malecns

https://natverse.org/malecns/
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### Install malecns and set Neuprint token

Source: https://natverse.org/malecns/index.html

Install malecns via natmanager, then set the Neuprint token in the R environment and verify access with dr_malecns(). The token is pasted into the .Renviron file after browsing to neuprint.janelia.org; ensure a new line at the end of the file. The example then fetches metadata, reads meshes, and plots them.

```r
install.packages("natmanager")
natmanager::check_pat()
natmanager::install(pkgs="natverse/malecns")
```

```r
# Set your Neuprint token
# will open your browser
# collect token by clicking on your account icon top right and then Account
browseURL('https://neuprint.janelia.org')
# back in R
usethis::edit_r_environ()
# paste in this text replacing with your neuprint token
# make sure you have a new lined at the end of the file
neuprint_token="eyJhbGci..."

# check everything's configured ok
dr_malecns()

# get some data
pnmeta=mcns_neuprint_meta('/.+_[adl]+PN')
table(pnmeta$type)
vm6=read_mcns_meshes('VM6_adPN')
plot3d(malecns.surf, alpha=.1)
```

--------------------------------

### Example: download, register, and transform with xform_brain

Source: https://natverse.org/malecns/reference/mcns_register_xforms2.html

Example workflow: first download the h5 deformation fields with mcns_download_xforms2(), then register them once per session with mcns_register_xforms2(), and finally transform a coordinate using xform_brain() with sample='malecns' and reference='JRC2018U'. The example is wrapped in \dontrun{} so it is not executed during package checks.

```r
if (FALSE) { # \dontrun{
# first time only
mcns_download_xforms2()
# then once per session
mcns_register_xforms2()
xform_brain(cbind(100000, 100000, 50000), sample='malecns', reference='JRC2018U')
} # }
```

--------------------------------

### mcns_neuprint example with token and neuprintr queries

Source: https://natverse.org/malecns/reference/mcns_neuprint.html

Example usage of mcns_neuprint. It shows logging in with the default token, then with a specified token and Force=TRUE, and then querying anchor neurons and plotting cumulative presynapses and postsynapses. The example is wrapped in \dontrun so it is not executed during package checks.

```r
if (FALSE) { # \dontrun{
cnsc=mcns_neuprint()

# log in using specified token rather than the one in the neuprint_token
# environment variable. This should then be cached for the rest of the R
# session.
cnsc=mcns_neuprint(token="XXX", Force=T)

anchorids <- neuprintr::neuprint_ids("status:Anchor", conn=cnsc)
# the last connection will be used by default
anchormeta <- neuprintr::neuprint_get_meta("status:Anchor")

plot(cumsum(sort(anchormeta$pre, decreasing = TRUE)), ylab='Cumulative presynapses')
plot(cumsum(sort(anchormeta$post, decreasing = TRUE)), ylab='Cumulative postsynapses')
} # }
```

--------------------------------

### mcns_scene example with open=TRUE and node='neuprint'

Source: https://natverse.org/malecns/reference/mcns_scene.html

Example usage of mcns_scene with a body ID, opening the scene in the browser, and specifying the 'neuprint' node. This example is wrapped in a dontrun block, so it is not executed by default.

```r
if (FALSE) { # \dontrun{
mcns_scene(4060524874, open=TRUE, node='neuprint')

} # }
```

--------------------------------

### Example of annotating a single body

Source: https://natverse.org/malecns/reference/mcns_annotate_body.html

An example wrapped in `if (FALSE) { # \dontrun{` that calls the function with a data.frame and the test parameter set to TRUE. The example is not run by default.

```R
if (FALSE) { # \dontrun{
# note use of test server
mcns_annotate_body(data.frame(bodyid=10005, group=10005), test=TRUE)
} # }
```

--------------------------------

### Example: choose_mcns_dataset with v1.0 and production

Source: https://natverse.org/malecns/reference/choose_mcns_dataset.html

Example usage of choose_mcns_dataset. The first call selects the v1.0 snapshot for the rest of the R session; the second selects the production version. The code is wrapped in \dontrun{} and is not executed.

```r
if (FALSE) { # \dontrun{
# use the v1.0 snapshot for the rest of this R session
choose_mcns_dataset("male-cns:v1.0")
# use production for the rest of this R session
choose_mcns_dataset("CNS")
} # }
```

--------------------------------

### mcns_islatest example

Source: https://natverse.org/malecns/reference/mcns_islatest.html

Example calls to mcns_islatest with a giant fibre neuron (returns TRUE) and an expired body (returns FALSE). The example is wrapped in \donttest.

```r
# \donttest{
# giant fibre neuron
mcns_islatest(10001)
#> [1] TRUE

# an expired body
mcns_islatest(49891)
#> [1] FALSE
# }
```

--------------------------------

### Example: read_mcns_meshes and plot3d

Source: https://natverse.org/malecns/reference/read_mcns_meshes.html

Example usage wrapped in \dontrun, showing how to read meshes for a body id and plot them with plot3d, or use wire3d for more control.

```r
if (FALSE) { # \dontrun{
ml=read_mcns_meshes(1796013202)
plot3d(ml)
# or if there's just one mesh, you can get more control with the rgl commands
# like wire3d
wire3d(ml[[1]])
} # }
```

--------------------------------

### mirror_malecns example with landmarks

Source: https://natverse.org/malecns/reference/mirror_malecns.html

Example from the documentation (wrapped in \dontrun). It loads the landmark CSV, mirrors the first three columns, and plots the mirrored points (black), original points (green), and a shifted set (red) to visually verify the mirroring.

```r
if (FALSE) { # \dontrun{
f3=system.file("landmarks/maleCNS_mirror_landmarks_nm.csv", package = 'malecns')
maleCNS_mirror_landmarks_nm <- read.csv(f3)[-1]
points3d(mirror_malecns(maleCNS_mirror_landmarks_nm[1:3]))
points3d(maleCNS_mirror_landmarks_nm[1:3], col='green')
# almost on top of the black points
points3d(maleCNS_mirror_landmarks_nm[4:6]+500, col='red')
} # }
```

--------------------------------

### Example: with_mcns with different datasets

Source: https://natverse.org/malecns/reference/with_mcns.html

Example usage of `with_mcns` with `mcns_body_annotations`. The first call uses the default dataset `male-cns:v1.0`; the second uses `CNS` and prints a message about switching. Outputs show the returned data frame.

```r
if (FALSE) { # \dontrun{
with_mcns(malevnc::manc_dvid_node(type = 'clio'))
} # }
# \donttest{
# This should work for both clio and neuprint calls, here clio:
# this body was typed after the v1.0 snapshot
with_mcns(mcns_body_annotations(194965), dataset = "male-cns:v1.0")
#>   bodyid celltype_predicted_nt celltype_predicted_nt_confidence
#> 1 194965         acetylcholine                        0.9525882
#>   celltype_total_nt_predictions  consensus_nt flywire_type group instance
#> 1                         22356 acetylcholine         Sm01 29457    Cm2_L
#>    predicted_nt predicted_nt_confidence soma_side   status   superclass
#> 1 acetylcholine               0.9600185         L Reviewed ol_intrinsic
#>   total_nt_predictions type  user       vfb_id  auto
#> 1                   66  Cm2 bergs VFB_jrmc35xh FALSE
with_mcns(mcns_body_annotations(194965), dataset = "CNS")
#> switching CNS dataset from `male-cns:v1.0` to `CNS`
#>   bodyid celltype_predicted_nt celltype_predicted_nt_confidence
#> 1 194965         acetylcholine                        0.9525913
#>   celltype_total_nt_predictions  consensus_nt flywire_type group instance
#> 1                         22356 acetylcholine         Sm01 29457    Cm2_L
#>    predicted_nt predicted_nt_confidence soma_side   status   superclass
#> 1 acetylcholine               0.9600217         L Reviewed ol_intrinsic
#>   total_nt_predictions type  user       vfb_id  auto
#> 1                   66  Cm2 bergs VFB_jrmc35xh FALSE
# }
```

--------------------------------

### mcns_set_dvid_annotations usage examples

Source: https://natverse.org/malecns/reference/mcns_set_dvid_annotations.html

Example calls to `mcns_set_dvid_annotations` showing how to set type, side, group, and how to unset a type by passing an empty string. The `if (FALSE)` wrapper indicates these are not run automatically. The last call removes a type and requires caution.

```r
if (FALSE) { # \dontrun{
mcns_set_dvid_annotations(10297, type = 'LHAD1g1')
mcns_set_dvid_annotations(10977, type='APL', side='L', group = 10540)
# only set the LR group
mcns_set_dvid_annotations(ids=c(13115, 14424), group = 13115)

# unset i.e. remove a type (careful!)
mcns_set_dvid_annotations(18987, type = "", user = "becketti")
} # }
```

--------------------------------

### Example: fancier labelling and interactive heatmap

Source: https://natverse.org/malecns/reference/mcns_cosine_plot.html

Example usage of mcns_cosine_plot with custom row labels, alternative metadata source, interactive mode, and returning an hclust object. This example is wrapped in a dontrun block.

```r
if (FALSE) { # \dontrun{
# fancier labelling of rows including soma side of neurons
r=mcns_cosine_plot("/name:LAL.+", partners='out', group=TRUE,
  labRow = '{name}_{group}_{mcns_soma_side(data.frame(bodyid, name, somaLocation))}')

r2=mcns_cosine_plot("/name:LAL.+", partners='out', group=TRUE,
  labRow = '{instance}_{group}_{soma_side}', metadata.source='clio')

# interactive version (open in browser)
mcns_cosine_plot('/name:Pm2.*R', group=T, interactive = T)

# just return an hclust (dendrogram) object without plotting anything
pm2.hc=mcns_cosine_plot('/name:Pm2.*R', group=T, heatmap=FALSE)
plot(pm2.hc)
} # }
```

--------------------------------

### mcns_body_annotations example with query and output

Source: https://natverse.org/malecns/reference/mcns_body_annotations.html

Example usage of mcns_body_annotations. The first call queries body ID "AOTU019" and shows the resulting data.frame with metadata columns. The second call (wrapped in if(FALSE) and \dontrun) queries with a list query for superclass existence and counts the superclass column. The example is not run by default.

```r
# \donttest{
mcns_body_annotations("AOTU019")
#>   birthtime bodyid celltype_predicted_nt celltype_predicted_nt_confidence
#> 1     early  10005                  gaba                        0.8305035
#> 2     early  10070                  gaba                        0.8305035
#>   celltype_total_nt_predictions consensus_nt flywire_type group hemibrain_type
#> 1                          5673         gaba      AOTU019 10005        AOTU019
#> 2                          5673         gaba      AOTU019 10005        AOTU019
#>    instance        itolee_hl predicted_nt predicted_nt_confidence soma_side
#> 1 AOTU019_R putative_primary         gaba               0.8350762         R
#> 2 AOTU019_L putative_primary         gaba               0.8259292         L
#>           status   superclass total_nt_predictions    type       vfb_id  auto
#> 1 Roughly traced cb_intrinsic                 2837 AOTU019 VFB_jrmc3hk6 FALSE
#> 2 Roughly traced cb_intrinsic                 2836 AOTU019 VFB_jrmc3hk5 FALSE
# }
if (FALSE) { # \dontrun{
mcns.superclass=mcns_body_annotations(query=list(superclass="exists/1"))
mcns.superclass %>%
  count(superclass)
} # }
```

--------------------------------

### GNU GPL v3 program notice template

Source: https://natverse.org/malecns/LICENSE.html

Use this notice to apply the GNU GPL v3 terms to a new program. Attach it to the start of each source file, including the copyright line and a pointer to the full license. The text includes the warranty disclaimer and a link to the license.

```text
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year>  <name of author>

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.
```

--------------------------------

### Example: cosine clustering with grouped output partners

Source: https://natverse.org/malecns/reference/mcns_cosine_plot.html

Example usage of mcns_cosine_plot to cluster based on grouped output partners. The example includes a warning about dropped neurons and synapses, and a warning about non-positive diagonal entries.

```r
# \donttest{
# cosine clustering based on grouped output partners (mainly DNs right now)
r=mcns_cosine_plot("/name:LAL.+", partners='out', group=TRUE)
#> Warning: Dropping: 94/24280 neurons representing 1658/866555 synapses due to missing ids!
#> Warning: diag(V) has non-positive or non-finite entries; finite result is doubtful

# }
```

--------------------------------

### Read and visualize neuronal skeletons

Source: https://natverse.org/malecns/reference/read_mcns_neurons.html

Examples showing how to read a neuron by ID, inspect the resulting neuronlist object, check its bounding box, and visualize the neuron in 3D.

```R
# nb convert
n30102=read_mcns_neurons(30102)
# neuronlist
n30102
#> 'neuronlist' containing 1 'neuprintneuron' object and 'data.frame' with 51 vars [165.9 kB]
boundingbox(n30102)
#>        [,1]   [,2]   [,3]
#> [1,] 168448  92160 197632
#> [2,] 279552 169984 274432
#> attr(,"class")
#> [1] "boundingbox"
# neuron
n30102[[1]]
#> 'neuron' with 2154 vertices in 1 tree and additional classes 'neuprintneuron', 'catmaidneuron'
if (FALSE) { # \dontrun{
nclear3d()
plot3d(malecns.surf, alpha=.1)
plot3d(n30102, lwd=2, soma=2000)
} # }
```

--------------------------------

### Example: retrieve all DVID annotations and plot type frequencies

Source: https://natverse.org/malecns/reference/mcns_dvid_annotations.html

Example usage of mcns_dvid_annotations with no ids, showing the returned tibble and a plot of type frequencies. The output is truncated with #> comments.

```r
# \donttest{
mda=mcns_dvid_annotations()
head(mda)
#> # A tibble: 6 × 49
#>   bodyid birthtime celltype_predicted_nt celltype_predicted_nt_confidence
#>    <dbl> <chr>     <chr>                                            <dbl>
#> 1  10001 early     acetylcholine                                    0.528
#> 2  10002 NA        acetylcholine                                    0.956
#> 3  10003 early     gaba                                             0.875
#> 4  10005 early     gaba                                             0.831
#> 5  10006 NA        acetylcholine                                    0.820
#> 6  10009 NA        gaba                                             0.866
#> # ℹ 45 more variables: celltype_total_nt_predictions <int>, consensus_nt <chr>,
#> #   flywire_type <chr>, group <int>, hemibrain_type <chr>, instance <chr>,
#> #   itolee_hl <chr>, manc_bodyid <dbl>, manc_group <int>, manc_type <chr>,
#> #   predicted_nt <chr>, predicted_nt_confidence <dbl>, soma_side <chr>,
#> #   status <chr>, subclass <chr>, superclass <chr>, synonyms <chr>,
#> #   total_nt_predictions <int>, type <chr>, vfb_id <chr>, notes <chr>,
#> #   supertype <chr>, user <chr>, halfbrain_body <dbl>, class <chr>, …
plot(table(mda$type), ylab='Frequency')


kcs=mcns_dvid_annotations("/KC.*")
mbons=mcns_dvid_annotations("/MBON.+")

head(mbons)
#> # A tibble: 6 × 49
#>   bodyid birthtime celltype_predicted_nt celltype_predicted_nt_confidence
#>    <dbl> <chr>     <chr>                                            <dbl>
#> 1 520151 early     glutamate                                        0.713
#> 2  10013 early     glutamate                                        0.713
#> 3 522749 early     glutamate                                        0.740
#> 4 522444 early     glutamate                                        0.740
#> 5 519373 early     glutamate                                        0.767
#> 6 521526 early     glutamate                                        0.767
#> # ℹ 45 more variables: celltype_total_nt_predictions <int>, consensus_nt <chr>,
#> #   flywire_type <chr>, group <int>, hemibrain_type <chr>, instance <chr>,
#> #   itolee_hl <chr>, manc_bodyid <dbl>, manc_group <int>, manc_type <chr>,
#> #   predicted_nt <chr>, predicted_nt_confidence <dbl>, soma_side <chr>,
#> #   status <chr>, subclass <chr>, superclass <chr>, synonyms <chr>,
#> #   total_nt_predictions <int>, type <chr>, vfb_id <chr>, notes <chr>,
#> #   supertype <chr>, user <chr>, halfbrain_body <dbl>, class <chr>, …
# }
```

--------------------------------

### Example usage of mcns_soma_side and mcns_somapos

Source: https://natverse.org/malecns/reference/mcns_soma_side.html

Example usage of mcns_soma_side and mcns_somapos. The first call predicts sides for a regular expression; the commented block shows comparisons between manual, instance, and predicted sides using mcns_body_annotations and mcns_neuprint_meta. The final lines plot soma positions and return a comma-separated string for a single neuron.

```r
# \donttest{
mcns_soma_side('/LAL04.*')
#>  [1] "R" "L" "R" "L" "R" "L" "L" "R" "L" "R" "R" "L" "R" "L" "L" "R" "R" "L" "R"
#> [20] "L" "R" "L" "L" "L" "R" "R" "L" "L" "L" "R" "R" "R" "R" "L"
# }
if (FALSE) { # \dontrun{
# All neurons with a type
table(mcns_soma_side('/.*'), useNA='if')

# compare manual with predictions
mcnswsoma=mcns_body_annotations(query=list(soma_side="exists/1"))
mcnswsoma$pside=mcns_soma_side(mcnswsoma$bodyid)
with(mcnswsoma, table(soma_side, pside))

# compare manual with instance
mcnswsoma=mcns_body_annotations(query=list(soma_side="exists/1"))
mcnswsoma$iside=mcns_soma_side(mcnswsoma$bodyid, method='instance')
with(mcnswsoma, table(soma_side, iside, useNA = 'i'))

# converse: compare instance with manual
mcns_instance=mcns_neuprint_meta('/name:.+')
if(!"somaSide" %in% colnames(mcns_instance))
  mcns_instance$somaSide=mcns_soma_side(mcns_instance, method='manual')
mcns_instance$iside=mcns_soma_side(mcns_instance, method='instance')
# many mismatches e.g. due to neurons without a soma including truncated
# sensory neurons, ascending neurons etc
with(mcns_instance, table(somaSide, iside, useNA = 'i'))

# we can parse that a bit by doing
mcns_instance %>% filter(soma) %>% with(table(somaSide, iside, useNA = 'i'))
} # }
sp=mcns_somapos('/LAL04.*', units='um')
plot(sp[,1:2])

mcns_somapos('LAL042', units='um', as_character=TRUE)
#> [1] "299.768,99.944,128.68" "438.56,87.904,117.6"  
```

--------------------------------

### Update malecns with natmanager::install

Source: https://natverse.org/malecns/index.html

Update an existing malecns installation by reinstalling from GitHub via natmanager.

```r
install.packages("natmanager")
natmanager::install(pkgs="natverse/malecns")
```

--------------------------------

### Configure Clio and DVID credentials in .RProfile

Source: https://natverse.org/malecns/index.html

Configure Clio and DVID user credentials for write access to the production dataset. Edit your .RProfile with usethis::edit_r_profile() and set malevnc.clio_email and malevnc.dvid_user; the dvid_user example is formatted as surname plus first initial.

```r
usethis::edit_r_profile()
# paste in this text, appropriately edited, and close the file
options(malevnc.clio_email="myuser@gmail.com")
# e.g. "jefferisg"
options(malevnc.dvid_user="<surname><firstinitial>")
```

--------------------------------

### Bounding box and plotting for malecns.surf

Source: https://natverse.org/malecns/reference/malecns.surf.html

Computes the bounding box of the neuropil surface model and shows plotting options in microns, raw voxels, and as a mesh3d object. The bounding box output is shown. The plotting code is wrapped in \dontrun and will not run in examples.

```R
# \donttest{
library(nat)
boundingbox(malecns.surf)
#>          [,1]     [,2]     [,3]
#> [1,]  45319.4  38146.7  79817.4
#> [2,] 726917.9 417784.6 341814.2
#> attr(,"class")
#> [1] "boundingbox"
# }
if (FALSE) { # \dontrun{
plot3d(malecns.surf, alpha=.2, col='grey')
# in microns
plot3d(malecns.surf/1e3, alpha=.2, col='grey')
# in raw voxels
plot3d(malecns.surf*(8/1e3), alpha=.2, col='grey')
# mesh3d gives more flexibility
plot3d(as.mesh3d(malecns.surf), alpha=.2, col='grey', type='wire', add=T)
} # }
```

--------------------------------

### Bounding box and plotting for malecns_shell.surf

Source: https://natverse.org/malecns/reference/malecns.surf.html

Computes the bounding box of the cortex surface model and shows plotting options in microns, raw voxels, and as a mesh3d object. The bounding box output is shown. The plotting code is wrapped in \dontrun and will not run in examples.

```R
# \donttest{
library(nat)
boundingbox(malecns_shell.surf)
#>          [,1]     [,2]     [,3]
#> [1,]  16898.1  31849.1  26324.4
#> [2,] 750590.0 441841.0 460357.0
#> attr(,"class")
#> [1] "boundingbox"
# }
if (FALSE) { # \dontrun{
plot3d(malecns_shell.surf, alpha=.2, col='grey')
# in microns
plot3d(malecns_shell.surf/1e3, alpha=.2, col='grey')
# in raw voxels
plot3d(malecns_shell.surf*(8/1e3), alpha=.2, col='grey')
# mesh3d gives more flexibility
plot3d(as.mesh3d(malecns_shell.surf), alpha=.2, col='grey', type='wire', add=T)
} # }
```

--------------------------------

### Query neuprint metadata with mcns_neuprint_meta()

Source: https://natverse.org/malecns/reference/mcns_neuprint_meta.html

Fetches neuprint metadata for all bodies and for subsets filtered by somaLocation, type/instance, or missing superclass with high synapse weight. The final query returns a data frame sorted by synweight; the displayed output shows the first 29 rows with columns including bodyid, synapse counts, and statusLabel.

```R
library(dplyr)
# fetch metatada for all bodies in neuprint
mnm=mcns_neuprint_meta()
# fetch metadata for all bodies with a somaLocation
mnm.soma=mcns_neuprint_meta("where:exists(n.somaLocation)")

# type or instance present
mnm.ti <- mcns_neuprint_meta('where:exists(n.type) OR exists(n.instance)')

# neurons without a superclass but quite a few synapses
mnm.nc=mcns_neuprint_meta("where:NOT exists(n.superclass) AND n.synweight>2000")
mnm.nc %>% arrange(desc(synweight))
#>       bodyid post pre downstream upstream synweight assignedOlHex1
#> 1     519161 2401 322       2717     2401      5118             NA
#> 2     555297  253 632       4061      253      4314             NA
#> 3     806539  202 447       3169      202      3371             NA
#> 4      73540  332 322       3016      332      3348             NA
#> 5     926152 1134 321       2087     1134      3221             NA
#> 6     956675   55 421       3139       55      3194             NA
#> 7      92244 1753 222       1426     1753      3179             NA
#> 8     942401  351 465       2705      351      3056             NA
#> 9     955927  163 401       2644      163      2807             NA
#> 10    104626   55 318       2732       55      2787             NA
#> 11    559672 1395 247       1385     1395      2780             NA
#> 12    110922   62 267       2640       62      2702             NA
#> 13    114076  150 300       2529      150      2679             NA
#> 14 516174168  128 319       2472      128      2600             NA
#> 15    959135   71 364       2507       71      2578             NA
#> 16    955615  450 382       2109      450      2559             NA
#> 17    118423  101 260       2443      101      2544             NA
#> 18    147605 1180 183       1207     1180      2387             NA
#> 19    956674  868 281       1506      868      2374             NA
#> 20    954995  399 305       1971      399      2370             NA
#> 21    555267   64 289       2300       64      2364             NA
#> 22    210839   70 268       2294       70      2364             NA
#> 23    908376   26 235       2318       26      2344             NA
#> 24    809279  107 279       2117      107      2224             NA
#> 25    810259   74 256       2039       74      2113             NA
#> 26    544325  238 254       1820      238      2058             NA
#> 27    147742  257 204       1790      257      2047             NA
#> 28    903906   52 327       1983       52      2035             NA
#> 29    946550  949 128       1067      949      2016             NA
#>    assignedOlHex2 flywireType group          name somaSide      statusLabel
#> 1              NA        <NA>    NA KC_L_fragment        L           Orphan
#> 2              NA        <NA>    NA          <NA>     <NA>           Orphan
#> 3              NA        <NA>    NA          <NA>     <NA>  Orphan-artifact
#> 4              NA        <NA>    NA          <NA>     <NA>       PRT Orphan
#> 5              NA        <NA>    NA          <NA>     <NA>  Orphan-artifact
#> 6              NA        <NA>    NA          <NA>     <NA>             <NA>
#> 7              NA        <NA>    NA          <NA>     <NA>           Orphan
#> 8              NA        <NA>    NA          <NA>     <NA>  Orphan-artifact
#> 9              NA        <NA>    NA          <NA>     <NA>             <NA>
#> 10             NA        <NA>    NA          <NA>     <NA>           Anchor
#> 11             NA        <NA>    NA          <NA>     <NA>           Orphan
#> 12             NA        <NA>    NA          <NA>     <NA>           Anchor
#> 13             NA        <NA>    NA          <NA>     <NA>           Orphan
#> 14             NA        <NA>    NA          <NA>     <NA>       PRT Orphan
#> 15             NA        <NA>    NA          <NA>     <NA>             <NA>
#> 16             NA        <NA>    NA          <NA>     <NA>             <NA>
#> 17             NA        <NA>    NA          <NA>     <NA>           Orphan
#> 18             NA        <NA>    NA          <NA>     <NA>           Orphan
#> 19             NA        <NA>    NA          <NA>     <NA> Partially traced
#> 20             NA        <NA>    NA          <NA>     <NA>             <NA>
#> 21             NA        <NA>    NA          <NA>     <NA>           Orphan
#> 22             NA        <NA>    NA          <NA>     <NA>       PRT Orphan
#> 23             NA        <NA>    NA          <NA>     <NA>  Orphan-artifact
#> 24             NA        <NA>    NA          <NA>     <NA>  Orphan-artifact
#> 25             NA        <NA>    NA          <NA>     <NA>  Orphan-artifact
#> 26             NA        <NA>    NA          <NA>     <NA> RT Hard to trace
#> 27             NA        <NA>    NA          <NA>     <NA>           Orphan
#> 28             NA        <NA>    NA          <NA>     <NA>  Orphan-artifact
#> 29             NA        <NA>    NA          <NA>     <NA>           Orphan
```

--------------------------------

### mcns_xyz2bodyid example with soma positions

Source: https://natverse.org/malecns/reference/mcns_xyz2bodyid.html

Example usage: maps soma positions from mcns_somapos to body IDs, returning a numeric vector. Also shows a commented-out call for the APL neuron using a raw coordinate offset.

```r
# \donttest{
# find the bodyids corresponding to set of soma positions
mcns_xyz2bodyid(mcns_somapos("/LAL04[12]", units='raw'), units='raw')
#> [1]  34187 512750
# }
# the APL
if (FALSE) { # \dontrun{
mcns_xyz2bodyid(cbind(24508, 15674, 26116)+4096)
} # }
```

--------------------------------

### Check malecns package options

Source: https://natverse.org/malecns/reference/malecns-package.html

Shows the current malecns package options, including the default dataset 'male-cns:v1.0'.

```r
options()[grepl("^malecns", names(options()))]
#> $malecns.dataset
#> [1] "male-cns:v1.0"
#> 
```

--------------------------------

### mcns_download_xforms2()

Source: https://natverse.org/malecns/reference/mcns_register_xforms2.html

Downloads JRCFIB2022M_JRC2018M and JRC2018U_JRC2018M h5 deformation fields (~3 GB total) from figshare. Only needs to be run once; the download is resumable and will skip files already present. Calls `mcns_register_xforms2` automatically after downloading.

```APIDOC
## mcns_download_xforms2()

### Description
Downloads JRCFIB2022M_JRC2018M and JRC2018U_JRC2018M h5 deformation fields (~3 GB total) from figshare. Only needs to be run once; the download is resumable and will skip files already present. Calls `mcns_register_xforms2` automatically after downloading.

### Usage
```r
mcns_download_xforms2()
```

### Arguments
None.

### Value
No explicit return value documented.

### Examples
```r
if (FALSE) { # \dontrun{
# first time only
mcns_download_xforms2()
# then once per session
mcns_register_xforms2()
xform_brain(cbind(100000, 100000, 50000), sample='malecns', reference='JRC2018U')
} # }
```
```

--------------------------------

### mcns_dvid_annotations

Source: https://natverse.org/malecns/reference/mcns_dvid_annotations.html

Documentation for the mcns_dvid_annotations function, including usage, arguments, return value, and examples.

```APIDOC
## mcns_dvid_annotations

### Description
Returns all DVID body annotations for the specified body ids, or all annotations if no ids are provided. The function can return a parsed data.frame or a raw list, and supports caching of results.

### Usage
```r
mcns_dvid_annotations(
  ids = NULL,
  node = "neutu",
  rval = c("data.frame", "list"),
  columns_show = NULL,
  cache = FALSE,
  ...
)
```

### Arguments
- **ids** (character/numeric) - Optional - A set of body ids in any form understandable to `manc_ids`.
- **node** (character) - Optional - A DVID node as returned by `manc_dvid_node`. Default is the current active (unlocked) node used through neutu.
- **rval** (character) - Optional - Whether to return a fully parsed data.frame (default) or an R list. The data.frame is easier to work with but typically includes NAs for many values that would be missing in the list.
- **columns_show** (character) - Optional - Whether to show all columns, or just those with '_user' or '_time' suffix. Accepted options are: 'user', 'time', 'all'.
- **cache** (logical) - Optional - Whether to cache the result of this call for 5 minutes.
- **...** - Optional - Additional arguments passed to `pblapply`.

### Value
A `tibble` containing columns including:
- **bodyid** (numeric) - The body id.
- **status** (character) - Status of the annotation.
- **user** (character) - User who made the annotation.
- **naming_user** (character) - User who named the body.
- **instance** (character) - Instance name.
- **status_user** (character) - User who set the status.
- **comment** (character) - Comment associated with the annotation.

NB: only one `bodyid` is used regardless of whether the key-value returned has 0, 1 or 2 bodyid fields. When `ids` are specified, missing ids will have a row containing the `bodyid` in question and all other columns will be `NA`.

### Details
See the Slack post from Stuart Berg for details. The original API call used `<rootuuid>:master`, but this has been changed to `<neutu-uuid>` as returned by `manc_dvid_node` because the range query stopped working on 16 May 2021, probably due to a bad node.

### Examples
```r
# \donttest{
mda=mcns_dvid_annotations()
head(mda)
#> # A tibble: 6 × 49
#>   bodyid birthtime celltype_predicted_nt celltype_predicted_nt_confidence
#>    <dbl> <chr>     <chr>                                            <dbl>
#> 1  10001 early     acetylcholine                                    0.528
#> 2  10002 NA        acetylcholine                                    0.956
#> 3  10003 early     gaba                                             0.875
#> 4  10005 early     gaba                                             0.831
#> 5  10006 NA        acetylcholine                                    0.820
#> 6  10009 NA        gaba                                             0.866
#> # ℹ 45 more variables: celltype_total_nt_predictions <int>, consensus_nt <chr>,
#> #   flywire_type <chr>, group <int>, hemibrain_type <chr>, instance <chr>,
#> #   itolee_hl <chr>, manc_bodyid <dbl>, manc_group <int>, manc_type <chr>,
#> #   predicted_nt <chr>, predicted_nt_confidence <dbl>, soma_side <chr>,
#> #   status <chr>, subclass <chr>, superclass <chr>, synonyms <chr>,
#> #   total_nt_predictions <int>, type <chr>, vfb_id <chr>, notes <chr>,
#> #   supertype <chr>, user <chr>, halfbrain_body <dbl>, class <chr>, …
plot(table(mda$type), ylab='Frequency')

kcs=mcns_dvid_annotations("/KC.*")
mbons=mcns_dvid_annotations("/MBON.+")

head(mbons)
#> # A tibble: 6 × 49
#>   bodyid birthtime celltype_predicted_nt celltype_predicted_nt_confidence
#>    <dbl> <chr>     <chr>                                            <dbl>
#> 1 520151 early     glutamate                                        0.713
#> 2  10013 early     glutamate                                        0.713
#> 3 522749 early     glutamate                                        0.740
#> 4 522444 early     glutamate                                        0.740
#> 5 519373 early     glutamate                                        0.767
#> 6 521526 early     glutamate                                        0.767
#> # ℹ 45 more variables: celltype_total_nt_predictions <int>, consensus_nt <chr>,
#> #   flywire_type <chr>, group <int>, hemibrain_type <chr>, instance <chr>,
#> #   itolee_hl <chr>, manc_bodyid <dbl>, manc_group <int>, manc_type <chr>,
#> #   predicted_nt <chr>, predicted_nt_confidence <dbl>, soma_side <chr>,
#> #   status <chr>, subclass <chr>, superclass <chr>, synonyms <chr>,
#> #   total_nt_predictions <int>, type <chr>, vfb_id <chr>, notes <chr>,
#> #   supertype <chr>, user <chr>, halfbrain_body <dbl>, class <chr>, …
# }
```
```
Tokens:
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