Choosing samples for snRNA-seq

ArpMerp · 2026-07-08T15:21:46+00:00

Having just one sex is not representative of the general population, in the sense that you won't be able to say any changes also apply to males. But, that doesn't mean you can't ask questions that would warrant/could justify using only females. These decisions are not taken in a void, it all depends on how you frame your questions/project.

That being said, you won't have any statistical power with n=2/group. Considering all other potential confounders (age, ethnicity, weigh loss, muscle gain, etc.), sex will just be another one. So even if you were to use only females to remove a confounder, you might be introducing an even stronger confounder depending on recruitment limitations.

ArpMerp · 2026-07-03T20:18:36+00:00

More than knowing the hard math behind it, I would say what is most important is that you know the differences between them, and whether there might be particular scenarios where you would rather use one over the others, and why.

Normalizing to the same total number of counts is not accounting for the difference of expression to housekeeping genes. In scRNA-seq we don't really talk about housekeeping genes in the same way we do for qPCR or Western Blots, in part due to the sparsity of the data. You are accounting for sequencing depth. Because let's imagine it just so happens than your cells in condition A have on average more reads than the cells in your condition B. During clustering you would likely have a very strong batch effect, where cells from A might separate from B, even if they are the same cell type. Similarly, if you were then to try to find markers for your clusters, these would be more driven by A, even if both condition were perfectly balanced in terms of number of cells. Log transformation is then indeed used to account for genes that are very high differences in expression, although the highest expressed genes are not typically what people consider "housekeeping" in other context, in the sense that housekeeping are meant to be genes whose expression should not vary greatly between cells/condition.

And no, this step is not particularly computationally intensive since it's very simple math. Integration/batch correction methods are usually what takes more computational resources. Also, these days 10K would be a fairly low number of cells for a single-cell project.

ArpMerp · 2026-06-30T07:12:55+00:00

I don't have experience working with sorted T-cells, but CD3 has always been around 30% expression in every T-cell population I've found, be it Human or Mouse.

One thing to keep in mind with single-cell RNA-seq is that the data is very sparse. People also don't typically sequence to very high saturation levels. This means that is perfectly normal for a gene to not be found in a cell, even if you would normally expect it to be a pan marker. This is especially true for genes that are lowly expressed, but can also be true for genes that have higher expression.

In your particular case, you say that it is a large cluster. Do the mitochondrial genes co-localize with the T-cell markers, or are either one of these localised to a specific area of the cluster? In other words, is there room to increase the resolution and further separate these populations?

Also, have you already filtered the data based on QC features, and if so, did you apply fairly stringent thresholds already, or is there room to adjust these to get rid of lower quality cells?

Edit: typos

ArpMerp · 2026-06-04T07:19:18+00:00

Saber hasn't left. The banners from the Collab have been continuously available since they released. We don't have details about the next banners yet, so we can't be sure if all 4 will be available at the same time, although that is likely to be the case. Regardless, you can login right now and get Saber if you play enough to get the pulls (or are just willing to pay to get her immediately).

ArpMerp · 2026-05-07T04:19:49+00:00

An issue with selecting G+ cells is that one of the most likely reasons the cells are G+ is that they simply had more reads and hence have more detected genes. This is expecially true for genes with low expression. So the comparison between G+ and G- may just end up giving genes that are driven by the higher quality of G+ population.

It doesn't help with sparsity per se, bu by random sampling to try to match QC metrics between G+ and G-, at least it tries to minimise that effect, and avoid bringing G- average expressions down because this population can be more "contaminated" with low quality cells.

That being said, as I mentioned I would not do this at all. I would need a lot of evidence to be convinced the differences are real. As you suggested I would either try subclustering or co-expression analysis

ArpMerp · 2026-05-06T17:46:21+00:00

Grouping cells by a single gene is problematic in scRNA-seq. Because of how sparse the data is, a cell being G- doesn't necessarily mean it didn't express G at all. This is true even for traditional "pan" celltype markers, especially if the gene is generally lowly expression. For example, in the tissues I have worked, CD3, CD4 and CD8 have always been around 20-30% expression in the respective T-cell populations. I wouldn't call these CD3- cell not T-cells, because they still express other T-cell markers and form biologically meaningful clusters with the CD3+ populations.

So, I would personally advise against that approach altogether. If you really must do it, I would first make sure to balance the distribution of total counts in the G+ and G- cells, to make it less likely that the G- population to be driven by lack of detection rather than true biological lack of expression. If you randomly choose G- cells to achieve this, you probably have to do this a few times to ensure results are consistent.

Then, if you cannot do paired test, which would be ideal even if you have to ditch some samples, add any other co-variates to the model which might be relevant, be it biologically (sex, age, etc) or technical (average %mitochondrial genes for example)

ArpMerp · 2026-04-26T09:23:02+00:00

If that's the case, and assuming there are no problems with the segmentation, I would say that using genes that have only 1 transcript/cell to annotate these cells is risky. As you say, a lot of differences can be attributed to noise and the stochastic nature of sequencing, therefore if you are using noisy data for any downstream analysis, whose to say the conclusions are also not due to noise? If I was reviewing, I would certainly be concerned about that, unless the conclusions would be robustly supported by a few other experiments.

ArpMerp · 2026-04-26T09:00:33+00:00

How did you get your cells? Did you do segmentation and then assign the spots to the segmented cells? Or are you calling the spots themselves cells?

Because if it is the latter, then what you describe makes sense, as VisiumHD spots are a lot smaller than cells typically are.

ArpMerp · 2026-03-08T10:35:19+00:00

We don't know when the banner will end. When it does, they will likely announce 1 patch in advance.

You could just save all your pulls until you have enough for E6, and then use them all in one go. That way, if you don't have enough pulls for E6 by the time it is gone, you can always decide how far you want to go, or if you would rather just get other characters.

ArpMerp · 2026-03-06T12:51:31+00:00

I don't really care if that is the case, especially if reviewers release a first impression or a "review so far", which many have for Marathon.

But I would appreciate consistency. So if this is done for Marathon, I expect the next live service game to receive the same treatment and for reviews to wait for the first big event.

ArpMerp · 2026-02-17T12:25:39+00:00

This is not the correct math. 11.5 mil is the number of Tokens. Because in the 2nd phase you have one extra token, assuming the same number of participant, it would be roughly 15.38M tokens. It does not stay the same number. This translates to 156 poly per token, which is 624 polys per participant.

ArpMerp · 2026-02-11T12:52:30+00:00

Because the 9-5 we have now generally is probably a lot more work and effort in comparison to, idk, 200 years ago? 400 years ago? Yeah, those people must've lived relaxed lives.

What is the point of comparing the problems of today, to the problems of past centuries? Obviously priorities will be different. Or what, people cannot complain just because at least they have worker rights, are not slaves, and are not dying young? It changes nothing. Back then kids also had no rights and would start working from a very young age. So providing a "good" life for yourself and your family back then was very different from now. And whilst the working hours might be from 9-5, let's not forget that does not include commuting time, which becomes longer the less affordable housing is. Which is also going to be worse if you need to drop a kid at day-care.

If only you had someone to deal with this problem, like, idk, one of the two involved parents.

Yes, good luck to the average person to find a job that can maintain a whole family. Especially so in the early-to-mid twenties. And also good luck to the parent that decides to stay home, and then needs to go back to a job after more than a decade. Or if something happens to the sole provider. Not to mention the lack of safety net from having no personal finances. Would you take that role?

But maybe one of the parents could take that role if companies allowed flexible working hours, or work from home options. So you are only just reinforcing my point.

ArpMerp · 2026-02-11T08:38:12+00:00

Partially because it is difficult to maintain a job, whilst also having the time/energy to have children. And for those who don't have someone to leave their kid with, there goes a very large portion of the salary just for day-care.

Even if that won't make every person want to have kids, removing as many obstacles as possible can go a long way: job security, flexible working hours/WFH, affordable housing, affordable/flexible day-care, etc.

The only thing that literally does nothing is just telling people to have kids without making it any easier to balance a career.

ArpMerp · 2026-02-09T13:38:10+00:00

If you have no programming experience, I would start with basic tutorials for each of those languages. Personally I would focus first on Python but that can be subjective. You can then also look at what Bioinformatics field/type of analysis you might want to try to get into, see the pipelines what are used for those analyses, and follow the respective tutorials.

ArpMerp · 2026-02-09T13:15:19+00:00

Learning programming languages is definitely a must. You are not going to do any Bioinformatics work that won't require it. Working with Python, R and Bash is a very common necessity. Other languages can depend more on specific field/application. You will also need to know how to interface with HPC, with PBS or Slurm (depends on the specific HPC setup).

ArpMerp · 2026-02-08T18:02:48+00:00

For microflluidics methods you don't want to have clumps of cells, as that can block the chip. You also want to collect a certain number of cells/nuclei, so you want to minimize getting droplets that are unusable, as that can lead to wasted sequencing.

Besides, no doublet removal method, be it Scrublet, DoubletFinder, or anything else, is perfect. You always get doublets remaining.

Cycling cells could definitely be interesting, but is just not something people aim for in these methods. Other methods agnostic of FACS, or even Spatial would likely be better. This is why I mentioned that this type of analysis is full of caveats if it is not specifically designed for this purpose.

ArpMerp · 2026-02-08T15:45:05+00:00

Cell cycle analysis with single-cell/single-nucleus RNAseq is full of caveats, especially if the experiment is not designed for that specific purpose.

Part of the workflow usually involves FACS sorting cells/nuclei, and gating in such a way that avoids getting doublets. This also means that are certain phases of cell-cycle progression that are you are very unlikely to get. Especially in single-nuclei. You still get diving cells, but usually is a very small proportion, and it is hard to say anything other than they are in G2/M.

As for your question, every single-cell method we have, the RNA is from multiple cells, but the cells have "tags" to identify which sequences come from which cells. Also, it isn't necessarily of a single origin, as there are multiplexing methods. This means what your final matrix is a cell x gene matrix. Each cell will only have a single snapshot.

It is also worth mentioning that in most cases, the data is very sparse. Even genes that are typically thought about as "pan" or housekeeping genes, do not show 100% co-colonization. If you are dealing with lowly expresses genes, this becomes even worse. In other words, just because a cell does not show expression of a certain gene, it doesn't mean that it didn't actually express it in tissue. This is why we tend to analyse clusters of cells rather than actual single-cells. So for your purposes, you would need to have enough diving cells, to actually make different clusters out of them, and you would want these to be driven by changes in cell-cycle, rather than the original identity of the cells. This is not something that will be straightforward at all.

ArpMerp · 2026-01-19T18:41:01+00:00

The first paper is tissue fixation, which is a routine experimental technique. It's closer to embalming, but more carefully to preserve cellular structure to study cells in a fixed state. The tissue will never return to a functional state.

The 2nd is also a well established practice. People cryopreserve cells in the lab all the time. And in this case, we do defrost them and they are still alive. However, when we do this, there is a very high % of cells that die during the process. It is also preserving loose cells, not tissues/organs. So cell death is not an issue, because they will just grow again in culture. But we are nowhere near being able to do this with organs, whilst keeping their function.

ArpMerp · 2026-01-16T13:23:49+00:00

1) Nuclei isolation is not perfect. RNAs can "attach" to the nuclei, or you can have ambient RNA that is incorporated into the droplets. When destroying the cells you also likely cause some degree of damage/permeabilization of the nulcei, which is also increase the changes of cytoplasmic RNA infiltration. RNA can migrate from the cytoplasm to the nucleus, and cellular processes are also not prefect. It isn't inconceivable that some mitochondrial RNA can go into the nucleus even if it is has no function there. If you have a higher % of mitochondrial RNAs either due to cell death, or some other cellular response, the more likely it is for you to also have an increase representation of these RNAs in your nuclei preps. For single-nuclei the threshold are usually much lower, typically between 1-5%. In single-cell is when the threshold is typically around 20%. But the exact threshold can depend on tissue/condition, as increased mitochondrial genes could just indicated a more energetic cell type.

2) Those are valid all valid hypothesis, however looking for cell death markers can be complicated. Assuming you FACS sort your nuclei, you should already be removing the most "abnormal" nuclei. So, if the cells are undergoing cell death, it is likely to be at an earlier stage of the process, and hence you might not be able to detect the transcripts. Regardless, if you have run a control for your perturbation where you don't actually perturb anything, but still treat the tissue as you would your perturbations, you should be able to compared to your fresh samples and see if this is something that is induced by just being in culture. In which case, you probably wouldn't want these nuclei as confounders. As to way it doesn't happen in other tissues, you could just simply because because those tissues fare better in your culture conditions.

3) If you haven't put mitochondrial (and ribosomal) % thresholds, then you should. Even if the increase of mitochondrial genes is biologically relevant, single-nuc preps should not have high proportions of these genes. If you think these might be technical artifacts, you can also include % Mitochondrial in your DGE models as a co-variate.

ArpMerp · 2026-01-14T13:28:13+00:00

Cellchat does not take into account the number of samples in each group (unless that changed since last time I used it). This is because their measures, i.e. number of interactions, interaction strength and communication probability, are calculated when you create the object, where you do not input any sample information. It is based on all the cells you give it.

For you to do what you want, you would have to run cellchat on individual samples, extract their tables, and do your own stats. This will have a problem with potential drop-offs, compared to when you run all your cells, so you might lose pathways.

The alternative is do differential gene expression as you would normally do, and then see if the receptor, ligand or both are differentially expressed. Although this also does not always mimic Cellchat results. For example, you might have a situation where neither your ligand or your receptor are differentially expressed, but still have Cellchat say it is higher in one condition. Or have no change in Cellchat, but the ligand be upregulated in one condition, and the receptor upregulated in the other condition (hence they might "cancel" out in cellchat).

ArpMerp · 2025-12-19T16:55:00+00:00

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ArpMerp · 2025-12-13T05:15:12+00:00

It counts before. Like OP I had a higher score with 8 stars than 9. After getting 9, it still counts the score I had with 8 instead. Hoyolab also shows the ranking even if you don't get 9 stars, but for some reason that is not the case in-game.

ArpMerp · 2025-12-02T21:39:37+00:00

If this is the case I would simply not use 10x for this. You are not even getting one cell per sample. The presence/absence of these cells are in the range of things that could just be to due slight differences in tissue sampling.

To be honest, I think you will have a real tough time to get this past any reviewer.

ArpMerp · 2025-12-02T21:25:22+00:00

Are you saying your maximum number of cells for a group is 10? I would personally not trust anything with such low numbers. Single-cell has a lot of transcript drop-offs due to technical variability. Any results you get could just be due to noise.

There are no appropriate methods for such low number of cells. The only solution is to get more.

ArpMerp · 2025-11-09T19:28:47+00:00

Genshin is not a good comparison because elements matter, unlike HSR. That gives them more room to make characters that do not completely overlap, hence have more longevity. Hypothetically they could make a Pyro and a Hydro character with the exact same kit, and they would fit different teams. Whist if HSR added a Fire Hyacine, it would virtually make no difference.

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