Will generate a trajectory using SLICE.

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

ti_slice(lm.method = "clustering", model.type = "tree",
  ss.method = "all", ss.threshold = 0.25,
  community.method = "louvain", cluster.method = "kmeans", k = 0L,
  k.max = 10L, B = 100L, k.opt.method = "firstmax",
  run_environment = NULL)

Arguments

lm.method

discrete; Select "clustering" based or "graph" based method to infer lineage model (default: "clustering"; values: "clustering", "graph")

model.type

discrete; The type of models that will be infered: "tree" - directed minimum spanning tree based, "graph" - directed graph based (default: "tree"; values: "tree", "graph")

ss.method

discrete; The method for defining core cell set for stable state detection: all - all the cells in a cluster constitute the core cell set; top - cells with scEntropy lower than the ss.threshold quantile of all the values in a cluster constitute the core cell set; pcst - cells with scEntropy lower than the ss.threshold quantile of all the values in a cluster constitute the prize nodes, linear prize-collecting steiner tree algorithm is used to approximate an optimal subnetwork, the cells in the subnetwork constitute the core cell set. Stable states are defined as the centroids of the core cell sets. (default: "all"; values: "all", "top", "pcst")

ss.threshold

numeric; The threshold used when ss.method is "top" or "pcst". Default: 0.25. (default: 0.25; range: from 0 to 1)

community.method

discrete; The method for network community detection. Most of the community detection methods implemented in the igraph package are supported, including "fast_greedy", "edge_betweenness", "label_prop", "leading_eigen","louvain","spinglass", "walktrap". If this parameter is set to "auto", the algorithm will perform all the community detection methods and select the one that generates the communities with best modularity. Only take effect when lm.method is "graph" (default: "louvain"; values: "fast_greedy", "edge_betweenness", "label_prop", "leading_eigen", "louvain", "spinglass", "walktrap", "auto")

cluster.method

discrete; Use "kmeans" or "pam" to divide cells into clusters. Only take effect when lm.method is "clustering" (default: "kmeans"; values: "kmeans", "pam")

k

integer; The number of cell clusters. If NULL, Gap statistic will be used to determine an optimal k. (default: 0L; range: from 0L to 20L)

k.max

integer; The "k.max" parameter of cluster::clusGap(); used when k is NULL. (default: 10L; range: from 3L to 20L)

B

integer; The "B" parameter of cluster::clusGap(); used when k is NULL (default: 100L; range: from 3L to 500L)

k.opt.method

discrete; The "method" parameter of cluster::maxSE(); used when k is NULL (default: "firstmax"; values: "firstmax", "globalmax", "Tibs2001SEmax", "firstSEmax", "globalSEmax")

run_environment

In which environment to run the method, can be "docker" or "singularity".

Value

A TI method wrapper to be used together with infer_trajectory

References

Guo, M., Bao, E.L., Wagner, M., Whitsett, J.A., Xu, Y., 2016. SLICE: determining cell differentiation and lineage based on single cell entropy. Nucleic Acids Research gkw1278.