And in this sub study we were interested in a mechanism behind this phenomenon, and that's why we measure the th one and th 2 response. So we did this by creating antigen stimulated t cells, and we measure the th one response by counting the T cells that secreted into gamma.
And then, on the other hand, we did the same for th 2 sales, where we counted the sales 5.
Then we created a manuscript of our results. But basically the main finding is that when we do correlation analysis on this th one over TH. 2. Polarized response. We see that people that have a more TH. 1 polarized response. We see that those people also have an early peak in their plasma of interferon Gamma.
And we also see that when we look at a transcriptome of the Pmcs. That those cells respond to interfa.
By, not by this. Not only yeah. Having it in a plasma, but also responding with all different kind of interferones.
So we believe that this memory, th one response is maintained via this early interferon response.
And if you're interested in this, you can already check out the results on bioarchives. The manuscript is currently under revision. So it's not officially out yet.
And yeah, I think promote will tell you more about the task itself. And of course, price.
Okay, awesome. Thanks, Lisa, nicely discussing the background of the new task. That which.
We are putting as a T sale response. So this is the revised task list that we have. So we had 3.
Task 3 group of tasks previously, and we leveled a self frequency task and gene expression tasks. And if you go on website, you'll find a 4th task, which is a T cell response task of, yeah, that is a bonus task where we ask rang the individuals. If I predicted. t 1, th, 2.
Or you know from gamma. I'll 5 polarization ratio prediction on day 30 post vaccination.
So this is the bonus task, and independent of the main challenge, and this the results are, the submissions of this task will be evaluated independently.
And the participation in the bonus task is optional. So if you.
Believe you can submit all 7 tasks, which is awesome. And also if if you if you wanna just focus on the main challenge or.
Or just a bonus, so that that is also the deadline, for the bonus task gets very similar to the main challenge, which is numbers twenty-twond.
And yeah, this is the task.
Short synopsis and the price week.
Will be awarding for just for the bonus, which is the $500, which is a cash price.
And this cash is, as I said, completely different from the main challenge winner task winner Pries. And yeah.
And the submissions with significant coalition will be, will will earn the co-authorship in the resulting manuscript, which is again the the very similar details that that we have the main challenge.
And an inclusion of this bonus task I had, and like, how do you submit the rank file? So for that purpose. We revise the submission file.
Now I'll be briefly talking about like how.
The submission file looks like. So if you.
I'll I'll be shortly going over the website and showing you that before that. You see, we have 6 tasks one until.
3.2, and now we have added extra column, which is for the 4.1.
So where you'll be inserting
The rancor's perpetrator ranks.
I'll be talking about the submission process.
In a while.
Yeah. So just to remind about this overall, you might be data we have.
Training data which comprises of 1, 1, 7 subjects and 50 per subjects. We have for the challenge in total, we have 1 71 subject data, which is divided and.
Challenge data. And and we have all these assay information available for making. And just to remind so all this data can be used for predict
Predict all this tasks, including the T cell response and there is no restrictions on like, how you use this data.
And again, just to remind data set comprises of 3 cohorts 2021, and 22. And and to make this data consistent.
Yeah, there are additional things which you might need to do, which is data processing. And for this also we have provided the data pre-processing and normalization pipeline, using using the various approaches which which are generally used in the field.
And we also made available this pipeline, using the r pub and github.
Yeah. And thanks for putting very nice inquiries on Solution Center.
About asking the details of the code and and various aspects of of the various aspects of like, how we do data processing and harmonization, etc.
So briefly. I'll just go over the Github pipeline just to remind one more time.
Like how the data processing is happening. So data processing is happening in 2 step, the 1st step is data harmonization where data.
Our data files are which we provide through the CI, Pb download, which are downloaded from there and standardize in in uniform fashion, so that you have nice compatible mattress standardized across.
All the data sets with respect to the identical features.
Which which we do, using the feature mapping. And then after you have those giant mattresses combined, together with respect to the different cohorts.
The next step we also did, which was the data limitation batch corrections which which we discuss in detail in in in the code files which which are in here.
Yeah, and yeah, and we welcome or encourage to ask you like all the questions you have on the solution center. If if I need.
Yeah. And we saw, there are a lot of.
Questions on on the solution, which I'll I'll go in a bit and quickly. I want to remind one more thing. Since we have added the bonus task.
We also provided the.
Interference. Gamma and alpha ratio pre calculated so that this information you can directly use while building the models.
So so this is the data download.
For the 3rd challenge. And as as you can see, all all the information about the raw data sets divided and challenge data set which is present here also, we provided the harmonic process data using the Tsv files and Lts file, which is also present here and along that we provided the
Wanna start data which is basically phones to polarization. With this file, you can download.
I'll show you how it looks like looks very quickly.
So it includes a specimen. Id, and these are the
Feature names we use in the T cell polarization assay data which we deposited there are in the process. So we kept those
And and and we provided additional information, which is like.
P. 0, 1, 5, 7, 9, which is basically uniproot id for indifferent. And yeah, this is id for aisle 5. So we provided this ratio information.
Yeah, which basically, you can use for building it model. And also not necessarily. You, you need to use this information. Only you can.
Use all the data we have for for building your models for this task.
Now I go back to the
Some of the post that we that were posted on the solution center.
So the.
There are a lot of
Post or core queries. Ask about the data submission and the criteria, and for which we made a separate post, clarifying all those.
Doubts feel free to read this post, and and also.
And and also, if you have any more confusions or queries just below
Auto query query it
Oh, the questions in there.
Briefly. I'll go to the Cpb.
And yeah.
Can we go for the solution center? And when I open 3rd challenge.
There are nice questions. I'll just take few.
The 1st question. I think it was asked by Iran yesterday.
Where it seems he was confused about the batch corrected data.
And how she can access that.
So basically, yeah, I responded in here. So.
So for individual assays we, since there is only one readout for those.
Assays we perform an organization on on just one data but for the gene expression data set, we provided both the raw accounts and Tpm. Counts, and and and both raw accounts, we perform the batch correction, and also separately. We perform the deep batch of a correction on the Tpm data. So all those different addresses. But provided.
And under the
And under the Gene expression data set. So which which you can access.
Yeah, so yeah, there are a lot of questions which we can discuss when and during the Q&A.
Next important thing is the submission process we have seen.
Some inquiries on the solution center and also a few participants also submitted this initial submission files. So so we wanted to discuss this in in detail. So what is the best way to handle or.
Submission process. So here is a quick demo.
So this is the submission template which I've created.
Now you see
Here I have manually inserted values.
So this is the correct file. So in which you can see the.
This is, these are random numbers so it starts with one, and we have.
The the highest rank, which is 51, so we expect all the ranks should put into.
This minimum one and maximum 54 range but also there can be possibilities where you might have.
Like.
Rank duplicates. And yeah.
And you can put it this way. There can be
Like the common ranks for 2 subjects or multiple subjects. Accordingly, you can insert those details.
But if you try to put values outside the.
Outside this rank list which is which is which will be handled by our submission portal. So, for an example, if I put they rank 50.
And try to submit this file. Let's see what happened.
I download this file as a Tsv.
Yeah, no, this file is here.
Now, yeah, I'll go back to the Cmb website.
Click on sign in, and then I click on the submit.
Now this is the submission ported.
So here the brief steps about the submission file preparations are mentioned. The 1st step, which is.
To download the template. So where is the template?
So you can click in here. And also you can go back, and you can find that template in here itself.
So after downloading that template? Yeah, you basically, yeah.
Like write the ranks.
Using the code which I'll be showing in a moment. Alternatively, you can directly do in the text like this.
So I just downloaded that file and I'll.
I'll go ahead and.
Try to submit that file.
Switches.
This one.
Now, since there were there was a 55 rank which I tried to submit.
So it shows me some validation.
But if my file is correct.
Yeah, there are different validations which we try to handle. But if the file is correct, like this.
Save it, and I download again, using.
When a file is downloaded, which is in correct order.
And when I.
Sorry.
Put it.
It passes the Qc. And it pops up the message saying that your submission is.
Recorded. And when I go to my email.
It should send me.
That file.
And the email message.
Seeing that. Yes, your file is been recorded, and you also get the
File, as as a, as a, as a, as a recorded version. For your own information.
And now, if you wanna access all your submissions.
You can go back to the sudden prediction.
Page, and then if you see on the left you have
Option to access all past submission. You go there, and all the files are listed there which you can download and and see what you submitted.
Now the next thing.
Like this is a a trivial data entry bar manually.
To make this more objective. We also have demonstration using the simple or the baseline prediction models. Basically, these models are the baseline models which we use for the past 2 challenges, and we expect, like
The actual submissions or actual models would be more complex. And and those models should outperform these baseline models. The 1st model is the pre vaccination levels of individual tasks. And, for instance, for for here.
For simplicity. We took the antibody titer levels of.
Itg, Pt, and and we just rank them and and then created the ranking. I'll I'll be showing the code in a moment. The second model, which is the chronological age of the subject.
We take the subjects, and we simply rank them and submit it in here.
If I go to the.
Our pub Jira. Explain the.
Process. So, as I mentioned the 2 models, the 1st one is the baseline Ig. Antibody against the Pt.
So I downloaded this data.
Then the model construction was very simple. I took the.
The baseline.
I'm a fine normalized data, and and then I rank them using the rank function in our.
And after having this, I.
I took those same ranks and passed it for the 1st 2 tasks, 1.1 and 1.2. Now my submission file.
Now, now the the data looks like this.
Now, yeah, it looks like this.
Now the final code is showing. Like, I took that data and passed into the suction file format.
And similarly for H.
I took the age as at boost, which was calculated using the year of birth and the data boost and the rank was calculated is the rank function, and which was passed to the.
All the task files.
So this is how the age rank looks like, and which is now filled for the all the.
And yeah, and these files were downloaded and and provided as the
As the submission examples which you can see in here.
The 1st file was the age.
And then second one was Aes Izz, so I'll just first.st
Download the age model.
Shoot.
I can see all the.
Tasks were filled out. Using this.
Same ranking calculated through age.
And.
The second matrix.
Go back, which is the Igg.
So this, are you using this model? I tried.
Fill out 2 columns, 1 point and 1.2, and and the all other columns for instance.
Like was not intended for this model, and and those were left blank. So you can either submit all the tasks, or or choose to submit few tasks as well.
As per your convenience.
Now I'll just demonstrate.
Like. I'll go back to the prediction.
I'll take one of this file.
Take age, model.
Uploaded, and I try to submit it. So it says the same message.
And when I go back to my email.
Gives me.
The recording of that file.
Okay, so this is how the submission process works so feel free to use your own criteria. And
Credit to process and submit the files. And we also give you this 2 models which we can.
And then the code which can use for for a reference.
Yeah, with this I'll pass it to Mari to discuss about reminders and.
Awesome. Thank you. Prud.
Next slide.
Okay, cool. So, as Shelby mentioned earlier.
We do have our office hours on October 8th so you can access the zoom through our private port or the solution center and also be sending out an email to everyone. So you can hop in and ask us any questions that you have about your particular model or about the submission process, and we'll be happy to answer.
Next slide.
Okay? And with that again, this is a quick reminder about everything that's on our Cmp website.
So.
Yeah, you can access everything through the prediction challenge section here on the left here.
And what promoters showing just earlier was the section called Examples of models.
And to the right we do have our solution center located on the right side at the bottom where promot is putting his cursor. And that's where if you can ask any questions for us we'll be happy to answer them as well.
Next slide.
Okay, cool. And as always, we just wanted to say to all our labs, and everyone who's worked with us throughout this project.
Next slide.
And these are just some of our current and past. See my Pv members. We just want to say to everyone for working with us throughout this whole process.
And with that we would like to end it off with some questions. So if you can use the react button at the bottom of your screen and use the hand motion, I mean, and then, if you could unmute yourself and.
Ask your question. That'd be awesome. And I do wanna clarify that you can ask questions about the submission process or about the data or the model doesn't have to be about something that we particularly presented about today. So.
Awesome lang. If you can unmute yourself and ask a question.
So I have a quick question about access data. I noticed that in the permanent data batch data, we only have the essay data. So is this true that we all needed to extract response data based on the test description from those as a data directly. And the.
And on the calendar data. I guess those columns corresponding to the response data will be gone. Is this correct?
So you mean?
That you're talking about the baseline, the longitudinal data.
For the ticketing.
Right.
So right? So we have several predation tasks. Right? So each of them has a response. But I noticed in the harmonized crack data we only have the essay, like, I say, File, so we don't have a separate file for response.
Right.
We do not have a separate file recording all the responses of interest.
So you described that we are going to call up from the as a data app itself.
Yeah, yeah.
Yup!
Yeah. So yeah, I think if I understood your correct question, whether, like the Igp at day 0 CC, at day 0, whether this
Recordings are present in the harmonized or the raw data set. So this information is present in all the files is just that. If you need to create a separate mask matrix, you need to extract by yourself and and and create it.
Yeah.
I said, I say, I say, got it got so we're going to on the training data. We're going to extract the data and delete them from the the training.
Right.
Like essays, and on the Channel data, those columns are already pre.
Preliminary Remove.
Yeah.
Thank you. Lane.
Okay. Next, I have Raphael.
So my question is about something you posted on the solution center some 10 days ago, you post a clarification about the use of external data and predictors. So it says, contestants may use external data set or predictors. If I'm not mistaken.
I think of using external data sets is a new rule. Because I was under the impression we couldn't in the past.
I I don't know if that's up. My question is really about reusing models. Are there any rules for using models past your contestants? Because in the last session.
People ask about you reusing their models. What happens if I also reuse their models.
And this is second part. That's the 2 part question.
You saw. You also said we encourage innovation rather than exact replication.
But is, does that mean something you practice? Are. You are more points awarded to original code or something.
Okay, I think.
Yeah, beyond, we can go ahead and
Yeah, if I can answer this, I think that is more aspirational than quantitative.
I mean, we are going to run the same models again.
That were done last time.
So if you submit the exact same predictions also.
You're pretty much guaranteed not to win.
Right, because there's going to be the same predictions going to be there.
So. But if you're using that, the models that were successful last time around.
And then add something innovative to them, to make them better.
That is very much encouraged, like there's nothing keeping you from doing that. Similarly.
If you identify another set.
But do you think it's gonna make it.
Your predictions better. That's awesome.
And again like, then the spirit should be.
So so I'm not aware of what kind of data set that would be. But if it would be great to know more about that, and that would be a key. Finding to understand that.
I mean in our past data analysis, so far.
The transferability between different vaccine settings has not been very impressive, for protesters.
So if somebody really identifies something that that works better.
And can utilize external data set in an innovative way, or an innovative or not.
I think that would be innovative by definition.
So very much. I mean ultimately, what counts is how your predictions rank.
Rafael, did that answer your question?
Awesome.
Thank you. Any other questions.
Absolutely.
Oh, and I also saw Maita posted in the chat. I think. This is the same question. But let us know.
If we didn't answer your particular question.
Yeah, that.
Okay. Awesome. Thanks. Maita.
And Nikki, I saw you. Do you have a question.
Oh, yeah, do. Thank you. Thanks for all the information. Today. Everyone.
I had a question about the new task. So in the downloads page there's a separate folder called bonus task data, where you've provided, I guess the ratio for the aisle.
Sorry. What is it? I'll
In different Gamma.
The th one interferon gamma aisle, 5.
But.
Is there data also includes in the harmonizing process data? Or do we have to add that.
Liberty.
Yeah, nice question. So if you see the Lds or the Tc files.
So when I tried to like when I tried to norm harmonize and batch process.
So I don't. I didn't identify any batch effects in T cell data.
So I directly went back and raw data for.
For the as as a like final data.
So, yeah, you can, you can see those notes in there. That's why, yeah, all the data for the T cell was the raw data is, which is the final data I I use for the processing pipeline.
Okay, so.
And which was used for calculating those ratios.
Okay. So in the.
In the raw data we should effectively have the Il 5 and the interfear on Gamma.
Okay, thank you for clarifying.
Thank you. Nikki.
Anyone else have any questions.
Yeah, just to clarify. We calculate this ratio and made readily available, so that.
Yeah, like, there is less confusion about like how to calculate this ratio and and how to use it for the.
Like making prediction.
Yeah, this is additional thing. But yeah, all the data about the interam and the aisle is already present in the T cell polarization as the data.
Awesome. Thank you. Okay, cool. If no one has any other questions. We'll end it. There.
So.
Actually, maybe I have one last quick question.
Absolutely.
I remember this was raised last year as well, and I can't remember the answer, but I guess the ranking is from high to low, right.
Right, so.
Yep.
If you have a.
Like. But if you have dedicated the highest value.
So that subject should be ranked first, st or one, and the the.
Vector values lower. So the that subject should be ranked like the highest number 54.
Okay, yeah, thank you.
And also just to add on that we are calculating Spearman coalition. So even if you messed up with banking ranking like you send it in reverse order.
Like that will be reflected in the spherum coalition. And we are taking absolute values of coalition coefficient to make the winner of that task. So so it doesn't affect.
But but it's yeah.
Alright. I don't see any other hands up, so we'll conclude there just before we end. Just a quick reminder again. So our second office hours are are on October 8.th
At 9 am. Pacific time. So, and we'll be sending out an email with the zoom information as well.
And as always, please. Post your questions on the solution center and we'll pick. We'll get back to you. On the website. So.
Yeah, awesome. Thank you. Everyone. So much for joining us today.
Thank you. Bye.
Bye, bye.
I.