# FateID

Will generate a trajectory using FateID.

This method was wrapped inside a
container.
The original code of this method is available
here.

ti_fateid(reclassify = TRUE, clthr = 0.9, nbfactor = 5L, q = 0.75,
k = 3L, m = "tsne", minnr = 5L, minnrh = 10L, trthr = 0.4,
force = FALSE)

## Arguments

reclassify |
Whether to reclassify the cell grouping. Default: TRUE.
Format: logical. |

clthr |
Real number between zero and one. This is the threshold for the
fraction of random forest votes required to assign a cell not contained within
the target clusters to one of these clusters. The value of this parameter
should be sufficiently high to only reclassify cells with a high-confidence
assignment. Default value is 0.9. Domain: U(0.1, 1). Default: 0.9. Format:
numeric. |

nbfactor |
Positive integer number. Determines the number of trees grown
for each random forest. The number of trees is given by the number of columns
of th training set multiplied by `nbfactor` . Default value is 5. Domain: U(2,
100). Default: 5. Format: integer. |

q |
Q real value between zero and one. This number specifies a threshold
used for feature selection based on importance sampling. A reduced expression
table is generated containing only features with an importance larger than the
q-quantile for at least one of the classes (i. e. target clusters). Default
value is 0.75. Domain: U(0, 1). Default: 0.75. Format: numeric. |

k |
Number of dimensions. Domain: U(2, 100). Default: 3. Format: integer. |

m |
Dimensionality reduction method to use. Can be tsne, cmd, dm or lle.
Domain: tsne, cmd, dm, lle. Default: tsne. Format: character. |

minnr |
Integer number of cells per target cluster to be selected for
classification (test set) in each round of training. For each target cluster,
the `minnr` cells with the highest similarity to a cell in the training set are
selected for classification. If `z` is not `NULL` it is used as the similarity
matrix for this step. Otherwise, `1-cor(x)` is used. Default value is 5.
Domain: U(2, 100). Default: 5. Format: integer. |

minnrh |
Integer number of cells from the training set used for
classification. From each training set, the `minnrh` cells with the highest
similarity to the training set are selected. If `z` is not `NULL` it is used as
the similarity matrix for this step. Default value is 10. Domain: U(2, 100).
Default: 10. Format: integer. |

trthr |
Real value representing the threshold of the fraction of random
forest votes required for the inclusion of a given cell for the computation of
the principal curve. If `NULL` then only cells with a significant bias >1 are
included for each trajectory. The bias is computed as the ratio of the number
of votes for a trajectory and the number of votes for the trajectory with the
second largest number of votes. By this means only the trajectory with the
largest number of votes will receive a bias >1. The siginifcance is computed
based on counting statistics on the difference in the number of votes. A
significant bias requires a p-value < 0.05. Domain: U(0, 1). Default: 0.4.
Format: numeric. |

force |
Do not use! This is a parameter to force FateID to run on
benchmark datasets where not enough end groups are present. Default: FALSE.
Format: logical. |

## Value

A TI method wrapper to be used together with
`infer_trajectory`

## References

Herman, J.S., Sagar, Grün, D., 2018. FateID infers cell fate bias
in multipotent progenitors from single-cell RNA-seq data. Nature Methods 15,
379–386.