good morning she chili one we got a one below zero right now it was about four below zero when the propane ran out last night I was trying to see what this little heater could do and she went through all the propane and that one can saw it middle of the night I went out and switched them over and then got her going again and it's 44 degrees in here with that thing on high gonna drive some coffee in me get some traps set up for big northern pike and then come back and cook up some breakfast while those traps are soaking got two live bait and three dead bait to soak today we'll see which ones work better that was the sixth night in a row on the ice out here trips going pretty well a lot of fun a lot of a lot of people on this trip doing solo camping but getting to see a lot of people each day coming in the fish going out to try to catch some other fish we got two big live shiners that the boys from Vermont caught on rodent real force yesterday I'm gonna deploy them see how they do I like live bait better I've been trying dead bait this trip but I think live baits always better let's go beautiful morning out here get this in fired up never gets old a little bit of a bite that's what we're into for live bait right there this one was caught on a rodent reel yeah so about halfway between the eye and the dorsal fin on him that's about a nine nine and a half shinier get him down he's gonna go where he wants to go I'm gonna show you guys the rubber band trick when you're using big bait that could trip your your set up your tip-ups or your jack trap so got a rubber band on the bottom there I'm gonna lift it up I get a fold I'm gonna tuck the fold underneath that rubber band so it's pinched so the bait is working against that right there when a pike hits he rips it out of that and then he's got free spooling so he doesn't feel anything any resistance and can can run and he won't drop the bait so that's how you set it up had to reach in the bucket and get that shinier out because my net's frozen solid as you can see I like to clear all the slush away from the hole when it's wet because as soon as that freezes it it's real grabby on your line so I got it away from the hole so that way when I'm fighting a big pike and I get him up to the hole and he takes all the line back the line won't shouldn't get hooked up on on any of that slush that's frozen around the hole well we got one in one more time tucking that in that big bait it's a little different angle on this put a fold then if you put it in the corners it's even tighter and that's it just like that well the rest of my dead bait that I had in my jet sled her the rest of my dead bait that I had in my sled last night froze a little too solid to use so luckily I vacuum sealed some the other day and they're in my shack and shouldn't be too frozen so we'll get those open them up and use them that's just a beautiful jack trap right there that's their burnt ash charred beautiful I like that one oh that's hot sit there for a minute between catching a 20 pounder and losing a 20 pounder on a cold day that slush is gonna harden like today and not only is it tough to kneel on it when you're trying to catch the fish when you're laying your line out it gets caught in it like crazy it's sticky it's like Velcro but when that fish has got to make a run and he's got to take some line you could have a snap off if you're hung up on the ice you could have a mess so that's one little piece of advice when you're big fish fishing always double check your knots your leaders everything because you're one flag away from catching the biggest fish of your life you've got to be the way you look at it so it's fishing guys and I know it's not a perfect science but I found that the big pike the really big ones feed between about 930 and 230 I don't know if it's a light thing or what it is but on several different bodies of water where I fish pike that's when I've caught my biggest pike in general now there's always those anomalies there's always it's always fishing you could catch one right at dark you could catch one first thing in the morning but I like to get my traps set up first thing in the morning so they're set ready ready for them could catch that anomaly and at least are good to go for when 930 comes around and I got no trouble and then you know anything before then as a bonus fishes is what I figure and then then once you hit that magical time slot then it's game on and then I fully expect to be getting some good bites out here had to run back and get a battery back to the GoPro now she's going okay we got something on whoa look at that she just took a run run like that you could usually set just pulled it right out of its face didn't feel very big but what a bummer what a bummer dang maybe I should let him eat easy to second guess wicked bummer you hate missing fish but felt like it might have been a hammer handle trying to eat that giant trainer let's see if he comes back all right it's about 8 8 30 we're gonna go in and cook up some breakfast get a full belly before we gotta go start pulling some big pike top so nice in here oh sweet oh nice well Benjamin you want to check this one it might just be laying there but doesn't look good well it took you did take some wine you know how to do it you got steel leader too so you can you can horse on them if you need to you guys are on camera is that all right he took a little bit so it wasn't just a perch sometimes they turn around you don't any bait at all Benjamin now always yeah yeah I had my bait charm put me up with yeah but she came out the first day out here yeah when did you first come out I think I've been here about six nights have you really making power I don't know what direction I'm going but when I when I kick the ice it started going so you could take it whenever you want yeah you got them feel big heavy you let me know if you need any line back a lot of times those big ones will come right at you and then they start fighting at the hole all right there's your bottom marker yeah within a foot or two there you go little hammer handle yes sir well guys I think I'm gonna take a load out of here back to the truck and start slowly slowly packing up for this trip I'm gonna fish the rest of the day and still have the tent and the floor and table and stove and all that stuff to take apart later well I'm still fishing but run a load back got a full load right here all right camps all picked up and clean gonna pick up my traps and that's gonna be it for this trip


Hi Shane, this is Ashley with Oxmore Toyota. Thank you so much for your recent interest in our pre-on 2013 Land Cruiser. When I hit and sent you over an email a little while ago answering the question set you had pertaining to this vehicle, when you get a moment please give me a call. My number is 502-247166 or you can also email me back. Thanks. Bye.


I don't know this, but it seems obvious to us that you've coached in person a lot more or first. Yes. Okay. 100%. I was in the trenches and I think anybody who wants to be a successful coach, you have to be in person. You have to be in person. Explain that why. We talk about that a lot. You will learn invaluable lessons from working one-on-one with an individual, both tactile cues, lessons. You will see the reactions in someone's face when they get it. You can't learn that from going from a textbook to just speaking on camera and just doing things online. Everybody wants to be an online coach because it's easy, right? But you have to earn your right to do that. You have to work in the trenches and you have to learn those lessons along the way of how can I make this work for someone before you can have the, dare I say, authority to move into the online world.


السلام عليكم ورحمة الله وبركاته بسم الله الرحمن الرحيم والصلاة والسلام على أشرف المرسلين سيدنا ونبينا محمد وعلى آله وصحبه أشبعين يا ربنا لك الحمد كما ينبغي لجلال وجهك وعظيم سلطانك اللهم افتح علينا افتح العرفين اللهم علمنا ما ينفعون وانفعنا بما علمتنا يا أرحم الراحمين اليوم سنقوم بفوكيس على سبه الثلاثة افتح العرفين افتح لنا أفتح علي الله سبحانه وتعالى وانتبعنا أكثر مستعدة لدينا لدينا when we leave this world to meet Allah سبحانه وتعالى so what is this new outer action of today it is remembrance of Allah سبحانه وتعالى which is doing zikr doing zikr is very important because سيدنا محمد صلى الله عليه وسلم talked about that and he said سيدنا محمد صلى الله عليه وسلم said the similarity between someone who remembers his lord someone who does this zikr and someone who does not remember him is like that of the living and the dead so it's like someone who is alive and someone who is dead الله سبحانه وتعالى ordered us in the holy Quran to make zikr and to remember him سبحانه وتعالى so he said in surah al-Baqara in ayah 152 فاذكروني أذكركم وشكروا لي ولا تكفرون so remember me I will remember you and be grateful to me thank me and do not deny me do not deny my blessings so why do we remember Allah سبحانه وتعالى why do we always make zikr why do we see gatherings of zikr and attend them سيد محمد صلى الله عليه وسلم لا يقعد قوم يذكرون الله عز وجل إلا حفتهم الملائكة وغشيتهم الرحمة ونزلت عليهم السكينة وذكرهم الله في من عندها سيد محمد صلى الله عليه وسلم when a group of people assemble for the remembrance of Allah when a group of people sit together to make zikr to remember Allah سبحانه وتعالى then what happens the angels surround them and Allah's mercy envelops them غشيتهم الرحمة ونزلت عليهم السكينة tranquility descends upon them وذكرهم الله في من عندها and Allah سبحانه وتعالى makes the mention of them before those who are near him who are those الملائكة so وذكرهم الله في من عندها سبحان الله and سيد محمد صلى الله عليه وسلم said in another hadith he was encouraging his companions to attend the gatherings of zikr so he said إذا مرتم برياض الجنة فرتعوا قال وما رياض الجنة قال حلق ذكر so and this is reported by Anas رضي الله عن that يد محمد صلى الله عليه وسلم said when you come upon pastures of paradise feed on them so try to gain as much as you can from these places and then one of the companions asked سيد محمد صلى الله عليه وسلم what are these places and said the Muhammad صلى الله عليه وسلم said they are the circles where people sit in circles to remember Allah سبحانه وتعالى so these are the circles so these are the gatherings of zikr so because we want to be of those whom حفتهم الملائكة وغشيتهم الرحمة ونزلت عليهم السكينة وذكرهم الله في من عنده angels surround us and mercy envelops us and tranquility and sakinah descends upon us and Allah سبحانه وتعالى will mention us before the angels then we want to be with these types of people with this group of people and سيد محمد صلى الله عليه وسلم told his companions he was sitting with them and he said سبق المفردون so the مفردون have gone ahead so they are winners so the companions asked يا رسول الله ملمفردون so who are these who are this group of people which is called المفردون سيد محمد صلى الله عليه وسلم said الذاكيرون الله كثيرا و الذاكيرات those men and women who frequently remember Allah سبحانه وتعالى so those are the winners those are the ones that are getting the high rewards in the day after so how can we be considered of these مفردون of الذاكيرون و الذاكيرات those who frequently who always remember Allah سبحانه وتعالى how can we be of them okay we have an answer from سيد محمد صلى الله عليه وسلم he said so when a man awakens his wife during the night and they both performed سبراكاز صلى الله عليه وسلم they are recorded among the men and women who remember Allah سبحانه وتعالى who celebrate remembrance of Allah سبحانه وتعالى you might ask what is the best zikr to say again we get the answer from our teacher from سيد محمد صلى الله عليه وسلم he said so he gives us the answer so the best the most excellent way to make mention of Allah سبحانه وتعالى the best zikr is to say لا إله إلا الله and the the best the most excellent supplication the best supplication is to say الحمد لله praise be to God so this is the best of supplications of دل الدعاء now we say we are all leaving this dunya and we are all going to meet Allah سبحانه وتعالى one day all our deeds are going to be presented to him and there will be a scale our deeds will be scaled so we have to get ready for that and going to Allah سبحانه وتعالى is the beginning of the road سيدنا إبراهيم talked about that so we want to we want to make sure that we can leave the things in the things that fascinate us in this dunya we want to leave these things behind we want to go to Allah سبحانه وتعالى we want to remember Allah سبحانه وتعالى so سيدنا إبراهيم عليه السلام said وقال إني ذاهب إلى ربي سيهدين this is in surah to saffat ayah 99 سيدنا إبراهيم said indeed I will go to my lord you will guide me so when you sit alone remembering Allah سبحانه وتعالى and doing zikr you might feel something special right you're sitting you're with Allah سبحانه وتعالى doing your zikr but after some time of doing this zikr you feel that you entered the stage of presence you feel that you are present with Allah سبحانه وتعالى and this stage remember if you get into this stage this stage does not does not last long it might be seconds it might be a few minutes so you have to keep doing your zikr then this state might become longer longer and during this state you might receive the secrets from Allah سبحانه وتعالى so the starting point of zikr is the zikr of the tongue while you are sitting you are saying أستغفر الله أستغفر الله أستغفر الله and you are thinking of so many things but you are saying أستغفر الله if you are remembering Allah سبحانه وتعالى then the next stage of zikr is the zikr of the heart you feel that your heart is longing to be with Allah سبحانه وتعالى to make zikr so you start by the tongue you go on then you move to the state of zikr by the heart when some people of zikr sit with people of dunya they feel sorry for them that they are deceived by this vanishing dunya they feel sorry for them that they did not taste the beauty of zikr which they themselves have tasted and when they are asked to describe this state and to describe what they know to describe what they feel about zikr to anyone who has not tried it they would say you know this is the same as describing the taste of ice cream to someone who has never tried ice cream so no matter how hard you try to explain it to explain the taste to explain the experience they will not they might not have an idea only but they won't get it so looking at سيد محمد صلى الله عليه وسلم we feel that all his life he was in a state of zikr when he would be teaching he would be remembering الله سبحانه و تعالى when he is with his family he will remember الله سبحانه و تعالى when he is in a fight he remembers الله سبحانه و تعالى when he is when he needs anything from الله سبحانه و تعالى he perfects supplications to الله سبحانه و تعالى so Prophet Muhammad صلى الله عليه وسلم used to make zikr all the time for each and every situation he is in and we have so many examples in the seerah that talk about the zikr of سيد محمد صلى الله عليه و تعالى so we just mentioned that سيد محمد صلى الله عليه وسلم used to make zikr all the time for each and every situation he he is in when he is with his companions he is teaching them he is remembering الله سبحانه و تعالى when he is with his family he is remembering الله سبحانه و تعالى when he is in a fight yeah in the battlefield then he remembers الله سبحانه و تعالى he does a lot of supplication so I am going to mention now a few examples about his zikr so جاب الرضي الله وان reported that I heard I heard سيد محمد صلى الله عليه وسلم saying إذا دخل الرجل بيته فذكر الله تعالى عند دخوله و عند طعامه قال الشيطان لأصحابه لا مبيت لكم ولا عشاء و إذا دخل فلم يذكر الله تعالى عند دخوله قال الشيطان أدركتم المبيت و إذا لم يذكر اسم الله تعالى عند طعامه قال أدركتم المبيت والعشاء so سيد محمد صلى الله عليه وسلم says if a person mentions the name of الله سبحانه و تعالى upon entering his house and when eating while eating then Shaytan will say he will address his followers he will say you will find nowhere to spend the night nor dinner in this house the person mentioned the name of الله سبحانه و تعالى then if the if a person enters without mentioning the name of الله سبحانه و تعالى Shaytan will say to his followers you have found a place to spend the night in and if he if the person does not mention the name of الله سبحانه و تعالى at the time of eating then Shaytan will say you have found a place to spend the night in and a place to eat till you have found a place to spend the night in as well as food so we always have to mention the name of Allah سبحانه و تعالى when we get into our homes and before we start our food another a zikr is when we hear the when we hear the when we hear the when we hear the when we hear the when we hear when we hear when we hear when we hear when we hear when we hear when you hear the then then Repeat what the the one whoQUALS the toances محمدًا الوسيلة والفضيلة وبعثه المقام المحمود الذي وعدته حلت له شفاعة يوم القيامة آتي محمدًا الوسيلة والفضيلة نعم حلت له شفاعة يوم القيامة when whoever says upon hearing the event oh Allah Lord of the perfect call Lord of the Dawah and the Lord of the established prayer اللهم رب هذه الدعوة تامة والصلاة القائمة آتي محمدًا الوسيلة والفضيلة grant Muhammad the wasila and the superiority وبعثه مقام المحمودة and raise him up to praise worthy to praise a worthy position which you have promised him so when you resurrect him give him the مقام المحمود الذي وعدته we all know that Sayyidina Muhammad صلى الله عليه وسلم is going to perform a sajdah on the day of judgment that he will praise Allah سبحانه وتعالى with the forms of praying that he has never ever said so whoever does this حلت له شفاعة يوم القيامة then it becomes incumbent that upon me to intercede for him on the day of resurrection so that person will be eligible for the intercession of Sayyidina Muhammad صلى الله عليه وسلم so when you hear the event when you hear the calling for the prayer you say اللهم رب هذه الدعوة التامة والصلاة القائمة آتي محمدان الوسيلة والفضيلة وبعثه مقام المحمود الذي وعدته إنك لا تخلف المعاد then the intercession of Sayyidina Muhammad صلى الله عليه وسلم will be given to that person another place of thicker is when you enter the masjid and when you go out كان رسول الله صلى الله عليه وسلم إذا دخل المسجد صلى على محمد وقال رب يغفر لي ذنوب وافتح لي أبواب رحمتك وإذا خرج صلى على محمد وقال رب يغفر لي ذنوب وافتح لي أبواب فضلك so when the messenger of Allah سبحانه وتعالى would get into the masjid he would he would say صلى الله عليه وسلم أبواب محمد and he would say رب يغفر لي ذنوب يا الله pardon my sins وافتح لي أبواب رحمتك and open the gates of your mercy to me and when he exits the masjid he would say صلى الله عليه وسلم أبواب محمد and then he will say رب يغفر لي ذنوب يا الله pardon my sins وافتح لي أبواب رحمتك and open the gates of your blessings for me another dhikr النبي صلى الله عليه وسلم كان إذا أوا إلى فراشه يقال بسمك الله هم أحيا وأموت so when the when say the Muhammad صلى الله عليه وسلم would go to his bed he would say بسمك الله هم بسمك أحيا وأموت يا الله with your name I live and die وإذا أصبح قال and when he wakes up in the morning he would say الحمد لله الذي أحيانا بعدما أموتنا وإليه النشور الحمد لله that he make us live again that we are living one more time we have another chance to live after we were dead because we all know that sleeping is a short death وإليه النشور and we will all be resurrected to him and when سيد محمد صلى الله عليه وسلم was teaching the Sahaba about all the types of dhikr one of them was entering the restroom before you enter the restroom you say اللهم إني أعوذ بك من الخبس والخبائس يا الله I seek protection in you from evil and from the male evil and female jinn okay so from the evil male and female jinn and when he used to get out from the restroom الحمد لله الذي أذهب عن so he would thank الله سبحانه وتعالى who has removed harm from him and kept him in health we all know that the best invocation is the dua on the day of Arafa and سيد محمد صلى الله عليه وسلم said خير الدعاء دعاء يوم عرفة وخير ما قلت أنا والنبيون من قبل لا إله إلا الله وحده لا شريك له له الملك وله الحمد وهو على كل شيء قدير so the best invocation is the day of Arafat and the best that anyone can say is what I and the prophets before me have said so what did سيد محمد صلى الله عليه وسلم and the prophets before him said لا إله إلا الله وحده ولا شريك له there is no god to be worshipped but الله سبحانه وتعالى alone له الملك وله الحمد who has الله has no partner he is the he is the one to be praised he is the one who owns everything he is the one to be praised and وهو على كل شيء قدير and he is able to do all things now if we stop for a second and think about think about remembering الله سبحانه وتعالى think about we can change we can flip our life from habits to worshipping الله سبحانه وتعالى من عادات إلى عبادات so every day the mother of the house and the lady of the house cooks then instead of just cooking either silently or thinking about some stuff or listening to music just mention the name of الله سبحانه وتعالى do zikr while you are cooking do zikr while you are doing your chores do zikr while you go to school do zikr while you go to work do zikr while you are driving do zikr while when you have any minute free then do zikr remember الله سبحانه وتعالى and some people have about 15-20 minutes in the morning 15-20 minutes in the evening just they sit alone with الله سبحانه وتعالى to remember him some people like to sit with their families some people like to go to zikr gatherings some people like to do it by themselves during the night so either way let your zikr every day have 100 استغفار 100 100 100 100 100 15-20 read this zikr sometimes we have another type of zikr which is the zikr of Quran so what is the zikr of Quran some people like to read a world of Quran before they go to bed أية الكرسي قلوا الله أحد ثري قولs قلوا الله أحد قل أودوا بربي الفنق قل أودوا بربي الناس الفاتحة some people like to read the first 10 ayahs of Surat Al-Qa'ab سيد محمد صلى الله عليه وسلم ساد من حفظ 10 آيات من أول سورة الكهف أوصم من الدجال وفي رواية أخرى من آخر سورة الكهف أبو أبو دردار رضي الله وعين ربورت that the messenger of Allah who ever commits to the memory is the first 10 ayahs of Surat Al-Qa'ab then he will be protected from the trial of a dajal from the Antichrist and in another narration it's the last 10 ayahs of Surat Al-Qa'ab so actually it's very easy just to memorize these 10 ayahs it's not difficult and we talked about how to memorize last session when we talked about reciting the Quran and we said that the easiest way is to follow a reciter and to listen you can do that okay now the Quran will protect you this Surat Al-Qa'ab these ayahs the first 10 ayahs or the last 10 ayahs of Surat Al-Qa'ab will protect us will protect us from the dajal from the Antichrist and Sayyidina Muhammad said that as we just mentioned whoever memorizes these these ayahs will be protected from the dajal so this is the first word of Quran the Quran also will protect us from the graves punishment after death when if we keep reciting Surat Al-Qa'ab and Sayyidina Muhammad said read Surat Al-Mulk to protect yourselves from the punishment of the grave another word that is Quran word that is Quran ذكر Sayyidina Ibn Mas'ood reported that Sayyidina Muhammad said من قرأ Surat Al-Waqa في كل الليلة لم تصيبه فاقة أبدا so Sayyidina سيد محمد صلى الله عليه وسلم said whoever recites Surat Al-Waqa every night will never be afflicted by want or by need and Ibn Mas'ood used to order his daughters to recite Surat Al-Waqa every night again we have from the treasures of the from the treasures of Ta'arsh the last two ayahs of Surat Al-Baqara and these were a gift from Sayyidina Muhammad صلى الله عليه وسلم to his ummah during the Israel war and again try to read the last two ayahs as your before you go to bed now we can say okay so we have so many things to do we can do a lot of dikker but there are some shortcuts and the reward for those shortcuts is immense Sayyidina Muhammad صلى الله عليه وسلم told us everything and he said in one of the narrations كلمتان خفيفة تاني على الديسان ثقيلة تاني في الميزان حبيبة تاني إلى الرحمن سبحان الله وبحمده سبحان الله العظيم two words are light on the tongue they weigh heavily in the balance and they are loved to the merciful one سبحان الله وبحمده سبحان الله العظيم so glory is to Allah and praise is to Allah سبحانه وتعالى Allah is the great the most great he is the greatest سبحان الله وبحمده سبحان الله العظيم one time Sayyidina Abu Zar sayyidina Muhammad صلى الله عليه وسلم and Sayyidina Muhammad صلى الله عليه وسلم said to him ألا أخبروك بأحب الكلم إلى الله shall I tell you the the most loved expression by Allah سبحانه وتعالى Sayyidina Abu Zar said yes and Sayyidina Muhammad صلى الله عليه وسلم said to him أحب الكلم إلى الله سبحان الله وبحمده the most loved words to Allah سبحانه وتعالى is to say سبحان الله وبحمده the mother of the believers رضي الله عنها was a جويرية أم المؤمنين رضي الله عنها was a righteous lady and she used to and she used to and she used to and she used to do she used to pray for her and then stay until until the rising of the sun until and she would be remembering but as I mentioned we are doing the shortcuts now so sayyidina Muhammad صلى الله عليه وسلم one day he he was with her and so he went out from her house one morning and that was at the time when he prayed and while she was still praying sayyidina Muhammad صلى الله عليه وسلم came back from the prayer and he returned to her when it was sunrise and he found her sitting and mentioning الله سبحانه وتعالى and doing and he asked her if she was still in the same position as that in which he had left her and when she replied that she was so he said so this is the shortcut imagine he said to her سبحان الله وبحمده عدد خلقه وريضا نفسي وزينة عرشه ومداد كلماته so he said to her I said three times four phrases if wait against all you have said today then will be heavier towards what I said سبحان الله وبحمده Glory be to God عدد خلقه the number of his creatures وريضا نفسه and in accordance with his good pleasure وزينة عرشه the weight of his throne ومداد كلماته and the extent of his words he's teaching us shortcuts also even in zikr سبحان الله so four times three times the four words that he said سبحان الله وبحمده عدد خلقه وريضا نفسه وزينة عرشه ومداد كلماته and another shortcut is what say the Muhammad tell us لقيته إبراهيم صلى الله عليه وسلم ليلة أسريبي so say the Muhammad he's saying I met إبراهيم صلى الله عليه وسلم on the night of ascension of يوم الإسرار فقال يا محمد أقر أمتك مني السلام so إبراهيم عليه السلام said could they my greetings to your أمه so now we know that he has greeted us we have to return the السلام ويقول وعليك السلام ويسيدنا إبراهيم وأخبرهم so tell them أن الجنة طيبة التربة tell them that جنة has a vast plane of pure soul عزبة الماء in sweet water وأنها قيعان it's a plane leveled land وأن غيراسها in the plants that grow there what is it سبحان الله والحمد لله ولا إله إلا الله والله أكبر so when you say سبحان الله والحمد لله ولا إله إلا الله الله أكبر then you have planted your place of جنة with these amazing phrases so these are the plants of جنة and another narration سبحان الله سبحان الله العظيم is the whenever you say it each time you say it you are planting a palm tree even during the time of calamities we remember الله سبحانه وتعالى we do zikr without noticing we thank الله سبحانه وتعالى so when there is a problem when there is a calamity we say this is the first word that we say الحمد لله الحمد لله that it wasn't bigger than this الحمد لله that it wasn't this affliction is not in my religion and we know that when we say الحمد لله then الله سبحانه وتعالى will highly reward us but you know what الله سبحانه وتعالى in صورة العصر in he said الله is swearing indeed mankind is in loss why الله swears that man is in loss so with all that said about the rewards of zikr about the shortcuts of zikr man still forgets to say these short forms of zikr which which generate a lot of the deeds and a lot of reward so he misses to collect abundant reward which might need just very short time to say so this is why الله is swearing that man is in loss because he is not collecting he is not easily collecting a lot of rewards a lot of good deeds so one of the benefits of zikr is to be with الله سبحانه وتعالى to feel that you are with الله سبحانه وتعالى now think a little bit more what is another benefit of zikr one of the best benefits of zikr is that it keeps shaitan away it awakens shaitan and what happens when shaitan is away and when he is weak then the soul gets stronger the relationship between the person and Allah سبحانه وتعالى gets stronger when the soul is stronger then a person starts to worship Allah سبحانه وتعالى and what does this remind us this reminds us of the soul in Ramadan in Ramadan the shaitans are chained and the soul gets stronger and stronger and that's why in Ramadan way much more way much more worship to Allah سبحانه وتعالى then we do outside of Ramadan we read more Quran we pray 20 rak'as of tarawih but on the second day after Ramadan the following day after Ramadan we don't do that so this is the benefit of getting shaitan the soul would get stronger and this is what we need in this dunya a lot of things a lot of errors are being shot against us these deceiving errors this deceiving dunya these calamities everything is against man but with zikr we get stronger to face everything because we are close to Allah سبحانه وتعالى and we mention that with zikr Allah سبحانه وتعالى would give us certain secrets he will give us certain light that would enlighten our path so كن مع الله ولا تبالي be with Allah سبحانه وتعالى and do not be afraid of anything else along the way Allah will protect you so zikr is getting us closer to Allah سبحانه وتعالى and Allah سبحانه وتعالى mentioned said in Surah Al-A'raf in ayah 180 ولله الأسماء والحسنة فدعوه بها and to Allah belongs the best names so invoke him by them Allah has 99 names so so many people memorize them so many people ask Allah سبحانه وتعالى with his last names and they know that Allah is close Allah said أستجب لكم I am close to you make dua and the best time to make dua the best time to remember Allah سبحانه وتعالى is before Fajr Allah would come would descend to the lowest the sky and he would say ألا من مستغفر فأغفر الله is there someone who is asking for مغفر for forgiveness and I will give it to him is there someone who is repenting and I would accept the repentance is there someone who is asking for anything and I would grant him but we have to be awake otherwise we would be of those not collecting these rewards that Allah has given us so ذكر is one of the outer actions that gets us closer to Allah سبحانه وتعالى it gets us more prepared for the day when we will leave this dunya to meet Allah سبحانه وتعالى on the day of judgment there will be a card written on it لا إله إلا الله whoever says لا إله إلا الله sincerely with a heart that's believing in it then this card will be thrown on his good deeds and it will have the best reward and whoever says لا إله إلا الله و هو موقن بها he would be highly rewarded و صلى الله عليه وسلم سيدنا محمد و على آله و صحبه و سلم يا ربنا لك الحمد و كما ينبغي لي جلالي وجهك و عظيم سلطانك and until we meet next week سيدنا محمد صلى الله عليه وسلم و السلام عليكم و رحمة الله و بركاته


Okay, thank you all for joining. I'm really excited to be your instructor this morning on day five of I'm sure what has been a really fun and exciting workshop packed with information for you. I'm Lauren Erdman. I'm a like fourth year starting in the fall PhD student in computer science. I've put this workshop like this portion actually together with my supervisor, and we taught it together for years and I have kind of taken it over in the past couple years. And so I'm excited today to talk to you about similarity network fusion clinical data integration. And then we're going to go deeply into survival analysis as well. So, this is all covered under creative commons license, which I believe means you can share and remix this work. Just make sure you attribute it and share like if you alter it. So like I said, this is going to be all about clinical integration. Of course, this is a huge field so I'm not going to cover it in completion in any way. I really encourage questions so we've got some awesome TAs, and I know they've been helping you all week and they'll probably be able to field your questions if you send them along on slack. It is going to be a little harder for me to keep up on the slack. So also feel free to open your mic for especially for the lecture portion. If you have a question that you kind of like us to, you know, stop and talk about. Or if there's a question on the slack that maybe should get attention. I just want to say the TAs like feel free to also open your mics and bring attention to that too. I'm happy to stop and I really want everyone to be on the same page in this. So, great. Without further ado, the learning objectives so just a quick overview of what I'm talking about when I say clinical data and some quick review on types of a single data analysis. We'll talk about some data integration methods, such as concatenating and clustering your data using iCluster and using SNF. As I say in the afternoon, you're going to be doing a lot of similar work. I think we, Shrata and I worked hard to kind of make sure that it's complimentary and it's really building on what, like what she's going to present later. We'll build on what I talk about now. So some of it may be review and some of it may be an extension. So also just keep in mind, a lot of these concepts may come up later, even in another session later today. We'll talk about some advantages and drawbacks of different methods you might choose and then how kind of survival analysis may come into the mix and the reason I'm including survival analysis here is to kind of drive it home like how do we make this now clinical and build a model that can be used clinically and evaluate it. Patient data. That's really what I'm talking about when I say clinical data. And so that could include, you know, genetic data, expression data, epigenetic data, microRNA protein data. So these are like omics data sets, but we could also have things that are like just clinical like from the chart data, for instance, that patients fill out so you can assess them, maybe some imaging that's been done on the patient or even aspects, whoops, even aspects of their diet. So we've worked with nutritionists, for example, who will have extensive diet information on their patient. But why would we want to integrate all of these types of data? Well, one reason might be to identify more homogeneous subsets of patients. These might respond similarly to a given drug. So if the patient's behaviors and their clinical history is similar, and that may be the case that the same drug will actually have a similar impact on these patients. They also might have a similar prognosis. So if all these features are similar about the patient, it may be that their actual clinical progression is similar as well. They may respond better to similar clinical management. So not just drugs, but different interventions that are done. So you may want to group them and identify those similarities there. So one type of single data type analysis, which I'm sure you're all familiar with is clustering. And so here, this is showing gene expression data for 1800 genes. This is from a PNAS paper in 2005. And so what they did was they collected gene expression data. They selected the most varied genes from those. So as you can see, this list is not 18,000 genes. And they performed hierarchical clustering. So that's what you see up here. This is a dendrogram hierarchically clustering these genes. And here they identify two clusters. So they identify genes that are associated with differing between these clusters using ANOVA. And they corrected for multiple testing or multiple hypothesis testing. And so that's what you see these genes here that are really driving kind of the separation between these groups. And from this, what they found really nice and neatly is a difference in survival of the patients with gluoblastoma multiforms. So it's a nice example of where you take data that is very omics based. You find a pattern in that and then you see that pattern kind of propagate to a clinical outcome. Okay. So I just want to dig into this KM curve that we're seeing. This is actually just plotting our data here. So in this case this is laying it all data. But what's happening here is you see that the survival for group one is much longer, the median survival is much longer than it is for group two here. So this data that's being plotted is showing the proportion of patients that are dying. From gluoblastoma multiform with each one of these events here each drop. And so as this falls faster, many more of these patients died sooner, whereas in group one, these patients survived much longer. And even at two years, more than half of that group actually was fine. They were, they were still surviving. So the probability, the way you could interpret this is the probability for surviving one year here is 80% for group one. So quite high for group one, but only 20% for group two. So prognostically these are very distinct groups here. Sorry guys, just lost my quicker. There we go. And then what else we can see is there's censored observations. So there's observations where we see this person lived at least half a year, but we don't actually have more information about them beyond that point. So that's why these points are here. Just showing this is the information we have on them. And we can include that in our model, but we can't say more about it so we don't know if they survived beyond that point. So, here, yes, yeah. Quick question, sorry, it's very early here. So is there a temporal component to the gene expression data, because there's no, there was. Yeah, exactly. There wasn't a temporal component. That's a really good question actually, off the top of my head from what I can remember. No, I believe this was baseline. I think it was from their tumor. But that's a great question because it could be that they're picking up just the progression of the tumor to. So it could be that maybe the people in group two, that gene expression data was collected at a different time point. It could look like, you know, it's further along, but maybe if you collect like a stage four tumor from these, these groups, one folks, then it could be that in that view their lifeline is quite shorter, but good question. And that's actually really important to consider when you're doing this kind of clustering. What pattern are you actually picking up here? Like, are you picking up a difference in the tumor at a given point in time? Or is there something actually, are you just collecting different samples from these patients? And maybe there's some different aspect that's that's going to reflect in this prognosis that you find. Yeah, thank you. Right. So, here, there's a work in cancer cell by Verhac et al, where they want to go beyond the single data type integration. And they essentially add more and more genes through this, sorry, they have gene expression data, but they select their genes based on more different types of data. So these are still gene expression data, I believe, but they're selecting genes based on, you know, copy number variation alterations, mutations, and, and then the actual gene expression itself. And then they also find that they're not actually able to, when they do this, they're not actually able to differentiate these different groups. So as you can see the p values differentiating the groups from the baseline group are very large. So it doesn't always work out. Sometimes you're finding things that are not going to be related to your actual clinical outcome. Yet, it's still identified pro-neural, neural, classical and mesenchymal groups. They just didn't have significant differences in their survival. So now, let's say, though, you don't want to just use your gene expression data like sync brought up, you know, there could be differences in that data that you want to kind of also include other data to augment that clustering that you're doing. Some approaches to doing this are, which I'll go over here, are concatenating and clustering your data. So essentially taking these different data types, putting them all together in one mega database, and now clustering that database. Another one is Shen et al's eye cluster, which is a popular method and has been developed since 2009, but I'm just going to talk about like the really basic version. And then similarity network fusion, which I'm also going to talk about in much more depth here. And there's more work worth coming on this. But we'll also use it as an example for what we're going to implement later, really to get your feet wet, kind of taking a new tool off CRAN and just using it and understanding it more deeply. This is not an exhaustive list. And in fact, later today, Shrata is going to actually show you even more of these kinds of techniques. So I'm not going to go too deeply into all of these because there's so much more to find. Okay, so concatenating cluster is super simple. It's truly what it sounds like.  So before, in the work I showed you before, they're only clustering gene expression data. Okay, and in this one, it would say, okay, now cluster, put these all together, treat this as one long kind of row of features that your patient has and now cluster your patients based on all of this data. Okay. So then you concatenated. Now you can cluster. And so here I'm going to just talk quickly about hierarchical clustering. And what you will have when you do the concatenating cluster or actually any clustering, you'll have a distance matrix, which is what I show here. So let's say each ABCD is a patient. Okay. So the distance matrix always has the same rows and columns. Okay, because it's comparing all to all. And so here in the middle, the distance between an individual to themselves is zero, right. And that's what that is interpreted as. And also, the lower and upper triangles are equal. So the distance from B to a is the same as the distance from a to B. This isn't always the case in the distance matrix, distance matrices you develop but far and away most of this is like by far the most common type. Now, each of these numbers is representing, for example, B to a, how far are these from each other. And so if we look across this we see that the minimum distance, other than zero so individuals to themselves is F to D. So this is the first kind of agglomeration or clustering that we would make when we make hierarchical clustering. So it's number one in the hierarchy. So when I talk about hierarchy, what I'm talking about is maybe dendrograms as you've seen them, or what we can see here, where we have F and D is the first one to get grouped together here. At point five distance at that point D and F become one cluster with each other. And then there's different ways to represent this grouped distance from new points, you could either average it you could take the maximum or you could take the minimum distance from new points. So each of these have their own implications. But here let's say we're averaging so then the next one, as you move up is going to be a to B. So these guys get grouped next. Okay. So if you were to split your clusters at this point so let's say we we did a split at point seven five here, we'd have a and B in a group and we'd have D and F in a group and everyone else. So D, C would be in their own group. They don't cluster with anyone yet because they're very far from other individuals. So as you go further out, we see that he is actually closest to the D F cluster and so then they would get grouped in there. So if you draw the line up here. So here we have D F and E all in a group. So here D F and E all in a group. We have C in its own cluster and then a and B are in their group. Okay, and then again as you go further see now gets grouped in with that D F E group. So if we split it up here at two, we could say, we've got one small cluster a B here, which does seem in our graph here. It does seem like it's a bit further away from everyone. And then we've got a kind of bigger group that holds the majority which is D F EC, and then these guys are all grouped together. Okay, so when you're looking at a glomerative clustering or hierarchical clustering. This is how you would be interpreting it. You see it stepwise growing into grouping everyone into some groups and at the end with the largest distance. Everybody is in a single group. So if we drew the line up at three, we say, yeah, this is this entire group is one cluster. Okay, so you can actually split it many different ways and it's a nice flexibility that comes with hierarchical clustering. So, but with this, like I said, you know, you can make that cut in many different places. What's the best one to do. So there's lots of options. You can just cut it by eye. And also not just by eye but by a kind of intuition or what makes the most sense when you look at those clusters after the fact. And so that is valid. I think clustering. It's, it's quite an art form. And I see it as much more for data exploration and really understanding the data you have in hand. So I think it's reasonable. But another thing you may want to explore is the silhouette statistic, which I'm going to go over in the next slide. The second gap from tips Ronnie actually is a another really nice option. And they're all very similar. Then there's many more actually that you can use that. Yeah, I encourage you to check out if you're interested. But that said, if you do these basic ones. Then you also should be fine. It's, it's more to understand the data that you have and really understand what your clusters are representing similar to what we saw here, where if you kind of just zoom out and look at the data. This a b group is pretty far from the others. So it would be reasonable to kind of draw the line up here and say, yeah, we've got this group that's far out here and then we've got this group that's in here. But if it seems like C is a real outlier, it also may be reasonable to cut it around here and say we've got a DFE group that does seem pretty close. We've got C that's a bit of an outlier and then a and B that are truly on their own. So like I said, you know it's it's more about really understanding the data that you have. A couple questions in slack. Maybe you could clarify for everyone. What does the X and Y mean in the clustering and which covariates are we clustering. Yeah, these are really good questions. Thank you so much, Heather. So here the X and Y axis. So these are the features that the clustering was actually based on so these two questions in this slide, they are related in the that the covariates or the data that you're clustering on. It creates this matrix and it can be whatever. So you could be clustering based on you could be clustering based on methylation you could be clustering based on methylation and gene expression so here. It could be these, but then here in this graph, we've got a two dimensional data set here. And so this graph here actually represents clustering between two features so we could say maybe gene one and gene two. So this could be gene one and gene two. And it's just much easier to interpret this on like to feature access so like X and Y axis. And so then the distances here represent the distance between this a which is defined by the X and Y axis points, and then be these X and Y axis points so each of them have two features that are used to create this distance value here that is representing how far they are from each other. Okay, so in this case this example. It's only two features so it could be one methylation probe and one gene expression value. But in practice, usually it's many like we saw before you know those top gene expression values it could be your entire gene expression array. You could be clustering based on your all of your methylation data. So it really it can be whichever features you'd like. But in this specific example, this is just arbitrary just two axes. Okay, I hope that was clarifying. All right, so the silhouette statistic, I really like the statistic for kind of understanding the intuition of what clusters you've come up with. And so it just a nice piece of history this has been around for a very long time. So from 1987. And it really is to show how graphically well each pattern but here we can say observation so when they're talking about a pattern, you can consider that as like a patient, an observation, or a individual. So for each pattern so each patient let's say in class CR, we're finding the difference between the average distance to all other patterns in other clusters. So all patients in the other clusters subtracted from the average distance to all the patients in the same cluster as that individual, and then divided by the max. So one thing straight off the top is, if one person if an individual is closer to to individuals in all the other clusters, then they are to their own cluster. This is going to be a negative statistic. And it's essentially representing that maybe that person should be in a different cluster. If they're closer to people in all other clusters then they are to individuals in their own cluster. It's kind of a bad sign. Right. So that's where the negative positive comes in, and then you can look at the scale so if people are like, pretty close to the other clusters but they're kind of close to the same cluster. Again, it seems like borderline it's almost like they're not in a cluster. So I'm just going to show know what we can see here. So an example of that is, let's say we put see in a cluster with E, and we wanted to compare the silhouette here. So a cluster of C and E, but we're looking at the silhouette for E is actually closer to D and F, if they're in a different cluster, it might make sense to actually put them in that in the cluster with D and F. So the silhouette statistic for E, if they're clustered with C is going to be probably negative, because they're not very close to see, but they're quite close to these guys in another cluster, D and F. Okay, so what silhouette does is it does that individual by individual and produces a plot like this. That's showing the silhouette value for each individual so the subtype here is the groups that you maybe have created in your clustering that need to be considered and then each line is one individual. And so you can see within each cluster, how much are the individuals how much should all those individuals be in that cluster, or maybe they're actually closer to individuals in other clusters so what we see really markedly here is in cluster one, a large group of those patients maybe should be in a different cluster. They're not closer to individuals in their own cluster than they are to individuals in different clusters and that's why they're showing up as negative here. And that's why I really like the silhouette statistic to give you just like a nice snapshot of, you know, are the individuals I've clustered close to the individuals they are clustered with, or should maybe they be put in a different cluster, because they're actually closer to individuals in those other clusters, or maybe it represents, if we have a lot that's close to zero, maybe it's kind of on a continuum, and our data isn't actually grouping well. That's also something that you may find when you're clustering that you're kind of just drawing lines in a continuous spectrum, and these groups are not kind of held together very tightly, as you might expect. All right. So I cluster. Okay, how do we reassign for values? Do you mind rephrasing that? Sorry, I meant how do we reassign for the bad assignment? Do we just reiterate over the process? Okay. Exactly. Yeah, no problem. Yeah, so you just reiterate, you know, sometimes you may find if you have many clusters that you're having a lot of negative values, and then maybe you just need fewer clusters.  Yeah, and then you could also try that because though with this silhouette, I might try to actually group them because if you're getting a lot of negatives, it means they're close to other like another group. So you could try a different random initialization. If it's a clustering that like jumps around a bit, you know, maybe it just got you into like a bad grouping. And hierarchical clustering is not random. So that will not help you like rerunning it. You should reproduce the same hierarchy. In which case, with hierarchical clustering, you may just want to cut it at a different point. So you may just find like I showed before, this wouldn't be the cluster you'd come up with. But if you're finding that your groups look wrong, you could also try a different clustering approach. But yeah, you'd really want to iterate it in different ways. But most often more groups is not what you actually will want to do if your silhouettes are negative, you'll usually want fewer groups. Thank you. Yeah, of course. Okay, so I cluster. Again, I cluster these all continue to be developed. So I just want to go over the real basics of it. But essentially, they're trying to find a latent variable a Gaussian latent variable model so it's a mixture of Gaussians here, and a mixture of Gaussians is one where you will have some kind of initialization. Alarm going off here. You'll have an initialization. And you could find that when you rerun it, you'll actually get slightly different clusters out. But what I clusters doing is actually building up that latent variable model using data from different groups so trying to find the same latent groups across different data types here. And so they're doing a sparsity regularization. So if you're familiar with lasso, what it's trying to do is basically drop out features that are not useful here so that are not actually contributing to that grouping that you're developing. And then the latent variable that is shared so that latent variable is the latent variable is the grouping. So the groups that are assigned, they're shared between these different, these different data types, and then it iterates so that you're finding the same latent groups. But the key thing is they're shared. So you're looking for information that is consistent across all of these different data types. So groupings that come up as consistent between the different data types. So a drawback to that is currently, it may take a bit more, but at the time. So even a few years ago, late group. Good question. So latent groups are different than collinearity, latent groups are really groups, sub types clusters. So what it's trying to do is find clusters of patients that are shared between these different data types. So finding, if we took the GBM example from before, finding those two clusters, but finding them through the copy number data, the mRNA data and the micro RNA data so finding that consistent grouping between them. So a latent group that would be like two latent groups, and it wants to find those two groupings in all of those data types and have them reinforce each other. Okay, so it's different from collinearity that way because it's, it's not the correlation essentially between features. Yeah. Awesome. So, yeah, so there's a lot of manual preprocessing though for this because you have these different data types and they're looking for latent groups. You can only kind of load so much in memory. So it takes about 1500 genes, and I'm sure you all have a good grasp of the genome that's a very small number relative to the whole genome so you're doing some major feature selection upstream, and that will dictate essentially what you find downstream. So depending on how that feature selection was done, you're kind of setting your course for what your results maybe. And in that way there's many steps in the pipeline for that. It's mostly done in the feature space. So I'm just, I'm actually going to read this because I'm not, I'm trying to think of what point I'm making here. So it's not combining the features to find a grouping. It's finding the groupings in the individual features. So if there's a grouping that you would get by combining the features you will not find it here. It has to be kind of a discoverable grouping from methylation and microRNA, for example, on their own without combining them, but they'll reinforce each other. And this is really important. It focuses on similarity across data types, and this is a similar point to what I was saying in this previous one. So what if there's complementary information? What if there is a grouping of patients that is very obvious in microRNA, but it's not obvious in your methylation data. That complementarity will not actually be found. It won't be used. And so that is, that to me is a major constraint here. And so it is just that which Wang et al set out to improve with similarity network fusion. So this is work by Bo Wang. He's actually at Princess Margaret as faculty in I think it's oh shoot now I'm going to butcher his, his faculty, but needless to say Bo is back. He left us for Stanford and he's now faculty at U of T. And he developed similarity network fusion when he was in on his lab in 2014. And the idea is instead of using the features as they are actually creating patient similarity matrix first. And then finding the complementarity and the similarity between these matrices by combining them through fusion. So I'm going to go through that I know those are a lot of like airy fairy kind of words, I'm saying here. So, step one of this is to create data type similarity, specific similarity networks. And so I before talked about distance matrices similarity matrices are essentially an inverse of that. Okay, so where before our center like our diagonal of this matrix was zero in the distance matrix before. Oh, thank you. And that's in laboratory medicine and path of biology. For some reason I thought he was in medical biophysics but I'm glad I didn't say. So, but the similarity matrix here, it is showing the most similarity on the diagonal because an individual is most similar to themselves. Okay, so in that way it's kind of inverting the logic of the distance matrix okay. So here, we've got our expression, our gene expression data, and instead of making a distance matrix, we're making a similarity matrix, and we'll be doing this later in R. And what's important, I'm going to show I'm going to be representing the similarity matrices like networks. And the key thing is, these are the same so this heat map here that is showing the similarity network here or the similarity matrix. So it is a network. Okay, so when there's no connection here. This means the similarity is zero is very very small. And when there is a connection that's very thick. It means that they are very similar, they're very closely related okay. We can think about these matrices as networks as well and so you work with side escape. Yesterday. So, similarly to that the similarity networks can be loaded into cytoscape and graft in that way to So here, we've got two networks for two different data types. And what happens with the fusion iterations is these matrices are essentially sparsified. So only the most similar individuals are kept or the most similar linkings are kept, and essentially a matrix multiplication they're multiplied by each other, and then updated, and this is done multiple times and that's what this is kind of representing here is that they're, they're updated by multiplying that sparse matrix of itself to the other data type and updated again and again, until they aren't changing much anymore so they kind of converge to a specific matrix that isn't updating. And then there's just a linear combination at that point because they are not changing and they're quite similar to each other. And that results in your fused similarity network. And in effect what that does is, it is able to pass the information between these two networks to each other to create something that includes the complimentary information. So if there's a very strong signal let's say, in this network, these guys are very, very tightly linked. And actually, so these guys, this will be dropped because this is absolute zero. It may be dropped it because it's zero but if it's small, it won't be totally dropped, it'll actually get reinforced and so that complimentary information will be included. But here, what else we can see is this link here. So it's very strong linkage here and a light link here, it will get reinforced. That's shared information that will be boosted through SNF. And so it's pretty nice for that but then you can also see where there's kind of like light linkage, not a lot from both of them so like, I would say like low information linking these two here. They actually end up getting dropped because it's like, they're not that related and all the data types are saying they're not that related. So it's kind of moving them all to more extreme version. All the links are being moved to more extreme version of themselves. So you get essentially a stronger signal from your data in completion, after running it through this procedure. So here's a case study and this is actually from the SNF paper. Okay, so here in the SNF paper, they're fusing the methylation data and gene expression data so mRNA expression and then micro RNA expression data. And then from each of these, they get a similarity network so I'm going to actually take a moment to go more in depth on how you would actually interpret these. What we see in this top one is pretty nice groupings here. So kind of a block structure and that's what you want to see when there's a good similarity network. Then you are going to see very strong blocking of your different groups. So these are patients here. And so we see these patients are quite similar to each other. These, this group of patients is quite similar to each other. This group, they're dissimilar from other groups, but they're not super similar to each other either so they're kind of the other group. Okay. And so then if we were to compare across data types, we can see in the micro RNA data, it's not so structured so it seems like in this data.  And then these guys seem somewhat clustering but honestly like this whole bit. They're kind of related to each other so in terms of how I would interpret this I would say, you know, you're not getting great clustering out of the micro RNA expression, but it may be that there's some similar group. So maybe this group down here this small group that's consistently grouping together, that'll probably get boosted now in our similarity network fusion. And so that's what we see actually. So we get this really tight group down here, we get this group that tightens up here, which is actually combining information from all of these. So some of these groups actually get combined to create this that it's not being kept in these, where in the gene expression data it seems like there's many more little clusters. It's grouping them all together. And then in, we have one big group that, again, is kind of our other group like they are the lightest color they have the least similarity to each other. And so, again, how you would interpret this is like, these guys are pretty heterogeneous. These guys are quite homogenous. They're very tightly linked and based on just like the brightness and the distinction of that grouping there. So this was done actually inside escape this this figure here. And, and it's just representing actually this exact thing. But what's nice is, but went ahead and colored the linkages between them, based on what is actually supporting that linkage there so what's supporting the similarity.  So it seems like the micro RNA data is not contributing a lot to a subset of these guys. But there are some that are being contributed to, though there's very few that are having the full contribution from every single data type so the nice thing about SNF is it can kind of be adding this information from different data types, and drawing away low information data types as well, just not really worrying about it. So, when they went ahead and looked at the clinical properties now of these subtypes. They were happy to find actually that, especially this subtype here. It has a longer survival time so there's a quite a distinction clinically between these groups that they're finding. We've also found that the age is lower so it's a it's a younger group of patients. And, and finally, the treatment response is really different for one of the subtypes as well so in subtype one, the treated versus untreated groups are really distinct this one. I'm not too sure what this last one was about. It must be the treatment response though it is only in subtype one. And so that would be this big guy here. Yeah. So just some advantages and disadvantages we've talked through it but to list them out here for you. And one great thing is you get integrative feature selection. You can grow the network. Advantage growing the network requires additional work so because you're doing everything in network space when you have more patients. You're making it a larger computational task a much larger computational tasks so I would say the computational limitation here is the number of patients. And the last example I showed you there's 200 patients. What we're going to do today has 200 patients. If you have I've tried it with 5000 patients, it takes a long, long, long time to run. So Bo is working on that solutions to kind of expedite this but that's just something to bear in mind here. Yeah, it's unsupervised so even though I'm linking this all to a supervised kind of outcome like survival or patient age or treatment response. At the base level SNF is unsupervised and so I think it's really important to kind of set your expectations about what you're going to get from it it's describing your data so one big thing is if you have kind of group effects or I should have group effects in your data. That's probably what you're going to find actually using SNF. It's a very effective way to find all the problems with your data. So I also recommend it for just exploring your data, even if it's not the main part of your analysis, finding kind of batch effects or effects that are consistent across a lot of your data types and may really mess up your analysis down the line. I think it's very good to check your data for that. But some really good things you know it creates a unified views of patients on multiple heterogeneous sources. So here, these are all omics data types I showed you here, but we're going to have no omics data in the data integration we do at in the lab. So you could really include any kind of data here you could be integrating different data types from this, including let's say we also had imaging data on these patients let's say we had some radiomic features on the imaging that had been done on the brains for these patients, then we could actually integrate that as well so that could be its own additional feature set, we make a similarity matrix for it we fuse it. So we have an even larger fusion so you don't have to have everything on the kind of same like omics scale you can also be integrating just totally different data into this so it's a huge flexibility. And yeah, just repeating what I just said, and no need to do gene pre selection, because you're doing everything in the patient similarity space. The number of genes doesn't matter. It just matters that you get that similarity matrix out from those genes. That said, you don't have to, but in practice sometimes it is useful to do this pre selection if that's what your actual research question is. So if you're really interested in a specific pathway. Subsetting your genes to that pathway or sub setting all of your data to only omics data that is in that pathway. It would be useful to do because it's it's what you're actually looking for similarly. Work I've done with using similarity network fusion in neuro imaging is to again if there's a pathway or certain circuit that you're interested in, including all that extra data. So all those extra measurements from other parts of the brain. It's not useful. It adds additional information that you're not actually caring about so including everything what I found in that analysis was including everything. It was giving me these really broad skill patterns that were like sex, you know, or age or something just really big things that I'm like okay that's not really what I'm getting at here like I want something more clinically relevant. So, by kind of making it only about specific circuitry, a specific portion of the brain. We were able to find actually much more interesting patient sub types that were more to do with actual clinical features beyond just age and sex. So you may find that doing a pre selection of some kind actually is very useful in practice to what you want to do, but you don't have to do it and there's no computational limitation, saying that you should do that. Yeah, and it's robust to different types of noise so I didn't show it here but it's pretty cool. It essentially is able to do a denoising. You have data that has noise different types of noise added to the same data. When you do fusion on it, it denoises it. So it's, it's pretty nice for finding a nice signal through noisy diverse data. After the network fusion, after the network fusion, can you extract the features, yes, that they separate the different groups. Yes. So, this integrative feature selection, I just want to make sure, oh yeah, okay scalable. You can. What you can do here, Astrid, this is a great question. So one thing here I'm showing different kind of different non omics data where they went through and evaluated you know how related are these different clinical features. to these clusters that I've come up with. But what similarly you could do is go through everything that was used to create these clusters and see how those relate to the clusters you found. And when you do that so what I'm saying here is essentially like a tea test, or welches to sample or a Crisco Wallace test here to say, you know, are the means of each probe different between these groups. And then you just rank them based on their p values and you find which features are driving this in essence.  And I'll show you everything but that I don't think I'm going to show you that exact procedure in the R script today. But I think it should be easy to do.  Or is there a difference of group numbers counts if it's categorical data between these groups, and then just rank them. And that's how you would, that's how you would see which features are really driving the clustering. So that's a great question about SNF working with missing values that that paper is forthcoming so I know that Bo and a couple students are actually working on it. It should be out or submitted at the end of the summer but right now, no, no missing data is okay. You can. You can interpolate your data like you can you can impute your data I should say, though I, the imputation. It's important to know who was imputed. When you go and evaluate it downstream, because what I found sometimes is, I'll just do like a median imputation because I want to like not have a very informative imputation. And then I'll find that kind of one group is like sort of my other group and they're just like the imputed group that just didn't have a lot of information so maybe they're just like low sample patients that I have in my set. So you can do that. Another thing is if you're doing imputation, you may be kind of helping drive the similarity. Because if you're imputing based on other features from other patients, and then kind of predicting the value the missing value, you are sort of contributing to the similarity between the patients that way. So that's where I would caution against kind of an informative imputation, but those most recent work coming forth will have like a different imputation method that allows you to kind of overcome that. But right now I would say it's a huge constraint. Yeah, the missing values hurt a lot. And so when using the omics data, what methods are available for scaling different data sets. Ah ha. Yes. So, um, in terms of scaling, you're always going to standard normalize, but the scale between the different data sets I should show here. The scale is actually the similarity space. So it's it's dealt with if you. Um, so for example, in this example here, thank you gave RNA seek and flow cytometry. What you would probably have is a RNA seek data set here and then a flow cytometric set here. And then within it, it's, it's dealing with the different scaling between the two sets by just transforming it into a patient, sorry, later into a patient similarity network. So you don't have to worry about scale differences between data types. But if you have continuous data, you will in general want to like have a normalization procedure so that especially I mean I think about this with gene expression you know, certain genes just are expressed more like you just have many more of them in the genome so they're kind of distributed on a different scale than other genes where you're just going to be observing less. And so you may want to actually do a standard normalization of those, because you'd want to say, you know, is this gene overrepresented versus if you don't scale it, it'll say the genes that are just expressed in higher values higher numbers, those are actually going to impact the distance between patients much more. So you'd say those are more important for determining how similar patients are to each other. And then those genes that are not expressed at high levels, those are less important. That's what it would essentially do. If you normalize it though, you're saying these are all have equal importance. And I want to say, you know, are you relatively over expressed or under expressed for each gene. So if you do that normalization within your data types, and then do similarity network fusion so you, you create these similarity networks, the scaling difference between different data types is totally handled. So you, there would be no scale difference because now it's all in the patient similarity scale. So I showed you before, you know, how a lot of times you can integrate this clustering or do your clustering, and then check after the fact, if you've distinguished groups that are surviving at different rates. You know, there's no intrinsic link between clustering and survival analysis. But because it is combined so often, I think it's really nice to go over how those are connected. And then in the lab, we're going to go over that kind of an input implementation sense. So, you know, do your clustering, get your clusters, and then do a survival analysis and evaluate your clusters based on some survival based outcome. So we're going to talk about survival data hazard rates survival functions, the Kaplan-Meier estimator which we already looked at and went through a log ring test that it's an example that we're going to go through previously this was cut but I'm going to keep it okay it's a little bit of math. And then the Cox proportional hazard ratio model, which I'm sure many of you are familiar with and is, I would say by far the most common model used even though the accelerated failure time models also very popular and I think very famously preferred by Cox himself so if you if you find you're dissatisfied with the Cox model, even he would support you using an accelerated failure time model. I'm not going to go over those models today, but I really encourage you to look them up and go through them there's great tutorials about the theory of those models as well as the actual implementation and are is very basic so if you can do the Cox modeling, you can do the accelerated failure time modeling just fine. So survival data survival data is actually got multiple components to you. The first component is the time to an event and it has to be one event. So there are many different types of survival modeling where, you know, you could have multiple events, you can have competing events.  So in its simplest form, you have one event and you assume that in the long extent of time at some point that event will happen. And some data on patients may be missing so we talked about this before those censored observations. And so it could be you, they've been lost to follow up. It occurred at some point but you never got to see it so you know that they survived at least a certain amount of time, but not, you don't know when the event actually occurred so we would call those censored data. Uncensored data is when we observe the actual death time. What we do is that we know it's beyond that time. And again, this is an assumption of survival modeling, you're assuming at some point, the death actually happens the event that you're interested in actually is happening. But you didn't actually see it. So really importantly is in creating a survival model, if you have censored data, you have to assume that it's not informative so it could be that you know all the patients that are censored. Actually, they live, or they live much longer there. They're all correlated with each other in some fashion. This has to not be the case, essentially, when you're doing survival modeling for that censoring kind of assumption to hold so it can't be the case that like this one, you know. Oh, I see. Yeah, you want to just say like yeah you know this person randomly moved out of town not like, oh yeah this whole group of people just went through a totally different kind of drug trial and they all have like a really different experience of life and we've only followed them up to this point right. So you need them to be uncorrelated, essentially. Your eyes are not your vision didn't just blur this is actually this slide but I really like this. At some point I'll make one myself this figure. So we have different cases so these are individuals. And this is the survival data we're observing here. So, we have the days to last follow up for the patients that we didn't actually get to see their event at the end of the study. So this would be a censored observation and we'd say six years is the event time for them, but the event itself of zero and it would be zero. So we would say at time six, you know, we saw for patient for no event at this point, but they lived at least that long. Whereas for patient five here, we'd say they lived one year and we observe that event we observe that they died within a year here. For patient three, we observe they died at four years. Okay. So these are our censored observations and they're both censored at six years. But in general the censored observations it'll be at different time points. There are two very important statistics here that I've been kind of alluding to the time to the event. And that time is reflected in your survival function so like we were seeing in that the KM curve. The survival function is showing the probability of a person being alive at T or any time above T. Okay. Whereas the hazard rate is the probability of an event happening at time T. So here it says in the next instant it's kind of if you think about a derivative like it's it's looking at at that point in time. How many like what's the likelihood of an event occurring. And what's really nice if you look at, you know, you look at your cam curve but then you have like a hazard curve you can see how the likelihood of an event happening kind of changes over time. So maybe there's a huge probability of death happening at one year, but then after that year, maybe it actually goes down quite a bit. So it can show this kind of transitional probabilities over the time course. So some examples of that hazard rate a concert, a constant hazard rate. So like no aging is kind of the idea but it's like you're equally likely to die at any point, like there's just no, whereas what we know is, as you age, there's a higher hazard of death. Though there is a pretty high hazard of death very early in life as well, particularly in certain groups of patients.  Essentially, this is what we're mostly familiar with. And a negative hazard rate is dying versus the highest at birth. So if there's high infant mortality. So if the rate is going down as you age and we see this in certain populations as I said before, where the likelihood of death very early on is high. And as you survive past those time points, your likelihood of dying is actually quite, quite low. Okay, and we can see this in the KM curve. It's not showing the hazards, so to speak, but it is reflecting that same data. It's plotting our survival function. Again, this is ancient from the perspective of everything we're looking at here. But the KM estimator is, it stood the test of time. And so it's just plotting your data. And that is the survival curve. It's the probability that a member from a given population will have a lifetime exceeding T. So you just have the number of people at risk. So that's just the number of people in your data at time T, the number of actual deaths at time T. And so then you say of the people who are at risk, how many died, and therefore how many are surviving here, and you aggregate that over time. And that's what's represented here. So this is what we were talking about before where you can say, you know, what's the probability at 500 days, you will be alive, given each of your groups.  You can also look at it from the percentage. So the median here and say the median follow up times here for the experimental group was 1000 days and for the standard group was actually a bit over 1500. Okay, so you can kind of slice this both ways. All right, so I'm going to go through a test you can do and we're actually going to do this test later in our, but I want to go through kind of the math of it, because I think it makes a lot of sense. And also it's good to just have this slide as reference so let's say we're comparing two groups, like we often are with survival. And J is distinct events in either group. The J's are the different groups. So for each J, we've got a number of people at risk. So that's just the number of people right the same N as before in each group and then overall and J is just combining those two groups. Okay, so group one and group two. And the O's are the observed number of events. Okay, so these are just the total number of people and these are the actual events. And then this is across both groups here. So the OJ's. The hypothesis is that the two curves are the same right that the the risk of an event in either group is actually it's no different. And that's what we want to test here with a log rank test so given that OJ happened. So given there's an event across both groups in time. We know that the event likelihood in one group is hyper geometric. And we can take that expected value here. So this expected value looks kind of weird at first, but all it is is saying, this is the number of people in your group. Okay, so a single group. This is the expected value for a single group. It is just the, the probability that it happens overall. That's just the expected value right so we just say, overall how many events out of how many people, and then take that proportion and multiply it by our individual group. This is a bit more of a complex term, but needless to say this is just derived from the hyper geometric distribution. But from this, we can actually get a Z score, summing up over our different groups to see how much are observed values. So here, it's not this OJ this overall, this is the actual in group observe value. So is it happening more than we would expect it to. Okay, so it's, it's just actually standard normal distributed here, very conveniently and so from this you can actually see, am I observing more events in my group, then I would expect given that my expectation my null hypothesis is that we all actually in all the groups have the same likelihood of an event happening. And from that you can actually just get your p value from a lookup table so these are easily computable values, standard normal distributed, and then you can compute, you know how likely is it under the null hypothesis that the distribution is the same. Okay, so it's quite, you know, classical statistics here. So again, sorry for a bit of a blur here but what I wanted to show you is the hazard ratio. And this is what is being used in Cox modeling. Okay, so the hazard ratio is taking what's observed versus expected so what we saw from the previous slide, these are the same statistics here so our observed group one versus our computed expected group one under the null hypothesis that they have the same likelihood of happening so that there's no difference between the groups okay. And so then this hazard ratio is looking at the relative risk of having an event relative to the expected likelihood of having an event in each group and comparing them in a ratio fashion. And so that is to say if you have a hazard ratio of 0.43 the relative risk of a poor outcome under the condition of group one is 43% of group two. So, group one has a lower risk than group to 43% lower. So the hazard ratio is two, then group one is two times as likely to have an event and group two right so this numerator is two times the size of the denominator here. Okay, so that's how these would be interpreted. So now we go to the Cox model and what their modeling is actually this hazard rate. So Cox captures how well multiple variables affect survival by using the Cox regression. So the hazard ratio of the risks here. It says here's the hazard ratio for a given individual at a given time. There are multiple ways to compute this baseline hazard here, but this can be thought of as kind of like an intercept. So this everybody has this baseline hazard rate. It says, Alright, given your covariates. How does this hazard this baseline hazard change. And what's really important is, it's exponentiated here, and it's multiplied to the baseline hazard. And so it's, it's a multiplicative effect on that hazard. So, H zero T here is the baseline hazard. Other predictors are exponentially increasing exponentially increasing hazard. So here, if you think about this, again, is the Cox model BJ it represents the log hazard ratio increase for one unit increase in the predictor holding all other predictors constant. Okay. And so here we have it up here. This is how much your hazard is increasing holding everything else constant. And the hazard ratio increase for one unit increase in XJ is the exponent of BJ. Okay, so you have one unit increase in X. So you have X, or your exponent to BJ, and you have this multiplied now by your baseline hazard. And that's your increase. If your coefficient is less than zero. It means you're increasing if you increase your X. It's a decrease in hazard and longer survival times. Okay. So you're increasing hazard rate, similar to that interpretation before you know if your hazard ratios point for three. It's a, it's a lower risk of an event, meaning a longer survival. So, let's talk about using an interpreting Cox pH. So, let's say we have a hazard ratio here for a subject I with a set of predictors X, and we're comparing it to subject J with a set of predictors X, right. So what it looks like is your hazard ratio IJ you computed this based on your Cox model and the baseline and so the ratio between the subjects actually comes down to the exponent of beta times the difference in your predictors. Okay. So it's just like linear regression and it can be interpreted as a percentage change in risk similar to kind of what we were talking about before, even bringing up Cox just the hazard ratio. It's a percent difference between groups. So if X is one, when treatments active and zero when treatments a placebo, if the hazard ratio is point eight, it means that there's a 20% decrease in mortality risk if using the treatment compared to placebo. Right, because this beta is activated when you have X equals one, and when X equals zero you have. X is not activated essentially so you're multiplying your baseline hazard by point eight. And so point eight is. All right, so how would you actually evaluate your survival model. One way of doing it is using the concordance index. So concordance actually captures the ability of your model to order the individuals correctly with respect to their survival time so it's essentially placing them in order as to when the events happened. And the way it's computed. Again, we're working all in the similarity space here. You look over every pair of individuals here. So first, this is just the number of all pairs. Okay. Over all individuals, how many of them are ordered every pair of individuals pardon how many of them are ordered correctly. Let's give them a one and any that are tied. And so they're just at the same time point. They're not ordered correctly necessarily but they're tied. So it's not before or after it's not right or wrong. So give them half a point, essentially for that. And this is computing, you know how well ordered are your patients. You know, you could be way off in your survival time predictions and have very good concordance. So it's something to consider when you're using this metric, but this is the only metric that actually captures the ordering of your individuals. So you can evaluate your model based on its ability to get the time right. But if you want the order right if you want to be able to say you know, this person's more likely to die early and this person's more likely to die later. Concordance is really going to be capturing that performance for you. Okay.


The hypothesis is what you believe that is true, and to test if that is that that your hypothesis is right, you need to do essays, those where we call experiments. And a hypothesis combined of results coming from those different experiments, you, combining with the data generating for those disasters and these experimentations, can tell you if your hypothesis is true or if it's false. If it's true, you can move forward and go for the next question you have and create new hypothesis. If it's not, you need to do some extra essays to try to understand where your hypothesis was wrong, and you can reformulate that to really achieve the main goal of understanding and establish the new knowledge.


Let's examine how you can craft a top grade nine response to question number three of the English language paper one exam. Now when it comes to this question, this is basically the structure question. You always tend to get asked by AQA how the writer has structured the text to interest you as a reader. Now that you know hopefully what to anticipate for question number three, I'd like to show you how you can write a top grade nine response based on the 2020 paper. Of course, with this response, have a think of how you can also apply some of the elements within this response to other question threes of paper one exams. Okay. So this question, as I mentioned, relates to the 2020 Rosie exam. This is the one to do with Rosie and the stranger child. However, of course, as I mentioned, this is a grade nine response, but think about and have a think as to how you can apply some of these methods to other question threes of language paper one. As I mentioned, this question is the structure question. It tends to ask you how the writer has structured the text to interest you as a reader and you're always interested as a reader. Okay.  Even if you find the text quite boring, you're always interested and you find elements, structural elements that illustrate your interest. Okay. Now, my suggestion when writing a response to this is I would suggest adopting the peel paragraph structure. Peel basically stands for point evidence explanation and link. Okay. You open with your point, which I've written in blue. Then you move on to your evidence, which I've put in red. Then you add your explanation. Okay. So this is where you talk about technique in this case, structural technique. And then you start unpacking that. That's the bulk of your marks. That's where a lot of your marks will be taken from. This is in your second in your pill paragraph. This is your explanation. And then of course you end by linking it back to the question. This is what I've written in purple, linking it back to the keywords within the question. So let's have a look at how to write a four mark paragraph for the 2020, 20 question paper. Okay. And this is a four mark one paragraph. I suggest you do these times two for the eight marker for question number three. So of course, as I mentioned, the question asks how the writer structure the text to interest you as a reader. And my suggestion in terms of the keywords in the question is always structure. So you're thinking about structural technique as opposed to language techniques and how this structure interests you as a reader. You were always interested. So let's have a look at the point that had made relating to structure for this particular question where Rosie sees a stranger child and then it disappears. So this is an opening point, the writer successfully interests us as readers by using a circular narrative at the start and the end of the passage. Circular structure is a really powerful device. If you're able to spot it, it's a great grade nine technique when it comes to structure. Circular narrative simply means it starts in a certain way and it ends in a really similar way. Okay. So for example, within this extract, the passage starts with an empty garden and it ends with an empty garden. It's in the middle where we meet the stranger child. However, it starts empty and ends empty. That's basically what I'm starting off with, but I don't stop there. I add an additional sentence to my opening point. The garden seems eerie and abandoned at the start and the end.  Not only have I mentioned the use of circular structure and circular technique by the author, which is structure, but equally I've talked about beginning versus end. I'm juxtaposing the two. So I'm really hitting all the different points in my AOs and what the examiner is seeking in this question. Okay. So that's the opening point relating to structure and how it interests you. By the way, if you're looking at an extract and it starts at one point and ends at a different point, you can still talk about how structure is interesting where it begins at a certain point and ends in a completely different place. That's still a really good structural technique to talk about. Let's look at the evidence. Okay. And obviously here within the evidence for question number three, I will suggest if you juxtaposing beginning versus ending in this case is structure, how it's circular at the start and the end. You want to find a bit of evidence from the opening paragraph and a bit of evidence from the end to support what you've written in your point. So this is the evidence. At first Rosie's children were playing in the empty overgrown garden. That's my first bit of evidence taken from early on in the text, but then I juxtapose it even if Rosie sees a child by the end when she stepped aside, the child was gone. I've added two bits of evidence, one from the beginning, one from the end to juxtapose the two. Now I'm going to launch into my explanation talking about the use of structure by the author. The writer uses a complex sentence. Again, I'm adding even more structure to really make my answer compelling for my examiner. My examiner has no choice but to say, this is an amazing paragraph. OK, so the writer uses a complex sentence, additional structural technique at the start of the extract to illustrate how empty the garden is. Aside from Rosie's children, it seems eerily empty. Yet Rosie sees the stranger child, but when she approaches it at the end, the final declarative sentence takes us back to an empty garden, which captivates us as we wonder whether Rosie is hallucinating or not. What I have done in my explanation is the first half of my explanation in my first two sentences. I've talked about how the opening is like. I've said in the opening it's basically empty and the writer uses a complex sentence to emphasize this. However, I've then juxtaposed it at the end. OK, and I've said, OK, the declarative sentence at the end actually also shows it ends off in an empty garden, even if in the middle there's a stranger child actually Rosie steps out and sees there's nobody there. By the way, if you're not sure what structural techniques there are, please do check out the language and structural techniques video that I've done where I'll go over a list of these different techniques. OK, so if you're not entirely sure what complex sentences or declarative sentence, just check out that video. It's only about five minutes. OK, anyway, so going back to this, the bulk of your marks are here. OK, so of course, you need to still for create foregrounding with your point, your evidence, right? You're setting out your argument as to why it's really interesting. You start off with your point, say circular structure. You then have your evidence from the beginning versus the end. However, you then now need to make a case and really dissect. OK, at the beginning, this is really interesting because it's empty. Even if there's a child in the middle, actually, by the end, the garden is still really empty. We have this eerie sense of feeling. And on top of that, I keep on referring back to us as readers. It captivates us. OK, I never lose sight of the fact that I have to mention this in relation to how this interests us as readers. Now, I then have to still add a final step in my pill paragraph. This is linking it back to the question. Hence the writer maintains our intrigue. So this is our interest as readers by using a circular narrative to create curiosity around the stranger child. We are keen to read on to discover if it will reappear in this overgrown garden. What I have done is ended by linking it back to the question and relating it to how it interests us as readers using two separate sentences. OK, so this is a perfect, I would argue, grade nine paragraph response. And you want to aim to do two of these in your question. Number three for language, people want. This is a structure question. OK, so do make sure you just have a look back at this video. Look at how I go over the separate parts of my pill paragraph in and try yourself to apply this. OK, try to apply this in your own writing for question number three.


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