So, we will look at differential gene expression analysis and also enrichment analysis. So, of course, once we have identified different cell types, now we are happy with our clustering. We have decided which clusters we want to keep. We want to start seeing which are the genes that are differentially expressed between different conditions, but also maybe identify marker genes that are more expressed in one cell type compared to the other. And then once we have a list of genes, we want to start to annotate them and see what functions they're involved in. So, we'll do enrichment analysis. So, first, for the marker gene identification, the implementation in Surat is used towards a function which is called find all markers. And this, by default, will use a Wilcoxon test to detect genes that are markers for cell clusters or cell types. Basically, what you will do, so, for example, you have five clusters of cells in your dataset or five cell types, you will use find markers to find the marker genes that are more expressed in one of the clusters against all other cells. So, for example, if you're interested to see what are the genes that are more expressed in cluster zero compared to the rest of your dataset, you can use this find all markers function. So, it's really taking one cluster against all other cells. And it does this for all of your clusters, so cluster zero against all other, cluster one against all others, etc. You will see if you look at the help that you have the option to only output the genes that are upregulated in one cluster or to include both. So, of course, you can also have genes that are less expressed in one of the cluster compared to the rest of the cells. Then, there is another function which is very similar. It's called find markers. And this is to perform pairwise comparison of genes. So here, it's not taking one cluster against all others. It's taking one against another, so specifically two clusters. It's really a pairwise comparison, so between cluster one and clusters two. Here the default is also using the Wilcoxon test, and there are other possibilities. You can have a look at the help to see what other methods are implemented. So, as we have seen already. Can I just add something? Yes. You can also find markers compare one cluster versus three others, for instance. Yes. But it's two comparisons that you will do. Exactly, yes. So every time you, for example, if you want to compare cluster one against cluster two and cluster one against cluster three, you will have to run two comparisons. So running these comparisons twice. That you can do. It's just for you to know it's only pairwise. So we have seen already yesterday and the day before that for a single cell data analysis, every time you want to do a task, several options are available. And of course, this is also the case for differential gene expression analysis. And this is one paper that it can be quite interesting. It is from 2018, but I think it's still valid. And here it's an evaluation of 36 methods that can be used for differential gene expression analysis in a single cell data. So if you're familiar with bulk RNA seek data analysis, at the beginning of the single cell data generation, people would use methods that were designed even for micro rays like Lima or for bulk RNA seek like the SEC applied on single cell RNA seek data. And then there were some new methods developed specifically for a single cell data. And in this paper, the authors compared actually these methods. So using the bulk methods and also comparing some of the single cell methods and see which could be, you know, the most appropriate. And I think they used simulated data to see how the methods would recover the truly differentially expressed genes. And one of the interesting figures in that article is this one. And here on the rows, we have all the different methods that they tested. And they had several metrics here in the columns that were evaluated for each method. So I'm not going to discuss them all, but we'll have a look maybe at some that could be interesting like scalability or speed or complex design. So here some of the methods maybe are familiar to you. So for example, the T test, and this can be used in any analysis of any type of data. So not only bulk RNA seek or single cell, also any type of data. And we can see that in all of the metrics, it performs quite well, except for complex design. And that means that when you have a T test, it's really a pairwise comparison. So like you have two groups, control and treated, for example, and you want to compare. In this case, it will be totally fine. You could use a T test. On the other hand, if you have a more complex design, like suddenly you will have batch effects, or you have like, I don't know, for example, five different experimental groups where you had different combinations of drugs, for example, a factorial design. Then of course, T test is not going to be applied on a more complex design. There are other methods that perform very well. And so for example, HR. HR is a method also designed for bulk RNA seek data. And within HR, you have several methods of analysis. And here would be the one that they found as the top ranking. And there is another one also masked. I think that was developed specifically for single cell RNA seek. And then there is another tool, which is called Lima, which was designed at the beginning for microarray data analysis. And it's also used a lot for bulk RNA seek analysis. And one that I often use, for example, is the VUM Lima method. And we can see that it's ranking at the top here. So during the exercises, you'll be able to test VUM Lima. And in terms of scalability, so this is something also important to take into account that some methods do not scale very well. So the more cells you have, the slower it will be and also for the speed. The nice thing about this paper is that the authors made available all of the code to run all of these methods. So if, for example, you would want to test the HRQLFD method, you could have a look at the GitHub repository for that paper. And you have the R script that you can take the code from and apply it to your data. So it's really a nice resource if you're not familiar with these packages. For example, also DSEC2, we use a lot for bulk RNA seek. You could also have a look at the methods there in these R scripts. All right, so describing the figure of the paper, I described these Lima or HR packages. So as I said, these are methods that were designed for microarray or bulk RNA seek. And the nice thing is that we can include complex designs. So if we have a batch effect, for example, we can include it in our model as a covariate. And this we will test in the exercise, including the donor ID as a covariate using Lima. Of course, you can use also these packages to analyze factorial design. So if you have a combination of genotype per treatment, you can easily use Lima and specify the interaction, for example, in the model. So here is another approach at looking at differences in gene expression across different cell types, for example. And this is called a meta cell. And so here you have the link to the paper. And here they applied, for example, to CD8T cells. So here what it does here, we have a dimensional reduction image of some immune cells that were classified according to their different functionalities, or like if they're dysfunctional or if they're more memory, et cetera. And the idea is that the authors wanted to see if there was a relationship between an expression score for a cytotoxic pathway, comparing it to the expression score of a dysfunctional pathway. So if you try to calculate the score, as we did yesterday with addModuleScore and create a scatter plot with XY axis, since the single cell data is sparse, for example, you probably will see quite a lot of noise if you look at that at the single cell level. And even more if you plot one gene against the other. So if you're interested in checking what could be the correlation of expression between gene A and gene B, if you create a plot like that, you will see a lot of noise. So to sort of reduce that noise and trying to obtain better correlations that are a bit cleaner, the idea here is to aggregate several cells into what is called a metasell. So for example, you take like 20 to 50 cells and you create like an aggregate of their gene expression. And so the noise that is within each will be sort of corrected for. So here, this is what we have here represented. So here, every dot is like the aggregate expression of several cells. And you do that for your different cell types. So you have these dysfunctional in the dark purple and light purple, transitional and the yellow cytotoxic. So you can see now that you have kind of a much cleaner inverse correlation between these two scores and like the transitional or in the middle. So this can be a nice approach to sort of clean up your your correlations and have clearer views. So that's another approach that you can use for looking at different differential gene expression or different scores among yourselves. So now many people use, for example, find markers to look at differential gene expression between a control group and a treated group. So let's say here we have healthy donor A and a patient. So we have two conditions, healthy and patient. And we have found that we have one cell type X of interest. So patient A has 98 cells and patient A here. Well, healthy donor A has 98 cells and the patient has 105 cells. And so maybe sometimes you can try to look at differential gene expression analysis between healthy and patient by using find markers, using all these cells against all these cells. But imagine that the fact that all of these cells come from the same patient, they're actually not really independent. So you have to ask yourself the question, how many independent replicates do you have? And this is something that is considered a lot in more like ecology. So for example, if you would like to compare the number of species between forest and grassland ecosystem, if you choose one forest and one grassland and sample several spots in each, and so you just run a statistical test to compare the species between forest A and grassland. So forest A, you think you have three replicates and grassland you have four. But actually how many real independent replicates do you have? And in this case, forest A should rather be considered as one replicates and grassland A should be considered as one biological replicates.  And of course, you can evaluate many spots within each ecosystem. But conceptually, each site is a different independent replicates. And so we can compare donors or individuals like individual mice as being the actual independent replicates. So in this case, instead of having like 200 cells of each, we actually have two independent replicates in each condition. And so what we will do to compare the differential gene expression between healthy condition and patient is to aggregate the gene expression for each patient to have one representative value for each independent replicate. And this can be done using a package which is called MOSCAT, which will do pseudo bulk analysis. So here we have our healthy donor A. We found 98 cells of cell type X and we create a pseudo bulk of the gene expression profile for the cell in this donor. So now we have one replicate and we do the same for the healthy donor B where we found 87 cells, for example, of cell type X. We have one replicate for this. So now we have two independent replicates for each condition. And if you have several cell types in your experiments, so in each patient, you didn't only evaluate one cell type, but for example, all these. So you had B cells, monocytes, et cetera. So here we have two conditions, control and stimulated, for example. So you will obtain one pseudo bulk per patient, per cell type, per condition. So here, for example, you can see that we have these FCGR3A monocytes that were aggregated. We had several patients. So for example, for the stimulated here, we had one, two, three, four, five, six, seven, eight donors. And then we have the same here in the control. So the same cells and also one pseudo bulk for every cell type for every donor. And after you obtain these aggregated gene expression per cell type per donor, you can use, for example, dimensionality reduction. So this is something similar to a PCA, for example. And then you can start to see if your conditions sort of diverge or not and how the cell type, where they're located in the dimensional reduction space. Then you can, of course, perform differential gene expression analysis, taking each donor as the independent replicate. And here we have an example, for example, of two genes that were differentially expressed in one of the cell types between control and stimulated. And of course, then you are free to go back to your single cell data and create a violin plot of these two genes within each cell. So here is going back to the single cell representation, showing in the single cell how this gene is distributed across donors, across the single cell of each donor in each condition. And you can, of course, create a heat map also at the donor level. So here we have some of the genes that were differentially expressed between the conditions and you have control and stimulated for one of the cell type, for example, for B cells. And these sort of figures you can create using this package, MOSCAT, and also the aggregation of the, so the creation of the pseudo bulk for every cell type, for every patient you can do with functions of the MOSCAT package. All right, so that was it for the background. So the question is to determine whether some genes are differentially expressed among cells coming from any of three groups, such as cells from newborn mice, middle-aged mice or old mice, which functions or methods would you use? So I haven't mentioned the F test, but this is basically like an ANOVA that you can do when you have more than two groups that is implemented in Lima package or defined markers function from Surat, which does pairwise comparisons, or the T test. One more second. Yeah, good. I'm glad to see the message mostly came through. So basically, if you have three groups, if you're working in a setting outside of single cell RNA-seq, you would probably use an ANOVA or an F test. And you could, if you have bulk RNA-seq, for example, or in this case single cell, you could use F test implemented in Lima, which is like an ANOVA, which will tell you for every gene if it is different in any of the three groups. So after you run an ANOVA, you could run a post-hop test to see which groups differ in terms of gene expression. If you use find markers, so that could also be an option, but you would have to compare one group against each other. And when you do that, you may be overestimate. So you don't correct completely the p-value because of the multiple comparison issue. And when you do an F test implemented in Lima, for example, this is already dealt with usually. With the find markers, you would have group one against group two. And then you adjust the p-value only within this comparison. So maybe you would have to further manually adjust for the multiple comparisons that you're doing. And then finally, a T test you cannot really use because here we have three groups. And T test could only be used for comparing newborn to middle-aged. But again, that's not what we want. We want to see if any of the three groups differ. All right. So once we have identified differentially expressed genes, what do we do? So I'm sure that if you have performed any bulk RNA-seq or even single cell RNA-seq before, you always end up with a long list of differentially expressed genes, sometimes hundreds of genes. And maybe you know what a few of them do. But how do you know what all of them do? So the idea is to gain biologically meaningful insights from long list of genes. And this is where we do enrichment analysis. And of course, several methods are available. And two that I will discuss are the overrepresentation analysis or ORA and the gene set enrichment analysis or GSEA. You can have different approaches to using these two methods. So first is if you have a particular expectation of what should be going on in your system. So for example, if you knock out a transcription factor that is involved in a pathway, you could have a look specifically if the genes that are involved in this pathway are enriched in your list of differential gene expression analysis or not. So you could just go ahead and test if this particular condition is enriched. The other approach is if you don't have an expectation and just want to see functionally what your differentially expressed genes are doing, you could test a large number of gene sets to see if you find interesting functions or new functions that you didn't expect. And you could test these gene sets or pathways that you found in collections online. And I will show you some ideas. So what is a gene set? And it can be, of course, many things. It can be the genes working together in a pathway. So here we have the citric acid cycle. And at each step, you have an enzyme involved. And so, of course, you can see if the genes that code for these enzymes are differentially expressed or not in your system. So, of course, a gene set could be all genes involved in a pathway. Or it could be all genes located in the same compartment in a cell. For example, you want to see if all the proteins that are usually located in the nucleus could be differentially affected by your condition. You could see if the proteins that are regulated by a same transcription factor are affected. So again, if you knock out the transcription factor and if you have a list of target gene of this transcription factor, are these differentially expressed or not? Then you could, of course, compile your own gene set by using a custom list that you found in a publication. So let's say you found an interesting publication of genes downregulated in a mutant. You could see if in your setting, you also see an enrichment for these genes in your system. If you're working on diseases, you could, of course, look if a list of genes that contain a SNP associated with this disease are also differentially expressed, et cetera, et cetera. So this is really not like a fixed thing. It's not just the gene that are involved in pathway, but it's any list of gene that do something together or are linked in some way. And in terms of where to find gene sets, if you don't have any as previous knowledge, you can find some that are grouped in knowledge bases or in databases online. One such example is the gene ontology. So it's a consortium who tried to develop a comprehensive model of biological systems. So you can find gene sets that are linked to the molecular functions at the organism level and they try to include a multiplicity of species in the tree of life. It's one of the I think it's the biggest knowledge base in terms of information on the function of genes and it is organized in what they call different ontologies. So different type of processes. One is called the biological processes. So here it's more linked in terms of maybe a bit more pathways and a function of genes. So specifically biological processes in the cell. Then you have the cellular components. So more where the different proteins and gene products are located in a cell. And then you have the molecular functions also. So you could have genes that have a similar like DNA binding domain, for example. And all of these genes that have this similar binding domain or sort of a similar enzymatic functioning, they would be grouped in a gene set of all of these genes under the molecular functions ontology. So the gene ontology gene sets are sort of organized hierarchically. At the top, for example, here is one sort of tree. You have one gene set that is called metabolic process. And for every gene set, you have what is called a go term. So it's like a number or an identity. So you have the name. It's metabolic process. And here is the the ID. And since it's at the top of the hierarchy, this gene set is very big. So you can have like 2000 genes, for example. So at the top of the tree, the gene sets are very big. And as you go down, they become more and more specific and specialized. So here at the bottom, we could have a very small gene set. Maybe could have like 20 genes. And it's very specialized and specific. And it's called hexos biosynthetic process. And it has its name and also its ID. And then you can see, so it's not always exclusive. Some gene sets can share genes with several other gene sets.  So you can have overlap of gene content among different gene sets. So if you run an enrichment analysis and find several of these gene sets that are significantly enriched, then it's probably because some of the genes that are differentially expressed are shared among several of these Go terms. So other sources of gene sets, if you don't have any previous expectation or want to browse a bit what type of functions you have enriched in your data set. Here you can find some online resources. So there is the molecular signature database for MCDB. It contains several types of gene set lists and collections. So it includes access to the gene ontology gene sets. But it also contains, for example, Hallmark. And these are 50 gene sets that were compiled by the authors of the gene set enrichment analysis method that I will describe a bit later. And these are sort of well-known gene sets. I would say they mostly apply to human and mouse. But they're well-known gene sets and well annotated and curated. So it can be interesting to have a look at these. And then in MCDB, you also have access to published gene sets. So you can download a list of gene sets that come from publications that are all linked to, for example, oncology or other types. I think now they have COVID also, but I'd have to check. So every time you have a publication that describes some genes that are involved in oncology, for example, it can be added to this collection. Then there is the KEG database. And this one mostly is linked and includes mostly pathways as we understand them. Like, for example, the citric acid cycle or other type of pathways. And one thing I like is that they have these images of how the genes are organized. So maybe we can have a look if I show you this. So the good thing about KEG is that it doesn't only include human and mouse, but you can have a whole list of different organisms. And if you want to search in KEG, you need to have an abbreviation of your species. So for example, if you're working with Arabidopsis, for example, you have it included here, Arabidopsis, two species or other species are also present. So you even lemur and other species. Let's have a look at human still. So for human, you have HSA, which is the abbreviation in KEG. And then you can, for example, type, let's see if we can find the cytokine pathway. Yes. For example, a signaling pathway in human. So you can have access to these maps here and see what are the genes that are upstream, what are the genes that are downstream. So I think it's quite interesting to have a look at these KEG pathways if you're not familiar with and it can give you some ideas whether they would be interesting for you to use or not. So this you can actually access also directly the list of all of these KEG pathways for human, I think, in MCDB. Then there is another database which is called the Reactome and there they try to classify the genes in terms of their, for example, reactions and thematic reactions or other type of reactions and how they interact. So each of these collections are compiled by a consortium of people that work towards one type of classification of the genes. So Reactome is one and there is the wiki pathway and wiki pathways is a bit like Wikipedia where anyone can suggest a pathway and make modifications. So it's more of a public effort to try to put genes together in terms of pathways. So these are a few that you can access directly from the MCDB web page. So here I would like to describe how the enrichment analysis works and the idea is that, so here we can have for example several donors that are healthy and some patients and we measure the expression of several genes and then we have some genes that are part of a pathway that we're interested in. We call it the blue gene set and we want to see if these genes are enriched in our differential gene expression results or not. So one option could be to sort these genes based on full change and so we have some genes that are down-regulated and others that are up-regulated and then we can see whether our blue genes are rather located at the top or at the bottom of this sorted list and this will tell us if the genes are enriched at the bottom or at the top that means maybe our pathway is enriched. So for the enrichment analysis method that's the overrepresentation analysis we actually look at the proportion of genes that are actually differentially expressed or not and that are part of our pathway or not and then we use a Fisher's exact test that here is an example of how it works in R. So there is an R function which is called Fisher test where you could run a Fisher test for any type of data as long as you have a count matrix. So what you have to count here is an example of this little code here. We create a contingency table of the counts of the differentially expressed genes so here we have two that are differentially expressed and that are part of our blue gene set of interest and five that are differentially expressed but they're not part of our blue gene set and then we have some genes that are not differentially expressed that can also be part of the blue gene set and 12 that are not differentially expressed and not part of the blue gene set. So the idea of the Fisher test is to see if this proportion two out of seven is different from this proportion three out of 15 and here if we run the Fisher test on our contingency table this is the output that we will obtain. So first the odds ratio will tell you whether this proportion is bigger or smaller than this one. So if it's higher than one then this proportion is bigger if it's lower than one this proportion is bigger. Then you can have a look at the confidence interval for your odds ratio and here we see that the confidence interval includes one. So basically we can expect that our odds ratio is not significant and indeed if we have a look at the p-value it's one so basically here we don't have an over representation of our blue gene set among the differentially expressed genes. So this is how it works in R. So in the example that I just showed we were testing if one gene set of interest the blue one was over represented in the differentially expressed genes but of course if you decide to test a collection of gene sets for example all of the pathways that are included in the CAG database then you could run individual Fisher exact tests for each gene set. So you would run one for the green gene set one for the pink gene set one for the blue etc. Once you obtain a p-value for every gene set since you run multiple tests you need to adjust for the multiple testing issue and so you adjust the p-value. This you will see when we do in the exercises we use a package which is called cluster profiler which has a function that allows you to run multiple Fisher tests for several gene sets at once. So basically the way that you run this analysis is by providing the list of differentially expressed genes that you had and the list of genes that were not differentially expressed and then you provide your collection of gene sets that you want to test and in the output you will obtain a list of all of the gene sets that were significantly enriched and the p-value will already be adjusted for the multiple comparisons. So you will see it's quite easy to do. One limitation of the Fisher test is that it's threshold based so basically what I mean by that is that each gene is classified as either a sort of black and white classification. The gene is either differentially expressed or not and this doesn't take into consideration the magnitude of the full change. So here we have three genes that were classified as being down-regulated but this gene is much more down-regulated than gene number nine for example and the same for the up-regulation. So there is another analysis that actually allows you to take into account the magnitude of the full change of the genes that are differentially expressed and this is the gene set enrichment analysis method that I will discuss just afterwards. So I just already mentioned this cluster profiler package so of course it is one possibility among several other packages to do enrichment analysis but why I like it is that these functions are more or less easy to use and it has also a nice tutorial online so you can see have a look at the vignette and it has a built-in access to gene sets for human, mouse, yeast and even other organisms and you can have access to gene ontology for example and keg. It also has a visualization function so once you have generated an enrichment analysis using cluster profiler the object that you create you can use in functions to create like bar plots or dot plots of p-values etc directly with the cluster profiler package so it can do several things which is quite nice. The only maybe difficult part of cluster profiler is to understand the arguments of the functions. So I wanted to take a moment to explain a little bit what are the arguments of the cluster profiler functions. So this is the function of the Fisher test that's part of the stats package so it's the one I just described before you have many arguments possible and so the important one is X which is the contingency table that you have to provide and maybe the alternative to either if you have if you don't have an expectation of whether your proportion should be higher or lower you can run a two-sided Fisher test but if you have an expectation you can either choose greater for example and so these are maybe the two options that you can choose in Fisher test. Now for cluster profiler functions so here's an example of one of the function which is called Enrich Go and this allows you to perform an overrepresentation analysis so it's like a Fisher test implementation in cluster profiler and the format as you can see is quite different from the Fisher test function because you don't provide a contingency table you provide just the list of genes that were significantly differentially expressed in this gene argument then you have to give an argument which corresponds to the species with which you are working with and this ORCDB actually this will call a package which are called ORCDB packages on Bioconductor which are like gene annotation packages for several species so if you want to run cluster profiler for human or mouse or other species you have to install the ORCDB package in R first and then in this argument you just provide the name of your ORCDB package that has to be used to find the correct gene annotations then you have another argument which is called key type which will actually be needed to inform the function how the genes are labeled in our gene argument so a gene can be labeled according to a symbol but also for example ensemble ID or other type of IDs you can even have uni-prots if you work on proteomics you could have enzyme ID even and the idea here is to tell the function how your genes are labeled here so if you use a symbol then you can change this key type to symbol then for the ontology so I have described to you that the gene ontology is organized in three ontologies the biological processes the cellular components and the molecular functions so here is the argument where you can select which ones you want to test so for example if it's here it's the molecular functions that is selected then for the p-value cutoff this is the for the output actually of the results do you want to output only the significant gene sets or all of them so by default it outputs only the significant one because the p-value is at 0.05 but if you want to output all even the non-significant maybe that can happen you can change the p-value cutoff to one for example then I told you about this multiple correction step that has to be done once you test several gene sets at once and this is performed directly by the Enrich Go function and the adjustment method is the Benjamini-Hockberg one and this of course if you have knowledge about different methods you can change you could use for example the Bonferroni correction if you only test like five gene sets so that's one of the options then the universe so the function needs a way to know which are the non-significant genes in your data set so you provide the list of gene symbols that were not significant as the universe argument. Finally the minimum gene set size and maximum gene set size so in some collections of gene sets some gene sets can be very small so I think in the Go collection some are less than 10 so we're not necessarily interested in testing for the small gene sets so we put a limit on at 10 and the same for the maximum gene set size I told you that some gene sets in the gene ontology are very long they have thousands of genes inside and so maybe they're not that interesting because they're very general so we put also a maximum set size threshold of 500 but that's of course up to you to decide if you want to change these. Now there is another function which is called enricher and this arguments are very similar to the one I just described to Enrich Go but this one you can use for a list of gene sets that you defined on your own so if you compiled a list of gene sets from several publications and want to perform over representation analysis for all of your 10 gene sets for example you could use the Enricher function. The first arguments are the same as I just described and there is an additional argument that's called term to gene that is actually a data frame that contains the list of your gene sets so you have 10 gene sets each one will have a name maybe with the author of the publication where you found it and will also have for every gene set the list of genes that it contains so this is a data frame that is formatted as two columns that you have to provide to this function and then the output will be very similar you have the list of significant gene sets you'll have the p-value for each and etc so with cluster profiler you can do a visualization of your results so here you can see I have a small example where we performed an over representation analysis of the gene ontology gene sets so the we generate an object that's called ego so in the format of this object is specific to the cluster profiler package and actually you can directly use this object with some visualization function and here for example is a dot plot of our results and we show for example 10 significant gene sets and here is an example so you have the different gene sets and the dot and is colored according to the p-value so you can see which are the most significant among all then the size of the dot is proportional to the number of genes that were significantly differentially expressed and that were part of the gene set and finally on the x-axis there is something that is called gene ratio and it's linked to actually the proportion that these genes represent so out of it it's it's not exactly the same information because if you have 10 genes in a gene set of 100 genes it's not going to be the same proportion as if you have 10 genes in a gene set of 20 genes for example so here we have this proportion that is represented so 10 out of 100 or 10 out of 20 for example so you have this on the x-axis then another representation that could be interesting is the enrichment map and for example I told you in the gene ontology collection some gene sets are maybe a bit redundant and they overlap in term of gene content and if you create an enrichment map you can start to have a look at the bigger picture of the type of functions or gene sets that are significant in your data set so here again we run an overrepresentation analysis providing our differentially expressed genes and we use the emap plot function which will create this enrichment map here the link actually shows that these genes share gene content these gene sets share some gene content so all of these gene sets are sharing some genes so they might be all linked as we can see here to some sort of cancer gene sets and then we have some other gene sets here more viral or disease linked and so you can start to see sort of what's happening at the bigger picture so maybe cancer related gene sets disease related gene sets were significant in my differentially expressed genes and then you have some that are more unique this one for example doesn't share any genes with the others another tool that I put here is called Revigo and this is a website where you can actually enter a list of go terms so all of these gene ontology gene sets will have a go term associated to each one so you can put the list of go terms on Revigo and it will create also a map a bit similar to that but the approach is different it's it will do a dimensional reduction analysis of the list of gene sets that you provide and put them in a 2D representation according to their gene content similarity and also on Revigo once you have created the plot you can actually download the R code to regenerate the same plot in R and if you want to customize it with like changing colors etc you can do it easily so it's also a nice tool to to try out all right now I will describe the gene set enrichment analysis method so I will try to explain it so that you can understand more or less how it works this one can be performed if you have some sort of statistical value for all genes that were detected in your single cell RNA-seq data set so in the previous over representation analysis I showed you that you just provide the list of significant genes and that this method is threshold based so you just have a gene that is significant or not and you sort of ignore the full change this analysis the over representation analysis you can do if you for example use find all markers and then you have the marker genes of cluster A and you provide this to the over representation analysis to see what functions these genes are involved in if you perform differential gene expression analysis between two condition and you use for example lima or hr in the output of these tools you will obtain for every gene statistic so if we compare phenotype A to B like in this example we will obtain a t statistic for every single gene even the non-significant ones so if we have 20 000 genes in our data set that will be assessed we will have a t statistic obtained for every single gene then what we do is we take our 20 12 000 genes and we rank them according to their t statistic or a full change it could also be so here at the top we have the genes that have a high full change for a phenotype A that means they're up-regulated in phenotype A then you continue and maybe in the middle you have genes that have full change close or around to zero so these are the genes that are not differentially expressed between the two conditions and then at the bottom you have the genes that have a negative value for the t statistic or the full change so these are the genes that are down-regulated in phenotype A compared to B and in the gene set enrichment analysis what you do is that once you have ranked your list of 12 000 genes you will walk along this list and every time you encounter a gene that is part of your gene set S so gene set S could be a pathway you're interested in so let's say pathway S so here you walk along your list and if the first gene you encounter is part of this gene set you draw a black line and if it's not part of the gene set you don't draw anything so you leave it white and here we see that if we walk along here now we encounter two or a few genes that are part of this gene set so we draw a black line and probably you have seen in publications this sort of chart or series of black lines which is sometime called the barcode plot if you take this little series of black line and you put it horizontally so this is exactly the same and here you will calculate an enrichment score for that gene set S this enrichment score actually starts at zero and then as you walk along your list of sorted genes so you have your 12 000 genes here this green curve actually shows the T statistic or the full change value for example so here at the top we have the genes that are upregulated in phenotype A and then in the middle we have the genes that have a full change close to zero and then finally the genes that have a negative full change so the ones that are down regulated in A and the enrichment score starts at zero and every time you encounter a gene that is part of the gene set the enrichment score increases and this increase is proportional to the magnitude of this value here so that's why I was saying that the gene set enrichment analysis actually takes into account the magnitude of the full change in that are included in in your gene set of these genes that are included in your gene set so you do this little walk you increase and here we encounter many genes that are part of the gene set so the increase is quite rapid and at some point most of the genes that we encounter are not part of the gene set so if we don't encounter the gene the enrichment score decreases again up to like reaching here zero for example then you will determine what is the enrichment score for your gene set of interest s and the enrichment score will be the maximum deviation from zero so here let's say we reach 0.3 for example so the enrichment score for my gene set of interest will be 0.3 it can of course also go down so if the gene that are the genes that are part of the gene set are rather located at the end of my ranked list then the enrichment score will start decreasing and going below zero because every time I encounter a gene that is not part of the gene set my enrichment score decreases and then it could go back up if most of the genes of the gene set are located here and then my enrichment score will be the maximum deviation from zero like it could be around here and in this case the enrichment score will be negative so the sign of the enrichment score can tell you if the gene set is upregulated in your condition A or if the gene set is downregulated in your condition A so you have an additional information based on the sign of the enrichment score. The last concept of gene set enrichment analysis is what is called the leading edge subset and the leading edge genes are the genes that most strongly contribute to the enrichment score and these are the genes that are located before we reach the maximum of the enrichment score so here we reach the maximum deviation and all the genes that are located here these ones and with the black bars are the genes that are the leading edge genes and this if you perform an analysis with cluster profiler you will get the list of these leading edge genes so maybe just make sure you don't when you obtain this list of leading edge genes you don't confuse them with all the genes that are significantly upregulated or not because a positive well a significant enrichment can be found by gene set enrichment analysis for a gene set even though the individual genes if you look at them individually maybe they're not statistically significantly differentially expressed but the fact that they're all located at like the top of the rank list is enough to have a significant enrichment of that gene set so that is the advantage of the gene set enrichment analysis compared to overrepresentation analysis is the fact that you can pick up a signal for a gene set even though at the individual level the genes are not significant in cluster profiler so I don't think we will perform this in the exercise but if you're interesting to perform a gene set enrichment analysis of the go gene ontology collection there is the gsc go function the important part is that you provide a list of genes so you have to provide the t statistic or the full change values for all of your genes that you have included in your data set so if you have 12 000 genes you have to provide the full change for all of your 12 000 genes because it requires the information also from the non-significant genes to calculate the enrichment score so and this list of full changes for all of your genes has to be ranked previously manually before you use the function so remember that you have to do that the gsc go function will not rank your full changes automatically and then you have the gsc keg function so it's exactly the same principle it's just to test the keg collection and finally if you have your own custom gene sets so you have your 10 gene sets that you compile from 10 different publications you can run gsa of these custom gene sets using the gsa function again you have to provide your list of full changes that are ranked here if you're interested in checking how this enrichment score is calculated in detail you can have the description in this paper here which is also the paper that compiled the hallmark gene sets that you can find on the mcdb website there is a question which is is there a python equivalent for testing user defined gene sets as with the enricher function and the honest answer is i have no idea i will see if i can find something i never use python personally so i don't know but i'm sure there is i know there is a package i have to check once i was teaching enrichment analysis and one of the participants told me that they were developing a python package for visualization of results similar to revigo for example so probably there is a hurt already posted so good gsa i just quickly google tia tia perfect i was gonna look already pretty good also great thank you perfect yes if you if you need info google it google doctor google has an answer all right so the vbox question i'll do it after the exercises so if i if i forget remind me to do it i think that's it for the presentation as usual i speak more than i wanted let's see so


Okay, welcome everyone. Good evening. We're so glad that you're able to join us tonight. For our bridging knowledge webinar series. I'm Cindy whole project manager, the scholarship program is sponsored by the IMLS. We're pleased that you're able to join. As students continue to join I will admit them in. But first I want to introduce our guest speaker. Tonight we are welcoming Alana Eiko Moore, who is executive director of Apollo, the Asian Pacific. American librarians association. And phenomenal librarian superstar extraordinaire. And I sent her just her bio and description out via email so you have access to that as well. This session will be recorded for those who are not able to stay during the duration, or for our students who have other commitments. We know that you're busy working professionals and we're very glad that you're able to join us this evening. So without further ado, I would like to welcome Alana to go ahead and introduce herself further and tell us a little bit more about what she's going to share with us tonight. Great. Thank you so much for that lovely introduction. I am so excited to be here tonight with all of the bridging knowledge scholars and I want to thank Cindy for this wonderful opportunity to speak with you tonight. I'm going to go ahead and share my screen. Okay. All right. So what I'm going to be talking about tonight is leadership care and community. And I'll just kind of touch a little bit on leadership values, the importance of community and the importance of self care. So it is finally, it's finally Friday, but it is not Friday, which shows how my mind is working. Time is a very strange thing, but it is not Friday, it is Thursday, but it feels almost like Friday for me. So if you could go ahead and go to Slido and then just one word describe how you are feeling right now. My word would be a little bit scattered because I did think it was Friday when I got started today. So how are you feeling right now in this moment? Are you feeling tired, relieved that it's kind of the end of the evening? And feeling a little bit looking forward to the weekend. How are you feeling today? Energized. I love that. I, my kids just got home from aftercare and they had so much energy. And so I was able to grab a little bit of that before joining you all. They are five and seven and have a ton of energy right now. So how are you feeling? How are you feeling right now? And if you're not able to get on Slido, you're more than welcome to drop that into the chat if you would like to do that instead. Okay, so we have someone feeling happy, someone feeling energized. I'm feeling a little scattered. Okay, so feel free to keep on adding in here how you're feeling ready for the weekend. I know me too. I'm definitely ready for the weekend. My partner is working all weekend, but I'm looking forward to having some time with the kids. We've had kind of a heat wave here. So maybe hitting up, hitting up some water as well, heading to the beach if we can find parking. Okay, thank you so much everyone for participating. Feel free to drop those things back in.  This is a photo by Lance Gerber. He took this photo of Indigenous artist Nicholas Galanin's Never Forget art installation. This is located outside of Palm Springs, California, and it was installed in 2021. So I would like to recognize and offer gratitude to the Indigenous people who have cared for the land I'm on today. I want to acknowledge that they have been dispossessed of their land through violence, deceptive processes, and colonialism. I would like to acknowledge the Kuma Yai people as the caretakers of this land and surrounding eco-scape in which I work and love. I support Indigenous peoples in this place, past, present, and emerging. And since land acknowledgments can be performative without action, I would like to make a donation today to the National Indigenous Women's Resource Center, and I'll go ahead and drop that into the chat. I'm sorry for those other notifications going on. So a little bit about me. I grew up in Hawaii as a fourth generation mixed race Japanese American. It wasn't until as much older that I realized that I was a settler and guest on colonized land. My maternal side came to Hawaii to work in the plantations, and my dad came to the islands from Missouri to work as a janitor at the YMCA, which is where he met my mom. I spent most of my childhood learning how to swim, do bone dance with the Japanese side, and I was a pretty rebellious teenager. I acted out a lot. So after high school, I wanted to go to college. We had no money to do those college tours that a lot of people do where they go and visit different places. But I had gotten a lot of financial aid from a really small college in Oregon. So I moved to Oregon and not being a librarian yet. I had no idea how to do research. And so I did not look at census data or demographics. And then when I got to Oregon, I had this huge culture shock. I had never seen so many white people in my life. The culture was different. People shouted out answers in the classroom and that kind of combative interaction in the classroom was very much encouraged. Nobody shared food, which I found really hard. And so in my four years in college, I actually really struggled. And I tried to adapt. I tried not to stand out, right. In my last year of college, I came out as bisexual. I now identify as queer. And I also got really involved in racial justice work and it was here that I, you know, was able to be in community with other black indigenous people of color. And I kind of felt like I found my place right. So I ended up working for eight years after college. I had a lot of nonprofits throughout Oregon around issues of racial justice, gender justice, domestic violence prevention, police and prison reform and LGBTQ human rights. I then did a really short stint in New York City, which was too crowded and did a few years in Chicago, which was too cold, but I did get my MLIS there. And then I landed in San Diego, where I now live. This is my house. This lovely person in the middle of the first photo on the college campus that I currently work at the UC San Diego. I work here as an ethnic studies, critical gender studies and sociology librarian. We were friends for a year. We dated for a year. We legally got married. And then nine months later, the state declared it was illegal to actually be married. It was interesting and awful at the same time. My partner identifies as transgender or non binary. And you can see that we have two incredibly beautiful amazing children that may or may not make an appearance at some point since this desk is in a corner of the kitchen. So in addition to identifying as mixed race, Japanese American queer, and as a woman, I also have invisible disabilities and I also have a lot of privileges. I'm light skinned. I'm cisgender. We are currently middle class. I'm able bodied and I'm also a college graduate. So when Cindy asked me to talk about leadership and the challenges of working as a black indigenous person of color in the LIS field. And I honestly, my first thought was like, oh my gosh, like what would I even have to talk about. Right. And I think that, you know, growing up being mixed race growing up being bisexual and queer, I've often struggled with feeling like I'm never enough, right. I'm a chaotic household. I never thought of myself as a leader. So I often am surprised when I realize I am in positions of leadership. And it really makes me think about, you know, what are the images that were often shown of what what a leader looks like and what a leader is supposed to be right. What characteristics to society and our profession tell us that leaders embody right. When we look at the heads of libraries we can see that despite a feminized profession that folks at the top are usually white male identified cisgender and able bodied right what we see are white men. And so that's problematic right because we are all leaders we all have the capacity to contribute in amazing ways to our profession. So, when I entered librarianship. You know, I felt like I didn't look the part I was like the small plug on the left and people who were leaders were like the big confident dog on the right right. I feel like I didn't look the part right I didn't speak with with authority and conviction I tend to. I tend to have a conversational style that you know ends with you know what some folks have told me sounds like a question at the end but really it's sort of the sing song way that I grew up talking to my family. It is a conversational style that I believe invites conversation and collaboration. So, I've actually had people coach me on my communication style and say like you need to lower your voice your voice is too high pitched. You always sound like you're asking questions you need to speak with more authority and conviction. And, you know, like I think I tried at first but it didn't feel like me. And so, I think now like many years in, like, I'm just me my voice is I pitched I speak the way I speak with the cadence that I have. I think a lot of times we think that our leaders. You know they use these big words they use academic language and a lot of times that wasn't me either like. I don't, you know, say therefore and here to for and you know, according to so and so like I tend to use smaller words I don't use academic language and at first I felt like that was a fault. That's not what leaders did but really I think that it makes us more relatable to the people that we serve it makes us more relatable to our colleagues. We're not putting on airs, you know, and I think that another thing that I do that isn't always seen as a leadership thing is, I say I don't know all the time. I don't know. There are so many things I'm still learning my, my people around me teach me things all the time and I do not have all the answers. So, the longer that we swim in a culture the more invisible it becomes right so this is an image representing like white supremacy culture right our profession has a long history of exclusion and assimilation. Our organizations are really white our graduate schools are really white, you know we're rooted in it right. The culture kind of includes a lot of those kind of norms of behavior right those unspoken behaviors those unwritten rules that language the structure those shared understandings that a lot of folks have around the way that you know things work right. Those white cultural norms policy isn't values really create barriers for librarians of color, right, and it affects our careers it affects our ability to lead it affects our ability to move up. So this next slide, kind of, is how I feel sometimes when I look at the whiteness of our organizations and China navigated, you know, from a perspective of a person of color like it makes no sense to me. You know that organizational culture that culture where some behaviors are accepted and others are not right. How do we navigate this when whiteness is pervasive when heterosexism is pervasive.  It could also be that this chart looks really awful to me because I also have ADHD so when I look at this I'm like this makes no sense. All right, so this is a quote from Porter and Vasquez about supervision, and I did a lot of reading around supervision and management. When I was a supervisor and a manager, but I really feel like this quote is also embodies a lot of what I feel about leadership right so the quote says that a feminist supervision but I've crossed it out feminist leadership model is about a collaborative relationship that's characterized by mutual respect genuine dialogue attention to social identity factors and analysis of power attention to diversity, social activism and self reflexivity and leadership to me is all of these things right collaboration respect, having genuine dialogue, you know, acknowledging that we're not all the same that we have different identities and power and privilege, you know, and that self reflection is also really, really important. And I feel like we each have responsibility to become an active player in the fight for social justice. Super important. There was a recent summit called path to leadership, and it was a national forum for advancing Asian Pacific Islander American leadership, and it explored the needs and barriers to leadership. And a lot of the things in there were about folks wanting to have more collaboration, right, more self reflection.  So, a lot of leadership is informed by values right and I think as I've moved through my career I've come to identify values that are really important to me. So I'd like to ask you now, what values inform your library practice or work what values do you want to bring with you into the discussion. What are things that help fill your caught what values do you hold dear. So you can go to slide.com, and the number is 2057217, and you can type in what values inform your library practice or work. If you don't have access to slide out you can feel free to drop that into the chat. But what values are important to you. So we have inclusivity, which I feel is really important to me as well, we're definitely having some great minds thinking alike here. We need to have a profession that embraces inclusion. Learning about our history is a super important value as well right we can't move forward in a positive way if we don't know where we came from integrity. Also super resonating with me as well. Yeah. What are the values that help inform your, your practice or your work things that are important to you that you want to make sure to bring into the field community that is one of my favorites, definitely that sense of community and humanity. Yes, these are all fabulous and thank you so much for adding these to the pool. Community, history, humanity, integrity, inclusivity and equity. Definitely all on the same page here. So when I reflect on values that inform my work that inform my leadership. One of the things that I think a lot about is authenticity and heart. So, you know, when I first entered the profession, after working for so long in communities of color, it was really charming for me, I again felt like that undergraduate freshman on that college campus, being like oh my gosh there's so many white people and where are the black people, you know, the color of my profession is people of color. And so I think when I first entered the profession, I wasn't authentic to myself. I think sometimes I tried to be somebody that I thought was the person that would be more successful the person that would be more palatable the person that would rock the boat less. I just feel really bad that it was actually attracting the wrong types of people to me, right, people who thought I was this person that I wasn't. And so I think I quickly realized that like, I can only be my full authentic self right and sometimes that might rock the boat, sometimes it might make people uncomfortable. But by doing so, I think that it also makes space for other people to be their authentic self right, and it allows me to lead with my heart and to lead with with compassion and with care. So I think authenticity is a really important value to me. I also try to be humble, part of my leadership is being humble. This is from Borkin and Coolhane Para, and this is their approach to cultural humility and this is actually the slide is borrowed from my friend to want to hodge. And this approach of cultural humility goes beyond that concept that we talked about of cultural competence. We actually really approach every encounter with the knowledge that our own perspective is always full of assumptions and prejudices. So we need to always keep an open mind and remain respectful of other people around us. So the age is being humble about the assumptions we make about knowing the world from our mentees' eyes, from other people's eyes, from our colleagues' eyes, how our background can impact the care of each other. This slide is specifically around mentorship, but this is for everyone, how we can learn more about each other's background, culture, beliefs, and practices, and incorporate this knowledge into our relationship with others and also incorporate lifelong learning and respect. Another value I really hold dear is connection. I think especially in this profession that there were many times where I felt like I was the only BIPOC in the room, the only BIPOC at an event, and really needing to find that connection with other people with other BIPOC.  And when we work in primarily white institutions, we really need those connections. You know, people always come first. That connection always comes first. And sometimes folks around us might be more concerned with, you know, getting things done. I always feel like we have to put that connection first, right? Then the getting things done will come after. Connection also is very clearly related to bridge building, where you want to, like, create those connections so that you can build bridges with other people to kind of promote solidarity between groups, bringing others across that bridge with you, and making things easier for those who come behind so that, you know, you're not just doing something by yourself.  And then finally, I think one of my last values is perseverance.  I think for me, perseverance means that, you know, I do get back up when I get pushed down. And sometimes, you know, it might take me a year, it might take me a month, it might take me however long it takes me, but I always get back up when I get pushed down. And I think that for me, that perseverance has really gotten me through a lot of really difficult situations in my life, through some deep depressions, through some barriers and obstacles at work. And I think that, you know, perseverance, overcoming those challenges and those barriers, you know, has kind of helped me to build those connections, build those bridges, and to still feel like really good about the work.  And, you know, I, I've been in my current position for 15 years. And I've been on this very awkward and strange backward trajectory where I quickly rose up through the ranks and then slowly started losing titles, losing people that I supervised. And I think that, you know, for a while it was really affecting me. I was like, what's wrong with me? Why is this happening to me? And I think that, you know, realizing that it wasn't about me. It was about, you know, the institution I'm working in. It was about certain people feeling very threatened and that that perseverance I think really allowed me to think about, you know, where else can I use my leadership skills and meet my goals? You know, where else can I can I actually get that leadership experience that I want and I need in order to like, you know, really help the profession. And so that brings me to my next, my next slide on community, which was one of the values that we talked about earlier, right? So, you know, data from the most recent Elise Statistical Reports shows that, you know, for graduate students, there's only 3.3 from, you know, Asian backgrounds and Native Hawaiians and Pacific Islanders are coming in at a 0.1 And Native, other Native and Indigenous people and American Indians are also very low at like 1%, right? So we make up like such a small percentage of the profession. We really need to have that community. For myself, you know, lacking opportunities at my current place of employment and really feeling isolated in many ways. I was able to find my community through the Asian Pacific American Librarian Association, which was a nonprofit created in 1982 and it works to enhance leadership opportunities for Asian American Native Hawaiian and Pacific Islander library workers and those who serve them. And Apollo was actually the first place where I got an opportunity to serve on a committee. And within the year I was chairing the committee because as many of you know, you know, our smaller community organizations are always looking for people to run things. Right. So within a year I was chairing a committee. I had never chaired a committee before. And getting that experience was really invaluable. And over the past 15 years with Apollo, going from a committee member to committee chair, I eventually ran for a position on the executive board as a member at large. And a few years ago ran for Apollo president, which, you know, I think if you would have asked me a long time ago if that was something I would have done. I would have said definitely not served as Apollo president for a year and then was asked a year ago to submit my application for the executive director. And I at first was like, no, I definitely don't need to be doing that. But when I thought about it, you know, serving as the president allowed me an opportunity to really work on my vision, my voice, you know, my advocacy. And the executive director role is more behind the scenes and is allowing me an opportunity to learn more about the financial and legal aspects of a nonprofit organization, which is a skill set that I don't currently have. And so I actually was like, okay, I'll throw my hat in the ring. And so I've been the executive director now for a couple months. Still learning the ins and outs, have some big shoes to fill. Lessa Kananiopua, Palayo Lazada was our previous executive director for three years and did a really, really great job. So, you know, it's been a really great thing to have the opportunity to practice leadership that aligns with my values and to be able to find communities through Apollo. And I think especially, you know, being feeling a little bit stuck at my current place of employment, being able to, yeah, Lessa is amazing looking at the chat. You know, being able to have other opportunities to lead and to feel like challenged to feel like you're having an opportunity to learn new things is so important. And, you know, all of the National Association of Librarians of Color really welcome having members take on those leadership roles. The American Indian Library Association does a lot of work with Apollo as well. We are sister organizations in many ways. And so, you know, Apollo is not perfect. No organization is perfect. But we've, we, I think we've been open to change and open to growing, right. So, when I first tried to join Apollo, I'd actually only been a librarian for a year, and I tried to join at one of those, like a volunteer type of fair. And the woman who was working in the Apollo table said, are you Asian? And I was like, I am, but does it matter because anybody can join Apollo, right? Like, it's for folks who, you know, are our Asian Pacific Islander Native Hawaiian or for folks who work with those communities or folks who are allies and supporters. And she was like, well, but I just, I'm curious. And I was like, yeah, I'm Japanese on my mom's side. And she goes, Well, you must look like your dad. And being mixed race, I had this moment of just being like, Oh my God, and feeling so much rejection for not being recognized as being part Asian. And I actually walked away from Apollo and actually did not join. And then over the next year, I met some other folks from Apollo, and we're really wonderful. And I told them what happened. And they were like, Oh my gosh, no, like, that person shouldn't have been at the volunteer table. And we definitely welcome mixed race folks. We would love to have you. And so I gave Apollo another chance. And, you know, it, Apollo has a lot of, I agree, Rebecca, that was totally messed up. You know, Apollo has a ton of mixed race folks now. And when I joined Apollo in the beginning as well, I also was like, you know, like, I'm feeling this disconnect between being with my people in Apollo, or going to the gay, lesbian, bisexual transgender group, which was mostly all white folks, right.  And so I definitely had another point of feeling like I am not sure where I belong. So I started really pushing Apollo, like, can we have more programming around like, you know, API and being GLBT, can we, can we, can we do some of that. And so we had a few sessions, and, you know, I'm proud to say like we have a lot more GLBT Apollo members now who are visible. We have some on our executive board. We are talking more about how, you know, the GLBT community a lot of times like is really invisible within our Asian Pacific Islander, Native Hawaiian, you know, Native Native American communities and so really making space for that. So, you know, no organization is perfect, but I feel like Apollo is willing to grow and learn. I know Ayla has also been really willing to grow as well. So this is the Koala Mountains in Hawaii. And I put this here because I really believe that community can help to support your growth and advancement and really help you to reach the top of the mountain, right. And we can't get there without help. And that's why community is so important. So, and I'd like to kind of close up with self care. So we talk a lot about self care, right, we talk about it as like getting a mani paddy, and it's an indulgence, right. And I think by thinking about it as something that's an indulgence. It makes self care an individual act, right. It white washes the concept and it distorts and kind of erases the necessity of care. And I do believe that in our communities, self care is not an indulgence, right. It is, it is, is an important way of like taking care of ourselves so that we can take care of others, right. It's a culture of care, and it is really directly connected to communities. You know, a lot of our organizations don't understand that self care is an ethical priority. They do not create a culture of care, which is why it's so important for us to have our communities to have our families to, you know, have those places where we feel like we can be our authentic self, because, you know, at work, we may be facing things like racism or microaggressions, like tokenism, you know, having to code switch. So we want to like really acknowledge that self care is so important, right. That, you know, a lot of us face structural oppression or inequity. And so self care is vital for our survival, and it's vital for our preservation, right. So that being said, what were you doing the last time you were taking really good care of yourself? Okay. What were you doing? It could be something as simple as, you know, you ate a really good meal. Maybe you took your dog for a walk. Maybe you lay down and had a really nice nap. Maybe you took some time to be outside and do some deep breathing. So what were you doing the last time you were taking really good care of yourself? Spending time with family. I agree. Spending time with family can be so incredibly restorative and healing. I think being with people who really see you. I think for those of us who have care obligations to other people, whether that's our elders, whether it's our children, that doing self care can be really important. Okay, reading in the cozy spot. I love that. I haven't been reading as much as I want lately, but I have been doing some audio books, which has been really nice as well. Exercising most days of the week at night. I love that. And I do know that for myself, I feel best when I get some exercise in, and it has definitely been something that's been falling by the wayside lately. So volunteering in the community and giving back is something that can make you feel like you're taking really good care of yourself. I love that. Talking story and laughing with my mom and sisters. Yes, eating good food. Yes, talking story. I love these. So take a minute. Look at what all of your friends are putting in here, maybe take one, write one down if it's something that, you know, you'd like to do that you haven't done in a long time. One of the things that I've been doing lately to take good care of myself is probably the reason I moved to San Diego is because I wanted to be closer to the ocean. When I first moved here, I went all the time. I would go at 530 after work.   So the day I had an hour break, I was working at home, and I jumped in my car and I drove and it took about 18 minutes to get there and find parking. And then I just sat, and I ate my little home lunch. And then I turned around and I got home in time for my next meeting. And you know, it was so wonderful. And it was really only 15 minutes of sitting staring at the ocean but I felt it was so healing. It was so meaningful. It's why I paid so much money to live in San Diego. And so it was a good reminder for me to do things like that, to take care of myself. And it's so important for us to like demystify self care and to share our experiences because, you know, mental health is, is really, really important for us. And it's really important for us to talk about what that means and how taking good care of what that looks like. Another thing I did today that was me taking really good care of myself was I went to therapy and I talked about some deep stuff with my therapist and, you know, doing things like that can be really about self care too, right? So we can fill our cups so we can fill our family's cup and fill our community's cup as well. And so, you know, a lot of what we're doing, right, is about inclusion. It's about co-creation. It's about building something together based on everything that you bring to the table. You have so many skills. You have so many things that you're bringing from your lived experiences that are going to make the profession richer and stronger and better. And you are all amazing, you know, students, amazing leaders. And, you know, we're so happy that you're here.   And so with that, I'm just going to close it out and I will drop my email into the chat. If anyone wants to stay in touch, I can go ahead and drop that into the chat and I will stop my screen share. And there we go. So thank you all so much. And let me drop my email in here if you want to stay in touch. And if folks have any questions, I'm more than willing to answer questions or talk story. Thank you so much. You really shared a lot of good information with us tonight. As always, inspiration, and I admire your leadership. I thank you so much for being here with us tonight. You're welcome. And I also encourage our students to ask a question. You can please feel free to unmute, turn on your camera, or add a question into chat as well. This is a great opportunity. Yeah, I'm happy to answer any questions. No silly questions. I have a good question for you. Well, okay. How did you choose the academic field. Oh my gosh. It was an accident. So I really, really wanted to be a public librarian coming from a nonprofit background. It really fit with that service to diverse populations that I really, really wanted to do. When I graduated, I applied for a job at Chicago Public Library because I was still in Chicago at the time and they were in a hiring freeze. And I needed a job. I needed to eat. I needed to pay rent. And so a friend was like, Oh, I saw that there was a job at this like technical school. And I was like, I don't know anything about engineering or computers at all, but I need a job. And so I applied for this job and I got it. It was just a reference librarian. And so it was one of those things where you kind of fake it until you make it. So I went to work every day and tried to learn as much as I could about engineering and computer science. And I just kept reminding myself that as a librarian, I don't have to know it. I just have to point someone to the information. So I just have to find the information point them to it. I don't have to know everything about it. And so I was there for a year and a half. And I was kind of looking around that whole time to see what else was out there and then saw this job at UC San Diego where I am now. And it was ethnic studies, gender studies and sociology. And I have a undergrad in social and gender studies. And then I thought, you know, doing racial justice work for eight years, like, I don't have the scholarship, but I have the practice, right. I can translate that. And so I applied for that job and I don't know how I got it, but I got it. And then I ended up in San Diego at this academic library and have been here for 15 years now. I honestly can say I did not plan a state as long as I have up into that point I had I think moved every five years, five to eight years to a new city. So I've been planning to move in five to eight years, but my spouse doesn't like to move. My spouse, they are, they are kind of send their ways. They don't like change. So it's like, okay, guess we're staying in San Diego. And there's, you know, that's kind of how I ended in academia, but I still think like public libraries. I'm like, oh my gosh, people working for the libraries are amazing. I would I wanted to be a public librarian. Well, thank you. Thank you. Yeah, that's great advice. They get till you make it, but you know, use research skills to. Rebecca has a question she put in the chat. Okay. Let's see what was the process like to get a job as an academic librarian. Higher education is having issues with tenure and many people entering the field are adjunct lectures. Yeah. I think that, you know, right now, I feel like right now is a good time to apply because like a lot of other sectors we are struggling to get candidates, believe it or not. And we have the process takes so long and I think that's part of the issue at least at my university sometimes like from initial advertisement to actual job offer can be a minimum of six months. Which we lose all the good people during that time, right? So I think if you really want to be somewhere, sometimes it's patients, sometimes it's putting in multiple application at the same time. I have a good friend who applied for a job with us. We weren't friends when he applied and it took so long that he got another job offer.  So sometimes it's just juggling that. At my university, part of the reason it takes so long is we're really, really big. So the library has a job description. It has to go to the campus HR and get vetted through, you know, different channels. And then it gets posted. We have it up for a while. We get the applications back. The screening committee goes through that. So they do a first round of phone interviews, which usually takes about a month. And then they do in-person interviews. But the in-person interview is an all day thing. So having an all day interview means that you have to coordinate the schedules of so many people that sometimes that pushes the interview out by a few weeks, right? So, you know, then they do that with maybe three candidates and then the final candidate gets the offer after the references get called. So, yeah, it takes so long. And I really feel like we lose a lot of really great people that way. Smaller colleges and universities, academic colleges and universities don't take as long, but ours is so big that that happens a lot of times. Which then I feel like somebody's like, oh my God, like we lost all the really good people of color, you know? And yeah. Wow, two months for public libraries? That's amazing. That's so amazing. So that's kind of the process with academic. And I know a lot of folks are just hiring like adjunct lecturers with limited contracts, which I think is unfortunate versus like a full faculty position. Good question, Rebecca. Other questions for folks? I want to ask a question. Yeah, go for it. Hi, my name is Halala Mo'o. I'm from Laie, Hawaii on the North Shore. Yeah, so I'm so happy to hear that you're also from Hawaii. You've lived here before. I'm native Hawaiian on my paternal grandmother's side. So I was really surprised about the statistics that you gave about native Hawaiian librarians. Yeah, so my question is basically so I'm just started at SJSU as an MLIS student. And one of the things that I want to ask is how I as a student could, I guess, promote equity and inclusion and diversity in my, I guess classes are in my maybe discussions with people or in my assignments, that kind of thing. Yeah, that's such a good question. And I'm from Oahu too. I actually grew up in Kalihi on the other side. My grandma grew up there too. Oh, really? I love it. Yeah, my mom is still there, but her house is kind of falling apart because you know a lot of the houses are built on the side of the mountain. And last time I went home, I was like, it's kind of sliding. Like, I know, it's unfortunate. Yeah, I actually went to, I'm sorry, I went to junior high school, so I was in that area a lot, yes. Oh my gosh, such a good school. I love that. I love that. Yay, connections.  Oh, that would be great. So I think like, what a great question.  Like, how do we get more underrepresented voices into our collections, into the books that people check out? How can we create our public services, right? So that we are being more inclusive of people, right? And it's like, you know, it could be like more inclusive of LGBT folks or more inclusive around race and ethnicity or something. I've been thinking about a lot is how to be more inclusive for people who are neurodiverse, you know, how can we create services that are more equitable and inclusive. So many different assignments that you get that you can definitely explore that. Within class, I feel like no matter what class you're taking, it's always ripe to have these discussions. And I know that a lot of faculty are starting to actually initiate that on their own, right? It shouldn't be up to like, you know, the people of color who are students in the room to always bring up these issues. And so I know a lot of faculty now are having more discussions where they're bringing in these different perspectives about things as they should. And, you know, if you haven't joined Apollo yet, we would love to have you. Great place to have all kinds of discussions about all kinds of things. So yeah, great question. Yeah. And I think I mentioned that our last Apollo executive director is native Hawaiian and she's also, as Cindy mentioned, our current president as well. She's amazing. She's a very good person to know. And she's, you know, so connected in a very genuine person. So she's someone who's always open and welcoming and that's very important. That's important in any field, but especially in ours. So I had a question about that, Alana. What would you recommend to everyone here when they begin their career? How can they help welcome colleagues and create that space of belonging? That is such a good question. I always feel like, I feel like sometimes the whiteness of our profession is so like formal, like, like, welcome to your new workspace, you know, let's onboard you and I feel like that's like, like, our style is more like, welcome, come on in. How's it going?  And I think that it can be sometimes difficult to hold on to that when you don't see that around you as much. But I feel that like goes back to that authenticity, right? Like how we present to others with that warmth, you know, with, you know, in Hawaii, that aloha spirit, that welcoming, you know, and I think especially for other BIPOC, like a lot of times like they might be the only one. So seeing another person, yeah, sharing food is so important, you know. I think I have, I mentioned before when I moved to Oregon, I was like, Oh my God, nobody is sharing food. This is, we're very much like, this is my potato chip. I'm not going to ever offer you one. You know, we went out to eat Chinese food and I had never heard the term family style before.  And I was like, you're really going to eat that whole plate of beef by yourself. That's just, it's so, so strange, right? So I think just being true to who you are, you know, making people feel like they belong is so important.  I saw someone speak on a panel recently and they were Asian and they were queer and they were in an academic library and they also disclosed that they had ADHD and I was like, I want to be friends with that person. And I sent them an email and I was like, you don't know me. We have all these things in common and I kind of want to be your friend. And they wrote back and we just started like doing a little chatting on text, and then we started having little zooms. And now we zoom once a month. And during the zoom, we support our ADHD by organizing. So during our two hours of zoom, we talk story, but we also spend that time just getting organized, you know, like for some folks that have ADHD, it's really hard to create systems that keep things organized or, you know, to keep track of things and not forget things because your brain just functions differently, which in my brain shift not being organized is, you know, you're not supposed to not be organized. So yeah, networking with someone just reaching out like that.  I don't want to reach out to someone and have them be like, I don't want to, you know, communicate with you but I think somebody's taking that risk can really pay off creating that community. Well, I appreciate that. And as in regards to organization, yikes, don't look in my basement.  Oh, all these folks that have ADHD. And you know, this kind of goes back to what I was talking about with self care and like destigmatizing something like ADHD, right, where I think in the past we wouldn't have wanted to talk about it in a professional setting, right?  They might not be a good librarian. This person might have trouble tracking things.  It can be your superpower. It is really fun to just organize with someone else. And honestly, I don't think I would do it if I didn't have that accountability, right? Of having someone else help me with that. I think also having people who understand how your, how your brain works and not seeing it as a fault more is like a quirk.  And she'll say like, let me see if I have it, you know, and, you know, so she's like, very good at like keeping track of things for me sometimes having a good friend who can then give you a copy of something that you might lose is really important too. What's that? I love Wendy. Oh my God, I love her so much too. I miss her. I miss her, I miss her. So for those of you who don't know Wendy, she worked at the American Library Association and she was the associate director, I believe, of the Office of Diversity. And she called me and told me that I won, or I was being awarded with the spectrum scholarship. And that's the first scholarship I would, I ever earned, and that was like my fourth degree at that point.  I couldn't believe it. I was like, oh my gosh, all these years, all these times I always applied for things. And, you know, I know that there's always a greater need than there is availability. But it was just so personal because it's something that I really felt passionate about. And so, you know, the thing about the spectrum scholarship program is once a, once a spectrum, always a spectrum. And you know, that's a family. Yeah, well you really deserved it. You're amazing. Well, thank you. I appreciate that. But I hope that everyone can still meet Wendy in some capacity again someday because you know there's 1400 spectrum scholars and we all know and love her because she brought us on board. Yeah, and I think she's a really good example of like a really strong white ally, where she really lifted up a lot of BIPOC but didn't take up a ton of space, but really advocated and supported folks. Yeah, Wendy's wonderful. Yeah. Do we have one last question tonight? We're almost at time. I want to make sure everyone has an opportunity if they want to put anything into chat. And if you're like me and you tend to marinate a little bit before you come up with a question, you can always ship me an email and I would be happy to respond that way as well. Well, Alana put her email into chat if you don't have that open. And we thank you so much for sharing. Thank you so much for inviting me. Yes, well this has been amazing. And you, I wanted to share one more thing. A few months ago I listened to you, you were presenting on something and you said that you keep a list of things that bring you joy. And I started that and I'm up to eight different things now on my list and I keep that right here. So I appreciate that advice. I love that. Yeah, I still, I think I'm still at eight. I don't think I've added any since our last conversation. But yeah, good. That's a good self care thing too. Yeah, what brings you joy. Definitely. Well, thank you all so much. So excited to be able to spend my evening with you all. Thank you so much. And please remember to take the survey students who received the link from Pat Hartnell. We appreciate you all. We know you're busy and hope you have a good evening and steady hard. Thank you. Thank you. Bye. Good night. Bye.


Hello and welcome to the Coach's Corner. I'm Joe Wamba, joined now with Graham Miller, head coach of the women's soccer team. And Coach, you guys head out to Widener to open up conference play and bring back a W. Yeah, it was a great start.  And then obviously, to go on the road against a Widener team that was coming into it, only losing one nothing to Haverford, who we had played a couple of weeks ago and tied 0-0. So we knew it was going to be a tough test against Widener. They're a very organized team, very well coached. We knew they were going to be defensive minded, which was going to make it tricky for us, since we have a couple of our forwards injured. So we were expecting a tough battle, and conditions weren't great. It was raining all day, but true Scottish style, the girls really played well in the rain. And we just went after it. And I was very, very proud of the girls to start conference play on the road with a win and a convincing win as well. I think we outshot them like 28 to 7 or something and to get three goals as well. And record a shutout for Tiffany with a strong defensive display was a really good start. And you got hood today, actually. What are going to be the keys to success for bringing another W in the conference? Well, we just could have built off our performance on Saturday. The girls look confident. The girls move the ball well. We got goals offset pieces, which is definitely a key for us this year with our long throw in and our corner kicks. So everything was really clicking on Saturday. So what we want to do is keep that momentum going into tonight's match. Hood's going to be the same. They're going to be looking for their first win in the conference. And they're going to be tough to break down. And we're going to have to be creative. We're going to have to work hard and really focus. And we don't take any team lightly. This is going to be a battle tonight again. And hopefully, we can continue where we left off on Saturday. Well, Coach, good luck in tonight's conference game and then in the future with Albright's conference game on Saturday when you guys travel there for that contest. Yeah, thank you. It's going to be another tough one as well on Saturday. Albright beat us last year 1-0. And it's going to be tricky. So we're going to, with two of our first conference games on the road, we need to take care of business in these first three games. And that will hopefully set us up for a run to the playoffs. He's Graham Allar. I'm Joe Waman. This has been the Coach's Corner.