newCellTypeHierarchy {monocle} | R Documentation

## Classify cells according to a set of markers

### Description

Creates a CellTypeHierarchy object which can store cell types with the addCellType() function. When classifyCells is used with a CellDataSet and a CellTypeHierarchy cells in the CellDataSet can be classified as cell types found in the CellTypeHierarchy

classifyCells accepts a cellDataSet and and a cellTypeHierarchy. Each cell in the cellDataSet is checked against the functions in the cellTypeHierarchy to determine each cell's type

### Usage

    newCellTypeHierarchy()

    classifyCells(cds, cth, frequency_thresh = NULL, enrichment_thresh = NULL,
      ...)

    calculateMarkerSpecificity(cds, cth, remove_ambig = TRUE,
      remove_unknown = TRUE)

### Arguments

`cds` | The CelllDataSet you want to classify
`cth` | CellTypeHierarchy
`frequency_thresh` | If at least this fraction of group of cells meet a cell types marker criteria, impute them all to be of that type.
`enrichment_thresh` | fraction to be multipled by each cell type percentage. Only used if frequency_thresh is NULL, both cannot be NULL
`...` | character strings that you wish to pass to dplyr's group_by_ routine
`remove_ambig` | a boolean that determines if ambiguous cells should be removed
`remove_unknown` | a boolean that determines whether unknown cells should be removed

### Details

CellTypeHierarchy objects are Monocle's mechanism for classifying cells into types based on known markers. To classify the cells in a CellDataSet object according to known markers, first construct a CellTypeHierachy with `newCellTypeHierarchy()` and `addCellType()` and then provide both the `CellDataSet` and the `CellTypeHierachy` to `classifyCells()`. Each call to `addCellType()` registers a classification function that accepts the expression data from a CellDataSet object as input, and returns a boolean vector indicating whether each cell is of the given type. When you call `classifyCells()`, each cell will be checked against the classification functions in the `CellTypeHierachy`. If you wish to make a cell type a subtype of another that's already been registered with a CellTypeHierarchy object, make that one the "parent" type with the `cell_type_name` argument. If you want two types to be mutually exclusive, make them "siblings" by giving them the same parent. The classifcation functions in a CellTypeHierarchy must take a single argument, a matrix of expression values, as input. Note that this matrix could either be a `sparseMatrix` or a dense matrix. Explicitly casting the input to a dense matrix inside a classification function is likely to drastically slow down classifyCells and other routines that use CellTypeHierarhcy objects. Successive calls to `addCellType` build up a tree of classification functions inside a CellTypeHierarchy. When two functions are siblings in the tree, classifyCells expects that a cell will meet the classification criteria for at most one of them. For example, you might place classification functions for T cells and B cells as siblings, because a cell cannot be both of these at the same time. When a cell meets the criteria for more than one function, it will be tagged as "Ambiguous". If `classifyCells` reports a large number of ambiguous cells, consider adjusting your classification functions. For example, some cells are defined by very high expression of a key gene that is expressed at lower levels in other cell types. Raising the threshold for this gene in a classification could resolve the ambiguities. A classification function can also have child functions. You can use this to specify subtypes of cells. For example, T cells express the gene CD3, and there are many subtypes. You can encode each subset by first adding a general T cell classification function that recognizes CD3, and then adding an additional function that recognizes CD4 (for CD4+ helper T cells), one for CD8 (to identify CD8+ cytotoxic T cells), and so on. `classifyCells` will aim to assign each cell to its most specific subtype in the "CellType" column. By default, `classifyCells` applies the classification functions to individual cells, but you can also apply it to cells in a "grouped" mode to impute the type of cells that are missing expression of your known markers. You can specify additional (quoted) grouping variables to `classifyCells`. The function will group the cells according to these factors, and then classify the cells. It will compute the frequency of each cell type in each group, and if a cell type is present at the frquency specified in `frequency_thresh`, all the cells in the group are classified as that type. If group contains more one cell type at this frequency, all the cells are marked "Ambiguous". This allows you to impute cell type based on unsupervised clustering results (e.g. with `clusterCells()`) or some other grouping criteria.

### Value

`newCellTypeHierarchy` and `addCellType` both return an updated CellTypeHierarchy object. `classifyCells` returns an updated `CellDataSet` with a new column, "CellType", in the pData table.

For a CellDataset with N genes, and a CellTypeHierarchy with k types, returns a dataframe with N x k rows. Each row contains a gene and a specifity score for one of the types.

### Functions

- `classifyCells`: Add a cell type to a CellTypeHierarchy

- `calculateMarkerSpecificity`: Calculate each gene's specificity for each cell type

  Computes the Jensen-Shannon distance between the distribution of a gene's expression across cells and a hypothetical gene that is perfectly restricted to each cell type. The Jensen-Shannon distance is an information theoretic metric between two probability distributions. It is a widely accepted measure of cell-type specificity. For a complete description see Cabili et. al, Genes & Development (2011).

### Examples

    ## Not run:
    # Initialize a new CellTypeHierachy

    # Register a set of classification functions. There are multiple types of T cells
    # A cell cannot be both a B cell and a T cell, a T cell and a Monocyte, or
    # a B cell and a Monocyte.
    cth <- newCellTypeHierarchy()

    cth <- addCellType(cth, "T cell",
                       classify_func=function(x) {x["CD3D",] > 0})

    cth <- addCellType(cth, "CD4+ T cell",
                       classify_func=function(x) {x["CD4",] > 0},
                       parent_cell_type_name = "T cell")

    cth <- addCellType(cth, "CD8+ T cell",
                       classify_func=function(x) {
                         x["CD8A",] > 0 | x["CD8B",] > 0
                       },
                       parent_cell_type_name = "T cell")

    cth <- addCellType(cth, "B cell",
                       classify_func=function(x) {x["MS4A1",] > 0})

    cth <- addCellType(cth, "Monocyte",
                       classify_func=function(x) {x["CD14",] > 0})

    # Classify each cell in the CellDataSet "mix" according to these types
    mix <- classifyCells(mix, cth)

    # Group the cells by the pData table column "Cluster". Apply the classification
    functions to the cells groupwise. If a group is at least 5% of a type, make
    them all that type. If the group is 5% one type, and 5% a different, mutually
    exclusive type, mark the whole cluster "Ambiguous"
    mix <- classifyCells(mix, Cluster, 0.05)

    ## End(Not run)

[Package monocle version 2.18.0 Index]