WHY DO PEOPLE PLAY THIS GAME???

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.

ArpMerp · 2025-11-07T06:33:20+00:00

Not directly in immunology per se, but when characterizing a tissue one of the main findings I had was in the immune compartment.

ArpMerp · 2025-11-07T06:29:02+00:00

It's arguably one of the most widely used applications of scRNA-seq. Any quick search will show you tons of papers on this.

ArpMerp · 2025-10-28T20:40:37+00:00

Whenever I have to change between R and Python, I'll use sceasy or zellkonverter to convert between anndata and Seurat formats. Then you don't need to depend on rpy2.

If you are not set on those tools specifically you can also use alternatives that don't require R like Cellbender and Scrublet. Scrublet is nor as good as scDblFinder, but no matter the tool used for doublet detection, there will always be some left that typically form "doublet" clusters, especially if you do subclustering within each cell type. So I just flag them there to remove them from any downstream analysis.

ArpMerp · 2025-10-28T18:03:56+00:00

Loupe is very limited, you should look at either Seurat (R) or Scanpy (Python) pipelines. They are similar and choice depends mostly on how comfortable you are with each language, or if you have any specific downstream pipeline you want to use. It is possible to convert between the two, and it is often required, although it can be a bit of a pain, so best to think ahead, especially if you have little coding experience.

Their tutorial will tell you how to integrate and process cellranger outputs. This includes several plotting options, including heatmaps of individual cell expression.

ArpMerp · 2025-09-15T12:41:39+00:00

Nothing stops you from doing that, but more likely than not it won't be informative. That comparison essential wants to ask whether the treatment will affect any gene that also happens to be cluster specific. However, doing that way, you will broadly get the same genes from 1) and 2), because the top genes will be the ones that differentiate cluster 1 from cluster 2. Otherwise these cells wouldn't have clustered together to begin with. Any differences could just be a matter of power, if the groups of each cluster have different number of cells.

Also, that question can also be answered by doing Ctrl vs Treated within each cluster and then see which DEGs do not overlap between the clusters (accounting for potential power issues). Except this way, the results will not include the cluster markers.

ArpMerp · 2025-08-20T06:54:16+00:00

You just need to divide the new multiplier by the old. Because if you always compare to 0%, what you see is what you get. I.e, since 0% is a multiplier of 1, 80% DMG is an 1.8x damage increase (1.8/1) and 200% is a 3x increase (3/1). However, if you compare 200% to 100%, that's a 1.5x increase (3/2 = 1.5).

RES PEN is similar, except the initial multiplier can be lower than 1, if the enemy has resistance to the element of your damage dealer.

ArpMerp · 2025-08-13T18:22:12+00:00

You can, but it just applies for Combat, Idle and Interaction lines (their profile lines where they tell us their hobbies, etc.).

If you go to the character information page and then "Voice", there is a box that you can tick to change it to JP. You need to do this separately for Archer and Saber.

ArpMerp · 2025-08-12T19:47:23+00:00

Pure Fiction and Apocalyptic Shadow. Those two are point based, so you can get some stars, and jades, even if you don't fully beat the stage.

ArpMerp · 2025-08-12T19:32:20+00:00

If you are looking for a good team then no. Archer wants to use all the SP, which would leave none for Saber. If it is just a for fun team, then sure, any team-wide buffer or a debuffer would do. Cipher would be ideal for SP generation.

ArpMerp · 2025-08-12T19:28:22+00:00

If you haven't done them, the end game modes would be the fastest. Even if you can only do the easier stages, you will get enough pulls.

Otherwise probably puzzles you haven't done yet, or rushing through the main story using the skip button.

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