 UUID or take a file ID to a case ID and things like that. So you have flexibility with working with other, so you don't have to get all of your data from our package, although you should, because it makes it pretty easy. But if you have some existing data and you want to translate some IDs, you could use our tools. For example, if you're getting data from the Genomic Data Commons, you may have some filenames, so if we run that, let's do Library Genomic Data Commons, and then run that again. So you may want to use Genomic Data Commons if you're interested in the newer harmonization that the GDC provides, and this will allow you to take the filename that you've downloaded and then convert these filenames into TCGA IDs, file IDs and sample IDs. So these are just utilities when working with the different sources of data for TCGA. And then, how are we on time? I think we're okay. All right, so I'll go over how to subset, which is an important operation when working with your data. So if you have like a couple of genes of interest, you could divide all of your assays by those genes in using this bracket subsetting operation. So there, you can see there are two commas here. So before the first comma corresponds to the rows, the second spot in between the two commas correspond to the columns or the call data, and the third spot or the rightmost spot here would correspond to the assays themselves. So here we are subsetting by the features across all of the experiments. So if all of your experiments are annotated by genes, you can simply do an operation like that. Well, first you have to load data and then subset by those genes. So it will only give you back the data that had those hits across all of those experiments. And if it doesn't have a hit, it'll just send you a data set with zero rows because those genes were not found in that data. And then if you have a phenotype variable or a clinical variable of interest, and maybe you only want to analyze stage four cases, you could use that to subset. So you do mini-ACC dollar sign, pathologic stage equals equals stage four. And then put that in between those two commas which correspond to either the call data or the column names, depending on the type of input. But here we're dividing our call data. And now we'll get a smaller mini-ACC where you have only 15 samples. And we can look at the call data. Let's assign this. We'll do call data on this. So now you see that we have a call data with 18 rows. So there are only about 18 patients who had a stage four ACC present. So it allows you to divide your data. And if you do divide by the call data, all of these samples automatically get updated based on what patients are left with observations across all of those assays. So it saves you a lot of time because otherwise you'd have to go through each of your assays and divide, figure out what patient has a sample in this assay and then remove that if they don't. And so it takes a long time. But here you can just do it in a simple one-liner here. And then lastly here, if you want to only extract, for example, one assay from the multi-assay experiment, you can do that and it will give you only the one that you wanted or that you're interested in. So last thing I'll mention are the complete cases. And if you do complete cases in the ACC, this will give you a logical vector of the column row names or call data row names or the patients that have data across all of the assays. So it gives you a nice way to be able to filter out if you're only interested in, say, maybe you have two assays that you're interested in and you want to look at patients that have information in those two assays, then you can do these complete cases and only analyze those patients that have information in those assays. So it makes it really easy to identify those and then subset based on that criteria. So I think that's, I'll open it up to questions now and maybe point you to the other vignettes that we have on the page here. So we have the small vignette for the studies, a summary of the studies, the reference vignette for curated TCGA data. So you can see a nice table of all the cancer types and a TCGA utility cheat sheet for what does what within the package. So feel free to pose any questions or email me. You can find me on GitHub as link-ny or on the Bioconductor Slack channel as well. And I'll be happy to answer any questions that you have. I know I went through a lot of stuff and so feel free to ask questions. Actually, thank you so much for this talk. And it is really great information, but so much information. So I do not have like right of a question, but certainly I'm working on TCGA data sets. So when working when I have a questions, I shall just reach out to you. Yeah, yeah, you can reach out to me and make use of these cheat sheets. They give you a nice summary of what's what functions we we've worked on. And if you don't see something that we've that you want and and you want present in the package, we can also accommodate that as well. So we're very responsive to issues on GitHub and questions on the support site. So yeah, feel free to reach out. Thank you so much. Thank you. Any questions online?