Build Clusters Based on PTM Data Under Like Conditions

This function takes the post-translational modification table and runs it through three calculations of distance: Euclidean Distance, Spearman Dissimilarity (1 - |Spearman Correlation|), and the average of the two of these. These calculations find the 'distance' between ptms based upon under what conditions they occur. These matricies are then run through t-SNE in order to put them into a 3-dimensional space. A correlation table is also produced based on the Spearman Correlation table.

MakeClusterList(
  ptmtable,
  correlation.matrix.name = "ptm.correlation.matrix",
  list.name = "clusters.list",
  toolong = 3.5
)

Arguments

ptmtable

A dataset for post-translational modifications. Formatted with numbered rows, and the first column containing PTM names. The rest of the column names should be drugs. Values are numeric values that represent how much the PTM has reacted to the drug.

correlation.matrix.name

Desired name for the correlation matrix to be saved as; defaults to ptm.correlation.matrix

list.name

Desired name for the lists of clusters to be saved as; defaults to clusters.list

toolong

A numeric threshold for cluster separation, defaults to 3.5.

Details

Please note: t-SNE involves an element of randomness; in order to get the same results, set.seed(#) must be called.

Examples

MakeClusterList(ex.ptmtable, "example.cor", "example.tsne", 3.5)
#> species scores not available

#> species scores not available

#> species scores not available