calculateDiversity | R Documentation |

This function uses various methods to estimate the clonotypic diversity of samples based on a matrix of clonotype abundances (samples are columns).

```
calculateDiversity(x, ...)
## S4 method for signature 'clonoStats'
calculateDiversity(
x,
methods = c("all", "nCells", "nClonotypes", "shannon", "normentropy", "invsimpson",
"ginisimpson", "chao1", "chaobunge"),
...
)
## S4 method for signature 'SingleCellExperiment'
calculateDiversity(x, ...)
```

`x` |
A matrix of abundance values where rows are features (clonotypes)
and columns are samples. This is created with |

`...` |
Additional arguments passed to external calculation methods. |

`methods` |
A character vector specifying which diversity measures to use
(default = |

Available methods are total cells with appropriate TCR data
(`'nCells'`

, not a diversity measure, but a useful point of
comparison), total clonotypes (`'nClonotypes'`

), Shannon entropy
(`'shannon'`

), Simpson index (`'simpson'`

), inverse Simpson index
(`'invsimpson'`

), Chao1 richness (`'chao1'`

), and Chao-Bunge
richness (`'chaobunge'`

). A special value of `'all'`

is also
allowed, which will run all methods listed above.

The `'chao1'`

and `'chaobunge'`

estimates assume all
abundances are integers. When this is not the case for the input matrix,
`k`

, all values are multiplied by the `scaling_factor`

and
rounded to the nearest integer. The resulting estimate is then divided by
`scaling_factor`

to return to the original scale. The
`'shannon'`

, `'simpson'`

, and `'invsimpson'`

methods work
with any input type.

A matrix of diversity estimates for each sample. Note that the
`'chaobunge'`

method also includes an estimate of the standard error.

```
data('contigs')
x <- clonoStats(contigs)
calculateDiversity(x)
```

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