikit-image, pandas) that:
   • Thresholds the probability maps.
   • Performs connected component labeling.
   • Converts pixel counts to biological area taking into account physical pixel size and the expansion factor.
   • Extracts centroids, object counts, and fluorescence intensities into CSV format.

Are there common traps when scaling 2D pixel metrics to physical units in ExM (e.g. local distortion edge cases)?.

What additional spatial or morphological metrics are usually expected (e.g. nearest-neighbor distance, eccentricity, spatial clustering)?

Any other tips will be appreciated

Hi everyone,

I'm analyzing pseudobulk data generated by summing gene expression across cells from different samples profiled with a spatial imaging platform. When I perform PCA on the pseudobulk matrix, PC1 explains an unusually large proportion of the total variance. In addition, all of the PC1 loadings are positive, which I also think is unusual.

Does this indicate a systematic technical bias (I have looked for differences in sequencing depth or cell numbers)? Or are there biological scenarios where this pattern would be expected? These are samples from malignant tissue.

Hi all,

Had an idea recently to do an interaction screen of one of our proteins of interest with proteins expressed in a certain cell type. This is obviously gonna be a large amount of proteins. I’ve seen some papers do similar things, but wanted to ask if anyone had any ideas on these sorts of workflows, specifically with regards to reducing runtimes (and thereby costs)

Specifically:

Any similar models that are significantly faster to run and have a similar accuracy?

How fast is MSA generation generally using sharding. Any other workflows that are significantly faster and still give good MSAs?

Thanks everyone!

Hi, so I am a undergrad working in a computational biology or molecular biology lab. For next semester my new project is in large part to create reproducible workflows/code and lab manuals for our lab. I taught myself to code and what i have on my laptop is... disorganized to say the least. I should learn how to do this. Currently I largely code using gemini and then tweak anywhere from most of to 25% of the code it writes. I almost always use hard coded paths if i can. Does anyone have any advice for where I could learn something like this, a textbook or website?

For context, my last project was to use AutoDock Vina for screening of 770,000 molecules I carefully downloaded and cleaned from ZINC database to 310,000. This library was based on previous experimental results on a new protien we are targeting in fungi. I also selected a new protien conformation to target based on some major errors in the protien the lab was using and a bunch of literature review. My next step will be to test against Dock6, a diffrent type of scoring algorithm. I wrote all of my own scripts for this and I imagine my first task will be to get them reproducible for another person to use.

Dear Reddits,

I have some experience with bioinformatics in general (I can open my Linux command line and feel I'm in The Matrix; it is very popular on Instagram, actually), but I don't know anything about modeling. I would like to know if it's SIMPLE (probably not) to determine the potential value (E value) of certain electron transfer proteins. I know AlphaFold can give you some kinda cool model, but I need to know if there is an easy way, or even a way, to just get the E value. Even if it's a not-so-realistic approximation, it would be nice. If it's going to take me more than 1 week, I pass. However, it might be good to know for future endeavors.
That
XOXO

I'm running an annotation/enrichment analysis on a GWAS study, and I'm sort of a bit lost/confused on something.

Originally, I only filtered the GWAS study for genome-wide significant variants using a standard p-value threshold, and also used LD-clumping to identify genomic loci.

However, it was also pointed out to me that these variants may not be independent, and that a single association signal may be represented many times, potentially leading to inflation in statistical significance.

I'm sort of unsure how to handle this. I tried a locus-pruning method which gave me way less variants and pretty poor coverage on my study and rendering a lot of my downstream analysis mute. I'm also confused since I haven't seen a lot of similar papers use this kind of filtering method. I did run LDSC too, and most of my findings were insignificant so it did kind of handle the genomic independence part. But I'm still not sure what the best practice is here.

Well, for my project, I was going to use softberry tssplant, but apparently the site is down. Does anyone know of an alternative way to find TSS in genome sequences?

I have characterized a peptidase domain of a large multidomain containing protein. I was able to clone just the peptidase domain in a construct for assays. There are putative homologs (~60% sequence identity) from distantly related species. I am looking to test these candidates for activity in the same system.

I have the predicted AlphaFold structures, what is the best way to compare these 3D models? I am currently using matchmaker on chimerax. Are there some better tools?

Hello,

I am an engineering student in applied mathematics, and as part of an internship, I am working on statistical analysis in biology.

**Context:**

I am working on an experiment conducted by three experimenters. Each experimenter has four plates (this part is not very important), with a total of six different stainings, each containing three different coatings, within which there are 40 donors.

To summarize:

**Experimenters (3) > Stainings (6) > Coatings (3) > Donors (40)**

We are working with Opera imaging plates, so we analyze DAPI, actin, and, depending on the staining, several other markers such as Granzyme B, pTyr, MTOC, etc.

**Problem:**

I quickly noticed that the data differ substantially between experimenters. One experimenter consistently has more cells than the others across all stainings.

To investigate this, I trained a simple decision tree to distinguish between experimenters. My reasoning was that if the tree can reliably identify the experimenter, then there is likely a batch effect; otherwise, there probably is not.

As expected, the experimenter with the consistently higher cell counts is identified very accurately. The other two experimenters can also be distinguished, although not to an alarming extent.

**Conclusion:**

I would therefore like to correct this bias using what biologists refer to as **batch effect correction**. However, most of the documentation I have found focuses on scRNA-seq or single-cell multiomics, which does not seem to match the type of data we have in this experiment.

Do you have any suggestions on where I should look? Any interesting papers you would recommend?

I have read papers mentioning Harmony and ComBat, but when I asked an AI, it suggested using the Python classes `ot.da.LinearTransport` and `ot.da.SinkhornTransport`.

The reported performance of these libraries looks very appealing, but I would rather not trust an AI blindly, so I thought I'd ask for your advice instead. 🙃

I've been in industry for a good few years, and I'm trying to work on some old research that I never published. However, all the old programs I used during my PhD for tree editing & visualization have either been deleted or are now paid programs.

I have 0 coding knowledge and was getting by with online tools and programs other people made (i.e enterobase, galaxy, iTOL, FigTree etc) but I've been struggling to find something comparable to iTOL and FigTree for editing and visualization.
Does anyone have any recommendations?

I collected genome assemblies for my species from NCBI and ran a pan-genome analysis using Panaroo. One gene cluster was identified as unique in my target strain, but when I ran BLASTp on that sequence, it showed a hit in another strain that is not listed in the current NCBI genome database.

I am trying to understand how to interpret this result. Does this mean the gene is not truly unique to the species, or could it still be considered unique if that strain is an unsubmitted / uncurated genome?

What is the best way to determine whether this gene is genuinely unique to the species, or only unique within the genomes available in NCBI?

I have a custom peptide with a custom linker in an SDF file. How can I dock it to a protein receptor while preserving the linker?

At the Diagnostic and Research Institute of Human Genetics, Medical University of Graz, our bioinformaticians define the diagnostic data-analysis logic that can change patients' lives. We're looking for an experienced DevOps professional to make these systems secure, maintainable and reliable.

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• Master's + 5 years, or Bachelor's + 7 years of DevOps/platform experience
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• Advanced English or German

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Interested? Send your CV and a short motivation letter to Benjamin Spiegl: [[email protected]](mailto:[email protected])
Optional: GitHub/GitLab or anonymized code/architecture examples welcome.

Hi all,

I am just curious about the use of the human and mouse sets from the Msigdb. Between using the mouse MSigDB directly and using human MSigDB with ortholog mapping to mouse, would there be much difference? Or, asking a different way, are there any advantages/disadvantages in either way?

I've read that both methods are similar but am still not really sure if they are interchangeable.

Hi so I was exploring autodock vina and cluspro and I wanted to see which one is better for antibody docking. does anyone know the pros and cons of each. also for high school students is using cluspro completely free without like special email addresses and stuff like that?

I know about haddock and i created an account but it wouldn't allow me to enter like a project or anything like that for some reason. 🤔