ambientProfileBimodal | R Documentation |

Estimate the concentration of each feature in the ambient solution from a filtered count matrix containing only counts for cells, by assuming that each feature has a bimodal abundance distribution with ambient and high-expressing components.

```
inferAmbience(...)
ambientProfileBimodal(x, ...)
## S4 method for signature 'ANY'
ambientProfileBimodal(x, min.prop = 0.05)
## S4 method for signature 'SummarizedExperiment'
ambientProfileBimodal(x, ..., assay.type = "counts")
```

`...` |
For the generic, further arguments to pass to individual methods. For the SummarizedExperiment method, further arguments to pass to the ANY method. For |

`x` |
A numeric matrix-like object containing counts for each feature (row) and cell (column). Alternatively, a SummarizedExperiment object containing such a matrix. |

`min.prop` |
Numeric scalar in (0, 1) specifying the expected minimum proportion of barcodes contributed by each sample. |

`assay.type` |
Integer or scalar specifying the assay containing the count matrix. |

In some cases, we want to know the ambient profile but we only have the count matrix for the cell-containing libraries.
This can be useful in functions such as `hashedDrops`

or as a reference profile in `medianSizeFactors`

.
However, as we only have the cell-containing libraries, we cannot use `ambientProfileEmpty`

.

This function estimates the ambient profile by assuming that each feature only labels a minority of the cells. Under this assumption, each feature's log-count distribution has a bimodal distribution where the lower mode represents ambient contamination. This is generally reasonable for tag-based applications like cell hashing or CITE-seq where data is usually binary, i.e., the marker is either present or not. We fit a two-component mixture model and identify all barcodes assigned to the lower component; and the mean of those counts is used as an estimate of the ambient contribution.

The initialization of the mixture model is controlled by `min.prop`

,
which starts the means of the lower and upper components at the `min.prop`

and `1-min.prop`

quantiles, respectively.
This means that each sample is expected to contribute somewhere between `[min.prop, 1-min.prop]`

barcodes.
Larger values improve convergence but require stronger assumptions about the relative proportions of multiplexed samples.

`inferAmbience`

is soft-deprecated; use `ambientProfileBimodal`

instead.

A numeric vector of length equal to `nrow(x)`

, containing the estimated ambient proportions for each feature.

Aaron Lun

`hashedDrops`

, where this function is used in the absence of an ambient profile.

`ambientProfileEmpty`

, which should be used when the raw matrix (prior to filtering for cells) is available.

`ambientContribSparse`

and related functions, to estimate the contribution of ambient contamination in each library.

```
x <- rbind(
rpois(1000, rep(c(100, 1), c(100, 900))),
rpois(1000, rep(c(2, 100, 2), c(100, 100, 800))),
rpois(1000, rep(c(3, 100, 3), c(200, 700, 100)))
)
# Should be close to 1, 2, 3
ambientProfileBimodal(x)
```

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