Hello everyone. Welcome to another NARC's Firely Live video. As you can see today, I am out by the beach. As the title reads, yes, we are going to be talking about stalking in this one. We're going to be talking about why the narcissist stalks you. Because clearly, yes, if you've clicked on this video, it has been a bit of a problem for you. You're tired of this person, they're constantly on your case. That's not to say that you were being mean, or that you're trying to push them away.  And of course you did the right thing by shutting them out. Because as I always have said in my videos, you should not tolerate abuse or disrespect. If someone does that to you, yes, you should get rid of them. But the problem that we have in these relationships is that even when you're trying to move on, it doesn't mean that they can move on. And yes, they will try to come back to you, or they may even try to stalk you behind the scenes. But either way, whatever they're doing, it gets to a point where pretty much everything they do, everything they say, it just becomes very irritating. Because they're not getting it, they're not understanding that you just want to be left alone. You just want to be left in peace.  You may not enjoy excessive stimulation.  And they may be following you around in person as well. And all of these things that they're doing, it's just really irritating you. Because you don't want anything to do with them. You just want to be left alone. But it's like they're not hearing you. You're not getting through to them. It's not making sense. And the reason why is because they actually don't care about how you feel or what you want. They don't even see you as a separate person with their own feelings and needs. They view you as an extension of themselves. So they expect you to feel how they want you to feel. And to want what they want you to want. As though you're like a puppet on a string. And they're also very envious of you. And jealous too. They have this desire to take something away from you. And not only that, but they feel like you're taking something away from them. Because you may not know it. Yes, you do possess this amazing energy as an empath. And it is very powerful and influential. It may often be drawing them in without your conscious knowledge. It's having a powerful effect on them. And they want some more of that. They want to get closer to you. So that they can participate in that. So that they can consume it and enjoy it. And let it become a part of them. But the reality is that even when they do that, they're still not satisfied. It just agitates them even more. It's never enough for them. And yes, that is why they keep coming back to you. Even though yes, they may have a new supply. They may be strangling you with this other person. But yet they still come back to Hoover you. They're still stalking you. They're still watching you. Because they're never satisfied. If they were satisfied, maybe they would just leave you alone. By recognizing what you want. By recognizing that you don't want any more of them. But their envy really overrides any empathy that they would otherwise have for you. So they're left with this inability to share your feelings and experiences. And there really is no way that you can get through to them. Because as I said, they're not exactly grounded in reality. They don't even see you as you actually are. And instead they have this fantasy. It's this thing that they've made up in their heads about how you want exactly what they want from you. As though you're on the same page. You desire the same things. It's all just this fantasy.  Which is why if you're around them, you can't even have a normal conversation with them. It always just becomes this shared fantasy. And at times it may seem like they're crazy. A lot of them really are psychotic. They're not grounded in reality. Which is why you may find yourself resisting them. You're constantly trying to move away. But when you do that, they just get closer. Because believe it or not, that actually makes you even more attractive to them. It's the opposite for them. For us empaths, we desire when only when someone desires to be close to us. And when they treat us right. Just as we would want to do for them. But with narcissists it is the opposite. When you pull away, they chase after you. They love you most when you've got one foot out the door. So what can help sometimes is just to get them a little something. Let go of your resistance even if it's just for a moment. And try to act crazy just like them. Give them a taste of their own medicine. It may replenish them for a moment. It may keep them off your back. But that's really as good as it's going to get. All it's ever going to be is an illusion, a shared fantasy. It's never going to be anything more than that. So you can let go of the resistance for a moment. Give them a little something. But yes, then you do need to pull away. Because it will get very dangerous for you. Before you know it you will be caught up in all sorts of things that you don't even like. You won't even know who you are. You will lose your sense of self. You will lose your identity. And you may even end up getting involved in drinking alcohol, drugs. Going places where you wouldn't normally go. Being around people who you normally wouldn't even be involved with. And it's all because you entered this shared fantasy. To where it becomes shared psychosis. And I know at times a lot of you, yes, you may have felt like you're going insane. Because you're being gaslighted. You're being caused to doubt your own memory, perception and sanity. And that is no good for you. Because when you look at it, I mean let's keep it real, let's be honest here. The reality is that this person is undesirable, unwelcome and attractive to you.  I mean maybe it will be a different situation. Although of course it would never actually be me, but if I was your stalker. If I was the one following you around. Maybe we could actually make something happen with that. We could actually be friends, who knows. Because the difference is, this narcissist is someone you do not want in your life.  All they're doing is taking things away. And maybe you're fed up with that.  You don't want to be gaslighted. When they come around you, that's all they're going to do.   And you need someone else outside of you to complete you. And as I said, that is all that you're ever going to get from them. Because otherwise you're going to wake up and realise that actually yes, there is no place for them in your life. But of course they don't want you to come to that conclusion. Which is why of course they're going to stop you from doing that. They're going to stop you from moving on. They're going to keep trying to come back to you. They're going to keep trying to hoover you. To try to convince you that no, maybe you got it wrong. Which is why I am here to tell you that no, you didn't get it wrong. You were right all along. This person that you're dealing with is actually no good for you. They are not someone that you should want around you.  Because just look back at what they've already done to you. Do you really want more of that? Do you really want more abuse, more mistreatment? More reality twisting, more gaslighting? Of course I already know that is not going to be any good for you. And in fact it's only going to further mislead you. It's going to make you lose touch with yourself and your reality.  Where you don't even know which way is up. And of course that is not what I want for you. I do not want you to enter that world again. Which is why I have to get on here and make these videos. It is why I have to do this. Because otherwise you will get wrapped up in the gaslighting and the confusion.  Because that's typically what happens to everyone. You get involved with these narcissists. They're all encompassing, they're suffocating. And you start to lose yourself. You start to forget who you are. And you start to pick up all of these things. But you never wanted anything to do with, to begin with. You never wanted to be involved in the things that they were displaying and portraying to you. You were on to something else until they came around. And then they pulled you into all of these things. Where it may be the opposite, maybe you lost all of your passions and interests. All of the things that meant something to you. All of the things that made you who you are. You lost all of that. And now you don't even know who you are. Which as far as I said, the best thing you can do is just to go no contact, block them and everything. Don't let them persist to be a constant influence in your life. Because all they're going to do is pull you away from yourself. And that is never going to be any good for you. And it is the only thing that they will do. It's never going to be any different than that. It's only ever going to be shared psychosis. They can only share what they have with you. They can't give you something that they don't even possess. And that is actually a very unrealistic expectation for us to have of them. Just get into the car here. It is beginning to rain. Like it got back just in time. Yes, but this is why they stalk you. They're envious of you.  They try to make it, they twist it around as though you're envious or jealous of them. And some of you, you might actually get pulled into it, but you've got to take a step back and realize, hold on a minute, why are you even coming around me in the first place? And of course it's because they decide to take something from you. Because they can't be satisfied with themselves. They can't be satisfied with anything in life. So they're always going to be on your case. They're always going to be trying to come back. And you just need to realize that no, you don't need them. They're not going to bring anything of value to you. They don't have anything of value of their own. So all they can do is take. And that's just that the longer you spend with them, the more your value is going to go down.  And then you will see by the end of the relationship or however many times they come back to hoover you, you will see that they begin to become more and more like you. It seems as though they're becoming more grounded, more comfortable, happier, more sane, more in control of their emotions. While it's having the reverse effect on you, you may find that you're becoming more destabilized.   You're thinking and feeling things that you would never normally think or feel in a normal situation, which is how you should know that yes, they're making you more and more like them.  So yes, be very careful when they stalk you because it will have a damaging effect on you. It will affect your identity and your sense of self because they have to take pieces of yourself away from you to replenish themselves. As people have said before, yes, they actually want to be you.  And that is why they come back. They come back to take more bits and pieces of you, more of your energy. So yes, this is it. This is why they stalk you. And I am very sorry for anyone who has to go through this. It's a horrible experience. I have experienced it myself. It's not nice when you're being stalked by this person who you do not desire for them to be around you. You don't want them anywhere near you. And yet they keep chasing after you. They won't leave you alone. And while doing that, they just become even more agitated. They're just constantly on your case. And you may find it's like wherever you go, they always have to be there. They show up at your house at your work. They follow you around. Maybe they contact your family or friends. It's like they just want to be completely immersed in your life to have control over you so that you can't get away. And of course they have to do that because they already know you want nothing to do with them. Of course they already know that. If that was not true, then why would they be so controlling of you?  So yes, of course, deep down, of course they know you do not want anything to do with them. And you're watching my videos because you want to learn more about what you went through so that you can protect yourself from them in the future. Or maybe from other narcissists as well. Because yes, these narcissists are everywhere, they're all over the world. And I actually had a very negative experience myself today in a Buddhist temple. Those of you who may know, I do enjoy visiting Buddhist temples. I've been doing it for over four years now. I really value the Buddha's teachings. And yes, I visited this temple. It was in Na Trang, in Vietnam. And it was very strange. I was being followed around by a few people. And I was trying to pray and just participate in the temple. And they were coming to me and telling me not to pray. I tried again. I said, you know, there's nothing wrong with praying, but they just didn't want me to do that. They seemed very envious of me and my spiritual connection. As many of you may know, yes, I am very spiritually connected. And yeah, it bothered me. So I just dropped the jaw sticks and I just walked out. Of course, I'm never going to go back there again. I don't want to put myself in that type of situation. But yeah, these monks are everywhere. Of course, they are in spiritual communities as well. Whatever your beliefs may be, you may find them in the mosques, in the churches. They are everywhere. So you do need to be very careful. And actually just Vietnam in general, it's not really the safest place. And I wish I didn't have to say that, but you know, I have to say the truth. I've been to a lot of countries and I normally have quite an enjoyable experience. But Vietnam, people are very loud, very noisy. It's almost like completely boundaryless. They will get right in your face when they're talking to you. They will touch you and think nothing of it. There's just no personal space. The roads as well are also very dangerous. There's a lot of bikes everywhere and they're all over the roads, sometimes going in the opposite direction. So, but yeah, I have had some good experiences here as well. I mean, as you may have seen in my past videos, some of the rooms I've had are really nice. The beaches as well are amazing. I actually really enjoy the Vietnamese food too. And I'm still here for a few more weeks. So, you know, I'm sure there are a lot more good things to see. I shouldn't really judge my entire experience just yet. So I can't really say whether or not I would recommend coming to Vietnam. I mean, there are definitely some things that I have enjoyed, some things I haven't. As many of you may know, yes, I do enjoy, I did enjoy Malaysia, Sri Lanka, South Korea, Japan. Those were all definitely very memorable places. But yeah, Vietnam so far, I don't know. I mean, one of the main reasons I came here was for the temples. Of course, my experience today kind of put me off. It's like they didn't even want me to pray. But at the same time, I saw other people praying right next to me. And no one said anything about that. So yeah, that wasn't a good experience. If you're coming here for the temples or to drive around in cars, might not be the best idea. The roads are very safe. For the guys, if you're coming here for the women, I don't know if maybe you have an Asian fetish. I know I do myself. But I have been to Sri Lanka, Malaysia, Japan and South Korea. I met quite a lot of beautiful women there. Had quite a good time, especially in Sri Lanka and Japan. The women there are very modest, they're very humble, very kind and easy to talk to. But Vietnam, to be honest, feminism is quite widespread here. The women are quite arrogant, quite narcissistic. I had this belief before I came here that they were going to be quite modest and humble, but that's not the case at all. Not like in Sri Lanka or Japan. So if that's what you're looking for, you're looking for an exotic Asian woman to love and care for you. Just as you might want to love and care for her. So far, my experience, I don't think you're going to find that here in Vietnam. You might find another foreigner, maybe a Russian woman. A few good things about them. If you are looking for something more exotic Asian, I'm not really so sure about that here. I mean, I myself, my experience, of course, as you can probably imagine, I have met hundreds, if not thousands, of women around the world, 34 still single. Probably as a result of my past experiences in narcissistic relationships. But yeah, I have met a ton of women around the world, especially in Asia here. I haven't really met any humble or modest women in Vietnam. So up until now, I wouldn't really advise that. But as for the food, Vietnamese food is really good. I've enjoyed it a lot on the beaches, as I said, but I guess I have spent a lot of time in the cities. And as we know, the cities aren't really the best place to be. I am going more towards the jungle. I think first I'm heading up towards Hanoi. Not sure what that's going to be like yet. But yeah, I give things a chance. I don't know if any of you here have been to Vietnam yet or any Asian countries. Let me know down below. What was your experience like? Any places you enjoyed? Any places you would go back to? Real Deal Prime says just here in Canada. I'm not sure what Canada's like. I've never been there. I would imagine that it's quite cold. Classy Cards then says no, but I met a nice guy from Korea here in the US some years ago. Yes, I've been to South Korea. The guys there, yeah, they are very kind, very nice people. The good guys had a great time there as well. And also Japan. I just really admire and respect the Far East Asian cultures and traditions. How in many places they still manage to uphold their traditions. You know, despite all of these other influences in the world, all of the social media, all of these things. So yes, I really admire and respect that. Anything traditional, anything cultural. Angela says, oh yes, Japan almost 15 times. South Korea once. Thailand was cool. Japan is my favorite. Sounds like you really enjoy Asian countries as well. Maybe you like the food. Or the beaches. Or is it the Asian men you're into as well? Yes, I just enjoy everything about Asian countries. As you may know, I am actually half Sri Lankan myself. So that's why I enjoy going to Sri Lanka as well. But yes, overall, I have been having a good time. I was in Malaysia for over one year. Sri Lanka was there for about six months, maybe more. So that's where I've been spending most of my time. I just really enjoying the cultures and traditions that they have. Traditional foods. Of course, we don't really have these kinds of things where I'm from back in the UK. So it's nice to see that. But yes, I'm leaving this city tomorrow. Driving up. Going to explore more. And despite my experience today, I am still looking forward to it. Of course, I don't want to let just one experience define everything. So yes, that's it. This is why the narcissist stalks you. Again, sorry for anyone who is going through that. I know it's not nice being stalked, being harassed, receiving this unwelcome attention, especially once you've begun to move on, you've begun to heal. Of course, these types of things are not going to be attractive to you anymore. You're not going to want to be around narcissistic people. But unfortunately, that's the only thing you're going to get because they're not going to heal. They're not going to become better. The only one who can do that is you. You have to heal yourself. You have to move on. Cut them off, go to contact, block them and everything. Let them find someone else. Let them move on with their new supply. I mean, I find it strange sometimes. It's like as soon as they find their new supply, they've got to come around and triangulate you with them. It's like, do you actually want this person or not? Because that's all that it should really reveal to you. It's like if you're so happy, you're so satisfied with them. Why do you keep coming around me? Of course, they don't think the way that we do. They're just all about themselves. And clearly, yes, if they are stalking you, they're hoovering you. They are not satisfied with their new supply. If they were, you would never see them. You would never hear from them. You wouldn't even know that they exist. They would be very quiet about it. So despite what they're trying to tell you or show you, let that tell you everything you need to know.  They're not happy. They're miserable. Just as miserable as they were when they were with you because they can never be satisfied with anything. And they're going to continue to be that way. So just leave them be. Let them move on. Forget about them. Don't get involved. Let them do what they're going to do. They're never going to find happiness. They're never going to be satisfied with anything.  You will be satisfied just by having peace on your own. And that may be the only thing that you want. And I do hope that you will find that for yourself. So that you can heal. You can become better. And then one day you can find someone. As I've said before, someone who loves the way that you love. Someone who will care about you the way that you cared about the narcissist. Just imagine someone with that type of energy. Just picture it in your mind. Visualize it. Because that will instantly raise your vibration. Because that is real love. That's exactly what love is. It's acceptance. And you accepted the narcissist. You cared about them probably more than anyone they had ever met in their entire lives. So just imagine someone giving you that unconditional love. That acceptance. That's real love. Visualize it. Enjoy it. Better bring you peace. Better help you to forget about this toxic, abusive, manipulative person. Because that's all that they're going to be. And you deserve so much better than that. You deserve so much more than someone who's just going to bring you down to their level. And make you feel small. They're just going to take your power away from you.  Block them out. Don't let them come anywhere near you because they're no good for you. They're of low value. All they can do is take your value away. That's just what it is. It's negative energy. It's not designed to bring anything of value to you. All they're going to do is take. They can't give you what you want. You're never going to get that from the narcissist. But you can get it from yourself. Learn to love and appreciate yourself. Practice gratitude. Something a narcissist is never going to do. Because if they actually did that, if they did actually practice gratitude and appreciation for themselves and their own lives, maybe then they wouldn't be so bad. Everything they do, it comes from dissatisfaction. And when you're around someone who is constantly dissatisfied, you may begin to feel that way as well. It will begin to affect you until you get away on your own. Then you will feel more at peace. So yes, of course I advise for them to not come around you. To not come anywhere near you. Because it's not going to be any good for you. What you need is some time alone. You may feel lonely. But you can watch one of my videos or other YouTubers as well to remind yourself of why you should not want these people in your life. They're only going to bring you down. They're only going to weigh you down. They are never going to lift you up. They can make it seem that way in the beginning. Remember that is the love bombing. That is the manipulation. Any narcissist can do that. But it's only for a short period of time. They're just reflecting your own energy back to you. Your own virtues and ideals. But it's only ever going to be lies and future faking. You've got everything you need within you. Trust your internal compass. Your gut instinct. Your intuition. These things will never lead you the wrong way. They will tell you everything that you need to know. So yes, that's it. This is it. This is why narcissists stalk you. I do hope it helps those of you who are in this situation.  And if this video has been helpful to you, you can give it a thumbs up down below. Let me know your thoughts as well in the comments section. And as always, yes, I will be back with another video very soon.


all right change payment done here at Royal Hibachi Buffet Route 46 West Saddlebook in Jersey it is a countdown for Thanksgiving Day 2019 we give a shout out to Commodore Jeff Sambello and Exorcist Bishop Brian the star of the show Ghost Adventures on the Travel Channel I'm going to get some lamb ribs now the owner actually he told me this is these are turnips that are sliced in long ways so I'm gonna taste it I would never know it was a turnip I thought it was maybe onions um let's see what we got here let me just take a look at the duck I know I am I am I usually get a carnivorous paleo Atkins style buffet crab soup lester crusty crustacean hearts crab soup I know I said it many times before always stir the bottom of soup before you put it in your bowl let's see how we're doing with the duck skin bone a lot of fat and lower percentage of meat I guess any waterfowl is going to be that way it's because they need insulation that's why so what am I gonna do what am I gonna do I'm gonna choose something else that's what I'm gonna do usual uh Chinese fare um let me check the American food okay there you go for the very best buffalo hot chicken wings to go for your very next sporting event come to Royal Hibachi buffet and buy it in bulk take it home for your sporting events party it could be any sporting events we're 46 west out of New Jersey oh look what we have here we've got the gyro with it that we didn't call it the ziki sauce look at it all right let's make this easy so I don't make a mess there's the asparagus make sure you're instinct to high heavens but it tastes great asparagus crab meat pie some more chicken gyro meat a few uh pita bread sir this is an extremely Atkins paleo plate the tzatziki sauce chicken kebabs well what's great about his uh chicken breast is he he does marinate it it's very tender and juicy there's the stuffed chicken breast okay I think I have enough on my plate now let me just take a quick look and see if there's avocado salad today don't see any hey salvatore mercurio fresh mozzarella with a slice of tomato in honor of salvatore mercurio don't don't mess with the moots salvatore don't mess with the moots please don't touch with your hands that's what that's what men usually say if they're in a go-go bar where they're giving hand jobs and you don't like you don't like the girls you don't look too sanitary please don't touch with your hand it's interesting uh this is turnip he said I thought it was onion oh no it's turnip I thought it was onion I thought it was onion uh this is good with the sauce I don't know what to get I want to get something I like to try to get something different you know so some weeks the food is not a different thing this is the king the king of Mongolian barbecue look at him look at him Jeff Sambo look at this guy he's the best look look at all the the separate meals he's he's trying to handle here he's juggling look at all the different meals he's doing this guy's amazing commoners in battle the vapors of life look at the shrimp and broccoli in a corner look at those vapors just going going up to the heavens uh look at that beautiful beautiful beautiful job king of mongolian barbecue this is the countdown for Thanksgiving 2019 I think I'm going to get some hardcore sushi not this stuff though I'm not going to get any of these rolls for the faint of heart I'm going to get probably salmon the tuna and the octopus and and the unagi the roasted eel okay this looks pretty black northern mussels cherry stone plumbery rings I actually prefer the tentacles to be honest with you but what are you gonna do here we go can't beat it can't be beat the black northern mussels you know bouillabaisse or bouillaballs salvatore mercurial like a bouillaballs like a bouillaballs I got to visit your uh your moots don't mess with the moots in honor of salvatore mercurial don't mess with the moots okay let me see what they got no no no avocado salad yet got the rolled grape leaves over there leaves don't touch with the hand all right that's it got the treleche treleche is a scream ice cream hey salvatore they should do instead of having jury duty they should force good-looking young large breasted females to report to a human dairy and have them milked to supply high quality milk in different forms could be powdered milk for athletes for male athletes who wish to make their protein drink with mother's milk okay no green tea ice cream unfortunately because I brag about it if I tell this guy how wonderful green tea ice cream is it's not too sweet he doesn't get it if I say nothing at all it comes in I have no idea this is I guess sherbet right which is like a sorbet I always wondered what the difference is between gelato sorbet and sherbet maybe sherbet was invented by a man by a man named herbert the sherbet okay I'm going to keep the sushi very simple I'm gonna get some unogny rosidio tuna tuna this has been a mega live 21 production


Hi, I'm Kokkei Yen. I'm the research coordinator and clinical fellow at the Department of Vascular Surgery at the Fue medical center in Amsterdam. We recently published a research article in Human Mutation entitled The Transdifferentiation of Human Dermal Fibroblasts to Smooth Muscle-like Cells to Study the Effect of MYH-11 and Octa II Mutations in Aortic Aneurysms. The study has been performed in the Institute of Cardiovascular Research within a collaboration of the Departments of Surgery, Clinical Genetics and Physiology. In this study, we successfully transdifferentiated dermal fibroblasts into smooth muscle cells within two weeks. The transdifferentiated cells show the same characteristics as smooth muscle cells obtained from the order. In patients with a variant in MYH-11 or Octa II, the effects of intronic variants on splicing were shown on messenger RNA level. Using this method, we were able to classify variants into pathogenic or non-pathogenic. Direct conversion of human dermal fibroblasts into smooth muscle-like cells is a highly efficient method to study the pathogenicity of variants in proteins of smooth muscle cells. You can read our article online and we're open for discussion and collaboration.


Okay, we're going to get started. Thank you for joining us then, where we'll be talking about analyze and clinical and genomic oncological data with the genie BPC and genome RR packages. I'm Sammy Brown, and I'm with Jessica Agri and Chris Awaiting, and we're all researched by statisticians at Memorial Sloan Kettering Cancer Center. In terms of our agenda today, we'll start with going over a brief overview of projects genie and genie BPC. Then we'll look at the clinical genomic data processing pipeline, and delve into a case study where we'll work through clinical data processing with genie BPC, and genomic data processing with GMR, and then we'll wrap it all up with a conclusion. The primary goal of genie BPC, or the project genie biopharma collaborative, is to augment the existing registry of genomic data from ACR project genie with enhanced clinical or phenomic data to support clinical genomic analyses. The phenomic data from in genie BPC are curated using the prism curation model to capture detailed information on a lot of different aspects of the EHR, such as cancer diagnoses, treatment regimens, disease status from radiology and pathology reports, and medical oncologist assessments. And these are structured over several different data sets with hundreds of different variables. And so one reason that we'll get into in a few minutes of why the package is so helpful is to streamline all of these, working with all of these data sets. And the analyses using linked clinical and genomic databases, such as genie BPC will help to drive advancements in precision oncology in identifying the genomic alterations and therapies that will optimize clinical outcomes. So to just skip a broad overview of the genomic data included in ACR project genie, researchers can receive genomic data from various different formats and types. And that ACR project genie data repository is comprised of one type of genomic data called tumor DNA sequencing assays. These are data that are collected from tumor samples via biopsy or resection, and they compare DNA sequence in cancer cells with that of normal cells. The sequencing assays can be broader targeted, and broad regions refer to whole genome or whole exome sequencing, whereas targeted regions refer to gene panels. And the genie data consists of data from targeted gene panels from high throughput or huge amounts of data, sequencing assays, and we also refer to these as next generation sequencing. The genie BPC data are publicly released by cancer cohort.  And new versions of the data are periodically released to include additional patients and variables and incorporate data corrections. The data files are stored as CSV and text files, and they're available for download from stage five networks, Synapse data sharing platform. And downloading each file individually poses challenges for efficient and reproducible workflows, which will lead us to our package genie BPC, which is a pipeline to programmatically access the data corresponding to each release from Synapse to support reproducibility and create data sets linking clinical and genomic data for analysis. And then moving into the genome R package, this package provides a consistent framework for genetic data rendering, processing, visualization, and analysis. Okay, so for our demo today, we're going to actually be working with the genie BPC data. And so the first step is to register for a Synapse account. So Jessica has put the Synapse link in the chat. And if you could just follow the instructions of registering, we'll give you a few minutes to do that. And one thing to note is please do not connect to Synapse via your Google account. Make sure you create a username and password. Okay, we're going to move on. And if you have any questions, please put them in the chat and we'll be monitoring throughout the hour. Okay, so the next step for our demo is to work on installing the genie BPC and genome R packages. The instructions to download these packages are shown here. And then also they are on our GitHub repository, which Jessica just put the link in the chat for in the demo.r script. And further, our package details are available on the package websites that are linked here. And just note that the R packages require a version, an R version of greater than or equal to three point sites. Okay, so with that, we'll move into the data processing pipeline for the clinical genomic data. First, the data is stored on Synapse. And so as we mentioned, this is the Sage Vi Networks data sharing platform. So the first part for the genie BPC R package is to import the data using call data Synapse and the Synapse version helper function. Then we'll get into actually data processing with the create analytic cohort and select unique NGS functions. We'll then go into data visualization with drug regimen sunbursts and then move into genomic processing with genome R using the create gene binary and TVL genomic functions. Okay, so we'll move into a case study that highlights all of these functions, where we create a cohort of patients who are diagnosed with stage 4 adenocarcinoma, mom's small cell long cancer, and received carbo cotton and pemicrexid with or without bevacismab or cisplotin, and pemicrexid with or without bevacismab as their first cancer directed drug regimen after diagnosis. And just a note to please follow along using the demo R script on our GitHub. All of the code is provided there. Okay, so our first step is to actually import the data into R. And so to do this, you need to set your Synapse credentials using the set Synapse credentials function, which will store your username and password for Synapse during each R session.  We also have this Synapse version helper function that returns a table of the genobpc data releases that are currently available. This function has one input, most recent, which is a true or false input and column most recent equals true. We'll return a table with each cancer code that's been released and its latest data release version. And calling most recent equals false will return all data releases available. So if we call this recent equals true, this is the table that's returned. And we can keep this in mind moving into the poll data Synapse function. For the poll data Synapse function, this pulls the genobpc clinical and genomic data directly from Synapse into R. It has two arguments, which are cancer type or cohort and the version of the data or version. And keep in mind that versions of data are updated periodically with releases of new variables and additional QA. And this function returns a list of data frames for each cancer site for the accompanying version of data release. So moving into our demo, and just to know that all of these yellow slides will involve everyone running code, and we'll give you a minute or two to do that. So we can first load the genobpc package library and set the Synapse credentials using your username and password for Synapse. And then we'll run the poll data Synapse function to pull the non-smell cell lung cancer or NSCLC version 2.0 public data. And we can name that the NSCLC Synapse data as is shown in the demo script. And what's returned will be a list of data sets. Okay, we're going to move on to the data processing aspect of the package and I'll pass it off to Jessica for that. Okay, great. Thank you, Sammy. So we'll talk about data processing for the clinical data to start and we'll talk about the functions create analytic cohort and select unique NDS. We'll come a little bit later and we'll talk also about visualizing some of that clinical data. So the create analytic cohort function is used to create a cohort from the genobpc data. So as Sammy mentioned at the beginning, the clinical data that are part of genie include cancer diagnosis information, including cancer cohort, treating institution, histology, stage of diagnosis. And we also have a lot of cancer directed regimen information, including the regimen name and the regimen order and the cancer that the drug regimen was given for, in addition to all of the pathology radiology and medical oncology information. So the create analytic cohort function will focus on the diagnosis and cancer directed regimen information to build a cohort. And then it will return all clinical and genomic data for the selected patients that met the criteria that we'll specify. So to go through the arguments for create analytic cohort, the first argument is data synapse, which is the list that was returned from poll data synapse on the previous demo slide. Next is an argument indicating the index cancer sequence.  So we're not going to touch on this too much today. This defaults to picking the patient's first cancer that had genomic sequencing. And that's what we'll leave it at. There are multiple institutions that contribute data to genobpc. So by default, create analytic cohort does not subset by institution. But if you wanted to, you could provide the institution input parameter to look at cases from a particular institution. We can build an analytic cohort based on stage diagnosis, which we'll do for our case study. By default, no subsetting is done based on stage, but you can subset based on stage one, two, three, one through three, not otherwise specified or stage four. Another way that you might build a cohort is based on histology. And so for our case study, we're going to look at amnocarcinoma histology. And for this input parameter as well, the default selection is to just not subset by histology, but the available histologies are listed to the right. When creating an analytic cohort by treatment information, you might be interested in a subset of patients who received a particular treatment. So you can specify regimen drugs. And there is a lookup table provided with the package so that you know exactly how the drugs are named in the data. And then regimen type indicates whether you want patients who received the exact regimen you specified or regimen that contained those drugs. So if you said regimen drugs was carboplatin and regimen type is exact, you would only receive patients, you would only get patients who received carboplatin. But if you said regimen drugs is carboplatin and regimen type is containing, you would get anyone who received any regimen that contained carboplatin. So that would include carboplatin alone or carboplatin given the cisplatin apotaxyl pymotrexin and so on. The other input parameters with respect to building a cohort based on regimen are regimen order and regimen order type. And regimen order refers to the order of the cancer directed regimen. So you might want to pick the first regimen that a patient received for their cancer. So you'd say regimen order is one and order type is within cancer. Or maybe you're interested in looking at the second time someone received a specific regimen. So you would say regimen order is two and order type is within regimen. The final argument to create analytic cohort is return summary, which specifies whether summary tables are returned using the GT summary package. And by default, this is false, but we really recommend setting it to true so that you can take a look at the data that's returned by create analytic cohort in some nice summary tables. So now we're on a demo slide and just a reminder that our case study is based on building a cohort of patients that were diagnosed with stage 4 adenocarcinoma, non small cell lung cancer, and they received carboplatin and pymotrexin with or without bevacizumab or cisplatin and pymotrexin with or without bevacizumab as their first cancer directed drug regimen after diagnosis. So to build this cohort, we're going to call the object NSCLC cohort and we'll call the create analytic cohort function. We'll first specify data synapse. So this is the list object that was returned from poll data synapse. So it's NSCLC synapse data and we'll indicate that we want the NSCLC version 2.0 data set for data sets. We'll specify stage of diagnosis is stage 4, histology adenocarcinoma. We're interested in four different drug regimens and we want those exact drug regimens. We don't want any other drugs given with those drugs. We want the first regimen. We want this regimen to be the first regimen that the patient received for their cancer diagnosis and we will return the summary tables. So we'll pause here to give you a minute to run this code and then we'll go through what the output looks like. Okay, so we'll take a look at what the result of running this create analytic cohort code chart returns. So the first summary table is just called TBL overall summary and it shows the number of diagnosis regimens and cancer panel test reports. We'll use cancer panel test report or CPT exchangeably with next generation sequencing and GS. So we'll see that we have data return for 241 patients and each patient had one diagnosis since the default was just a lot quicker to index cancer per patient and because we specified a particular drug regimen and we wanted it to be the first regimen, we have one regimen per patient. So we have 241 regimens and we'll come back to this in a little bit, but we'll see that some most people only had one next generation sequencing report, 92%, but there are a handful of people who did have multiple next generation sequencing reports performed over time. So we'll talk about how to handle this in the coming slides. The next summary table is a summary of the cohort. So we can see that we have patients who have lung cancer. We did a subset by institutions. So we get DNA Farber, MSK, and Vanderbilt cases. Everyone was staged for a diagnosis and their histology was at no carcinoma. So our case study is clean. So basically this table just kind of tells us that the function is working as we expected it to, but if you did have additional, like let's say you wanted to look at stage one and two, you would see the breakdown of stage on this table or if you had multiple histologies, it would be informative to tell you the breakdown of each of these characteristics across your cohort. The drug regimen table includes the cohort and institution variables again and also tells us the breakdown of the proportion of patients that received each of the four drug regimens that we specified. So the most common was carboplatin-penetraxin, followed by carboplatin-penetraxin with bevacizumab, and then this is platin-penetraxin with or without bevacizumab, followed after that. This is the summary table for the next generation sequencing data. So we'll see that the oncotree code, which is a way of characterizing the type of lung cancer, was most frequently LUAD, which is lung adenocarcinoma, which makes sense given that we specified we wanted adenocarcinoma, histology and explicitly. And then we'll see here that there were multiple sequence AXA IDs or multiple panels that were used to perform the next generation sequencing. And we'll talk a lot about the impact of having different panels or patient sequence on different panels when we get to the general part of the demo today. Next, we will visualize the drug regimen data for patients in our cohort. So we specified what we wanted their first drug regimen to be, but we can visualize all of the treatment information for patients in our cohort. So this is an example of a sombrous plot to the right, and it's a way to visualize the complete treatment course for a selective cancer diagnosis. And each ring corresponds to a regimen where the innermost ring is the first regimen, the second innermost ring is the second regimen, and so on. And when you run this in R, it comes out as an interactive figure. So you can hover over regimen names. You can hover over bands in the plot to see the regimen name and the percent of patients that received that regimen pattern. The input parameters for this function are data synapse, data cohort, and the maximal number of regimens. So we'll specify in data synapse all of the data that was returned. So this is the entire non-small cell lung cancer version 2.0 public release. Then our data cohort is specifying the specific cohort that we're looking at return from create analytic cohort. And maximum number of regimens is just a way of sub-setting the sombrous so that it doesn't get too unwieldy if you're only interested in the first few regimens. You can hover again on this. So we're at another demo side. So we'll pause for a little bit here. We'll create an object called NSCLC underscore sombrous, where we'll call the job regimen sombrous plot function and specify data synapse as NSCLC synapse data with the non-small cell lung cancer version 2.0 object. And then data cohort is our NSCLC cohort and we're not going to specify some number of regimens.  Okay. So the next slide, we have the resulting sombrous plot. And if you hover over the orange band here followed by the green band, you can see that 10.4 percent of patients had curve a button from the trexid followed by a nebola band. And you can hover over the different trajectories. And looking at this, you can really see that there's a lot of heterogeneity in the treatments that patients received. You'll also notice when you run it interactively, there is a little box on the side that says legend, but we recommend turning that off and really just hovering over the portions you're interested in to look at the legend up on the top left so that you can see which color corresponds to the trigger. Next, we're going to talk about the genomic data processing with one function from the genic APC package and then the rest come in from the genome R package. So we're on the last box here in our pipeline. We'll talk about creating gene binary to process the genomic data for mutation, copy, number alterations, infusions. And we'll talk about TBL genomic and a couple of other functions for summarizing the genomic data in tables and figures. So back to our case study. We have patients with non small cell one cancer in this nsclc copart object that we created with create analytic copart. And we'll talk about how to process the data into an analysis study matrix of gene alteration events. And we will summarize the genomic alteration through the season analyze difference between men and women study. So we can see on this table to the right that 60% of the 241 patients that met our inclusion criteria were female to recap the genomic data that is available in genie VPC. We'll be processing and analyzing data on mutations, copy number alterations or CNA and fusions and that names of each of those objects are listed here and more information about them can be found in the genomic data guide posted by ACDR. So to jump into a summary of some issues when processing multi institutional genomic data. One issue we saw on the next generation sequencing data summary. Most patients do only have one next generation sequencing report, but in the case that they have multiple, we need to figure out how to select a single sequencing report for analysis. So we'll talk about that with the select unique NGS function. Other issues that we'll go into greater detail on in a little bit include data formats and gene standards often being inconsistent. So variable names, data formats, gene names may even differ between studies or sometimes within studies, cohort inclusion. So in those mutations fusions and copy number alterations file, only instances of genes that were altered are included. So if someone did not have any alterations, they might not be in that data set. So we'll have to be really careful about making sure we don't accidentally drop patients from our analysis. And then lastly, multi institutional studies use several gene panels. So just samples might be sequenced on it on different panels across different institutions and even within the same institution over time, next generation sequencing panels have evolved to include more genes as time goes on. So we need to be careful about annotating mutation status and analyzing overlapping genes. So we'll start with selecting one sample, one next generation sequencing sample per patient. And we'll do this with the select unique NGS function. And this function prioritizes characteristics of interest. And if importantly, if the patient only has one report, it's returned regardless of the criteria in this function. So the data cohort argument is the output of the CRE analytic cohort function on poetry code is a way of characterizing the cancer diagnosis and is reported for all next generation sequencing reports. Sample type will specify which type of genomic sample to prioritize. So if the patient had multiple next generation sequencing reports, sometimes that happens if their primary tumor was sampled. And then later on in time, their site of the test assist was sequenced as well. So you can specify which of the two you would prefer to include if they did have multiple. And lastly, you can specify selecting a sequencing report with respect to time. So you can select early sequencing report or the latest sequencing report. So we are on another demo slide. So we can see that in the NSELC cohort, the cohort NGS data set had 262 rows, even though we only had 241 patients. So we know from here and from our summary table, returned from CRE analytic cohort that there are patients with multiple sequencing reports. And so we'll run select unique GS and we'll specify data cohort as the NSELC cohort, the cohort NGS data set. And we'll specify the LUAD and aqua tree code. So this is long abnormal carcinoma. We'll prioritize that if if there is a long abnormal carcinoma aqua tree code sample available, if not, we'll look for a NSELC metastatic sample. And if there are still multiple, we'll pick the one that is latest in time. And you can see that running this returns 241 rows corresponding to one row per patient. So we'll pause here for a little bit to allow you to run this code. Next, we'll talk about the format of formatting data in an analysis writing matrix. So the create gene binary function from GMR will give us a data frame of N patients by X alterations, as you can see here. The alteration columns are denoted by the gene name if it's a mutation, such as TP53, or the gene name plus aesthetics of dot amp, dot fuse, or dot dial for other alteration types. So we can see here that the MYC dot dial is a deletion. And each cell will have a zero if there's no alteration, a one if there is an alteration, or an NA if that gene was not tested in that patient. So to get the data in a standardized format, we need to talk about the data formats and gene standards that can be inconsistent in multi-institutional data. So the genome mark functions are designed to work with standard alteration data formats, using common data platforms like C-Biot portal. The gene copy number alteration and fusion data are slightly different than the standard. And so we need to reformat it using the reformat fusions and pivot CNA bomber functions. So looking at the reformat fusions function, initially in the gene fusions data, we have, for example, these two genes that are fused together. And it's in a lock format. And when we run the reformat fusion function, we get just one row to indicate one fusion between the two genes. Next, using the pivot CNA bomber data in the gene raw CNA data, we have it in a lock format. And the standard is to have it in a longer format. So the pivot CNA bomber function transposes this, the copy number alteration data into a longer data set. And so here we'll get ready for the demo where we call the mutations copy number alterations and fusion data as objects to save in the R environment. And then we reformat the fusions and reformat the CNA data using the reformat fusion and pivot CNA bomber functions. Now that we have the copy number alteration and fusion data reformatted, we're ready to run the gene binary. Add additional arguments to create gene binary to help address the remaining data processing issues. So the next issue that we're going to discuss is cohort inclusion, where samples with no alterations may be dropped when pulling the raw genomic data. And so this samples argument will ensure that all study IDs have a row in the resulting data, even if they're not present in the genomic files. We'll also cover how multi-institutional studies use several gene panels. And so samples may be sequenced using different panels and therefore may have non-overlocking genes that we need to annotate as missing. And so the specified panels argument and create gene binary can insert NAs when we know that the gene was not tested for a specific patient or a sample. So to use the specified panels argument, we need to first create a data frame indicating which patient IDs were sequenced on which panels. And so we'll use the dataset from the select unique NGS to call the sample ID and the panel name or the assay ID to create a two-column data frame of the samples and their corresponding panel names. And so after running the specified panels argument, we can see that whereas if we do not specify the panels, it looks like the genes that were not tested are actually missing. Whereas when we include the specified panels argument using the data frame of the panel, of the sample IDs and panel names, we see the correct indication that these genes were not tested for these samples. We need to make sure that the gene names are consistent across all studies. And so we do that with this recode aliases argument. And it's important to note that some gene names have evolved or been updated over time. And so this argument ensures that all panels use the most up-to-date gene names for consistency.  All right. I will pass it off to Carissa to continue with the genomic data processing. Thanks, Amy. So now I'm going to talk a little bit about how to analyze this data that we've just processed. So we're going to use the data in the previous section to just make some basic summary tables and do some exploratory analysis using the genome R package. So as with processing data, there's some common issues that arise when you're analyzing multi-institutional genomic data. Firstly, often we're working with pretty large targeted panels. So if we do want to do some analysis with clinical endpoints or associations between gene frequencies and clinical characteristics, we might be conducting a lot of hypothesis tests. And as we know, the large number of statistical tests, the greater risk that some of those significant findings are significant due to chance. And additionally, a lot of times we're working with pretty low prevalence events or low prevalence alterations, which are not very informative. So one way to handle this that's often done is to choose a threshold, depending on your sample size, it'd be 1%, 5% or more before you start conducting your analysis. And also you might want to consider adjusting your p-values for multiple testing, and there are a couple ways to do that. So there's some genome R helper functions to help with that. Additionally, as I mentioned, a lot of times we're working with low event frequencies or low prevalence alterations. So depending on your cancer type or your disease area, it might make sense or might be biologically meaningful to summarize on the gene level or the pathway level as opposed to the specific alteration level. So there's also some genome R functions to help with that as well that we'll talk about. Returning to our case study, we're going to take our processed gene binary object, which is just that processed binary data frame, and we're going to summarize the genomic alterations overall in the cohort. And also we're going to do a summary by sex. So in this case, just male or female, given the data that we have. In order to do this, first, we're going to need to join our clinical sex variable onto our processed genomic data frame. So on this slide, and again, this code's kind of long, but it is available in that demo.rfile, we're just using dplyr select and joins to grab the patient ID in that sex variable and then marry that onto our processed gene binary object. So we'll give you a couple seconds to do that. Okay, so as I mentioned, we might want to subset by a prevalence threshold. So the subset by frequency function in genome R will help you do that. So you pass your processed gene binary data into subset by frequency, and you choose the threshold, which is the T argument, which can be between zero and one or zero and 100% prevalence. And then you'll get returned from that a data set with your sample ID, as well as only the genes that meet or exceed that given threshold. Additionally, there's another argument, other bars that helps retain any other variables you might want to keep in your analysis data set. So in this case, we want to retain that sex variable, because we're going to do some analysis with the later. So we just pass that to the other bar's argument. So here's an example we can quickly run through where we take gene binary pass it to subset by frequency. Here I chose a pretty high prevalence that probably is not too commonly chosen in a real analysis, we did a 40% prevalence threshold, but just for example purposes. And then I specified that I wanted to retain that sex variable in the final data set. So as you can see, we started out with 1400 ish genes in the resulting data set. We have six columns, one of them is the sample ID column, one of them is that sex column, and then the other four are the only genes that met or exceeded that 40% threshold. So we'll give you about 10 more seconds to go through that. Additionally, I just wanted to point out this other helper function that works in a similar way, but instead of subsetting by a certain alteration threshold, it subsets by a specific targeted panel. So in the package itself in the Genomar package, there's a lot of sort of built in panels that are kind of commonly used panels and agrogenic research. And so you can just call out one of those IDs in the panel ID argument. And you can look at the documentation to see what those IDs are. And then similarly, you'll get out a data set with just the genes within that given panel, as well as any other variables that you want to retain. So here we as an example, we looked at the MSK impact 300 panel, and our resulting data set has 220 genes that were both in our data set as well as in that panel, and then also our sex variable. So just useful, especially if you're working with multi institutional data that might have several different panels at once. So next, we're going to create a simple summary table of our frequency alterations using the table genomic function. So table genomic is a function in genome are, but it is built on top of the GT summary table summary function with some specific defaults that make it helpful for presenting genomic data. So if you haven't heard of it, GT summary is a wonderful package written by Dan sober that helps you create summary tables and arts very useful for a lot of applications. But because this table genomic object is a GT summary object, you can also leverage lots and lots of customization functions available in GT summary for making your table look more, you know, more presentable, look a little bit nicer, you can use some different statistics, you can, you can basically specify any aspect of the table. So check out the GT summary documentation, and we'll show an example of using the bold labels function here. So here we can just run through. So now we have subset our data. So this and SCLC subset is the 40% or higher cut off version of our data. And then we're going to pass it to table genomic. And then we're going to add that GT summary bold labels on top just to make it look a little nicer. And we get this really nice table out of the box on the right. And it's showing us those four alterations that reached that prevalence threshold. So as I mentioned, depending on the cancer area, the disease type you're working on, it might make sense to analyze on the gene level as opposed to the specific alteration level. And that basically means that the, that you're going to bend together things like copy number alterations, deletions or fusions on a gene with the mutations and count any of those things as an event for that patient on that gene. So in Gina, Martha is a helper function called summarized by gene that you can pass your data to. And if your data is coded in the documented way, it will give you that data set summarized on the events on the gene level. So here on the right is an example of what that gene level table would look like if we ran it through the table genomic code. And I'll just point out like KRAS, for instance, here we have 107 events and are 241 patients. And then if we go back to the previous slide, you'll see KRAS is 98. And that's because in this version, that is not by the gene level, but the alteration level, these are just counting the mutations, whereas in the next table, we're counting any sort of genomic event from our data as a KRAS event. Just to note that if you are going to use this, you definitely want to subset by frequency after you summarize by gene, just because if you first subset by frequency, you might be getting rid of some low level events that might count towards those gene level events. So first summarize, then subset, and then you can pass it to table genomic, or any plotting functions or anything you want to do with it after that. Additionally, you might want to analyze even one level up from the gene level, which is on the gene pathway level. So pathways are groups of genes that work together biologically to control processes like tumor growth or a response to treatment or cancer progression. And there's some common pathways often used in cancer research analysis. You can check out this paper that I cited at the bottom that has some of those common pathways. And a lot of these pathways we built in by default to the package to allow you to process your data and analyze these pathways pretty quickly. You can do this using this add pathways function. So basically, if you take that processed binary data set and you pass it through add pathways, it'll add a column for each of these common oncogenic pathways onto your data set. So here, this is a list of those default built-in pathways. And again, these will just be automatically added as additional event columns to your binary matrix. Additionally, on the next slide, we'll talk about the custom pathways argument. So certain cancer types or disease types have very specific genomic pathways outside of those kind of common oncogenic pathways. So we wanted to allow the functionality to analyze those easily as well. So here, you can pass custom pathways and specify the specific alterations that you want counted towards that new pathway. And it'll process it and add it to your data set, just like it did for those default pathways. So here we can take 30 seconds to just add this add pathways function onto our process data. And if you run through this and put this through table summary, you'll get this nice just pathway specific table on the right. And again, this is counting any gene that's within that pathway. If that's altered, then that pathway is then activated for that patient. So again, let's go back to the issue of presenting a table that split the alteration frequencies by male and female. So we can take our previous table genomic code and just add one small tweak, which is the bi argument. So we subset the same way we pass the table genomic, and we just specify bi equals that sex code variable. And then we both label it just to make it look nicer. And we get this really nice table on the right that has our overall column, which we've seen before, as well as this split frequencies by male and female in this case. And then as we mentioned before, we might want to additionally do some adjustment for multiple testing. GT summary makes this really easy. They have this add p function, which by default will add a Pearson kai square test p value onto this. And then also add q, which will do multiple testing adjustment of that p value by default uses false discovery rate, although you can specify pretty much any method you want there's some built in, or you can write your own custom one and pass it to add q. So it makes it really easy to just kind of quickly do these exploratory analysis and add these multiple testing adjusted q values. We'll just take a minute to go through that. And then I think there's a question about analyzing other types of pathways from other sources. So in the custom pathways argument, I believe you can also pass a named list of several other pathways, like a list of custom pathways you want to analyze. So I think you can pretty easily turn a JSON into a list in our and then you'd be able to pass that list into the function. But I haven't really tried it. So check it out and file an issue. If you have any issues with that, we can, we can work it in this package is still under development. And then lastly, I'll just quickly mention there's a lot of default plots and custom tables and plots you can use aside from the ones that we've gone over in the genome or package, including this is just a basic commutation plot using the top altered genes, you can pass a math file here and you know, I'll give you this kind of nice out of the box visualization. And most of the visualizations in genome are our ggplot objects, you can then add additional ggplot customizations on top of them pretty easily. This is another one that I use a lot, which is just looking at the top genes and splitting them by the brand classification. So that was just a couple of visualizations, but there's there's several more in the package, as well as color palettes that are useful for genomic data, because a lot of times it's genomic data, you want a lot of contrasting colors if you're working with, you know, high dimensional data. So genome are has some built in color palettes that are especially useful for that. Additionally, depending on where your data source from, you might need to do some additional data checks. So the genome are vignette, which is linked here has some helpful tips on just queuing this is the sort of genomic data in general. It might be appropriate to oncocopy annotate your data and only analyze oncogenic or likely oncogenic alterations. So I recommend checking out oncocaby.org and there's some functionality that's woven into genome are to do that as well. And lastly, this one's kind of under development, but we're working on some additional functions to look at CNA segmentation data. So there's some forthcoming documentation and some additional functions that can help you make really cool, like segmentation heat maps very easily. So that's coming soon. So I'm going to pass it back to Jessica to just kind of wrap us up. Okay, so all together, these two packages, the genie VPC package and the genome are package offer reproducible pipeline to create cohorts for clinical genomics data analysis. And starting with the genie VPC package is streamlined data access from the Synapse platform into the art environment for clinical data processing from multiple complicated, unwieldy clinical data files of varying structure with a high that are high dimensional. There are a lot of variables and a lot of different data files. So the genie VPC package allows you to, in a streamlined way, read in the data and create your analytic cohort. And then from there, the genome are package facilitates annotation and analysis of complicated genomic data. And importantly, we want to highlight that genome are we use today for the genie VPC data, but it is it can be used more broadly than that it can be used for genomic data processing and analysis of genomic data outside of the the genie VPC project. So thank you all for being here today. I know we had a lot of information to go over. And so the slides are available on GitHub. The demo code is available on GitHub. We appreciate your time. We're happy to answer questions. If you reach out to us via email or on GitHub, since I know we're almost at time. And we want to say special thanks to Anna Fuchs, who contributed a lot to the packages in this presentation, but going to be here today. And also to others in our department here at MSK who have contributed to these packages along the way. So thank you all. All right, thank you. I'm going to go ahead and end the session now.


Welcome back to further discussion on rotational contribution to molecular partition function. We have been discussing different contributions to overall molecular partition function which is equal to product of partition functions for different contribution there for example, Q translational into Q rotational into Q vibrational into Q electronic and if there is any other contribution. In the previous couple of lectures we have discussed rotational contribution for a linear rotor and when we talk about linear rotor it could be diatomic, it could be triatomic but as long as the molecule is linear. Then this linear molecule for example, if we talk about diatomic molecule it can be homonuclear it can be heteronuclear. The difference in the final expression for homonuclear and heteronuclear diatomic molecule has been the inclusion of the symmetric symmetry number and therefore, we need to have a prior knowledge of what the symmetry number will be. Today we will further extend the discussion on rotational contribution to molecular partition function for a linear rotor we will solve some example and then we will proceed towards discussion on rotational contribution to non-linear rotor. Let us proceed in the previous lecture let us have some background of that. We have discussed that in general rotational contribution to partition function is given by k t divided by sigma h c b and remember that this expression is based upon the assumption that the rotational energy levels are close to each other and under normal conditions of temperature there are several rotational energy levels which are populated and the approximation was that we can consider the summation as equal to integration. Now above what temperature that approximation is valid will be decided by this equation in this equation theta r is the characteristic rotational temperature. So, above this temperature if the temperature is much much higher than theta r then we will say that it is safe to assume high temperature approximate approximation limit otherwise we will have to go by exclusively summation method. And when we were talking about heteronuclear and homonuclear diatomic molecules we talked about the inclusion of symmetry number in the denominator. For heteronuclear diatomic molecules sigma is equal to 1 for homonuclear diatomic molecules sigma is equal to 2 the reason for sigma equal to 1 and sigma equal to 2 we have discussed in the previous lectures. So, one of the ways of representing rotational contribution to partition function for a linear rotor is k T by sigma h c b and if we express in terms of rotational temperature where h c b upon k we will write equal to theta r. So, therefore you can also write rotational contribution for a linear rotor to overall partition function equal to T by sigma into theta r. So, therefore whether you use this equation or you use this equation it essentially means the same thing. After having this background let us try to solve some numerical problem. Let us take a look at the problem given to us is that the bond length of oxygen molecule is 120.75 picometer. Use the high temperature approximation to calculate the rotational partition function of the molecule at 300 kelvin. So, it is given to us that you use the high temperature approximation. So, if such an information is already given to us then we do not need to worry about calculating theta r unless you are using this expression. So, since it is given that use high temperature approximation we will straight away use q r is equal to k T over sigma h c b. The given information to us is the bond length of oxygen. So, let me say r of oxygen is given as 120.75 picometer which is equal to 120.75 multiplied by 10 raise to the power minus 12 meter. This information is given to us. Now if you carefully look at this expression we have to now transform this expression to something else where you use the given information k is Boltzmann constant it is a constant e temperature is given to you it is 300 kelvin. Sigma is symmetry number oxygen oxygen is a linear molecule. So, therefore, 180 degree rotation leaves the molecule into an indistinguishable state. Therefore, sigma we can straight away write sigma is equal to 2. Then next c is the speed of light which is fixed and b which is rotational constant is h cross over 4 pi c into i which is equal to h cross is h over 2 pi. So, it becomes 8 pi square c and i which is moment of inertia this is equal to mu r square.  In the given case it is oxygen. So, therefore, 1 and 2 both are oxygen oxygen atoms. So, this will be equal to m square where m is the mass of oxygen atom over 2 m which is equal to m by 2. So, now I have b is equal to h over 8 pi square c and what I have mu r square you can for the time being let me retain mu r square and this b which is h over 8 pi square c mu r square can be substituted over here. Once you substitute over here you will get this expression when you put b over here which is h over 8 pi square c mu r square and then c and c will get cancelled eventually you will have 8 pi square k t mu r square sigma h square and what is mu? Mu can be expressed in terms of mass of single oxygen atom and you are given information on the bond length. Let us use this information. So, the expression that I am going to use is q r is equal to 8 pi square k t mu r square divided by sigma h square sigma h square all right. So, q r is equal to 8 into 22 by 7 square k is 1.381 into 10 raise to the power minus 23 joules per Kelvin temperature is 300 Kelvin mu is half m that means 8 into 10 raise to the power minus 3 over 2 into n a n a is Avogadro constant half m. So, half m 8 into 10 raise to the power minus 3 to convert into kg per mole and divided by Avogadro constant where you are converting it into per molecule or per atom and what you have is now r 120 into 10 raise to the power minus 12 whole square mu r square divided by sigma is equal to 2 into h square 6.626 into 10 raise to the power minus 34 joule second square. So, therefore, now your q r when you calculate this it turns out to be 71.2. So, carefully you check q r is 8 into pi pi is 22 by 7 whole square into 1.381 into 10 raise to the power minus 23 joules per Kelvin 300 is the temperature and half mu r square which is 120 into 10 raise to the power minus 12 whole square sigma is 2 and the Planck's constant is here 6.626 into 10 raise to the power minus 34 all right. So, this calculation gives you a q r value of 71.2. Now, this value of 71.2 is a sizable value that means at 300 Kelvin 71 thermally accessible rotational states are present. If you increase the temperature carefully if you examine this q r rotational partition function is directly proportional to temperature it is directly proportional to bond length and it is inversely proportional to the symmetry number. The variables in this if you see k is constant pi is constant temperature can affect the value of rotational partition function. So, as long as the molecule is fixed means a fixed value of mu fixed value of r fixed value of sigma q r will increase directly as a function of temperature. It makes a sense when the temperature becomes higher more and more thermally accessible rotational states are present. Therefore, the value of 71 for rotational partition function which itself suggests significant population of the upper states can further increase as the temperature is further raised all right. After having discussed the linear rotors let us now have some discussion on non-linear rotors non-linear molecule. Some examples of non-linear molecules are given in this table for example, water water is a non-linear molecule ammonia is a non-linear molecule CH4 methane is a non-linear molecule benzene C6H6 that is also a non-linear molecule. For linear rotors we have derived expression for rotational partition function and I am reiterating again and again that in order to use high temperature approximation we need to have knowledge of characteristic rotational temperature. So, therefore, as long as the experimental temperature is much higher than characteristic rotational temperature we can use the high temperature approximation result. For linear rotor we did a detailed derivation for non-linear rotor the derivation is quite involved. So, therefore, we are not going into too much mathematical complexity those who are interested can separately do that. We will write the result and have a discussion on the result. The rotational partition function for a non-linear rotor is given by 1 by sigma k T by h c raised to the power 3 by 2 pi by a b c square root where a b and c are rotational constants for the rotation of molecule or associated with the rotation of molecule in three dimensions. It is possible that depending upon the type of rotor a b or c any of these two may also be equal. So, therefore, experimentally to obtain the value of rotational partition function we need to have information on these three rotation rotational constants a b and c all in centimeter inverse we need to have experimental in information on these like b is equal to h cross by 4 pi c i a and c also have the similar form only thing is there the moment of inertia for the rotation of the molecule in three dimensions or three dimensions respectively will be different all right. Once we have that experimental information we can plug in those numbers and at a given temperature we can calculate the value of rotational partition function for a non-linear molecule. However, the caution that we must divide by the symmetry number. So, therefore, a knowledge of symmetry number becomes important we have earlier discussed that what is the need to include this symmetry number. For example, if we consider even water let us consider water water is a bent molecule. So, if you consider let us say I will write somewhere here H O H. Now if I rotate water you know around this axis it interchanges the two atoms this atom will occupy this position and this atom will occupy this position. So, that means its rotation by 180 degree will leave the molecule in two indistinguishable forms in an indistinguishable form. So, therefore, the symmetry number for water will be equal to 2. Now consider ammonia if you consider ammonia and then if you consider its C 3 axis you will see that every 120 degree rotation will leave the molecule in an indistinguishable form. I repeat take the C 3 axis rotate ammonia molecule it will be observed that every 120 degree rotation will leave the molecule in an indistinguishable state. So, that means the symmetry number in case of ammonia is 3 and the higher the value of symmetry number the lower becomes the rotational partition function because you do not want to over count the number of rotational states which are thermally accessible. Now let us consider about methane you know methane has tetrahedral arrangement around carbon atom. So, pick up any C H bond and rotate the molecule by 120 degree you will see that the molecule is in the indistinguishable form. 120 degree rotation means in total 360 degree rotation there will be 3 indistinguishable forms, but methane has 4 C H bonds. So, therefore, you can choose any of the C H bond and carry out 120 degree rotation each rotation will leave the molecule in an indistinguishable form. Therefore, the symmetry number in this case is going to be 3 into 4 3 comes from each 120 degree rotation and 4 comes from 4 C H bonds. The symmetry number becomes 12 benzene is also 12 any of 6 orientations around its C 6 axis leaves it unchanged and rotation of 180 degree around any of its C 2 axis in the plane of molecule. So, therefore, the symmetry number in this case will become 12 I have discussed for simple molecules, but for complex molecules definitely if you have studied symmetry that will help you in finding out the symmetry. But for our purpose what is important is to recognize that symmetry number is required because you do not want to over count the number of accessible rotational states. So, what we have discussed by now as far as the rotational contribution to partition function is concerned first of all we should identify whether the molecule or the rotor is linear or non-linear. Because for linear rotor the formula is different for non-linear rotor the formula is different. Second thing in order to use the high temperature approximation even this one even this result is a high temperature approximation. That means, even to use this result you must first calculate A B and C you must have information about A B and C and then from these calculate the characteristic rotational constants. Then if the experimental temperature is much higher than the characteristic rotational temperature we are free to use this high temperature approximation result. After having decided whether to use direct summation method or to use high temperature approximation then we need to have information about symmetry number. Once you have these informations it is very easy to calculate the rotational contribution to partition function. But let us keep in mind that rotational energy levels are not very far separated. Therefore, under normal conditions of temperature several rotational states will be thermally accessible. Therefore, the value of rotational partition function is going to be sizeable. Now after having discussed the rotational contribution to partition function we will move to discussion on vibrational contribution and electronic contribution to partition function molecular partition function. But that we will discuss after solving few more numerical problems. So, I hope that with the knowledge of the rotational partition function for non-linear and linear molecule we will be able to apply to a variety of problems. Thank you very much.


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