Last updated: 2021-04-17

Checks: 2 0

Knit directory: lieb/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Repository version: a493056

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version a493056. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    analysis/.DS_Store
    Ignored:    analysis/.Rhistory
    Ignored:    analysis/pairwise_fitting_cache/
    Ignored:    analysis/preprocessing_cache/
    Ignored:    analysis/running_mcmc_cache/
    Ignored:    data/.DS_Store
    Ignored:    data/.Rhistory
    Ignored:    output/.DS_Store

Unstaged changes:
    Modified:   analysis/candidate_latent_classes.Rmd
    Modified:   analysis/downstream.Rmd
    Modified:   analysis/preprocessing.Rmd
    Modified:   analysis/priors.Rmd
    Modified:   analysis/running_mcmc.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/index.Rmd) and HTML (docs/index.html) files. If you've configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
html f3413aa hillarykoch 2021-04-17 Build site.
html dd0b829 hillarykoch 2020-10-15 replace cache
html ed24c1b hillarykoch 2020-05-08 update toc
html 1c43a7f hillarykoch 2020-04-29 rebuilds the html because I forgot to
Rmd db94e3b hillarykoch 2020-04-29 update landing page and remove excess output files
html 16778b1 hillarykoch 2020-04-28 add some downstream analysis
Rmd bf87fa0 hillarykoch 2020-04-28 add some downstream analysis
html cc98d2b hillarykoch 2020-04-24 replace cache and update to describe flex_mu
Rmd 828e725 hillarykoch 2020-04-23 add a preprocessing section
html 828e725 hillarykoch 2020-04-23 add a preprocessing section
html c5fd6fc hillarykoch 2020-04-23 change nav bar to accomodate a menu
html 5d5c3dd hillarykoch 2020-03-07 Build site.
html 7c42345 hillarykoch 2020-02-20 Build site.
html 2a5b7c0 hillarykoch 2020-02-20 Build site.
html 3a9bf9d hillarykoch 2020-02-20 Build site.
html 72e6fec hillarykoch 2020-02-20 Build site.
html 862bc02 hillarykoch 2020-02-07 big updates for mcmc processors
html 8b3e556 hillarykoch 2019-12-03 Build site.
html 39383ac hillarykoch 2019-12-03 Build site.
Rmd 645d408 hillarykoch 2019-12-03 workflowr::wflow_publish(files = "*")
Rmd e467a51 hillarykoch 2019-08-16 different shiny location
html e467a51 hillarykoch 2019-08-16 different shiny location
html d58e1a6 hillarykoch 2019-08-16 resource data
Rmd 38fb1c0 hillarykoch 2019-08-16 edit shiny
html 38fb1c0 hillarykoch 2019-08-16 edit shiny
Rmd 550fb1e hillarykoch 2019-08-16 add shiny
html 550fb1e hillarykoch 2019-08-16 add shiny
html 228123d hillarykoch 2019-07-17 update up to obtaining the hyperparameters
html c1dc0c1 hillarykoch 2019-07-17 update up to obtaining the hyperparameters
html 674120c hillarykoch 2019-07-17 update about page
html e67a3a2 hillarykoch 2019-07-17 update about page
html 2faf446 hillarykoch 2019-07-17 update about page
html 2177dfa hillarykoch 2019-07-17 update about page
Rmd 8b30694 hillarykoch 2019-07-17 update about page
html 8b30694 hillarykoch 2019-07-17 update about page
Rmd e36b267 hillarykoch 2019-07-17 update about page
html e36b267 hillarykoch 2019-07-17 update about page
Rmd a991668 hillarykoch 2019-07-17 update about page
html a991668 hillarykoch 2019-07-17 update about page
html a36d893 hillarykoch 2019-07-17 update about page
html e8e54b7 hillarykoch 2019-07-17 update about page
Rmd f47c013 hillarykoch 2019-07-17 update about page
html f47c013 hillarykoch 2019-07-17 update about page
Rmd 50cf23e hillarykoch 2019-07-17 make skeleton
html 50cf23e hillarykoch 2019-07-17 make skeleton
html a519159 hillarykoch 2019-07-17 Build site.
Rmd b81d6ff hillarykoch 2019-07-17 Start workflowr project.

The Composite LIkelihood eMpirical Bayes (CLIMB) methodology was designed for Gaussian mixture models where the behavior of each component in the mixture is dictated by a latent class \(h_m\) which has the form \(h_m=(h_{[1]},\,h_{[2]},\ldots,h_{[D]})\), where \(h_{[d]} \in\{-1,\,0,\,1\}\) for \(d\in\{1,\ldots,D\}\) and \(D\) is the dimension of the data. For even moderate dimensions, this model becomes computationally intractible to fit directly because the number of candidate latent classes is \(3^D\). The CLIMB procedure circumvents this computational challenge, estimating which latent classes are supported by the data, and fitting the model in an empirical Bayesian hierarchical framework with Markov Chain Monte Carlo (MCMC).

CLIMB occurs in 4 major steps:

  1. Pairwise fitting of the Gaussian mixture over pairs of dimensions
  2. Enumerating candidate latent classes based on the output of the pairwise fits
  3. Pruning the candidate list of latent classes based on computed prior probabilities of each class's mixing weight
  4. Fitting the final model using MCMC

Each step of analysis CLIMB requires a bit of attention because of their memory/processor requirements. That is, Steps 1 and 3 require low memory but can be parallelized for faster performance. Meanwhile, Steps 2 and 4 cannot be parallelized, and Step 2 in particular in some cases may require high memory. To be efficient when running an analysis with CLIMB, we recommend splitting the steps up accordingly.

Each step of CLIMB has its own page in the "Four-step procedure" menu. We walk through a complete analysis of a simulated dataset throughout these 4 pages. Though the simple analysis provided here can be run on your personal laptop, in practice with real genomic datasets, one will need access to a computing cluster. We assume the user knows how to request multiple cores for parallel jobs or more memory for high-memory tasks. Navigate to any step's page to learn more.

CLIMB's output is rich for exploration. We demonstrate how to produce some of the results or figures in the CLIMB manuscript here.