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https://github.com/chanw0/SparseMCMM

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https://github.com/chanw0/SparseMCMM

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---

title: "SparseMCMM"

output: github_document

---



```{r, include = FALSE}

knitr::opts_chunk$set(echo = FALSE)

```



# SparseMCMM



*Author & Maintainer: Chan Wang  and Huilin Li *



Sparse Microbial Causal Mediation Model (SparseMCMM) is designed for the high dimensional and compositional microbiome data. SparseMCMM utilizes the linear log-contrast regression and Dirichlet regression to quantify the causal direct effect of the treatment and the causal mediation effect of the microbiome on the outcome under the counterfactual framework while addressing the compositional structure of microbiome data. Further it implements regularization techniques to handle the high-dimensional microbial mediators and identify the signature causal microbes. Furthermore, a splitting strategy ([Rinaldo et al; 2019](https://projecteuclid.org/journals/annals-of-statistics/volume-47/issue-6/Bootstrapping-and-sample-splitting-for-high-dimensional-assumption-lean-inference/10.1214/18-AOS1784.full)) is incorporated to account for the biases introduced by the regularization techniques employed.



SparseMCMM is particularly effective in examining the mediation effect of the microbiome within a standard three-factor (treatment, microbiome, and outcome) causal study design ([SparseMCMM](https://academic.oup.com/bioinformatics/article/36/2/347/5536874)). Moverover, the analytic procedure of SparseMCMM can be harnessed to explore the influences of the microbiome on health disparities.This is depicted in an extension of the model, [SparseMCMM_HD](https://pubmed.ncbi.nlm.nih.gov/36712075/), as elucidated in Wang et al. (2023). We also discuss the differences and relevance between SparseMCMM and SparseMCMM_HD (Wang et al; 2023). It's noteworthy that the mathematical expressions of the Residual Disparity Measure (RDM), Manipulable Disparity Measure (MDM), and Overall Disparity Measure (ODM), proposed by SparseMCMM_HD, align precisely with the formulas for the Causal Direct Effect of treatment (DE), the Mediation Effect through the microbiome (ME), and the Total Effect (TE) on the outcome, as formulated in our SparseMCMM. To simplify the discussion, we will refer to these as DE, ME, and TE henceforth.



SparseMCMM consists of three components:



Component 1: Report the estimates and if applicable standard errors of DE, ME and TE respectively based on the split strategy.



Component 2: Report the point and if applicable 95\% confidence interval estimates of $ME_j$ for each microbe.



Component 3: Report the overall mediation test results: OME and CME tests.



Consequentially, SparseMCMM provides a clear and sensible causal path analysis among an exposure, compositional microbiome and outcome of interest.



## To install the latest release version of SparseMCMM



```r

library(devtools)

install_github("chanw0/SparseMCMM")

```



## SparseMCMM main function



#### Usage:



*	```SparseMCMM(Treatment, otu.com, outcome, n.split, dirichlet.penalty,lm.penalty1, lm.penalty2, low.bound1, up.bound1, low.bound2, up.bound2, num.per)```



#### Arguments:



*	```Treatment```: A numeric vector of the binary treatment (takes the value 1 if it is assigned to the treatment group and takes the value 0 if assigned to the control group) with length = sample size (n).



*	```otu.com```: A n*p numeric matrix containing compositional microbiome data. Each row represents a subject, and each column represents a taxon (given the rank, for example, the genus rank) or an OTU. The row sum equals 1.



*	```outcome```: A numeric vector of the continuous outcome with length = sample size (n).



*	```n.split```: An integer value. The number of repetitions regarding the split strategy. See **Details** for a more comprehensive discussion on the split strategy



*	```dirichlet.penalty```: A numeric vector that includes potential tuning parameters employed during the estimation process in relation to Model 2.



*	```lm.penalty1```: A numeric vector that includes potential tuning parameters employed during the estimation process in relation to Model 1.

*	```lm.penalty2```: A numeric vector that includes potential tuning parameters employed during the estimation process in relation to Model 1.



*	```low.bound1```: A numeric vector representing the lower bounds of the controls employed during the estimation process in relation to Model 1.



*	```up.bound1```: A numeric vector representing the upper bounds of the controls employed during the estimation process in relation to Model 1.



*	```low.bound2```: A numeric vector representing the lower bounds of the controls employed during the estimation process in relation to Model 2.



*	```up.bound2```: A numeric vector representing the upper bounds of the controls employed during the estimation process in relation to Model 2.



* ```num.per```: An integer value, the number of permutations.



#### Value:



*	```Esitmated Causal Effects```: Estimates of direct effect (DE), mediation effect (ME) and total effect (TE). See **Details** for a more comprehensive discussion.



*	```Estimated component-wise Mediation Effects```: Estimates of component-wise MEs for all mediators. See **Details** for a more comprehensive discussion.



* ```Test```: P-values for tests OME and CME if num.per is not NULL, otherwise, no this item.

    



### Details:



Within the SparseMCMM framework, regularization techniques are employed to carry out variable selection, aiding in the identification of signature causal microbes. To account for the biases introduced by the regularization techniques employed, we further implement splitting strategy (Rinaldo et al; 2019), which can handle arbitrary penalties and provide asymptotically validated inference. Specifically, we randomly divide the dataset into two equal halves: the first half is utilized for variable selection, while the second half is dedicated to parameter estimation. The estimates of DE, ME, TE, and component-wise MEs were then calculated. We repeated this data splitting procedure multiple times (n.split times) to ensure robustness and accuracy in our estimations and inference.



If n.split > 1, the 'Esitmated Causal Effects' component is a 2 x 3 matrix. The first row contains the average estimates of DE, ME, and TE  based on n.split times of repetitions. Correspondingly, the second row provides the standard errors associated with these averages. If n.split=1,  the 'Estimated Causal Effects' component is a vector of length 3. This vector contains the estimates for DE, ME, and TE, which are calculated based on a single, random data split.



If n.split > 1, the 'Estimated component-wise Mediation Effects' component is a 3 x p matrix.  The first row contains the average estimates of compoment-wise MEs for all p taxa based on n.split times of repetitions. Correspondingly, the second and third rows provide the lower and upper 95\% confidence interval associated with these averages. If n.split=1,  the 'EEstimated component-wise Mediation Effects' component is a vector of length p. This vector contains the estimates for compoment-wise MEs which are calculated based on a single, random data split.



## Reference:



**SparseMCMM**: Wang C, Hu J, Blaser MJ, and Li H (2020). Estimating and testing the microbial causal mediation effect with high-dimensional and compositional microbiome data. Bioinformatics. 36(2):347-355.



**SparseMCMM_HD**: Wang C, Ahn J, Tarpey T, Stella SY, Hayes RB and Li H (2023). A microbial causal mediation analytic tool for health disparity and applications in body mass index.



## Examples



### Simulation Data



```{r datageneration}

library(SparseMCMM)



Treatment=SimulatedData$Treatment;

otu.com=SimulatedData$otu.com

outcome=SimulatedData$outcome

```

### Example 1



Calculate estimates of DE, ME, TE, and component-wise MEs based on a single random data split.



```{r example1}

set.seed(1234)

res=SparseMCMM(Treatment, otu.com, outcome, n.split=1, num.per=NULL)

```



### Example 2



Calculate estimates of DE, ME, TE, and component-wise MEs based on 10 times of repetitions.



```{r example2}

set.seed(1234)

res=SparseMCMM(Treatment, otu.com, outcome, n.split=10, num.per=NULL)

```



### Example 3



Calculate p-values based on 200 times of permutations in a single random data split 



```{r example3}

set.seed(1234)

res=SparseMCMM(Treatment, otu.com, outcome, n.split=1, num.per=200)

```