`slanted_orders.Rd`

For a matrix expressing the cross-similarity between two (possibly different) sets of entities,
this produces better results than clustering (e.g. as done by `pheatmap`

). This is because
clustering does not care about the order of each two sub-partitions. That is, clustering is as
happy with `((2, 1), (4, 3))`

as it is with the more sensible `((1, 2), (3, 4))`

. As a
result, visualizations of similarities using naive clustering can be misleading.

slanted_orders( data, order_rows = TRUE, order_cols = TRUE, squared_order = TRUE, same_order = FALSE, discount_outliers = TRUE, max_spin_count = 10 )

data | A rectangular matrix containing non-negative values. |
---|---|

order_rows | Whether to reorder the rows. |

order_cols | Whether to reorder the columns. |

squared_order | Whether to reorder to minimize the l2 norm (otherwise minimizes the l1 norm). |

same_order | Whether to apply the same order to both rows and columns. |

discount_outliers | Whether to do a final order phase discounting outlier values far from the diagonal. |

max_spin_count | How many times to retry improving the solution before giving up. |

A list with two keys, `rows`

and `cols`

, which contain the order.

#> $rows #> [1] 15 25 4 16 8 22 6 5 23 17 14 12 13 7 24 1 2 9 29 11 10 27 19 21 3 #> [26] 32 26 18 31 20 30 28 #> #> $cols #> [1] 15 25 4 16 8 22 6 5 23 17 14 12 13 7 24 1 2 9 29 11 10 27 19 21 3 #> [26] 32 26 18 31 20 30 28 #>