R/cosine.R
neuprint_cosine_matrix.RdCalculate a cosine similarity matrix for neuprint neurons
neuprint_cosine_matrix(
ids,
...,
threshold = 5,
partners = c("outputs", "inputs"),
group = FALSE,
groupfun = NULL,
details = NULL,
conn = NULL
)Passed to neuprint_ids
Optional filter expression defining which partners to include
An integer threshold (connections >= this will be returned)
Whether to cluster based on connections to input or output partner neurons (default both).
Whether to group by cell type or another named column.
A function which receives the metadata for all partner neurons and returns a single grouping vector (see the details section).
Optional character vector naming metadata columns to fetch for partner neurons.
optional, a neuprintr connection object, which also specifies the
neuPrint server. If NULL, the defaults set in your
.Rprofile or .Renviron are
used. See neuprint_login for details.
matrix or list of two matrices (input and output)
For most purposes you can use neuprint_cosine_plot
directly, but it can sometimes be useful to use
neuprint_cosine_matrix to have more control over how partner neurons
are grouped (see e.g. groupfun) or which partner neurons are
included in the results (.
The groupfun argument can be a powerful way to construct flexible
grouping strategies for partner neurons. It was added in order to use
information present in fields such as the group, serial or instance/name
columns in the male VNC/CNS datasets. It will receive as input a dataframe
and expects to receive a single vector of length matching the number of
rows in the input dataframe. The input dataframe will contain the standard
columns returned by neuprint_connection_table but you can
request extra columns if necessary by naming them in the group
argument.
# \donttest{
# NB the second (unnamed argument) filters the partner neurons
# so that only those with type containing the regular expression ORN are used
neuprint_cosine_matrix("/DA[1-3].*PN", grepl("ORN",type), partners='in')
#> 1734350788 1734350908 1765040289 5813039315 722817260 754534424
#> 1734350788 1.0000000 0.7911385 0.7838174 0.7508534 0.7478463 0.7899044
#> 1734350908 0.7911385 1.0000000 0.8131651 0.8421953 0.7844044 0.8548269
#> 1765040289 0.7838174 0.8131651 1.0000000 0.8310386 0.8299156 0.9000086
#> 5813039315 0.7508534 0.8421953 0.8310386 1.0000000 0.8099328 0.8491668
#> 722817260 0.7478463 0.7844044 0.8299156 0.8099328 1.0000000 0.8307328
#> 754534424 0.7899044 0.8548269 0.9000086 0.8491668 0.8307328 1.0000000
#> 754538881 0.7874977 0.7809437 0.8708951 0.8050826 0.8282373 0.8532473
#> 733316908 0.7594145 0.8497124 0.8137838 0.8343710 0.8326044 0.8468566
#> 1796817841 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 1796818119 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 1797505019 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 1827516355 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 818983130 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 1702806746 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 1702815630 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 1703683361 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 666135689 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 754538881 733316908 1796817841 1796818119 1797505019 1827516355
#> 1734350788 0.7874977 0.7594145 0.0000000 0.0000000 0.0000000 0.0000000
#> 1734350908 0.7809437 0.8497124 0.0000000 0.0000000 0.0000000 0.0000000
#> 1765040289 0.8708951 0.8137838 0.0000000 0.0000000 0.0000000 0.0000000
#> 5813039315 0.8050826 0.8343710 0.0000000 0.0000000 0.0000000 0.0000000
#> 722817260 0.8282373 0.8326044 0.0000000 0.0000000 0.0000000 0.0000000
#> 754534424 0.8532473 0.8468566 0.0000000 0.0000000 0.0000000 0.0000000
#> 754538881 1.0000000 0.8238283 0.0000000 0.0000000 0.0000000 0.0000000
#> 733316908 0.8238283 1.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 1796817841 0.0000000 0.0000000 1.0000000 0.4512069 0.3026890 0.3648613
#> 1796818119 0.0000000 0.0000000 0.4512069 1.0000000 0.5869982 0.7033427
#> 1797505019 0.0000000 0.0000000 0.3026890 0.5869982 1.0000000 0.6480365
#> 1827516355 0.0000000 0.0000000 0.3648613 0.7033427 0.6480365 1.0000000
#> 818983130 0.0000000 0.0000000 0.4413857 0.6897041 0.5350274 0.5396716
#> 1702806746 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 1702815630 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 1703683361 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 666135689 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 818983130 1702806746 1702815630 1703683361 666135689
#> 1734350788 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 1734350908 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 1765040289 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 5813039315 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 722817260 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 754534424 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 754538881 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 733316908 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 1796817841 0.4413857 0.0000000 0.0000000 0.0000000 0.0000000
#> 1796818119 0.6897041 0.0000000 0.0000000 0.0000000 0.0000000
#> 1797505019 0.5350274 0.0000000 0.0000000 0.0000000 0.0000000
#> 1827516355 0.5396716 0.0000000 0.0000000 0.0000000 0.0000000
#> 818983130 1.0000000 0.0000000 0.0000000 0.0000000 0.0000000
#> 1702806746 0.0000000 1.0000000 0.9713775 0.3657420 0.3632341
#> 1702815630 0.0000000 0.9713775 1.0000000 0.3593996 0.3463019
#> 1703683361 0.0000000 0.3657420 0.3593996 1.0000000 0.9335473
#> 666135689 0.0000000 0.3632341 0.3463019 0.9335473 1.0000000
# }