Create multimedia.qmd by YihanLiu4023 · Pull Request #768 · microbiome/OMA

YihanLiu4023 · 2025-07-14T17:36:48Z

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antagomir

Thanks!

Check the suggestions and the following:

increase support for the mia and (Tree)SummarizedExperiment methods when possible, this is very useful for compatibility with many other methods; I added suggestions. Also multimedia seems to support SE so this should be done.
Let us try to avoid repetition in the code; this has now two almost identical parts. Shall we prepare example data that is readily usable for the examples?
I am wondering if there would be a way to combine the two mediation chapters into one?
If we consider these methods widely usable we should consider providing wrappers for the interpretation, summaries, and visualizations of the results; either as contributions to "multimedia" package, or into mia or some other pkg (doesn't matter which pkg as long as it works)

antagomir · 2025-08-06T19:23:15Z

Could you resolve the cases you have closed (from the "Resolve" buttons)?

YihanLiu4023 · 2025-08-06T20:06:07Z

Fixed most of the detailed comments, still working on the more genral ones like creating pkg. Will finish this asap

antagomir · 2025-08-17T20:25:09Z

Can you resolve the completed suggestions above and confirm if this is ready to merge?

Just fixed some minor inconsistence in the name of variables

antagomir · 2025-09-17T21:14:16Z

To be checked in more detail before merge, regarding the following:

May require additional wrapper functions to extract the relevant information from data objects more intuitively (but those should be in multimedia pkg or mia, so not sure if it is feasible to do as part of this PR - but if not, then it is unclear when this would take place).
Are the examples now sufficiently short & concise (data cleaning steps usually avoided in OMA examples; some explanations could be added instead).

YihanLiu4023 · 2025-09-18T22:22:16Z

To be checked in more detail before merge, regarding the following:

May require additional wrapper functions to extract the relevant information from data objects more intuitively (but those should be in multimedia pkg or mia, so not sure if it is feasible to do as part of this PR - but if not, then it is unclear when this would take place).

Are the examples now sufficiently short & concise (data cleaning steps usually avoided in OMA examples; some explanations could be added instead).

Hi Leo, thank you for the suggestions. I have added some wrapper functions, especially to where you indicated. For the data cleanning steps, do you mean we can remove those part, and add some texts to explain instead?

antagomir · 2025-09-19T07:07:13Z

The data cleaning part would work best if we include a suitable cleaned demo data in the R pkg, then we can skip the data cleaning steps in OMA. The problem is that if we could do this with every single method but then more than half of the book might easily be data cleaning examples, and this is also shifting the focus from the actual method to general data processing steps -> can we include demo data set/s in the multimedia, mia, or other pkg, or can we use already existing demo data sets?

antagomir · 2025-09-19T07:07:47Z

Regarding the wrappers, we could see if we can include these in a package if they would be generally useful anyway.

himelmallick · 2025-09-19T10:31:06Z

Hi Leo – just wanted to quickly chime in here. This dataset is a special case where we don’t have a conventional, well-defined outcome for mediation analysis. Instead, the outcome is a dysbiosis score derived from taxonomic profiles, as described by Lloyd-Price et al. (2019), which, to my knowledge, hasn’t been implemented elsewhere. We are already drawing the data from curatedMetagenomicData, but creating the dysbiosis score is a necessary step to showcase the mediation analysis.

antagomir · 2025-09-20T21:01:55Z

Thanks. I think that dysbiosis score could then stay there. Let's check @TuomasBorman feedback.

TuomasBorman

Thanks, looks very good! However, there are couple points to discuss.

This book is intended as teaching material to demonstrate specific analyses and tasks. The main focus is on the underlying ideas and concepts rather than implementation details. Therefore, the code is kept as simple as possible to achieve the intended outcomes. More polished analyses, including advanced plots, are better suited for workflow packages (we are also considering creating workflow package to showcase more complex analyses).

There are couple lengthy code chunks that do operations to results. Having these long chunks have opposite effect as readers are exhausted by the amount of code and they cannot focus on the main points. Everything that is not relevant for the main point should be removed even though plots or names would not be optimal.

Ideally, these wrappers will be implemented in a package, but of course it takes some time. At least, we should have plan to implement them. If we just leave it here, these will be never implemented as we are very busy with everything.

antagomir · 2025-11-19T11:30:22Z

Can you @YihanLiu4023 confirm when you're ready with the updates? Can you also press the "Update branch" button above to ensure that this PR is in sync with the latest devel branch.

YihanLiu4023 · 2025-11-20T23:37:59Z

Updated file submitted!

antagomir

Thanks - some points to clarify still.

Overall this looks good but it will be essential to consider the following:

Data cleaning operations should be either ignored, or readily cleaned demo data set should be used (it can be placed to one of our packages). Otherwise OMA would be primarily a data cleaning example collection..
We need to check if any ready-made functionality is available to fetch and visualize results from mediation analyses. Now this is done with custom code. Maybe multimedia package has some utilities and if not, we should see if some tasks are so central that they should be standardized (either into multimedia package as contributed PR, or into mia).

antagomir · 2025-11-21T21:29:01Z

+# Run mediation (non-delta) #
+#############################
+
+# Convert the TreeSummarizedExperiment to be SummarizedExperiment


Why is this needed?

Note that TreeSE is an instance of SE

I just realized here is an issue:

It looks like the package is kind of hard-coded when recognizing the types - it does not handle TreeSummarizedExperiment in mediation_data() and cannot recognizes it as SummarizedExperimentin this situation. Thus it seems like we still need to convert to SummarizedExperiment before running the mediation.

I simplied a little bit on this as the following:
se_relative <- as(tse, "SummarizedExperiment")
if (is.null(rownames(se_relative))) rownames(se_relative) <- rownames(rowData(se_relative)) <- make.names(rownames(tse), unique = TRUE)

The multimedia developer kindly fixed this for us: krisrs1128/multimedia#7

Now you can directly input TreeSE to multimedia_data

antagomir · 2025-11-21T21:37:43Z

+# Clean the pathway names
+pwy <- rownames(summary_df)
+pwy <- gsub("PWY0\\.", "PWY0-", pwy) # PWY0.xxx → PWY0-xxx
+pwy <- gsub("PWY\\.", "PWY-", pwy) # PWY.xxx → PWY-xxx
+pwy <- gsub("\\.", " ", pwy) # leftover dots → spaces
+pwy <- gsub("__", ": ", pwy) # double underscores → colon
+pwy <- gsub("_\\.", " ", pwy) # underscore then dot → space
+pwy <- gsub("_", " ", pwy) # remaining underscores → space
+pwy <- gsub("\\.\\.", " ", pwy) # double dots → space
+pwy <- gsub("\\.$", "", pwy) # remove ending period
+# pwy <- trimws(pwy) # final cleanup
+rownames(summary_df) <- pwy


Can we prepare demo data in one of the R packages (e.g. mia) that is sufficiently clean?

OMA is dedicated to show how methods work, and data cleaning is something that is so common that it will overwhelm all examples in the book if we are not actively avoiding it.

Yes, I can do that. Do I just need to create a new pull request to the mia package for the cleaned demo data?

Yes, a PR. It should follow similar conventions than the other demo data sets.

Resolve this when done.

ibdhdb is already a mia dataset so it is not ideal to create a second version of this dataset just for the multimedia chapter.

Instead, let's simplify and minimise preprocessing just enough so that multimedia doesn't complain.

@YihanLiu4023, is it necessary to clean the pathway names? If it is necessary, could we simply replace all dots and underscores with space or another accepted character, in a single line of code?

antagomir · 2025-11-21T21:38:28Z

+ggplot(summary_df, aes(x=estimate, y=reorder(mediator, estimate), color=significant)) +
+  geom_point() +
+  geom_errorbarh(aes(xmin=lower, xmax=upper), height=0.2) +
+  geom_vline(xintercept=0, linetype="dashed", color="grey") +
+  theme_classic() +
+  labs(
+    x = "Observed Indirect Effect with Bootstrap CI",
+    y = "Mediator",
+    title = "Forest Plot of Mediation-specific Indirect Effects for Pathway"
+  ) +
+  scale_color_manual(values=c("black","red")) +
+  theme(legend.position="bottom")
+


Can we consider having default visualization method for mediation analyses, for isntance in miaViz pkg (if not multimedia pkg itself)?

We can add a package-native mediator visualization using plot_mediators(), which shows the observed outcome–mediator relationships (colored by treatment) for the top pathwise indirect effects. Here is the example plots for species:

(Mediators are standardized (z-scored) for comparability across features; hence values may be negative. Besides, many microbial features are sparse; several mediators exhibit near-zero values for most samples, producing vertical bands.)

While a miaViz-based visualization would instead emphasize the taxonomic/pathway abundance structure (e.g., distributions/compositional patterns) rather than mediation-effect estimates, our original bootstrap forest plot complements plot_mediators() by summarizing effect uncertainty via CIs across mediators. Do you want plot_mediators() to be the primary/default visualization (with the forest plot as optional), or should we keep both as complementary views?

We could include both for now imo,

No problem, I would keep both for now.

antagomir · 2026-01-09T18:20:04Z

This seems nearly complete.

I picked the following points from above. Can you confirm this is OK @YihanLiu4023 ?

Data cleaning operations: add and use readily cleaned demo data set for the examples. This will keep the examples more focused on the actual core point (mediation). If suitable demo data does not exist, we can add it in mia or another package (should be done before merging this but is straightfwd if the data is already prepared - we can help with this as necessary).
Minor update on the visualisations as discussed above. Mostly just cite the multimedia homepage (and publication) for further guidance and examples.

YihanLiu4023 · 2026-01-25T23:22:39Z

This seems nearly complete.

I picked the following points from above. Can you confirm this is OK @YihanLiu4023 ?

Data cleaning operations: add and use readily cleaned demo data set for the examples. This will keep the examples more focused on the actual core point (mediation). If suitable demo data does not exist, we can add it in mia or another package (should be done before merging this but is straightfwd if the data is already prepared - we can help with this as necessary).

Minor update on the visualisations as discussed above. Mostly just cite the multimedia homepage (and publication) for further guidance and examples.

YihanLiu4023 · 2026-01-25T23:24:50Z

This seems nearly complete.

I picked the following points from above. Can you confirm this is OK @YihanLiu4023 ?

Data cleaning operations: add and use readily cleaned demo data set for the examples. This will keep the examples more focused on the actual core point (mediation). If suitable demo data does not exist, we can add it in mia or another package (should be done before merging this but is straightfwd if the data is already prepared - we can help with this as necessary).

Minor update on the visualisations as discussed above. Mostly just cite the multimedia homepage (and publication) for further guidance and examples.

For the data cleanning, I'm preparing the data demo and will pull request to the mia package soon. For the visualization part, I just upload a version that keeps both of the things we discussed above. I would also uplaod a cleaned version (using the updated data demo in mia) very soon after the PR for data is merged. Thank you!

YihanLiu4023 · 2026-01-26T00:37:04Z

I have create a PR to the mia package for the data demo, but it seems like the data file is not named correctly (may showing "Add files via upload"), please let me know if you cannot open it.

antagomir · 2026-02-03T13:03:54Z

I have create a PR to the mia package for the data demo, but it seems like the data file is not named correctly (may showing "Add files via upload"), please let me know if you cannot open it.

@0xMuluh could you have a look?

antagomir · 2026-05-24T07:24:10Z

To complete soon.

RiboRings · 2026-07-13T10:03:08Z

Hi!

After meeting @himelmallick at the office, I had a brief look at this chapter and it looks very nice and almost ready!

@TuomasBorman @antagomir, from your side what still needs to be done to finalise the chapter?

From my side, I would have a couple of suggestions to simplify the code in addition to the others' comments above.

Check if miaViz can replace ggplot "manual" visualisations. miaViz is our to-go option for semi-automatic visualisation in the OMA book. It greatly reduces the burden of plotting for users. Just to name a couple, it may be possible to replace the histogram here and the forest plot here with miaViz plotHistogram and plotForest functions, respectively.
Stick to code standards used across the book. In general, keep code as simple as possible (think how to avoid complex expressions like this). Use 4-space indentation instead of current 2-space indentation. Add spaces around math signs, around = and after , when defining function arguments. It is a good idea to go through other chapters and mimic their flow + formatting as much as possible.
Avoid non-standard use of comments, such as 3-line headers (like here) and in-line comments (like here). Instead of using comments as section headers (like here), it is better to split the code into multiple chunks, and insert md text in-between the chunks to describe what will happen next in the very following chunk.

Hope this helps, and let me know if you need more feedback! :)

antagomir · 2026-07-13T11:12:41Z

I agree about these updates.

When we already have readily available plug-in methods in miaViz indeed good to use those to simplify the workflow and enhance standardization. If suitable visualization method does not exist it is a good moment to consider if we can benefit from a dedicated visualization method for mediation analysis. But such new work could be a new issue and PR, not slowing down the merging of this one.

2-3. Agreed, co-pilots can probably handle much of this semi-automatically.

How shall we proceed, is @YihanLiu4023 able to finalise these, or support needed?

RiboRings · 2026-07-13T11:49:19Z

For the records, this is the reference list of miaViz: https://microbiome.github.io/miaViz/reference/index.html

For here, at least the vignettes of plotMediation, plotForest and plotHistogram might be relevant to check.

antagomir · 2026-07-13T12:01:31Z

Thanks. It is also possible that (some of) these might not be directly applicable but the general idea is to streamline the analyses by providing easy-to-use default tools for standard use cases. The current case is also helping to identify where gaps/needs may exist.

YihanLiu4023 · 2026-07-19T15:50:47Z

Hi, I uploaded a new version according to the comments but had 1 failing check: Style code. I also ran the style workflow locally in R. However, the automated styler check reformatted the R chunks back to 2-space indentation. Is there any way to solve this?

RiboRings · 2026-07-19T18:45:17Z

Thanks!

Looks pretty good! I still have a couple of comments.

RiboRings

I left some comments, please have a look below and resolve them when addressed.

RiboRings · 2026-07-19T18:52:16Z

+#| label: load_iHMP_relative_demo_data
+#| message: false
+
+load("/Users/lyh_ciel/Library/CloudStorage/OneDrive-YaleUniversity/WCM/OMA/multimedia_data_demo.rda")


Instead of importing a ready-made object, preprocessing steps should be kept here as previously, similar to the other OMA chapters.

P.S., now it should be possible to pass TreeSE to multimedia without having to convert it to SE: krisrs1128/multimedia#7 (comment)

Sorry for the confusion, I forgot to change the path here while testing. Actually, I uploaded the preprocessed data to the mia pacakge with a new pull request as discussed before. I'm just wondering if we still want to load it through mia or we can just keep the data preprocessing here as previously?

No problem, sorry I wasn't aware of it.

You are using the ibdhdb dataset from HMP2, correct? If so, it's already in mia: https://microbiome.github.io/mia/reference/ibdmdb.html.

You could import it with data("ibdmdb", package = "mia") and preprocess it for the multimedia analysis.

Linking to the related PR: microbiome/mia#802 (comment)

Thank you for this update! That's exactly the PR I created for this data. But this one is already preprocessed, so I may load it directly. Please let me know if you think it's better to show some preprocessings here in multimedia, I can make some changes.

RiboRings · 2026-07-19T18:53:54Z

+    mediators = medi_idx
+)
+
+mdl <- multimedia(exper)


While the three-line headers were not needed, the actual comments describing each line of code should be kept here and elsewhere.

I added back some comments for understanding, but please let me know if you think more comments need to be added

Thank you! If possible, please add comments to all lines of code, there are still some parts without comments.

Also, remove full points (.) at the end of the comments

I've added as many comments as possible, while also trying to keep things concise

RiboRings

Thank you so much, I'm confident we'll be ready in one or two more rounds of review. Please keep up the great work! 🥇

RiboRings · 2026-07-20T18:59:17Z

+clean_pathway_names <- function(pathways) {
+    pathways <- gsub("PWY0\\.", "PWY0-", pathways)
+    pathways <- gsub("PWY\\.", "PWY-", pathways)
+    pathways <- gsub("__", ": ", pathways)
+    pathways <- gsub("_\\.", " ", pathways)
+    pathways <- gsub("\\.", " ", pathways)
+    pathways <- gsub("_", " ", pathways)
+    pathways <- gsub("\\s+", " ", pathways)
+    trimws(pathways)
+}


As mentioned above, is this preprocessing necessary? If it is, can we try to reduce it to the minimum, 1/2 lines?

It is only used to make pathway labels more readable in the plot. If you think it's okay, I can delete them. But currently, I just reduced this to two inline cleanup lines and removed the helper function.

Thanks! It's this one, right?

pwy <- gsub("PWY0\\.|PWY\\.", "PWY-", rownames(summary_df)) pwy <- trimws(gsub("\\s+", " ", gsub("[_\\.]+", " ", pwy))) rownames(summary_df) <- pwy summary_df$mediator <- pwy

How about using stringr for all string processing in the chapter? That way would be more readable and it also supports pipe operations. For example, the code above becomes:

summary_df$mediator <- rownames(summary_df) |> str_replace_all("PWY0\\.|PWY\\.", "PWY-") |> str_remove_all("[_\\.]+") |> str_remove_all("\\s+") |> str_trim()

To keep it simple, you can also check if the plot still looks good enough even when skipping some of those operations.

RiboRings · 2026-07-20T19:04:18Z

+diss <- as.matrix(
+    getDissimilarity(
+        tse,
+        method = "bray",
+        na.rm = TRUE,
+        assay.type = "relabundance"
+    )
+)


Tidy operations are easier to read. For example:

# Calculate Bray-Curtis dissimilarity across samples dist <- tse |> getDissimilarity(...) |> as.matrix()

Could you try to make this chunk (load_iHMP_relative_demo_data) more user-friendly through more tidyverse/dplyr verbs and |> pipes when possible?

I just uploaded a version that load dataset after the preprocessing from mia, if you want to include some data cleanning here, I will revise this part later.

In my understanding, the dataset you are using is ibdhmp from HMP2, correct? If so, we should use the original dataset, and apply all preprocessing steps in the multimedia chapter (while also keeping them minimal).

Please use:

# Import dataset data("ibdmdb", package = "mia") mae <- ibdmdb

RiboRings · 2026-07-20T19:06:20Z

+    mediators = medi_idx
+)
+
+mdl <- multimedia(exper)


Also, remove full points (.) at the end of the comments

YihanLiu4023 · 2026-07-22T04:51:12Z

Sorry I just realized the data loading is not yet updated (all the other comments till now is updated). I would push a new one right away.

YihanLiu4023 · 2026-07-22T04:56:14Z

+# Use syntactically valid mediator names in model formulas
+raw <- sub(".*s__", "", rownames(tse))
+clean1 <- str_remove_all(raw, "\\[|\\]")
+clean2 <- str_replace_all(clean1, "[: \\.,]", "_")


clean1 and clean2 are used to make the mediator names formula-safe and more readable. I can delete this if you think this cleanup is unnecessary.

It's ok to keep this, but as I commented above, let's make tidy and more readable using stringr verbs and the pipe:

# Use syntactically valid mediator names in model formulas rownames(tse) <- rownames(tse) |> str_remove(".*s__") |> str_remove_all("\\[|\\]") |> str_replace_all("[: \\.,]", "_") |> make.names(unique = TRUE)

Change accordingly here and elsewhere.

RiboRings

A few more comments.

RiboRings · 2026-07-22T11:26:03Z

+#| fig-cap: Directed acyclic graph illustrating multimodal mediation where an exposure affects multiple mediators across modalities (microbial species and metabolites), which in turn affect the outcome. A direct path from exposure to outcome is also included (dashed). While gut microbial species biologically influence metabolite levels, for tractability in high-dimensional mediation analysis we assume species and metabolites act as conditionally independent parallel mediators given exposure, acknowledging this as an approximation
+#| echo: false
+
+library(DiagrammeR)


No need if it's too complicated, but would it be possible to plot this DAG with the ggdag package instead of DiagrammeR? That would align it with the OMA chapter on mediation.

RiboRings · 2026-07-22T11:28:17Z

+
+# Select the top mediators for the default mediator-level plot
+top_meds <- summary_df %>%
+    dplyr::arrange(dplyr::desc(abs(estimate))) %>%


Remove package:: from function calls across the chapter unless necessary.

RiboRings · 2026-07-22T11:29:55Z

+
+7.  Repeat the process for the iHMP microbial pathways.
+
+### Performing multimodal mediation analysis for iHMP species relative abundance {#sec-relative-abundance}


Shouldn't this be a second-level header (##)? Same also for the next section.

RiboRings · 2026-07-22T11:34:03Z

+pwy <- gsub("PWY0\\.|PWY\\.", "PWY-", rownames(summary_df))
+pwy <- trimws(gsub("\\s+", " ", gsub("[_\\.]+", " ", pwy)))
+rownames(summary_df) <- pwy
+summary_df$mediator <- pwy


id.var argument of plotForest is "rownames" by default, so I don't think you need this line (457).

antagomir · 2026-07-27T16:04:07Z

@YihanLiu4023 can you resolve the remaining comments, then we approach completion?

antagomir · 2026-08-02T13:30:48Z

@YihanLiu4023 kindly confirm when this is complete based on the feedback.

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Multimedia preprocessed demo data microbiome/mia#802

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		7. Repeat the process for the iHMP microbial pathways.

		### Performing multimodal mediation analysis for iHMP species relative abundance {#sec-relative-abundance}

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