I think we're good to go. People have gotten drinks and food and excellent, everything. So we're going to continue into a bit more nitty gritties of once we've planned our single-cell RNA-seq experiment, what do we do now? And once we've got our scone. Yeah, so we're going to look at how we quantify expression in single-cells. So I was supposed to do learning objectives because that's how we do teaching here. I hate learning objectives, so I'm not going to use them. Instead, you all chose to be here. So I want you all to think about what you want to learn in this course. And if we don't cover it or if I don't cover it, ask. Make sure you learn what you wanted to learn. And I want to give you two minutes now. I hope that you're on the slack. If you could post two things you want to learn from this course on the slack right now, that'll be super helpful. And I will make sure I cover all of those things tomorrow. Hopefully you've all written down a couple of things you want to learn. So we'll start off with just how single-cell RNA-seq actually works. So you've heard about all these technologies from Trevor earlier this morning. But they all sort of break down into this pipeline. We start with isolating our single-cells. We collect the RNA out of the single-cells. We turn that RNA into DNA. We amplify that DNA until we have a ton of it instead of one single-cells worth. We then turn that pile of DNA into a sequencing library. We throw it on a sequencer, usually a new Illumina, but other sequencers are available. And then we take that sequence of data back, we process it, and we turn it into a count matrix of our gene expression. And we do all kinds of downstream access to it. So first step, where we have choices coming. So obviously, there's lots of ways of isolating your cells. Trevor already covered that. He also briefly mentioned different ways of turning our RNA into your DNA. So you can do full-length RNA single-cell RNA-seq in one version, or you can do tag-based. I'm not going to talk about full-length because quantification for full-length single-cell RNA-seq is essentially the same as bulk RNA-seq. If you want to learn that, you can do them, of course, just before this one. Instead, for single-cell RNA-seq, almost all of you will probably be using tag-based methods, and they are significantly different from bulk RNA-seq. So that's what I'm going to talk about. So this is your mRNA. So first, to capture this mRNA, we use a poly-DT sequence that hybridizes to the poly-A tail. To this, we add a whole bunch of barcodes to record exactly where this mRNA molecule came from. So first, we add the unique molecular identifier, the UMI. So this is a randomly synthesized barcode. They're typically between 8 and 12 base pairs, giving you over 50,000 possible combinations. So in theory, this should be unique to a particular mRNA molecule in a particular cell. In practice, it's not quite that straightforward. So we end up actually having to use the combination of what Reed does my UMI link to and what is my unique molecular identifier to identify a unique molecule in our synthesis. We then have to add a cell barcode. These are 14 to 18 base pair sequences, and they come from a predetermined pool. These are predetermined so that we can make sure there are at least two base pair differences between any two cell barcodes, which makes it much easier to determine precisely which cell our RNA reads come from, because there are, of course, sequencing errors. So we can get one or two errors in these cell barcodes, but as long as we've got two base pair differences between our different barcodes, we can get almost all of our reads correctly matched to the correct cell. So if you're using 10x genomics, they have a whole bunch of different whitelists. Depending on which particular chemistry you used, you need to make sure you're using the right whitelist. So they're all available, I'm going to say. So once you've got your cell barcode, you then also add a 10x barcode. These are 10 base pair differences, base pair sequences, again, from a predetermined pool. They come in a kit of 96 different ones, and they're used to identify a particular sample. So if you sequence 12 different mice, you give them each a different 10x barcode, then you can pool them all together when you sequence them, and your sequencing costs less, which is good. Then we add a PCR primer so that we can amplify this like crazy. This gets reverse transcribed by reverse script days into CDNA. It ends with some poly C at the end, which allows us to hybridize it, switch all of the nucleotide to those poly Cs. With our other PCR primer, reverse transcript days goes back to synthesize the other strand. Now we have a CDNA that we can amplify with standard PCR. So next we amplify this like 12 times through PCR. So we get tons and tons of DNA. And then to prepare our libraries, we have to fragment this into 10 base pair, 300 base pair fragments for our sequencing machine if we're doing alumina. If you're doing nanopore, you don't fragment it and you sequence the whole transcript. But almost nobody does that because it's expensive and single cell is expensive enough. So we're going to alumina with 300 base pair fragments. And we do paired and sequencing to get to 100 base pair reads back from our sequencing machine. All right, so what are these reads? So first read one is purely dedicated to our barcodes to make sure we sequence all of those barcodes all together. And then it will run into the poly A tail, but most of the time just going to be a whole bunch of A's. So that's not actually useful for us at all. So we just trim that off and throw it away and we just get our set of reads from read one. Then our matching read or paired read read two is in our transcript fragment. And that's where we can actually see what gene does this molecule belong to. You may also get an eye read if you use cell ranger to generate your fast cues from your sequencing machine output. The eye read just contains the sample index. It's not really actually important. You can do everything you need but just read one and read two. Now there's two different versions of 10x genomics and some other protocols. So you can do either three prime or five prime sequencing. And all that's different here is which. All ago, we're attaching all of our barcodes too. So if you wanna do five prime single cell RNA seek, take all those barcodes, attach them to the switch all ago and you're done. You're now doing five prime single cell RNA seek. Everything else is exactly the same. Yeah. Is there a high-resonance reduction? Nope. Because it's a lot more common. Three prime is much more common basically because it was first, because we were using the polytease to capture the RNA. So it was like, oh, well, we'll just add our barcodes to that and we'll do sequence of three prime end. In theory, there's no efficiency difference. However, you will know, note if you do both 3 prime and 5 prime, you cannot just put those back together again. Because of the tons and tons of amplification we do, obviously the sequence at the 5 prime end of your gene is different than the sequence at 3 prime end of your gene. So any little bias in that amplification that depends on the sequence, like the GC content will cause a bias on your technology on which end you're using, and you can see that. So if you take 3 prime and 5 prime from the same sample, stick them together, they don't integrate unless you apply some sort of integration to it. No, so for TCR and BCR, you have to do the 5 prime end because that's where the variable section is. So if you do 3 prime sequencing, you can still see the constant portion of your TCRs and BCR, but you cannot see the variable portion because that's at the 5 prime end of the gene. Oh, so why don't you have it after the fragmentation? Because the switch all ago is at the 5 prime end. Oh, don't have it any? Oh yeah, so it is possible that after fragmentation you can get your breed 2 to just be polyatail. Yeah. Oh yeah, sure. Yeah, so the question is, is it possible that after fragmentation you won't have any gene sequence in your fragment anymore? And that is possible if you have a particularly long polyatail, though it's fairly unlikely, but it can also happen if your sample is really badly degraded and all of your transcripts are really short now, and then you'll get lots of poly T in your breed 2s. I don't see any other questions. And again, our breed 1 is going to be all our barcodes, and our breed 2 is in our CD9 transcript, the actual gene. So that's what in theory should happen, and that should be awesome, but people make mistakes. So what would happen if your sample is super degraded and you had really short transcripts? I already kind of answered that.  But what would happen if you use the wrong cell barcode whitelist? Or if there's a strong bias when you're generating your UMI's towards certain sequences? Maybe a couple minutes to think about these and feel free to discuss with the people next to you and go through the answers. What would happen? Okay, I think everyone's had time to think about these. So answers from the crowd. What would happen if you use the wrong cell barcode whitelist? No one feeling confident in their answers. Oh, yeah.  So you'd end up with like half or a third the number of cells you're actually expecting to see, and all the rest would have gotten thrown out by CellRanger or whatever tool you're using to quantify your single cell RNA seed. What would happen if there's a strong bias towards certain sequences in your UMI's? So it wouldn't affect the differential expression of certain transcripts. Yeah, so when the UMI's are independent of the genes, so it won't affect any gene in particular, they're random in theory, but they're generated by a particular process in the lab. So when we say random, they're technically random, but they're not actually random. If you actually sequence and count the number of UMI's you get of different sequences, they are not evenly distributed across all possible sequences. There are biases towards certain sequences in there. Yeah. So your expression level quantification will be an underestimate because you'll have multiple repeated UMI's that are actually taking different molecules, but they just happen to have the same UMI because you have a bias in your UMI pool. So the true frequency of UMI's is not completely random, which is why we end up using both the gene your UMI is linked to in that second read and the UMI code when we're counting how many molecules there are for each gene in your pool because those UMI's are not actually going to be unique in a single cell because they're not actually, there isn't actually equal frequencies of 50,000 different UMI's. Certain UMI's are more frequent than others. Okay. So this is how single cell RNAseq works. So what can we capture with single cell RNAseq? So mRNA, we're going to capture really well because those are poly A tails. Minor congeal RNAs, RNAs from the mitochondrial genome, we also capture really well. They have poly A tails. Micro RNAs you do not see. T RNAs you can't see. And RNA, so ribosomal RNA, you're also not going to capture. So they don't have poly A tails. But viral transcripts sometimes maybe you can capture them because if you have a transcript that is very high in A's and T's that you can have in certain viral genomes, you might have a very high AT rich genome for your virus. There you can get internal priming. You can just have a spontaneous set of poly A's in that, in a gene and then that will get captured. The same goes for bacteria. If you have a bacteria that's very AT rich, you can have spontaneous poly A sequences just in the middle of your gene and get internal priming and get recorded gene expression for that gene. But if you have a very GC rich genome, you won't see it. In theory, yeah, if you do single nucleus RNA seek, you should get no mitochondrial RNA reads. But in practice, it depends on what protocol you use to extract the nuclei from your cells. Some of them will truly get you zero mitochondrial reads. Some of them you can still get some mitochondrial reads because it captures not just the nucleus but some of the stuff stuck to the nucleus as well. Not really.  You'd have to look in the literature for your particular system if you want to know if your virus can be captured or not. Or download a publicly available dataset for your system and see if you can find it because a lot of people don't check to see if they can find it or not. There's tons of datasets available. Generally, all the tools we use to do the quantification throw out everything that doesn't match the human genome. So if you take everything that doesn't map from your initial mapping and then basically remap it to viral, yeah, it's a second pipeline of that leftovers. You can look at whether they map to bacterial transcripts, viral transcripts. I've heard a report that if you take that sort of junk that's thrown away and map it to microRNAs, sometimes you can get one or two microRNAs in there as well. But it's a secondary pipeline. You'd have to design yourself. I don't think the pipeline that was used for those studies is actually available. Oh, yes, link RNAs. If they're poly A, have poly A tails, you can capture them. They don't, you can't. And the problem with link RNAs is also they tend to be very lowly expressed. So even if in theory you can capture them, just by chance you might not see them. Except for mallet one, which you will see everywhere and everything and it drives everyone crazy. Yeah, everyone here who's done some analysis themselves has seen mallet one. It is everywhere. It can. Yeah, it can't. I would not recommend it though. If you're going beyond sort of model systems, I would recommend using star solo instead of cell ranger, because I'll cover this a bit later in this talk star solo allows you to tune all of the parameters used in mapping, which can greatly improve your sensitivity for non model organisms. Cell ranger in theory, you it's all open and you can tune the parameters in practice. It's very difficult to change the parameters. So it's basically a black box, what you put in your input and you get out your output and you can't really change anything about it. This brings me to the next slide, which is talking about that exactly. So quantifying gene expression. Okay, so we've got we've thrown our data on the sequencing machine. We've got our fast Q files back. Now we need to take those fast Q files and turn them into you and my count matrix. Right. So the first step is to map the reads to the transcriptome. So here you need to consider what genome you're using. So if you're using master human, you can easily download the genome. If you're using a non model organism, you may or may not have a good genome available. So you also need to consider your annotation quality because we're mapping to the transcriptome. If you're transcriptome, your transcript annotations are garbage, you're not going to get very much out of your single cell RNA seek. So you may want to consider recalling your transcripts using your single cell RNA seek data if you're using a bad genome for some weird non model organism. You also need to consider about whether you're going to include introns and exons. So until last year, the recommendation was to only include exons in your transcriptome. As of July 22, 2002, 2022, it's now recommended you include introns when you're mapping your reads because you get about 10% to 15% more reads mapped and counted in every map. Every read counts when our number of UMIs for a lot of our genes are one or zero. You then have to assign reads to cells. So this is where your whitelist comes in and then you count your UMIs. So things to consider here is how you deal with multi-mapping reads. If you have a read with a UMI that maps multiple different genes, which genes you count that UMI for and how do you do with sequencing errors in your UMIs? You could have two different UMIs mapped into the same gene that are off by one base pair. Are those actually two different molecules or is that the same molecule? But you have a sequencing error in one of those UMIs. And our standard tools for doing this are CellRanger, which probably most of you, if you've got data, have used or has had your data run on it because that's released by 10x Genomics designed for 10x Genomics staff. We're not going to run it in this course because it's super boring to run it. It's one command, put it on to Linux, you wait eight hours, you get your UMI count matrix back. There's also StarSolo. So CellRanger is based on the alignment tool map alignment tool Star with some slight modifications of parameters in the genome. People who made Star weren't too happy about CellRanger getting all the credit. So they made their own version of CellRanger called StarSolo, which again is still based on Star. And it tries to do all the downstream steps as close to CellRanger as possible. So in theory, you get the same answer. In practice, you don't because there's secrets that 10x Genomics doesn't tell you some of the time. So it's not quite the same, but it's in theory the same. The benefit of StarSolo, as I mentioned, is you can tune any parameter you want, which means the key ones there are the mismapping rate. So if you're using, say you're using a new species that doesn't have a genome available and you're using its closest relative that does have a sequence genome, then you're going to have a higher mismapping rate. And if you use CellRanger, a lot of your reads are going to be thrown away. If you use StarSolo, you can change that mismatching rate to be more permissive. And all that read, all those reads come back and can be useful. There's also Alvin, which is based on pseudo alignment. I've met one person who's used it, so I'm not going to talk about it. Okay. So I mentioned sequencing errors. So when I talk about sequencing errors, the people who are Illumina doesn't get too happy, because they say there are no sequencing errors. They're 99.9% accurate. When we're talking about sequencing errors, in the Biomex context, we're talking about errors by reverse subscript days, errors by DNA polymerase when doing the amplification, as well as errors in the actual sequencing. All of those things can contribute errors. Some of those we can see from our sequencing quality score, most of them we can't. So they'll get through and we'll have to deal with them in computation. So the way CellRanger deals with sequencing errors in their UMI's is they allow for one mismatch. And so if there's two UMI's that match to the same gene with one mismatch between them, and one of those UMI's has significantly lower numbers of reads, they all merge those two together into one UMI. Is that perfect? Probably not. There are tools that are better. So if you want to look at UMI tools, it's technically better, but it's slower and you're only getting like 1% or 0.1% more counts. So people can't be bothered and they just use the CellRanger one mismatch. And a sort of a demonstration of why these mismatches are important to consider, and the sequencing errors are important to consider, is mismatching reads. So here on the left, I have a plot where on the x-axis is the gene expression level. So it's the log expression level. And on the y-axis here is the number of cells where the gene expression for that gene was zero. So lowly expressed genes are zero in almost every cell, whereas highly expressed genes are detected in every cell. And you can see we have this main curve in gray where most of the genes fall. So obviously there's a pretty strong relationship between the expression level and the number of cells. You see. But you can see the second curve in orange that is basically the same as the main curve, but it shifted to the left by a small portion. And if you look at what genes those are, those are processed pseudogenes. So processed pseudogenes are genes in the genome that have no promoters. They have no enhancers. They have no regulatory sequences at all. They don't even have introns because they are mRNAs that got reverse transcribed somehow out in the wild and then got embedded into the genome. So these things really should not be expressed. They have no promoters. They have no regulatory sequences. But here if you look at them, we do have expression of them. So the truth here is that all of the reads mapping to these pseudogenes should actually be from the original gene that it's a copy of. So it's a copy of a gene that already exists. But what we observe is about 4% of the reads or the omias get mapped to the processed pseudogene and not the main gene. And it's because of this that cell ranger, all of their genomes have removed the processed pseudogenes. So that's one of the differences. Whereas if you start solo, you probably won't do that. And you'll see processed pseudogenes in your data because of this effect. Yeah. So if you see processed pseudogenes in your data, they did not use cell ranger. Or they used a very, very old version of cell ranger before I presented this at a talk in Ox in Cambridge where there was a cell ranger representative and they're like, oh, we should get rid of that. Yes. So star solo is designed to work on 10x data just like cell ranger and it's designed to mimic cell ranger's pipeline. Star solo can, because everything is customizable, you can customize the barcode structure in star solo. So it can be used for other data sets as well. So you can use it for drop, secret, and drop, or other technologies as well. Whereas cell ranger only works for 10x. Yeah, if we assume 1% sequencing error rate and 100 base pair reads, this 4% corresponds to reads that have three or more sequencing errors, which seems reasonable for those to then be mismapping. So once we've got our UMI by cell matrix, the next thing that we do is create a barcode rank plot. So here we just take the total number of UMI's met to each of our whitelist cell barcodes and see how many UMI's were assigned to that and plot it on this log log plot. And you can see that we get reads for 100,000 droplets, even if we only loaded 10,000 cells. So why did we get all of these reads mapping to these droplets that clearly aren't cells, because we only loaded 10,000 cells? So then we have to think about how the droplets are actually generated. So we have these beautiful pictures, that is the ideal case, right? So we flow our barcoded beads and our cells together, we get a droplet with one cell and one barcoded bead in it. In practice, this isn't what happens, because we have no way of recording to check whether this is happening or not. So we can't have these two, so it writes about barcoded beads, and the cells are flowing through our microfluidic device. We have no way of waiting until there's one cell and one bead in each droplet before pinching off that droplet. It's happening way too fast, we're doing thousands of droplets per minute, that's simply not practical. So what we actually do is we control the flow rate so that on average, we tend to get only one cell and one barcoded bead for most of our droplets, where we get a cell. To achieve that, we have to have a lot of droplets where we don't have a cell. We also want to only have one barcoded bead per droplet, which means we also have a whole bunch of droplets where we don't have a bead in it. So this is our ideal case, and you'll see already in my ideal case, I've added something that's not on that diagram, which is ambient RNA. So in our cell and our cell mixture, probably there's going to be some ambient RNA from our dead or damaged cells, that's just floating around and sticking to our other cells, and that will end up in our droplet along with our single cell. So there's always going to be some ambient RNA in there. Most of our droplets are actually empty. A lot of them will still have a barcoded bead in it, which is why they will match to a whitelist barcode. So here we have ambient RNA and a bead. So we've got some RNA that can get captured, and we've got a bead that can capture it, so it's going to get captured and sequenced. And we have ones that are truly empty. We can also have droplets, right? So we have one droplet with two cells in it, and we can also have what's known as a barcode multiplet, where we have a droplet with two barcoded beads in it and one cell. So this will end up as basically a photocopy of that cell in our data set at the end. In practice, there's nothing we can really do to correct for barcode multiplets. We just have to keep in mind that some portion of our data is actually just copies of the same cell. It's just something that happens. Yeah? Okay. I've been thinking about this since the beginning, but I'm just sort of specific of how we might get attached to one-to-one mapping with the mRNA of interest. So I'm trying to imagine in this process, maybe you're wrong, it could open up and it would sort of match it, but if you could like, can we touch on it, or maybe you'll talk about it later? Oh, sure. No. I had finished talking about that, so I'll go back to that. So the way that works, so the way we get a unique molecular identifier attacking to each mRNA is these barcoded beads. So I can't really tell you exactly how it works because it's a trademark secret of 10x genomics. How do they make these? But in these droplets, we have our polyase sequences and they randomly, they basically generate a whole bunch of UMI's, take their poly-A tails, throw them in, got this big mixture of UMI's and poly-A, poly-T sequences, they're going to just capture them, and then you throw in some DNA ligase and ligate on one of these UMI's to each of the poly-DT sequences and you hope it's different. And then you take that pool and you shove it into one of these hydrogel beads that then goes into the machine. Yeah, I can understand the randomness process if you only put like a certain, you know, say like theoretically like one UMI of one type, you know, and it contains just only one kind of mRNA type, but you have to make sure that that mapping is actually isomorphic with mapping all the other cells. So, you know, the other barcode, it should work the same way, but in this process, it sounds like, you know, in one bead, one cell, you could have one UMI map of one type of mRNA, but in the other kind of, you know, one cell, one barcode, one thing, you could have like the same UMI map of different mRNA. Yes, yes, so that is how it works. So the UMI is randomly associated with your mRNA, right, through the hybridization. So you'll have the same UMI repeated across multiple droplets, but mapped to different genes. Yeah, by design, yeah. A lot of this relies on random chance. So how do we distinguish these droplets that have a single cell in them, and those droplets that have doublets of them, computationally? Oh, and also, we can affect a lot of these things. So here I've got a set of numbers that are from this paper, they're sort of average typical numbers. But depending on how you design your experiment, these numbers can change. So different depending on our experimental design, we get different numbers of doublets, we can get different numbers of cells captured, and different amounts of ambient RNA. If you have a couple minutes to think about what can affect what parameters of your experimental design kind of influence these three factors. And again, feel free to chat with your neighbor if it's helpful. Okay, I think everyone's had time to discuss this. Hopefully you've got some answers. So what about our experimental design can affect the number of doublets we see? Yep. Yeah, so your cell concentration that you put onto the 10x machine or whatever experimental capture system you're using. More cells means more likelihood of doublets. Anything else? Yep. Yeah, if you change the flow rate, so if you're using a system other than 10x, you can change the flow rate. So you can change the flow rate to increase or decrease your chance of doublets. Yeah, so how sticky the cells are and how thoroughly you dissociate them, right? So you can change your dissociation protocol to isolate single cells or be more gentle and then you'll get more doublets. Yep. No, so in theory, you should actually see fewer doublets in single nucleus than single cell because it's less likely your single nuclei will stick to each other. Whereas your single cells, if you're taking them from a solid tissue, a lot of them are used to being stuck together. So they will tend to stick together more and you'll get real doublet, what I call real doublets, where it's a doublet because your cells are actually stuck together rather than just the random chance of them being put in the same droplet. Yep, sure. Well, you mentioned that we've got a protocol that's not like understand after when we are doing the analysis and we just assume that this happens and there's nothing we can do. But isn't there a possibility of, because I have two protocols in one cell, that my total counting of UMI's are going to be like too low when I do this exercise? In theory, and you might think that having two barcode beads in the same droplet will have the number of UMI's you get for each of those. In practice, there's so much variability in the number of UMI's we capture per cell or per droplet, just in the normal case of one cell per droplet, that we can't really tell that apart. It's the same thing with the doublets. In doublets, you might expect there to be twice as many RNAs, so you'll capture twice as many UMI's. In practice, you can't really tell and there's so much variability that even when we can detect doublets using the other methods, sure they tend to be at the higher end, but they're not exclusive to that higher end. There's plenty of real valid single cells in that higher end. Yeah, that's the same thing at the low end. Yeah. Yeah. Yeah, absolutely. And even experiments where you don't deliberately do that, we've accidentally found cell-cell interactions because we see an over abundance of doublets with two particular cell types involved. Yeah, so the one we saw it for was macrophages and endothelial cells. We tended to see a particular type of macphage and each particular type of endothelial cell forming doublets more often than we would expect just based on the math of the randomness. So, doublets can actually be interesting. How about the number of cells captured? I mean, we kind of covered this. Yeah, so the number of cell captured is basically how many cells you're loading and what concentration you're loading them at, as well as how healthy those cells are.  What about the amount of ambient RNA? Yeah, degraded samples, so making sure your sample is good quality, cell viability. Yeah, if lots of your cells are dying, you're going to have more ambient RNA than if they aren't. Not so much. So yeah, how you're manipulating the cells and in particular, yeah, if you're doing this, yeah, if you're doing your dissociation for a very long time, you'll have more destroyed cells, a lot more ambient RNA. The other thing you can do is you can wash your cells before you put them on the 10x machine to try and reduce the amount of ambient RNA. But then, of course, your cells are dissociated for a longer amount of time, so your RNA is going to be degraded more, so it's sort of a trade off. Depending on your particular system, you may want to wash them or you may not want to wash them. Yep. So we've seen in our data sets that single nucleus RNA-seq tends to have more ambient RNA, because if you think about the nucleus, it's covered in pores that are designed to export mRNA from the nucleus. So it makes sense that mRNA is getting shunted out of those nuclei in solution while you're loading them on the 10x machine, so you get more ambient RNA. However, it does differ between different papers. Some papers do see this, some papers don't see this, but we do see it, so I believe it, but it's up to you. Current controversy. Yep. Yeah, it shows for other reasons. Well, one, we already sort of talked about if you use the wrong cell barcode whitelist, then you're going to get an underestimate of the number of cells that should be there. Other issues would probably be more if your hydrogel beads are like a really old kit for 10x genomics and they've started degrading, who might get fewer than you expect, or loading issues. So one thing that's quite common, not using the 10x machine, if you use like a drop-seek machine, you can have the beads, so it uses beads instead of hydrogel ones, they can get stuck together, and then you'll get fewer cells captured. Yeah, I don't think drop-seek has improved much, but lots of the others have improved, yeah, but yeah, so which technology can massively influence how many cells you capture? There's also just random chance, right, where capturing, I am putting these cells into droplets just at random, so you can get just super unlucky, and you may end up with high doublets, or lots of yourselves end up in droplets that don't have a bead in them, just by random chance. So we typically see in the days that I've looked at, between, so you can have like three or four full difference in the number of nuclei or cells you capture, just between different samples, even if you run them all identically, just because of random chance. So you do the experiment and over a number of days, yeah, the tissue differences is usually your dissociation. So PBMCs, you can capture really well, neurons, not so much. So they're getting destroyed or damaged during the dissociation or the tissue hand line. All right, so this is sort of the table of empirically how many doublets you get versus the number of cells loaded. I'm not going to go through it, but you can see more cells below the higher doublet rate. All right, so we've got our barcode rank plot, so now we want to find which of these droplets contain cells. So here, this is a pretty easy one, right? We've got a whole bunch of droplets up at this high end, where we have our real cells, and they end up about like 5,000 here, real cells on this curve, just where this knee is. And that's probably how many we were expecting. And then we have a whole bunch of stuff down here at like 10 UMI per droplet. So those are definitely going to be empty. Complexity comes in if we consider a tissue where the cells aren't all the same size. So maybe you have or had the same amount of RNA. So maybe you have a tissue where you have some really big metabolically active cells in some really small, relatively inert immune cells. Now what happens?  What happens if our sample contains a lot of ambient RNA? Oh, we get the same thing. We had two humps, one where we have high ambient RNA, and one where we have high ambient RNA and a barcoded bead, right? So we can capture that infant RNA, and we get another hump where we have all our droplets where we didn't have a barcoded bead. And we have a problem because we have this middle hump where it could either be a bunch of small cells with a low amount of RNA in them, or it could be droplets with a high amount of ambient RNA. So how do we tell the difference? So if you use the old version of CellRanger, it didn't even try, and just said this first hump, that's your cells, everything else is not your cells. The newer version of CellRanger uses a method inspired by empty drops where you can have this region where you definitely have cells at the top, and then you have this region where you have some cells and some not cells. So how does it actually do this? Well, I haven't looked into the CellRanger code for what inspired by empty drops really means. Just to talk about what empty drops does, because you can use it yourself as well, right? So for empty drops to identify cells, say, okay, we've got this bunch of droplets that have really low counts, we know that definitely doesn't have cells in it. So we can use those droplets to estimate the amount of ambient RNA, and what the gene expression pattern for the ambient RNA should be, so which genes have high expression in the ambient RNA, and which genes have low expression in the ambient RNA. We can then take each of our droplets and ask, is the distribution of gene expression significantly different from the ambient RNA? So here our black line is what our ambient RNA should look like, and here's the significance of whether each of our droplets significantly deviates from that distribution. And everything that's below significant threshold, we say that is a cell, and everything that is above that is not a cell. So here you can see at the high end, here we have our 100% cells, and then as we go down, we get portions where we have some cells and some not cells. And lastly, I'll just talk about how we identified doublets. So similar to barcode multiplets, you have homotypic doublets where you have two cells of the same type, you can't tell that apart from a droplet that has one cell of that type. But you can identify these heterotypic doublets where you have two cells of different types. There's a whole bunch of methods for this, they all do basically the same. So they first randomly generate doublets from measured cells, take some sort of classification method, train it to identify these doublets we've invented that we've created from the true real cells, and then we take that classifier and apply it to our original data. Based on, because we can calculate the expected number of doublets based on how many cells we loaded, figure out how many doublets we expect to see, and draw a threshold based on our classification score for everything above that threshold is a doublet and everything below it is a single cell. There's tons of methods for this. They all do the same thing, just with different classification methods, different numbers of synthetic doublets they generate in slightly different ways they generate those synthetic doublets. In the lab, we're going to cover doublet finder because it's sort of a nice trade-off between how fast it takes and how accurate it is. I would also notice that the accuracy here, this score should go up to one, and we're maxing out at 0.55. So it's not that great, but it's better than that. And I'll just quickly summarize. Yeah, so talk about all the stuff and I think I'm about out of time. So any last questions?


Could I also get just some desserts? Yeah, deal with the pizzas can you get like a garlic bread and drink? Well, could I please get the um like a garlic bread, but could I get the cheesy garlic? Could I also with the drink is it possible? I'm sorry See you later


To my delight, we found that Los Angeles in the US had a climate almost identical to that of Mordor. His climate modelling also explained the poor performance of the English cricket team. One of the test matches that we lost was almost right bang in the middle of the most Mordor-like climate. I think that almost certainly explained our whitewash by the Aussies this year. Lump's next task was to communicate the results of his research. But what other surprises lain store for the climate model of Middle-Earth? With the climate modelling completed, the next step for Lump was to do what all scientists or wizards do. Write a paper about it. That was always the idea for this work, was to communicate with the public about climate science. We didn't really want it to be purely a fun exercise if you like, just about Middle-Earth. Originally, I envisaged this perhaps being a page on the university web page where we mentioned something about the Middle-Earth simulations and then talked a bit about our own work on past climate simulation. But actually, the more I got into it, the more I realised there was actually a potential to do more than that and actually write something a little bit more substantive. I can't remember at what point, but at some point I had the idea of actually writing almost a mock paper about these simulations. In fact, in some ways it grew and grew. I originally intended it to be quite short, but in the end I think it's one of the longest papers I've ever written. Lump wrote his paper under the pseudonym Radagaster Brown. The first step in any writing is know your audience. It's sometimes difficult to know whether you're writing for a Tolkien fanatic audience or your climate science colleagues or Joe blogs on the street. But in the end, hopefully we got the balance between those three. Lump received a great deal of feedback from those most difficult to please fans, Tolkien purists. The thing I got the most correspondence about after the work, the most complaints about, certainly was our pretty feeble attempt to translate our paper into Elvish and Dwarvish. In fact, the way that I did that was I confess I just changed the font of the paper into Elvish script and Dwarvish runes and didn't actually change any of the words itself and so I got lots of complaints about the fact that I hadn't done that properly. So for all those people I do apologize. However, one piece of feedback that surprised and delighted Lump came from Australia. So another interesting thing that happened was just after the paper was published I actually got an email from an Australian geologist who'd done a lot of field work in Australia. And one of the things we said in our paper was that one of the regions of our real Earth that was very similar to the climate of Mordor was very close to Alice Springs in Australia. That was sort of one of the bull's eyes of Mordor climate, if you like. And I got a letter a week later from an Australian geologist so I was expecting him to be very rude at me for highlighting this. But he said actually no, it's actually completely true because just outside Alice Springs there is a region known as Mordor Pound because in the 70s some geologists were there and thought the climate and just the whole feel of the place was so like Mordor from Tolkien's books that they named it Mordor Pound. So it was actually almost like art imitating life or life imitating art or the other way around it at me. But perhaps the most interesting feedback comes from Lunt himself. As a scientific expert in climate modelling what did he think of Tolkien's efforts to describe the climate of mid-life? It is incredible actually the way that a lot of the model predictions actually were in agreement with what some of the things that Tolkien mentions in his books. And I think like you say that a lot of that was because Tolkien was such a careful scholar if you like. And it wouldn't surprise me actually if he spent some time thinking himself about the climate of his Middle Earth. One of the things we did do is we did place Middle Earth in our simulation somewhere similar to where Europe is in the real world and I think that's part of the reason why we get climate similar to that which Tolkien predicted because it's quite possible that he had the European region in mind when he thought about the extremes. For example if you look at the climate of the Scandinavian countries the north of the Scandinavian countries going right down to the south of Spain and North Africa is quite similar to the sort of climatic range that Tolkien had in Middle Earth. So yeah I think he probably was a very good meteorologist as well as being a very good linguist. Lunt continues to develop his climate models in the geographical sciences department at the University of Bristol. A labyrinth like building more complex than the minds of Moria. After Lunt's success in modelling Middle Earth what's next in his future? Could he apply his modelling skills to other alien worlds? Could he for example explain the one climate planets of Star Wars? So I've heard people say that it's interesting that Star Wars planets have the same climate over the whole planet. I'm not sure that that is the case. I know for example the ice world of Hoth certainly is cold everywhere but you'd still imagine if it is indeed a spherical world and orbiting a single sun and didn't have a completely unusual orbit or axis of rotation we would expect it to still be warmer at the equatorial regions in the middle of the earth than it would be at the pole but it's certainly possible. Maybe it has some chaotic orbit and it's tumbling around so that's why all parts of the planet receive on average throughout a year the same amount of radiation so maybe it is possible I don't know. Possibly the most burning question for Lunt is this will he create a climate model of the world in Game of Thrones? So that was also mentioned to me that we should try and do the Game of Thrones climate so that's something that may be under development in the near future I don't know. Perhaps fans will finally have an answer to that all important question is winter coming to Westeros? Maybe I'll start the model running and I'll let you know. I have three major passions in my life is climate change, motherings and cricket That's very similar to me. That's very similar to me actually. I'd probably football in that as well. Ning, Ning, Ning, Ning, Ning.


Good morning everyone, Basel Chapman here for the Tiger Financial News Network, 10 a.m. market update and thank you Tommy for that early morning kickoff that you give us at 9 with great information. Let's go to the DAO, the DAO is up 160 at 38,954. You can see it broke the uptrend channel line. Now it's gone back into what was a support that never came a resistance. Now we'll see where it is at 38,962. If by Tuesday or Wednesday I'm going to draw this in right now as we're talking this Chapman falling affirmation. If we close it sharply above the, if it goes into the 39,000s I think they would be a very good sign. We'll watch this closely over the next two days. That's two sessions. The S&P right now trading at an all-time high at 51,84.97. Right into the inside track repellent zone. How does it last today? Well so far the market's taking the economic news positively. That's the jobless jobs number. Let's go to the QQQ. The QQQ is up $1.94. All-time high ESB speaking 447.40 IWM. So the QQ is up 0.43%. The IWM is up 1.39% at up 287.209.85. This is what I was saying the other day that if it was able to get to the 208.209 area that's going to impart some kind of information that says any market pullback in March which often happens we watch this closely because it could become a leader back going into April-May. We're watching this very closely to see are the small caps of 2000s starting to move. We're looking at gold another big day in the upside up 14 at 21.80. It's almost like a single leg aid to the upside. It broke that inside track repellent line in the weekly chart. We haven't closed the session yet but so far it's well above it and it's going to the monthly longer term inside track repellent. So a fabulous action dollar therefore is going down. Yep it's down 28 ticks at 102.54. Crudor looked like it was going to break out the other day kind of slowing down it's down 50 cents at 78.40 and this is what we're going to be looking at bonds. Bonds are up only 230 seconds. So there's something going on here that's quite interesting. I'll be back for the Tiger Tech missions in a few moments. Hope you can check out both and see you in a few and have a great weekend.


Hello to all that read this, mostly my therapist, I guess. I used to be a game mod developer who worked on a lot of source mods, basically I made things for games, like Half-Life 2, and Counter-Strike Global Offensive. I apparently need to write this. There's been a hell of a lot going on, and only now have I been able to get on top of it. What I'm about to tell you is what I've experienced when trying to work on my previous project. I can account for some of what happened, but not everything. For warning. Even if I have been getting help, I'm still trying to cope with the paranoia, so I'm sorry for any nervous sticks that I may have in my writing. So yeah, to start from the beginning, I was working on a little campaign for a mod called, Underhell. When I wasn't so messed in the head, I remember this game being extremely good. I wanted to make a city that became slowly corrupted and haunted, whilst the people living in the city tried to figure out what to do. The project went well, the city was made, voice acting was acted, and skeletons were spooked. Forgive me, humor helps me right now. I was just working on one of the first scares that the players get to see, but I was having problems setting it up. Basically, the player would start in a corridor with a dank sodden grayish blue carpet, and dirty beige walls. It was quite claustrophobic, and almost seemed to get smaller the farther you went. When the player got halfway through the corridor, a door at the end of the corridor opened into a room where she was inside. The lights were off in the room she stood in, but you could see her silhouette in the leftover light of the corridor. When you got near the end, she'd start walking towards you, and you'd back away only to find the corridor behind you had suddenly vanished. The problem with this was that the door that she had behind, would rotate a whole 360 degrees. It would go through the wall and she would keep bumping into it, and wasn't able to get out. I tried to sort it out by making the door only be open one way, and only be able to move 90 degrees. I thought that I had sorted it, so I started the game and loaded the map to test, and oddly a lot had changed from the get go. The floor was plain concrete, and the walls were of tattered wall caper and scratched plaster. I closed it to see if the editor had accidentally messed up my map, but it was the same as it ever was. I felt quite unnerved, so I had a little breather, and decided to try launching the map in game.  I walked across the floor, and all that I could hear were the loud clacking footsteps of my character, getting the concrete floor. I was nearing the middle of the corridor, and I started to get very tense, I still get scared by my own work. I hit the trigger that caused the door to open, but the door opened incredibly slowly. I stood still in the middle of the corridor waiting to see into the room, but as the door inched its way open, I realized with a cold dread that the room was empty. As soon as the door fully opened, I hear footsteps coming from no discernable direction. They sounded like their feet on stone, except it was almost like the person was slapping their feet on the ground, trying to make as much noise as possible. I got incredibly scared by this and lowered my audio, expecting the worst. The footsteps got louder and louder, until they were just as loud as they were, with the audio turned up, and then they stopped. In the cold sudden silence, I covered my eyes, lowered my audio even more, and tried to hear if anything was going to screen out at me. I heard nothing but a raspy, stunted breathing. And at that moment I couldn't take it anymore, and quit the game. I deleted that whole level, and tried to forget that happened. When I finally got the courage to start mapping again, I said about remaking the whole level again. I tried to get the previously mentioned part of the level over with first, so that I wouldn't have to come back to it later. All went fine, until I tried to set the prop dynamic, this basically lets you set which model you want and manipulate it in a variety of ways, with the model of her, but I couldn't find the object anywhere. I searched through all the folders in the object browser, but couldn't find the model. I decided to save my progress, and look through the game's install directory myself. I clicked out to my desktop and got a sudden chill. The model, its textures and even a PNG of her was on my desktop. She wasn't that scary when you looked at her, but the feeling of what happened last time I made that level came back. It felt like I was being watched. I put them back in the directory without any problem, and just made sure to keep my antivirus looking for any incoming and outgoing files from that particular folder. I quit mapping for the day, and tried to get my head clear. I watched some TV, and that's when my antivirus came up with a massive pop-pop and alarm sound, that was only reserved for the grandest of dangers to a computer. I muted my headphones, and took a look at the warning that came up. The antivirus showed a message, stating that I needed to remove 14 objects immediately. 10 had a yellow alert, 3 had an orange, and 1 had a red. I looked at the list of files infected. Most were normal trojans, adware and the likes, but too caught my attention and made me freeze. One was called winlogger.zuse.icu, and one called themealex.mgl, that one stung my senses of laser. That was the file, the file of her model. I pulled back my chair, mouth agape, and I quickly made sure to remove the files.  I made sure I got rid of those viruses, uninstalled steam and all my games for it, backed up the barebones level of my project without any textures or models, and I rolled back my computer to when I never started that project, March 30th. The first thing I did with my freshly rolled back computer was install every good antivirus I knew. I did multiple scans to make sure those god damned files were removed. I even thought about smashing the USB that killed those levels. I was extremely tempted to say the least, but I left it on my desk for the time being. After nearly six hours of scanning, I decided that my computer was okay and went back to reinstall everything I wanted. I even reinstalled steam, although not under hell, nor the program formatting, just in case this wasn't some horrendous fluke. I used my computer normally, and finally I started to feel like maybe I was safe. I went to bed a couple of hours later. When I woke up, my computer was already on. Concerned, I looked closer, but everything appeared as it should be. I used it for a while and looked for my USB, found it and took it into my kitchen, where I relentlessly smashed it to pieces with a Nintendo riser. I couldn't help but grind. I felt like I had stocked my creation, well to be frank, someone else's creation that I made something for, from becoming even more horrible. I went to sleep and woke up to my alarm. The day was August the 28th, and I was already late for a day out, so I quickly got ready and was about to go on my computer when I saw I was already logged on. I felt a doubtful fear and slowly cracked up to my computer, as if it was going to pounce out at me. A folder called player group was opened, and a single text document was inside titled, femalex.txt. I clicked it instantly and saw only one word, behind.   Something grabbed my shoulder, and I heard this horrendous blood curdling shrink wrack through my body. I didn't even realize it was coming from me, until the person behind me turned me to look at them. It wasn't her, it was a neighbor of mine. I couldn't face reality and my paranoia that had been ever increasing took hold. Maybe he was what the computer was warning I had to run. Maybe he was the one doing the things that made me like this. I hit him and ran out onto the street, tears in my eyes. I kept seeing her every corner I took. I ran harder, merely getting hit by passing cars multiple times, and ran farther than I'd ever ran before, so much so that I passed out just as my adrenaline wore off. When I awoke, I was in the hospital. I was shaking with fear. She could be anywhere here, and all these electronics could spell any kind of message to taunt me. I tried to get out of bed, but the doctor tried to lightly tell me I'm safe, but I disregarded. I pushed him down and tried to run, but security was called. I felt a stab and then I woke up in restraints. I didn't see or hear anything. I was too stuck in my own head trying to calm down, but all I could surmount was this rapid chant of, see no evil, hear no evil. I couldn't feel a thing. I don't even know how much time it took to get out of that state, but when I did, whatever clicked in me to lose those senses just came back. I could reason, I could feel, but I was still paranoid. I tried to disregard the doctors, but all that happened was that I was made to undergo psychological evaluations. The therapist told me that whatever I saw that made me like this induced some kind of psychosis. I don't remember giving answers, but he told me that even when answering, I said I heard her and saw her, that she was wherever I made her not be, wherever she was never meant to even comprehend. I don't know if I said that, but he certainly sounded like he was quoting me. I just don't know. I'm a lot better now, and I only see her rarely, but I've had severe sleep paralysis since. I see her a lot when waking up, and I can't even move. It's horrible, but I'm getting used to it little by little. I just pretend to myself that my house is on fire, and then I always regain the ability to move and she disappears. I just, I don't know how this could have thought to be up so much. My therapist told me writing it all out would help, but I don't know. In any case, whoever reads this, just please be careful.