COMPASS {COMPASS} | R Documentation

## Fit the COMPASS Model

### Description

This function fits the `COMPASS` model.

### Usage

    COMPASS(
      data,
      treatment,
      control,
      subset = NULL,
      category_filter = function(x) colSums(x > 5) > 2,
      filter_lowest_frequency = 0,
      filter_specific_markers = NULL,
      model = "discrete",
      iterations = 40000,
      replications = 8,
      keep_original_data = FALSE,
      verbose = TRUE,
      dropDegreeOne = FALSE,
      init_with_fisher = FALSE,
      run_model_or_return_data = "run_model",
      ...
    )

### Arguments

`data` | An object of class `COMPASSContainer`.
`treatment` | An R expression, evaluated within the metadata, that returns `TRUE` for those samples that should belong to the treatment group. For example, if the samples that received a positive stimulation were named `"92TH023 Env"` within a variable in `meta` called `Stim`, you could write `Stim == "92TH023 Env"`. The expression should have the name of the stimulation vector on the left hand side.
`control` | An R expression, evaluated within the metadata, that returns `TRUE` for those samples that should belong to the control group. See above for details.
`subset` | An expression used to subset the data. We keep only the samples for which the expression evaluates to `TRUE` in the metadata.
`category_filter` | A filter for the categories that are generated. This is a function that will be applied to the treatment counts matrix generated from the intensities. Only categories meeting the `category_filter` criteria will be kept.
`filter_lowest_frequency` | A number specifying how many of the least expressed markers should be removed.
`filter_specific_markers` | Similar to `filter_lowest_frequency`, but lets you explicitly exclude markers.
`model` | A string denoting which model to fit; currently, only the discrete model (`"discrete"`) is available.
`iterations` | The number of iterations (per 'replication') to perform.
`replications` | The number of 'replications' to perform. In order to conserve memory, we only keep the model estimates from the last replication.
`keep_original_data` | Keep the original `COMPASSContainer` as part of the `COMPASS` output? If memory or disk space is an issue, you may set this to `FALSE`.
`verbose` | Boolean; if `TRUE` we output progress information.
`dropDegreeOne` | Boolean; if `TRUE` we drop degree one categories and merge them with the negative subset.
`init_with_fisher` | Boolean;initialize from fisher's exact test. Any subset and subject with lower 95 Otherwise initialize very subject and subset as a responder except those where ps <= pu.
`run_model_or_return_data` | `character` defaults to `"run_model"` otherwise set it to `"return_data"` in order to not fit the model just return the data set needed for modeling. Useful for extracting the boolean counts.
`...` | Other arguments; currently unused.

### Value

A `COMPASSResult` is a list with the following components:

`fit` | A list of various fitted parameters resulting from the `COMPASS` model fitting procedure.
`data` | The data used as input to the `COMPASS` fitting procedure – in particular, the counts matrices generated for the selected categories, `n_s` and `n_u`, can be extracted from here.
`orig` | If `keep_original_data` was set to `TRUE` in the `COMPASS` fit, then this will be the `COMPASSContainer` passed in. This is primarily kept for easier running of the Shiny app.

The `fit` component is a list with the following components:

`alpha_s` | The hyperparameter shared across all subjects under the stimulated condition. It is updated through the `COMPASS` model fitting process.
`A_alphas` | The acceptance rate of `alpha_s`, as computed through the MCMC sampling process in `COMPASS`.
`alpha_u` | The hyperparameter shared across all subjects under the unstimulated condition. It is updated through the `COMPASS` model fitting process.
`A_alphau` | The acceptance rate of `alpha_u`, as computed through the MCMC sampling process in `COMPASS`.
`gamma` | An array of dimensions `I x K x T`, where `I` denotes the number of individuals, `K` denotes the number of categories / subsets, and `T` denotes the number of iterations. Each cell in a matrix for a given iteration is either zero or one, reflecting whether individual `i` is responding to the stimulation for subset `k`.
`mean_gamma` | A matrix of mean response rates. Each cell denotes the mean response of individual `i` and subset `k`.
`A_gamma` | The acceptance rate for the gamma. Each element corresponds to the number of times an individual's `gamma` vector was updated.
`categories` | The category matrix, showing which categories entered the model.
`model` | The type of model called.
`posterior` | Posterior measures from the sample fit.
`call` | The matched call used to generate the model fit.

The `data` component is a list with the following components:

`n_s` | The counts matrix for stimulated samples.
`n_u` | The counts matrix for unstimulated samples.
`counts_s` | Total cell counts for stimulated samples.
`counts_u` | Total cell counts for unstimulated samples.
`categories` | The categories matrix used to define which categories will enter the model.
`meta` | The metadata. Note that only individual-level metadata will be kept; sample-specific metadata is dropped.
`sample_id` | The name of the vector in the metadata used to identify the samples.
`individual_id` | The name of the vector in the metadata used to identify the individuals.

The `orig` component (included if `keep_original_data` is `TRUE`) is the `COMPASSContainer` object used in the model fit.

### Category Filter

The category filter is used to exclude categories (combinations of markers expressed for a particular cell) that are expressed very rarely. It is applied to the `treatment` counts matrix, which is a `N` samples by `K` categories matrix. Those categories which are mostly unexpressed can be excluded here. For example, the default criteria,

`category_filter=function(x) colSums(x > 5) > 2`

indicates that we should only retain categories for which at least three samples had at least six cells expressing that particular combination of markers.

### See Also

- `COMPASSContainer`, for constructing the data object required by `COMPASS`

### Examples

    data(COMPASS) ## loads the COMPASSContainer 'CC'
    fit <- COMPASS(CC,
      category_filter=NULL,
      treatment=trt == "Treatment",
      control=trt == "Control",
      verbose=FALSE,
      iterations=100 ## set higher for a real analysis
    )

[Package COMPASS version 1.30.0 Index]