multimedia is an R package for multimodal mediation analysis
of microbiome data. It streamlines data curation, mediation and outcome
model specification, and statistical inference for direct and indirect
effects. By defining causal relationships across data modalities, it can
support principled data integration. You can read more about the package
in our preprint:
The preprint describes the scientific context and interpretation for two of the vignettes in this package. One gives a multi-omics analysis of IBD, and the other describes how to simultaneously model 16S profiles and survey responses in a mindfulness intervention study.
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("krisrs1128/multimedia")
Here is a simple example of estimating direct and indirect effects. The
data are randomly generated (no real effects), but we imagine that the
ASV columns mediate the relationship between treatment and PHQ. This
is mimics the possibility that the microbiome (ASV = Amplicon Sequence
Variant) mediates the relationship between a treatment and depression
(PHQ = Patient Health Questionnaire). You can find more details in the
random.Rmd vignette.
The original data here are a SummarizedExperiment. The package can
also take phyloseq objects and data.frames.
library(multimedia)
demo_joy()
## class: SummarizedExperiment
## dim: 5 100
## metadata(0):
## assays(1): counts
## rownames(5): ASV1 ASV2 ASV3 ASV4 ASV5
## rowData names(0):
## colnames: NULL
## colData names(2): treatment PHQ
Next, we specify which columns are the treatment, mediators, and
outcomes. Notice that we can use tidyselect syntax to match multiple
columns.
exper <- mediation_data(demo_joy(), "PHQ", "treatment", starts_with("ASV"))
exper
## [Mediation Data]
## 100 samples with measurements for,
## 1 treatment: treatment
## 5 mediators: ASV1, ASV2, ...
## 1 outcome: PHQ
Next, we fit all mediation analysis components and estimate effects. By default, the package uses linear models – see the vignettes for examples using sparse regression, random forests, and bayesian hierarchical models instead.
model <- multimedia(exper) |>
estimate(exper)
model
## [Multimedia Analysis]
## Treatments: treatment
## Outcomes: PHQ
## Mediators: ASV1, ASV2, ...
##
## [Models]
## mediation: A fitted lm_model().
## outcome: A fitted lm_model().
In any mediation analysis, there are several types of effects that could
be interesting, each corresponding to different ways of traveling from
the treatment to the outcome in the mediation analysis causal graph. In
the block below, direct_effect captures treatment effects that bypass
the microbiome; indirect_effect are effects that are mediated by ASV
relative abundances. Since this example uses a linear model, the effects
are identical for the two indirect settings.
direct_effect(model, exper)
## outcome indirect_setting contrast direct_effect
## 1 PHQ Control Control - Treatment 0.09314376
## 2 PHQ Treatment Control - Treatment 0.09314376
indirect_overall(model, exper)
## outcome direct_setting contrast indirect_effect
## 1 PHQ Control Control - Treatment 0.02256029
## 2 PHQ Treatment Control - Treatment 0.02256029
The package also includes helpers to visualize and perform inference on these effects. For example,
boot <- bootstrap(model, exper, c(direct = direct_effect))
library(ggplot2)
ggplot(boot$direct) +
geom_histogram(aes(direct_effect), bins = 20) +
scale_y_continuous(expand = c(0, 0)) +
theme_classic() +
labs(x = "Direct Effect", y = "Frequency", title = "Bootstrap Distribution")
If we want to use a different type of model, we can just modify the
original multimedia specification. Below we use a sparse regression
model, which correctly recovers that the direct effects are 0.
multimedia(exper, glmnet_model(lambda = .1)) |>
estimate(exper) |>
direct_effect()
## outcome indirect_setting contrast direct_effect
## 1 PHQ Control Control - Treatment 0
## 2 PHQ Treatment Control - Treatment 0
We welcome questions and comments about the package either through github or email.
sessionInfo()
## R version 4.4.1 Patched (2024-08-21 r87049)
## Platform: aarch64-apple-darwin20
## Running under: macOS Sonoma 14.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/Chicago
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggplot2_3.5.1 multimedia_0.2.0 tidyselect_1.2.1 ranger_0.16.0
## [5] glmnetUtils_1.1.9 brms_2.21.0 Rcpp_1.0.13
##
## loaded via a namespace (and not attached):
## [1] gridExtra_2.3 inline_0.3.19
## [3] permute_0.9-7 sandwich_3.1-0
## [5] rlang_1.1.4 magrittr_2.0.3
## [7] multcomp_1.4-26 ade4_1.7-22
## [9] matrixStats_1.4.0 compiler_4.4.1
## [11] mgcv_1.9-1 loo_2.8.0
## [13] vctrs_0.6.5 reshape2_1.4.4
## [15] stringr_1.5.1 pkgconfig_2.0.3
## [17] shape_1.4.6.1 crayon_1.5.3
## [19] fastmap_1.2.0 backports_1.5.0
## [21] XVector_0.45.0 labeling_0.4.3
## [23] utf8_1.2.4 rmarkdown_2.28
## [25] UCSC.utils_1.1.0 purrr_1.0.2
## [27] xfun_0.47 glmnet_4.1-8
## [29] zlibbioc_1.51.1 GenomeInfoDb_1.41.1
## [31] jsonlite_1.8.8 progress_1.2.3
## [33] biomformat_1.33.0 highr_0.11
## [35] rhdf5filters_1.17.0 DelayedArray_0.31.11
## [37] Rhdf5lib_1.27.0 prettyunits_1.2.0
## [39] cluster_2.1.6 parallel_4.4.1
## [41] R6_2.5.1 stringi_1.8.4
## [43] StanHeaders_2.32.10 GenomicRanges_1.57.1
## [45] estimability_1.5.1 rstan_2.32.6
## [47] SummarizedExperiment_1.35.1 iterators_1.0.14
## [49] knitr_1.48 zoo_1.8-12
## [51] IRanges_2.39.2 bayesplot_1.11.1
## [53] igraph_2.0.3 Matrix_1.7-0
## [55] splines_4.4.1 abind_1.4-5
## [57] yaml_2.3.10 vegan_2.6-8
## [59] codetools_0.2-20 curl_5.2.2
## [61] pkgbuild_1.4.4 lattice_0.22-6
## [63] tibble_3.2.1 plyr_1.8.9
## [65] withr_3.0.1 Biobase_2.65.1
## [67] bridgesampling_1.1-2 posterior_1.6.0
## [69] coda_0.19-4.1 evaluate_0.24.0
## [71] survival_3.7-0 RcppParallel_5.1.9
## [73] Biostrings_2.73.1 pillar_1.9.0
## [75] phyloseq_1.49.0 MatrixGenerics_1.17.0
## [77] tensorA_0.36.2.1 checkmate_2.3.2
## [79] foreach_1.5.2 stats4_4.4.1
## [81] distributional_0.4.0 generics_0.1.3
## [83] hms_1.1.3 S4Vectors_0.43.2
## [85] rstantools_2.4.0 munsell_0.5.1
## [87] scales_1.3.0 xtable_1.8-4
## [89] glue_1.7.0 emmeans_1.10.4
## [91] tools_4.4.1 data.table_1.16.0
## [93] mvtnorm_1.3-1 tidygraph_1.3.1
## [95] rhdf5_2.49.0 grid_4.4.1
## [97] tidyr_1.3.1 ape_5.8
## [99] QuickJSR_1.3.1 miniLNM_0.1.0
## [101] colorspace_2.1-1 nlme_3.1-166
## [103] formula.tools_1.7.1 GenomeInfoDbData_1.2.12
## [105] patchwork_1.2.0 cli_3.6.3
## [107] fansi_1.0.6 S4Arrays_1.5.7
## [109] Brobdingnag_1.2-9 dplyr_1.1.4
## [111] V8_5.0.0 gtable_0.3.5
## [113] digest_0.6.37 operator.tools_1.6.3
## [115] BiocGenerics_0.51.1 SparseArray_1.5.31
## [117] TH.data_1.1-2 farver_2.1.2
## [119] multtest_2.61.0 htmltools_0.5.8.1
## [121] lifecycle_1.0.4 httr_1.4.7
## [123] MASS_7.3-61