YSPH Biostatistics Seminar: "SPRUCE and MAPLE: Bayesian Spatial Multivariate Mixture Model for High Throughput Spatial Transcriptomics Data"

December 01, 2021

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Dongjun Chung, PhD, Associate Professor, Department of Biomedical Informatics, The Ohio State University

November 30, 2021

ID7227

To CiteDCA Citation Guide

00:01<v ->College of Medicine and he is a member</v>
00:04of the Pelotonia Institute for Immuno-Oncology
00:08of Ohio State University as a candidate and a member.
00:12His research focuses on
00:15(mumbles)
00:18for integrative analysis on synaptic and genomic data
00:22with biomedical real data.
00:26So welcome back Dongjun Chung.
00:29(audience member claps)
00:32<v ->Okay.</v>
00:34Thank you Wei, for the kind introduction
00:37and it's so great to come back.
00:39Although it's all virtual.
00:43I hope someday we can see in person.
00:46So today I will discuss our recent project
00:52about the SPRUCE and MAPLE: Bayesian Multivariate
00:57Mixture Models for Spatial Transcriptomics Data.
01:01Oh, by the way, can you hear me well?
01:03<v ->Ah yes, we can hear you.</v>
01:05<v ->Okay, great.</v>
01:07So, let me start us from some quick introduction
01:12about the single cell genomics.
01:16So in some sense,
01:17we can say that the last decade was the era of single cell
01:21genomic experiments.
01:24So it changed science in many ways.
01:26And also a great amount of the data has been generated
01:32using the single cell genomic technology.
01:36Single cell genomic experiments
01:38provide high-dimensional data at the cell level.
01:42By doing so,
01:44it allows to investigate cellular heterogeneity
01:48within each subject or the patient
01:52which was not possible previously
01:54with the bulk of genomic data.
01:56Which means that genomic data collected at the tissue level.
02:04So some kind of standard visualization
02:09of the single cell genomic data is called a UMAP.
02:13And here,
02:14this UMAP shows the distribution of the different clusters
02:18in the tumor,
02:20including the different immune cell type.
02:25And in this way,
02:27we can interrogate different types
02:29of the immune cell composition.
02:32And also there,
02:33we can look at what kind of general feature
02:37imaged for each cell cluster.
02:40One of the recent (mumbles)
02:43is the emergence of the high-throughput
02:46spatial transcriptomics or the HST technology.
02:51So, with the emergence of the HST technology,
02:56we do not only look at the gene expression
02:59in the cell level or the close-to-cell level.
03:03We can now also notice that there are cross pointing
03:06spatial information.
03:08The figure at the bottom shows one example.
03:12And here it shows the mouse brain tissue,
03:16and each cell cone.
03:19Here cross pointer to one spot
03:21which is a group of the smaller...
03:24small number of like two to ten at most.
03:29And color here indicate expression level of different gene.
03:34So left one cross point to the Hpca gene.
03:39Right one cross point to the Ttr gene, for example.
03:48And with the HST data,
03:50we can do a lot of interesting science
03:54to improve the parity in current medication.
03:57So for example,
03:59we can now look at the spatial information
04:01of the tissue architecture at the transcriptomics level.
04:07And then we can also investigate
04:09the cell-cell communication with the spatial information
04:13in our hand.
04:15So at the figure at the bottom left shows the UMAP.
04:19And here,
04:20the different color indicates a different cell cluster.
04:24And if you look at the figure on the right,
04:27then you can see that there are a cluster
04:30in a meaningful way on the tissue.
04:33So in this way, we do not look at the different cell types
04:36within a tissue.
04:38But also look at their spatial information at the same time.
04:47And there's many exciting applications
04:49of the HST experiment, including the neuroscience.
04:57Including the brain cancer study such as the immuno-oncology
05:02and the developmental biology
05:04which looks at the changes of the cellular composition
05:08across the different stage of the development.
05:12And here I specifically discuss the application
05:16in the cancer, especially the tumor microenvironment.
05:20And with the spatial information,
05:22we can now study their location of the immune cell
05:27and the tumor cell in the tumor tissue.
05:31We can also interrogate implication of distance
05:35on the tissue and their corresponding density.
05:39And we can also study the distribution
05:42of the immune regulator.
05:45And finally, the special spacial patterns
05:49such as the tertiary lymphoid structure.
05:56Then from the statistical point of view,
05:59how the HST data look like.
06:05The first observation is in the HST data spatial structure,
06:10in the tissue architecture in a meaningful way.
06:13So as you discussed earlier,
06:16we can see a similar type of the cell cluster
06:19often located in the close proximity in the tissue.
06:27And even after we exclude such kind of cell competition
06:32in the spatial location,
06:35we can start to see some spatial pattern in the patient
06:38on the tissue.
06:40So the figure on the top shows the expression pattern
06:43of the three genes,
06:46PCP4, MBP and MTC01.
06:50After regressing out, with respect to the cell clusters.
06:56And as you can see, even after considering
06:58the cell cluster patterns,
07:00you can start to see some interesting spatial patterns.
07:05That the figure at the bottom shows the distribution
07:09of each gene for each cell cluster.
07:14And you can see that sometimes it's asymmetric
07:17but also often we can see non-symmetry
07:22in vascular distribution for each gene.
07:27So these are some of the key features
07:30of the HST data we want to consider
07:33in the modeling of the HST data.
07:37So if I profile pick somebody,
07:39Gene expression outcomes feature complex correlation
07:43such as the spatial correlation,
07:46and also gene-gene correlation,
07:49which mainly effects the biological pathway.
07:52Spatial structure can be
07:55(mumbles)
07:56cellular clustering entity expression patterns.
08:00And gene expression densities,
08:02often feature skewness and or heavy tears
08:06due to outlier cell spots.
08:09So ideally we seek to provide a model
08:13for identifying the tissue architecture
08:16while accommodating these challenging features.
08:24So, especially during the last two years,
08:28several statistical methods have been proposed
08:32to model HST data.
08:34And still many of them are network-based approaches.
08:39Partially because the stragglers; the very famous packages
08:43for the single cell genomic data analysis.
08:46And network-based approach has been proven
08:49to be powerful in this context.
08:51So based on that multiple network-based approach
08:56have been proposed including the Giotto, Seurat and stLearn.
09:03Because in the statistical model,
09:07recently BayesSpace was proposed by the group of the
09:12(mumbles)
09:13at the Fred Hutchinson.
09:15And essentially,
09:16it uses a multivariate-t mixture model
09:21to cluster cell spots.
09:24It implement spatial smoothing of clusters
09:27via a Pott's model prior on cluster labels.
09:32And interestingly,
09:34they try to predict sub-spots to increase the resolution.
09:41In spite of such interesting features,
09:44it has also some number of drawbacks.
09:47For example,
09:49it assumes the symmetry of the gene expression densities,
09:52and it also relies on the approximate inference.
09:58And here our goal is to develop a statistical model
10:03that overcome these limitations
10:06and also provide the optimal tissue architecture prediction
10:11using the HST data which we call SPRUCE
10:18or the spatial random effects-based clustering
10:20of the single cell data.
10:30So this is our SPRUCE model.
10:35So here we use the i as the index for the cell spot
10:40in the tissue sample.
10:42And then we denote y i
10:45as the length of gene expression vector for spot i.
10:50And based on the y i, we also may find a mixture model
10:56of the form.
10:59So here we assume the k number of the mixture component.
11:03or the cell spot clusters.
11:06Theta k indicates the set of the parameters
11:10specific to mixture component k.
11:13Pi k is the probability of the spot i
11:17belonging to the component k.
11:22We further introduce z1 to zn,
11:27which are the latent mixture component indicators
11:31for each spot.
11:33And zi can have the value between one to k.
11:37And as I mentioned earlier,
11:40can you see the gene-gene correlation
11:42are key features of the HST data?
11:47So to account for skewness and gene-gene correlation,
11:51we assume a multivariate skew-normal distribution.
11:56Where is the parameters?
11:59So first one indicates the main vector for spot i,
12:03and alpha k indicates gene-specific skewness parameters
12:07for mixture component k.
12:10And omega k is the gg scale matrix that captures correlation
12:15among the gene expression feature in the component k.
12:24And then we further represent MSN distribution
12:28using a convenient conditional representation.
12:31We use mu k for the mean of component k,
12:36phi i for the spatial effect,
12:38and t i and ksi k for the component-specific skewness
12:44of each gene.
12:47Epsilon i for the multivariate normal error.
12:53And then in order to further accommodate spatial dependence,
12:58we used the multivariate intrinsic
13:00conditionally autoregressive,
13:02or the CAR prior for phi i.
13:05So essentially,
13:08given all the spots except for spot i,
13:12we might suggest pi i as the normal distribution
13:17with the mean of its neighbors.
13:21And with the covariance matrix denoted as the lambda.
13:33And as you can see earlier,
13:35we see the two different levels of the spatial patterns.
13:40One for the spatial pattern of defect clustering.
13:44And another one is the spatial pattern
13:46of the gene expression.
13:48So for the spatial pattern of the cell clusters,
13:53we want to allow the probability of pi
13:58of belonging to each mixture component.
14:01Also to vary spatially as well.
14:03So in order to do so,
14:05we extend model I showed previously
14:09using the pi i k,
14:12which is the i specific.
14:14And then here we modeled this one as the sigmoid
14:18of the two parameters.
14:21And then part one in the interceptor
14:23for the baseline propensity of the membership
14:28into component k shared by all cell spots.
14:32And second term indicates the spatial random effects
14:35allowing the variation about the intersect.
14:42And again,
14:43to introduce the spatial association
14:46into the component membership model,
14:49we further assume the univariate intrinsic CAR prior.
14:53As you can see here.
14:55And here the one computational challenges,
15:01if you're interested, is format.
15:04Then it do not allow us to...
15:06It do not provide the closed form posterior distribution,
15:10which prevent Gibbs sampler.
15:12And in order to address this computation challenge,
15:17we extended our model
15:20using the results from the Polson et al in 2013, Jasa
15:25on Polya-Gamma data augmentation to allow for Gibbs sampling
15:30of the mixing weight model parameters.
15:34And essentially,
15:34we could assume that this can be represented
15:38as the Polya-Gama Data Augmentation.
15:42And by doing so,
15:43everything can be implemented as the Gibbs sampler.
15:49In the case of the further outliers or heavy-tails,
15:53we can even further extend the model
15:56to the multivariate skew-t distribution
15:59that you can see here.
16:00Which can be very easily implemented
16:03given the existing model.
16:07To complete our model specification,
16:10we use the weekly specified prior,
16:14and then the quantity of prior.
16:16And by using this conjugate prior,
16:19we can do everything using the fully Gibbs sampler
16:23of the closed form
16:24which provide the best computation.
16:29And some additional consideration.
16:33So here,
16:34the one question is the optimal number of the k
16:38worked in number of disparate clusters.
16:41So for the proposal,
16:42we use the product of the model selection approaches,
16:46and specifically we use the WAIC,
16:49or the widely applicable information criterion.
16:55In the patient mixture it's very common
16:57to observe the label switching program.
17:00So to protect against the label switching issue
17:03in the MCMC sampler, we use the canonical projection of z
17:08using the Peng and Cavalho, in 2016.
17:13And finally for the actual implementation,
17:17we use the Rccp
17:19to further improve the computation efficiency.
17:27We implement the proposed model as on our package SPRUCE,
17:33and it's currently available from our data page.
17:38Here.
17:40And then the figure shows our digital page.
17:45When we developed our software,
17:49one of the popular software to pre-processing
17:53and analyzing the HST data
17:57is the Seurat workflow.
18:00So when you develop our software,
18:02we provide integration with the Seurat workflow
18:05so that our software can be embedded
18:10as part of the (mumbles) flow.
18:12So for example,
18:14the data can be loaded into our using the Seurat,
18:19and then people can apply the pre-processing
18:23using the Seurat workflow.
18:26And then that objective
18:26can be fed into the SPRUCE analysis workflow.
18:31And then the output from the SPRUCE can, again,
18:34fit into the Seurat workflow for the visualization
18:39and downstream analysis
18:46So first for the simulation,
18:50the first for the simulation is about the...
18:54Has the two purposes.
18:56So first one is to assess the validity
18:59of the parameter estimation algorithm.
19:02And second is to quantify the effect
19:05of ignoring skewness and spatial information.
19:09So in order to make our simulation more realistic,
19:13we use the sagittal mouse brain data as the tissue shape
19:18and the spot location.
19:20And we simulated the full clusters
19:23from the multivariate skew-normal distribution
19:27with the 16 genes.
19:31We considered the 26...
19:352696 spots.
19:38And then we considered three models,
19:41including the multivariate normal,
19:44multivariate skew-normal,
19:45and with no skew-normal with no spatial.
19:49So first one shows the implication
19:51of inadequate study of skewness and spatial.
19:55Second shows the implication
19:58of ignoring the spatial structure.
20:00And the final was our proposed model.
20:05And here the top left figure,
20:08shows the true cluster labels.
20:11And top of right shows
20:12the UMAP reduction of the gene expression pattern.
20:18And as you can see, we can make the orange and the green,
20:22which is far away from each other,
20:24similar in the gene expression,
20:26so that it can be more challenging in the prediction.
20:30And we really test the performance of each model
20:35using the ARI where the very close one
20:39indicates the better performance.
20:42And as you can see here, when we ignore
20:47the skewness and the spatial pattern,
20:51there is the big loss of the ARI.
20:55And by considering the skewness,
20:57we gain some but still that there is being lost.
21:00And by further considering the spatial pattern,
21:04we can improve the high level of the ARI.
21:11And for the real data application,
21:14we consider the two applications.
21:18So,
21:20to compare the performance of the SPRUCE to existing tools,
21:26we used the 10X Visium human brain data
21:30from the Maynard et al, 2021, Nature Neuroscience.
21:36Here at the rehab we have about the 3000 spots.
21:40And one of the good aspect of this data is
21:45It's very well annotated.
21:48So, the author,
21:51using his expert knowledge,
21:54they annotated the 3000 spots into the 5 brain layers.
22:00Including the white matter and the frontal cortex layers.
22:05And as I mentioned earlier,
22:06we use the standard Seurat pre-processing pipeline,
22:11including the normalization of using the sc transform
22:16and also selection of the most variable genes
22:20using the existing pipeline.
22:22We consider the top 16 most variable genes.
22:29And we also consider the three other existing algorithms
22:34including BayesSpace, stLearn, Seurat and Giotto
22:40as the computing algorithms.
22:42And we use the default parameters for each of them.
22:49Here it shows the regions
22:52and top left figure shows the manual annotation
22:54provided by the author in the paper.
22:58And you can see the nice, five spatial clusters
23:03from inside out.
23:05And also there you can see
23:08that there is one, narrow cell cluster
23:11corresponding to the number four.
23:16Here we showed the real data for the SPRUCE,
23:18BayesSpace, stLearn, Seurat and the Giotto.
23:24And in this case, the network-based approaches,
23:28including the stLearn, Seurat and the Giotto,
23:32all showed a lower performance compared to those algorithms.
23:38The BayesSpace showed relatively higher performance
23:42about the ARI of 0.55.
23:46SPRUCE further improved the performance
23:49compared to the BayesSpace.
23:52And one thing I noted here is the...
23:58The narrowed cell cluster,
24:00could it be identified by the SPRUCE?
24:04Which is interesting.
24:06And as the second example.
24:10So first one is the more labeled data.
24:13We can compare our prediction to the existing annotation.
24:17And to further demonstrate the application of the SPRUCE
24:22to unlabeled data, we analyze the publicly available
24:26human invasive ductal carcinoma breast tissue.
24:31Again using the 10 X Visium platform.
24:36And we essentially followed the similar workflow
24:38and we identify the top 16 most spatially variable genes.
24:45And those included several tumor associated antigens,
24:50TAA, in creating the GFRA1 and CXCL14.
24:56And also that there is the tumor suppressive gene,
25:00like MALAT1.
25:04And we use the SPRUCE to identify the 5 sub regions
25:10using these 16 features.
25:12This shows the 16 most variable genes.
25:16And you can see that there are very clear spatial patterns.
25:22For example the CXCL14 and GFRA1,
25:28expel on the right bottom side.
25:30While the MALAT1 express higher in the top left side.
25:38And this is the cluster prediction
25:42made by the SPRUCE algorithm.
25:46And you can see that it identified
25:48the cluster too, which it highly coincide with the CLCX14
25:55and GFRAI1 with a study on.
25:59(mumbles)
26:01What the cell cluster 1,
26:05Is the MALAT1
26:09which is more tumor suppressor.
26:13So here we can see that the SPRUCE can identify
26:17the different group of the tissue architecture,
26:20such as the tumor suppressor and then tumor related
26:25(mumbles)
26:33And we can also easily look at there,
26:37within cluster expression pattern
26:40and gene-gene correlation.
26:43As you could see earlier,
26:44on cell cluster 2 which equals 0.2 to the right
26:49higher than the GFRA1 and CXCL14.
26:52One, which is the cross point here is the high-end MALAT1
26:57and so on.
26:58And also, in the case of cell cluster 2,
27:02there's a very strong gene-gene correlation pattern.
27:06So we just support the proposed model that considered
27:11spatial pattern and also gene-gene correlation
27:14simultaneously.
27:20So,
27:21so far I discussed the method
27:26for our SPRUCE and its application.
27:33And that we essentially expanded our work a little bit more
27:38to the MAPLE,
27:39which is the multi-sample spatial transcriptomics model
27:44Why we care about the multi-sample analysis of HST data?
27:49So currently most algorithms are designed in a way
27:53that it can more focus on a single sample.
27:57But even intuitively,
27:59joint analysis of the multiple HST data
28:03can potentially boost the signal
28:06by sharing the information amongst samples.
28:09And also the joint analysis of the different samples
28:13can allow the differentiation analysis of the HST data.
28:18So very often, each tissue is not our main interest.
28:24But we also want to compare tissue architecture
28:27between the different samples.
28:30For example, between the disease group versus the controls,
28:35responders versus the non responders to 13 treatments,
28:39such as the cancer immuno-therapy.
28:41So to offset this limitation, we proposed MAPLE.
28:47And actually our existing SPRUCE framework
28:50already allows this one naturally.
28:55So, simply what it can do is
28:57instead of now analyzing each sample individually,
29:01we can jointly analyze all the samples together.
29:05And then by doing so,
29:06we can share information
29:08about the modeling of each cell spot cluster,
29:13and also their spatial pattern.
29:17But by introducing the sample-level covariate exp xi
29:22in the cell type composition,
29:27we can see the impact
29:29of the different sample-level covariate.
29:33Which I show more in detail in the coming slides.
29:41So the first application is the same mouse brain data,
29:45the human brain data...
29:47Sorry this should be the mouse brain,
29:49and here we see the two anterior parts,
29:54which look very similar.
29:56And then as you can see here,
29:57when we jointly analyze the two sample
30:01cross pointing to the same part of the brain.
30:04It nicely identifies the cross pointing part
30:08between the two sample.
30:10Like one in the end, three on the top,
30:14five at the bottom and so on.
30:17And because this is the Bayesag framework,
30:21it can also provide uncertainty measures
30:25about our clustering prediction.
30:28And as you can see usually there is more uncertain
30:31about the clustering prediction
30:35around the boundary between different cell clusters.
30:38Which kind of makes sense,
30:40because we expect that maybe cell type
30:43might be more mixed together in the same cell spot.
30:48Also, there are some cell clusters
30:50with the higher level of the uncertainty
30:53of which we are still trying to understand more
30:56at this point.
30:58And this kind of the figure is the...
31:01what utility of this kind of joint analysis.
31:06So, for the identifier with T,
31:09we set the first cell cluster as the reference.
31:14And then here we see the two (mumbles)
31:16The top one shows the intercept,
31:20and then we can interpret this one as the relative size
31:25of each cell cluster.
31:26So then compared to the one,
31:29we can say three and the six are larger.
31:32So the three and the six are larger, compared to the one.
31:36Why the four is the smaller,
31:39well just smaller compared to the one.
31:40So this is what it can see by eye
31:45from the tissue prediction region.
31:48But good thing is that this model allows us to quantify,
31:52what you see by eye.
31:55And what is more interesting is the second one.
31:57So this one,
31:58is about the difference between the two sample.
32:02So again,
32:04so basically if it's higher,
32:07then it means that certain tissue spot cluster
32:12getting bigger in the second sample.
32:14And if it's lower immune state is a kind of smaller
32:18in the second sample and so on.
32:20So in this way,
32:21we can quantify the change of the tissue architecture
32:26between different cell clusters.
32:30And another interesting example is this one.
32:35So here, the image of 2D to anterior samples,
32:39we now also look at the posterior sample as well.
32:44So because this is two parts of the brain
32:48anterior and the posterior,
32:50the issue is kind of continuous between two.
32:53And as you can see here,
32:54cell cluster three is connected to the posterior side here.
33:00Cell cluster one is connected to here and so on.
33:05And then this kind of pattern is not clear
33:08if you analyze each data independently.
33:12And our MAPLE framework nicely captures
33:16such kind of sharing pattern.
33:18And also the difference pattern
33:20between the different samples, interestingly.
33:23So at this point,
33:25we are working on more simulation study
33:27and the real data analysis
33:29to further show the performance
33:33and understand the properties of the MAPLE at this point.
33:39So then I can't summarize my presentation today.
33:45So the high throughput spatial transcriptomics, or HST,
33:50provides unprecedented opportunities
33:54to investigate novel biological hypotheses,
33:57such as the tumor microenvironment and certain structure
34:05about the human brain and Alzheimer,
34:08and so on.
34:10And here we propose SPRUCE,
34:13a Bayesian multivariate mixture model
34:16for HST data analysis.
34:19SPRUCE has multiple strengths
34:23including the novel combination
34:26of the skewed normal density,
34:29Polya-Gamma data augmentation,
34:31and spatial random effect.
34:35Altogether, it allows to
34:37precisely infer spatially correlated mixture component
34:41membership probabilities.
34:44In our simulation study and real data analysis,
34:49we could see that SPRUCE outperforms the existing method,
34:53in the tissue architecture identification.
34:56And finally our recent extension of the MAPLE
35:01allows the joint clustering and differential analysis
35:05of multiple HST data.
35:09So at this point SPRUCE is on the review in,
35:13(mumbles)
35:15in the biometrics.
35:17Cross pointing manuscript is available in the bio archive.
35:22And there are multiple ongoing work
35:25regarding the HST data modeling
35:28in our lab.
35:30So we are actually currently working on further improving
35:35the SPRUCE and the MAPLE
35:37by incorporating other characteristics
35:39of the HST data, such as the relationships among cells.
35:44For example,
35:46we know that there are some likened and receptor,
35:50for example.
35:51Which we expect that they interact with each other
35:55in their cell structure.
35:57And then by incorporating different prior information,
36:01we can further improve the SPRUCE and MAPLE.
36:06We are also working on the other statistical models
36:10for somewhat relevant, but different tasks.
36:14For example,
36:15currently we are also working on the streamlining framework,
36:19especially the graph neural network,
36:22which is called RESEPT.
36:24And then using the gene framework,
36:28we tried to come up with good embedding
36:30of the HST gene expression pattern.
36:34Our current results show that such a combination
36:38of the stem learning and the statistical model approach
36:41can provide nice prediction performance.
36:47For this proposal, we developed a framework called RESEPT
36:52and cross pointing bio archive
36:55is also available publicly.
36:57And then cross pointing paper
36:59is now under revision in the nature communications.
37:06Regarding cell-cell communications,
37:09using network-based approaches has some benefit
37:12because the cell-cell communication can be nicely
37:16and naturally modeled using AGR network.
37:21So we have the parallel work called the the Banyan
37:25to identify the cell-cell communication
37:27and tissue architecture using the network-based approaches.
37:31And finally, there are the multiple effort experimentally
37:37to generate the spatial multimodal data.
37:42For example,
37:43the effect to seek such as the single cell genomics,
37:48proteomics and the T-cell receptor at the same time.
37:53And very soon,
37:54everything are expected to be combined
37:57as the spatial transcriptomic structure.
38:01We are working on the direction
38:03to develop the statistical model
38:06for integration of the HST data with other matched data.
38:13So I would like to acknowledge my research team at OSU.
38:18Carter Allen is the main driver this project,
38:23and also my pitch assistant
38:27Qin Ma and Yuzhou Chang is my close collaborator
38:32for the HST data modeling project.
38:36And Zihai Li,
38:38who is the director of the Immuno-Oncology Institute
38:42and also the expert in cancer.
38:46Won Chang at the University of Cincinnati
38:49who are the spatial statistics expert,
38:53and MUSC collaborator Brian Neelon
38:56and my grant support.
39:00So, and this is the end of my presentation,
39:03and you can find my manuscript
39:05and the software from the link here.
39:10If you have any questions and comment,
39:13please let me know by email at chung.911@osu.edu.
39:17So thank you for your attention.
39:29<v ->So thank you.</v>
39:32Do we have any questions from the audience in the classroom,
39:36or from the audience on zoom?
39:43<v ->Can I ask a question?</v>
39:45Can you hear me?
39:46<v ->Yes, mm-hm.</v>
39:47<v ->Right, Dongjun welcome back.</v>
39:50Great work, it's a nice presentation.
39:52I'm just wondering, like,
39:53when you do this from your own experience
39:56on the cell clustering,
39:57how much the spatial information contributes
40:00to the clustering.
40:03<v ->Sure.</v>
40:09So,
40:10(mumbles)
40:15If you're here,
40:16so if you look at the Seurat workflow,
40:18you can see there's a still lot of the, kind of,
40:22local boundary between different cell spot clusters.
40:28And when you analyze the same data using the SPRUCE,
40:32you can see much cleaner boundary.
40:34And often it will coincide with the
40:36expert analogy annotation.
40:40So given that there is the significant contribution,
40:46of course even the gene expression,
40:49we still get some big picture, as you can see here.
40:52But spatial information provide much cleaner prediction
40:57about the tissue architecture in general.
41:01<v ->I see.</v>
41:02And also the skewness.
41:05Do you estimate that or that's like your heart
41:08was persuaded by the skewness?
41:12<v ->You mean which one?</v>
41:14<v ->On k model.</v>
41:16Your model to specify, the k model you have there.
41:19I missed that part.
41:20Like, do you need to specify the skewness?
41:24<v ->Or learn from data.</v>
41:26<v ->Oh, I see.</v>
41:27But from the data, how skew?
41:29I mean, just in terms of how stable
41:31that alpha k can be estimated.
41:36<v ->So maybe I can answer it in two different ways.</v>
41:40So if there is this skewness in the data, I think yes.
41:44So we'll say it depends on how processed the data as well.
41:49So usually there's three different approaches
41:51to model the HST data in closed spatial embedding gene.
41:57And so you can see here,
41:58who are the people using the principle components?
42:02Who are the people use the team learning
42:05as the embedding step?
42:08If you use the team learning or the PCA
42:12it's more likely symmetry in the real data.
42:16If you consider the spatial embedding gene,
42:20we often hope to have the skewness, as you can see here.
42:24And then regarding your question, overall it works well.
42:30I don't have the exact quantification, but it works well.
42:34Especially stably in most cases.
42:37<v ->Yeah, I read the spatial Bayes paper.</v>
42:39They seem to be working on the principle components, right?
42:41They do not work on individual genes, right?.
42:43<v ->No, yeah.</v>
42:44They base this on the PCA.
42:46<v ->Yeah, that's why it's completely puzzling me</v>
42:47while you're doing that.
42:48But anyway, yeah.
42:49Thank you.
42:50<v ->Yeah so, so...</v>
42:51(mumbles)
42:54so they mainly target the PCA.
42:55So they only can start the multivariate distribution.
42:59And also because of the same reason,
43:01their equivalence metrics means less density.
43:05<v ->I see.</v>
43:08Thank you.
43:09<v ->Thank you.</v>
43:22<v ->Do we have any questions from students in the classroom?</v>
43:32<v ->Wait, can I ask another question?</v>
43:36So, towards the end,
43:37you mentioned you tried
43:38to look at the cell-cell communication.
43:45That part.
43:46I'm very interested in that
43:49From our experience on the single cell spatial data are...
43:55Are you talking about you're learning from the single cell,
43:58or the spatial single cell?
44:02<v ->So, regarding the cell-cell communication</v>
44:05it's still very ongoing research at this point.
44:10I mean, not just our side but in general.
44:13Because most of the cell-cell communication prediction
44:17based on the database.
44:20So based on data, like on the receptor,
44:22pairing the database and checking
44:25their cross point on the expression in cross point spot
44:28of the cell.
44:29And then by checking that the cross pointing pair
44:33of the expression pattern
44:34between the like and the receptor.
44:36They want to model cell-cell communication.
44:40It's not perfect, as you know,
44:42because it's like a computer.
44:45If you look at the chip, it's almost like
44:48(mumbles)
44:48but more like motive analysis.
44:51So there's some limitation,
44:53but it's a more likely general limitation at this point.
44:57<v ->Yeah,</v>
44:58I'm asking because we've been looking
44:59at some of the spatial single cell data
45:02that were too noisy for the like
45:04and receptor gene expression levels.
45:07Just couldn't make it too far.
45:09(mumbles)
45:11But for a single cell, may be different?
45:13I mean, probably there'll be more that, like...
45:17<v ->Yeah, three already.</v>
45:18I mean, so if you go to high-resolution,
45:22it's a very noisy,
45:24so very often we need to do some simplification.
45:28Like looking at multi-modal or the cell cluster,
45:31rather than the cell.
45:34It's still very multiple experimental limitation,
45:38at this point.
45:39(mumbles)
45:40Thank you.
45:51(class teacher addresses classroom)
46:00<v ->On the data from multiple samples</v>
46:03So, if we have samples from...
46:06(mumbles)
46:18<v ->Oh yeah, that's a very good question.</v>
46:22So,
46:23actually we can answer in the two different ways.
46:29In some sense,
46:30good pre-processing is still important
46:35because it still depends on the expression patterns.
46:43But still regarding the differences
46:46between the different tissues.
46:48If there is a big difference,
46:49it can still detect the difference
46:51between the different sample.
46:54So, it can detect spots.
46:55But still like a main goal is more
46:59for the similar type of tissue.
47:01If it's too different,
47:02maybe it's different research project.
47:05So, for example,
47:07here our targets is more about, for example,
47:10like same breast tissue,
47:13but with a different responders and non-responders group,
47:18for example.
47:19Or like a cell-cell long tissue, but the tumor but not tumor
47:24and so on.
47:25If you like a human and mouse,
47:29then it might be somewhat different story,
47:33which might need much more work.
47:38<v ->Do we have any more questions here?</v>
47:58Okay, can we have all the questions
47:59from the audience on zoom?
48:21Okay, so it looks like we don't have any more questions.
48:26So Dr. Chung, thank you again for your nice presentation.
48:31Look forward to meeting in person sometime soon.
48:36<v ->And then thank you again Wei and Hongyou</v>
48:38for the invitation
48:40and it's a great come back, although virtually.
48:43And I hope to see you again.
48:46<v ->We'll come by in person.</v>
48:49<v ->Hopefully someday soon.</v>
48:53Okay, thank you.