Package 'SMARTTR'

Description

This function takes an experiment object and mouse object and adds the mouse object to the experiment. The mouse's unprocessed data, including all of it's individual slice information and raw imported segmentation and registration data will be not be added from the mouse object to save space. Any desire to modify this data must be done at the mouse object level before continuing further.

This function will also read the individual mouse attributes and automatically populate the experimental attributes that are relevant. For example, the 'group' attribute of a mouse will be read and automatically added to the experiment object's 'experimental_groups' attribute if it is a new unique experimental group name.

Usage

add_mouse(e, m, replace = FALSE)

Arguments

Value

an experiment object

Examples

e <- experiment(experiment_name = "example")
m <- mouse(mouse_ID = "Mouse1")
e <- add_mouse(e, m)
e <- add_mouse(e, m, replace = TRUE)

Description

Add slice to a mouse object

Usage

add_slice(m, s, replace = FALSE)

Arguments

Examples

m <- mouse()
s <- slice()
m <- add_slice(m, s, replace = FALSE)
m <- add_slice(m, s, replace = TRUE)

Description

This function takes a slice object and first applies a filter with default settings to the image set as the slice registration path. An interative user loop allows for easy adjustment of the brain threshold since the wholebrain GUI tends to be a bit buggy. This function then returns a filter with the adjusted brain threshold.

Usage

adjust_brain_outline(s, filter = NULL)

Arguments

Value

filter (list) wholebrain compatible filter

Examples

## Not run: 
# Adjust the brain threshold, then run register on the slice object
filter <- adjust_brain_outline(s); s <- register(s, filter = filter)

## End(Not run)

Description

A custom list to match attributes of a mouse and experiment object respectively.

Usage

attr2match

Format

A list

Description

Run this as a quality check after importing an external dataset using import_mapped_datasets(). This goes through all dataframes for all channels imported and replaces any non-matching acronyms and region names with those as they are exactly coded in SMARTTR.

Usage

check_ontology_coding(e, ontology = "allen")

Arguments

Details

Note: Processing times scales with the size of your dataset so it may take a few minutes if your dataset is large...

Value

e experiment object

Examples

## Not run: 
e <- check_ontology_coding(e, "allen")

## End(Not run)

Description

Check for redundant parent regions included in a list of acronyms in a plate. For example, if all the the subregions for the hypothalamus are represented, the HY should not be included in the list.

Usage

check_redundant_parents(acronyms, ontology = "allen")

Arguments

Value

A list containing two elements: one vector of unique child acronyms, a vector of the parent regions considered redundant

Examples

check_redundant_parents(acronyms = c("ACA5", "ACA1", "ACA"))
check_redundant_parents(acronyms = c("VMHDM", "VMHSh", "VMH"), ontology = "unified")

Description

This function also stores the mouse attribute names (not experiment attributes) as columns that will be used as categorical variables to make analysis subgroups. The values of these attributes (group, drug, age) will automatically be converted to a string values for consistency.

Usage

combine_cell_counts(e, by)

Arguments

Value

e en experiment object

Examples

## Not run: 
e <- combine_cell_counts(e, by = c('groups', 'sex'))

## End(Not run)

Description

The data from two different analysis groups are compared by specifying the correlation_list_name_1 and correlation_list_name_2 parameters. Note that both of these analysis groups must have the same number of channels to compare. The functions get_correlations() needs to have been run for each of these analysis groups prior to running this function. The test statistics used is the pearson values of those in correlation_list_name_2 subtracted from corresponding Pearson values in correlation_list_name_1.

Usage

correlation_diff_permutation(
  e,
  correlation_list_name_1,
  correlation_list_name_2,
  channels = c("cfos", "eyfp", "colabel"),
  n_shuffle = 1000,
  method = "pearson",
  seed = 5,
  p_adjust_method = "none",
  alpha = 0.05,
  progressbar = TRUE,
  ...
)

Arguments

Value

e experiment object. The experiment object now has a list called permutation_p_matrix stored in it. Elements of this permutation_p_matrix list are the outputs of different permutation comparison analyses. These elements are named by the groups that were compared.

See Also

get_correlations()

Examples

## Not run: e <- correlation_diff_permutation(sundowning,
                                            correlation_list_name_1 = "female_AD",
                                            correlation_list_name_2 = "female_control",
                                            channels = c("cfos", "eyfp", "colabel"),
                                            n_shuffles = 1000,
                                            seed = 5,
                                            p_adjust_method = "none"
                                            alpha = 0.001
                                            )

## End(Not run)

Description

Create a joined network to visualize overlapping connections with the same outer joined node set.

Usage

create_joined_networks(
  e,
  correlation_list_names = c("male_agg", "female_non"),
  channels = "cfos",
  ontology = "unified",
  alpha = 0.001,
  pearson_thresh = 0.9,
  proportional_thresh = NULL,
  alpha2 = NULL,
  pearson_thresh2 = NULL,
  proportional_thresh2 = NULL,
  proportional_sort = "alpha",
  filter_isolates = TRUE,
  anatomical.order = c("Isocortex", "OLF", "HPF", "CTXsp", "CNU", "TH", "HY", "MB", "HB",
    "CB"),
  export_overlapping_edges = TRUE
)

Arguments

Value

e experiment object. This object now has a new added element called networks. This is a list storing a graph object per channel for each network analysis run. The name of each network (network_name) is the same as the correlation_list_name used to generate the network. This network_name is fed as a parameter into the plot_networks() function.

See Also

plot_networks()

Examples

## Not run: sundowning <- create_joined_networks(sundowning,
correlation_list_name = "female_control", alpha = 0.05)

## End(Not run)

Description

Create graph objects for plotting different analysis subgroups.

Usage

create_networks(
  e,
  correlation_list_name,
  channels = c("cfos", "eyfp", "colabel"),
  proportional_thresh = NULL,
  alpha = 0.05,
  pearson_thresh = 0,
  ontology = "allen",
  anatomical.order = c("Isocortex", "OLF", "HPF", "CTXsp", "CNU", "TH", "HY", "MB", "HB",
    "CB"),
  filter_isolates = FALSE
)

Arguments

Value

e experiment object. This object now has a new added element called networks. This is a list storing a graph object per channel for each network analysis run. The name of each network (network_name) is the same as the correlation_list_name used to generate the network. This network_name is fed as a parameter into the plot_networks() function.

See Also

plot_networks()

Examples

## Not run: 
e sundowning <- create_networks(sundowning, correlation_list_name = "female_control", alpha = 0.05)

## End(Not run)

Create a permutation difference network This function requires export_permutation_results() to have been run for the specific permutation analysis of interest, with the filter_significant parameter set to FALSE, so that users can set their own p-value threshold for filtering out edges.

Description

Create a permutation difference network This function requires export_permutation_results() to have been run for the specific permutation analysis of interest, with the filter_significant parameter set to FALSE, so that users can set their own p-value threshold for filtering out edges.

Usage

create_perm_diff_network(
  e,
  permutation_group,
  channel = "cfos",
  p_value_thresh = 0.001,
  ontology = "allen",
  anatomical.order = c("Isocortex", "OLF", "HPF", "CTXsp", "CNU", "TH", "HY", "MB", "HB",
    "CB"),
  community_detection_function = tidygraph::group_fast_greedy,
  ...
)

Arguments

Value

e experiment object

Examples

## Not run: 
e <- create_perm_diff_network(e, permutation_group = "Control_vs_Isoflurane", channel = "cfos", ontology = "unified", p_value_thresh = 0.001, weights = NULL)

## End(Not run)

Description

Quality check function to make sure that the regions included for analysis show up in more than 1 slice object, otherwise the user can remove exceptions from the mouse object.

Regions counts derived from only one image may be less accurate. This function can generate a log of these regions so the user can qualitatively evaluate the raw data. Users also have to option of removing these regions automatically from normalized_counts dataframe.

The user should run normalize_cell_counts() and get_cell_table() functions prior to using this function.

Usage

detect_single_slice_regions(m, remove = FALSE, log = TRUE)

Arguments

Value

mouse object

Description

Check if there are enough mice per analysis subgroup across all regions. if the normalized counts data sets are split by specified grouping variables. This function also automatically keeps only the common regions that are found across all comparison groups.

Usage

enough_mice_per_group(
  e,
  by = c("group", "sex"),
  min_n = 5,
  remove = TRUE,
  log = TRUE
)

Arguments

Value

e experiment object

Examples

## Not run: 
e <- enough_mice_per_group(e, by = c("group", "sex"), min_n = 4, remove = TRUE, log = TRUE)

## End(Not run)

Description

Method for excluding user specified regions, layer 1, and contralateral hemisphere per slice. This function automatically excludes the default regions included in the attribute "regions_excluded" in each slice IN ADDITION to the regions added to the 'exclude_regions' parameter. Regions added to the 'exclude_regions' parameter will then be updated in the slice attribute to keep track of what was excluded. Note: Please see the simplify_regions and simplify_keywords parameters. By default, if a subregion can be folded into a parent region based on a certain keywords, then this function will automatically exclude the entire parent region as a conservative exclusion approach. Keep simplify_regions=TRUE if the final analysis will contain simplified regions.

Method for excluding cell counts in specified regions, layer 1, out-of-bounds cells counts, and hemispheres per mouse. This function automatically excludes the default regions included in the attribute "regions_excluded" in each slice IN ADDITION to the regions added to the 'exclude_regions' parameter. Regions added to the 'exclude_regions' parameter will then be updated in the slice attribute to keep track of what was excluded.

Note: Please see the simplify_regions and simplify_keywords parameters. By default, if a subregion can be folded into a parent region based on a certain keywords, then this function will automatically exclude the entire parent region as a conservative exclusion approach. Keep simplify_regions=TRUE if the final analysis will contain simplified regions.

Usage

exclude_anatomy(x, ...)

## S3 method for class 'slice'
exclude_anatomy(
  x,
  channels = NULL,
  clean = TRUE,
  exclude_right_regions = NULL,
  exclude_left_regions = NULL,
  exclude_hemisphere = TRUE,
  exclude_layer_1 = TRUE,
  include_right_regions = NULL,
  include_left_regions = NULL,
  simplify_regions = TRUE,
  simplify_keywords = c("layer", "part", "stratum", "division", "leaflet",
    "Subgeniculate", "island", "Islands", "Fields of Forel", "Cajal", "Darkschewitsch",
    "Precommissural"),
  plot_filtered = TRUE,
  ...
)

## S3 method for class 'mouse'
exclude_anatomy(
  x,
  slice_ID = NA,
  hemisphere = NULL,
  channels = NULL,
  clean = TRUE,
  exclude_right_regions = NULL,
  exclude_left_regions = NULL,
  exclude_hemisphere = FALSE,
  exclude_layer_1 = TRUE,
  include_right_regions = NULL,
  include_left_regions = NULL,
  simplify_regions = TRUE,
  simplify_keywords = c("layer", "part", "stratum", "division", "leaflet",
    "Subgeniculate", "island", "Islands", "Fields of Forel", "Cajal", "Darkschewitsch",
    "Precommissural"),
  plot_filtered = TRUE,
  ...
)

Arguments

Value

a mouse or slice object

m mouse object

Examples

## Not run: 
 s <-  exclude_anatomy(s, channels = c('cfos', 'eyfp', 'colabel'), clean = TRUE,
 exclude_regions = NULL, exclude_hemisphere = TRUE, exclude_layer_1 = TRUE, plot_filtered = TRUE)

## End(Not run)
## Not run: 
m <- exclude_anatomy(m, slice_ID = "1_10",
hemisphere = NULL, channels = c('cfos', 'eyfp', 'colabel'), clean = TRUE,
exclude_regions = NULL, exclude_hemisphere = TRUE, exclude_layer_1 = TRUE)

## End(Not run)

Description

Excluded user chosen regions by entering acronyms

Usage

exclude_by_acronym(
  e,
  acronyms = "fiber_tracts",
  ontology = "allen",
  channels = NULL
)

Arguments

Value

e experiment object

Examples

## Not run: 
e <- exclude_by_acronym(e, acronyms = "CBL") # exclude the cerebellum

## End(Not run)

Description

Excluded user chosen regions by keywords found in long-form name

Usage

exclude_by_keyword(e, keywords, channels = NULL)

Arguments

Value

e experiment object

Examples

## Not run: 
e <- exclude_by_keyword(e, keywords = "tract")

## End(Not run)

Description

Your dataset may contain redundant information because it includes counts at different "ontological" resolutions. SMARTTR initially operates at the highest "resolution" and later on, can fold sub-regions into parent regions later. Therefore redundant counts from parent regions should be removed from datasets using this function prior to analysis and plotting functions.

Usage

exclude_redundant_regions(e, ontology = "allen", channels = NULL)

Arguments

Details

To check and see redundant regions contained in a list of acronyms, see the function check_redundant_parents().

Value

e experiment object

Examples

## Not run: 
e <- exclude_redundant_regions(e)

## End(Not run)

Description

experiment() constructs an S3 object of class 'wb_experiment'. An experiment object consists of a list of processed mouse objects with raw data from slices omitted, and experimental attributes stored as a list.

Usage

experiment(
  experiment_name = NULL,
  experimenters = NULL,
  channels = NULL,
  experiment_groups = NULL,
  drug_groups = NULL,
  sex_groups = NULL,
  cohorts = NULL,
  strains = NULL,
  genotypes = NULL,
  reporters = NULL,
  ages = NULL,
  output_path = "set output path for your experiment",
  ...
)

Arguments

Details

The experimental attributes can be assigned as arguments to the experiment constructor function. See the parameters listed for the default values for these attributes Note that you are able to add custom attributes as keyword pairs, if you would like to keep track of an additional piece of information. However, this will only serve a descriptive purpose and will not be used for analysis. You may not need to use all experimental attributes but fill out as many are applicable to your experiment.

Value

An experiment, a colloquial term for an object of class 'wb_experiment'. An 'experiment' object is also a list, with class list.

See Also

See also mouse() for the description of a mouse object and it's attributes.

Examples

my_experiment <- experiment() # constructs an experiment object

Description

Export the permutation results as a csv file. This automatically saves into the tables folder.

Usage

export_permutation_results(
  e,
  permutation_groups = "all",
  channels = c("cfos"),
  ontology = "allen",
  filter_significant = TRUE
)

Arguments

Examples

## Not run: 
e <- export_permutation_results(e, permutation_groups = "all", filter_significant = TRUE)

## End(Not run)

Description

A filter with parameters for registration used in wholebrain functions.

Usage

filter

Format

A list of parameters to filter features of interest in an image

alim

price, in US dollars

threshold.range

weight of the diamond, in carats

eccentricity

eccentricity (elongation) of contours sets how round you want cell bodies to be. Default is 500 and smaller values equal to more round.

Max

Maximum value to display in the 8-bit rendered (sets sort of brightness contrast)

Min

Minimum value to display in the 8-bit rendered (sets sort of brightness contrast)

brain.threshold

the exact value where you want to start segmeting the brain outline in autofluorescence

resize

resize parameter to match the atlas to your pixel resolution, should be between 0.03 and 0.2 for most applications.

blur

blur parameter that sets the smoothness of the tissue outline, if magaded or jagged edges increase. Using a value fo 4 is usually recommended.

downsample

downsample, default is set to 0.25 and images with a size of 15000 x 8000 pixels can then usually be run smoothly

...

Description

Filters to chosen base parent regions and all child subregions

Usage

filter_regions(
  e,
  base_regions = c("Isocortex", "OLF", "HPF", "CTXsp", "CNU", "TH", "HY", "MB", "HB",
    "CBL"),
  ontology = "allen",
  channels = NULL
)

Arguments

Value

e experiment object

Examples

## Not run: 
e <- filter_regions(e, "Isocortex", channels  = "cfos")

## End(Not run)

Description

Detect, log, and remove outlier counts. This function removes any normalized regions counts that are more than n_sd standard deviations (default = 2) higher than their cohort mean.

Usage

find_outlier_counts(
  e,
  by = c("group", "sex"),
  n_sd = 2,
  remove = FALSE,
  log = TRUE
)

Arguments

Value

e experiment object. Outlier counts in the experiment object are removed if remove = TRUE.

Examples

## Not run: 
e <- find_outlier_counts(e, by = c("group","sex"), n_sd = 2, remove = FALSE, log = TRUE)

## End(Not run)

Description

This function stores a list in a mouse object that is the length of the channels parameter. Each element of the list is a dataframe containing combined cell counts for one channel across all slices processed for that mouse. By default, if one slice has no dataset processed for that particular channel, that slice will be skipped over. The function will run properly but a warning will be thrown indicating you should go back and generate a mapped dataset for that particular slice and channel.

Usage

get_cell_table(m, channels = c("cfos", "eyfp", "colabel"))

Arguments

Value

m mouse object

Examples

## Not run: 
m <- get_cell_tables(m, channels = c("cfos", "eyfp", "colabel"))

## End(Not run)

Description

This analysis will get regional cross correlations based on cell counts normalized by region volume.

Usage

get_correlations(
  e,
  by,
  values,
  channels = c("cfos", "eyfp", "colabel"),
  p_adjust_method = "none",
  alpha = 0.05,
  ontology = "allen",
  method = "pearson",
  anatomical.order = c("Isocortex", "OLF", "HPF", "CTXsp", "CNU", "TH", "HY", "MB", "HB",
    "CB"),
  region_order = NULL
)

Arguments

Value

e experiment object. The experiment object now has a named correlation_list object stored in it. The name of the correlation object is the concatenation of the variable values separated by a "_". This name allows for unambiguous identification of different analysis subgroups in the future.

See Also

Hmisc::rcorr()

Examples

## Not run: 
e <- get_correlations(e, by = c("sex", "group"), values = c("female", "AD"),
channels = c("cfos", "eyfp", "colabel"), alpha = 0.05)

## End(Not run)

Description

This analysis will only include common regions that are included in both the colabelled and cfos or eyfp channels. The colabelled percentage of individual animals will be calculated with the option to export the data.

Usage

get_percent_colabel(
  e,
  by,
  colabel_channel = "colabel",
  channel = "eyfp",
  save_table = TRUE,
  rois = NULL,
  individual = TRUE
)

Arguments

Value

e experiment object with colabelled percentage table stored in it.

Examples

## Not run: 
e <- get_percent_colabel(e, c("group", "sex"), channel = "eyfp"))

## End(Not run)

Description

Calculate the registered area (microns^2^) and the regional volumes (microns^3^) of all regions contained in a slice.

Note: Simplification of the analyzed regions by keywords is highly recommended because there are errors in the wholebrain basecode that results in a mismatch between the region acronym mapped to and the actual registration contour based on the region acronym. This mismatch is most notable in the dentate gyrus subregions. If simplification by keywords is used, this will circumvent the errors.

This function also automatically removes parent regions that are redundant, e.g. "CTX" should by volumetrically represented by summing all subregions, but there is a tiny amount of potential space that allows for cells to get mapped to slim spaces between subregions. This potential anatomical space should be ignored.

Calculate the registered area (microns^2^) and the regional volumes (microns^3^) of all regions contained in a slice. Note: Simplification of the analyzed regions by keywords is HIGHLY RECOMMENDED because there are errors in the wholebrain basecode that results in a mismatch between the region acronym mapped to and the actual registration contour based on the region acronym. This mismatch is most notable in the dentate gyrus subregions, where certain regions are represented twice because the DG curve along the rostral caudal axis. If simplification by keywords is used, this will circumvent the errors.

This function also automatically removes parent regions that are redundant, e.g. "CTX" should by volumetrically represented by summing all subregions, but there is a tiny amount of potential space that allows for cells to get mapped to slim spaces between subregions. This potential anatomical space should be ignored.

Usage

get_registered_volumes(x, ...)

## S3 method for class 'slice'
get_registered_volumes(
  x,
  simplify_regions = TRUE,
  simplify_keywords = c("layer", "part", "stratum", "division", "leaflet",
    "Subgeniculate", "island", "Islands", "Fields of Forel", "Cajal", "Darkschewitsch",
    "Precommissural"),
  ...
)

## S3 method for class 'mouse'
get_registered_volumes(
  x,
  slice_ID,
  hemisphere = NULL,
  simplify_regions = TRUE,
  simplify_keywords = c("layer", "part", "stratum", "division", "leaflet",
    "Subgeniculate", "island", "Islands", "Fields of Forel", "Cajal", "Darkschewitsch",
    "Precommissural"),
  replace = FALSE,
  ...
)

Arguments

Value

a mouse or slice object

s slice object with a stored dataframe with columns 'name' (full region name), 'acronym', 'area' (in microns^2^), 'volume' (in microns^3^) 'right.hemisphere'

m mouse object

Examples

## Not run: 
s <- get_registered_volumes(s)

## End(Not run)
## Not run: 
m <- get_registered_volumes(m, slice_ID = "1_10", hemisphere = "left", replace = FALSE)

## End(Not run)

Description

This function takes an experiment object and imports externally mapped datasets.

Usage

import_mapped_datasets(e, normalized_count_paths, ...)

Arguments

Value

e an experiment object with the imported dataset

Description

Custom method for importing segmentation data for a slice object. This is a flexible method for importing channels aside from cfos and eyfp that use the same ImageJ segmentation macros provided in the SMARTTR pipeline. This method works best when following saving segmentation data with the same naming conventions using the 3D Roi Manager within the 3D ImageJ Suite. Segmentation data is saved in two'.txt' files which output the results of the Measure 3D and Quantif 3D options of the 3D Roi Manager plugin,respectively. A description of what each of those these options measure is provided in the online documentation.

The naming conventions for the ".txt" file storing the Quantif3D results are ⁠Q_*_{channel}_*_{channel}.txt⁠, where the * indicates a wildcard character(s) and {channel} is the channel name without brackets. E.g. "Q_G_eYFP_258_1_1_eYFP.txt"

The naming conventions for the ".txt" file storing the Measure 3D are ⁠M_*_{channel}_*.txt⁠ where the * indicates a wildcard character(s) and {channel} is the channel name without brackets. E.g "M_G_eYFP_258_1_1.txt".

The wildcards characters may be used to store things like date or slice naming information.

The locations of these files must be specified in the slice_directory attribute of the slice_object. Otherwise, the root folder containing the registration image is searched. This attribute can be stored when initializing the slice object or can be edited afterwards.

Method for custom importation of segmentation data for a mouse object

Usage

import_segmentation_custom(x, ...)

## S3 method for class 'slice'
import_segmentation_custom(
  x,
  channel,
  x_col = NULL,
  y_col = NULL,
  meas_path = NULL,
  quant_path = NULL,
  ...
)

## S3 method for class 'mouse'
import_segmentation_custom(
  x,
  channel,
  slice_ID = NA,
  hemisphere = NULL,
  x_col = NULL,
  y_col = NULL,
  meas_path = NULL,
  quant_path = NULL,
  ...
)

Arguments

Value

a mouse or slice object

s slice object

m mouse object

Examples

## Not run: 
s <-  import_segmentation_custom(s, mouse_ID = "255", channel = "cfos")

## End(Not run)
## Not run: 
m <-  import_segmentation(m, slice_ID = "1_10", channels = c("PV"), replace = FALSE)

## End(Not run)

Description

Method for importing segmentation data for a slice object

Method for importing segmentation data for a mouse object

Usage

import_segmentation_ij(x, ...)

## S3 method for class 'slice'
import_segmentation_ij(x, mouse_ID = NA, channels = NULL, maxdist = 10, ...)

## S3 method for class 'mouse'
import_segmentation_ij(
  x,
  slice_ID = NA,
  hemisphere = NULL,
  channels = NULL,
  maxdist = 10,
  replace = FALSE,
  ...
)

Arguments

Value

a mouse or slice object

s slice object

m mouse object

Note

The designated colabel channel name in this pipeline will auto import the output of the batch_3D_MultiColocalization.ijm macro provided in the pre-processing pipeline. If you have a separate method used for detecting colabelled cells, please use a different naming convention for this channel, e.g. "colabel_PV_cfos", and import using a customized import function such as import_segmentation_custom().

Examples

## Not run: 
s <-  import_segmentation(s, mouse_ID = "255")
s <-  import_segmentation(s, mouse_ID = "255",
channels = c("cfos", "eyfp", "colabel"))

## End(Not run)
## Not run: 
m <-  import_segmentation(m, slice_ID = "1_10",
channels = c("cfos", "eyfp", "colabel"), replace = FALSE)

## End(Not run)

Description

Make a wholebrain compatible segmentation object for a slice in a slice object

Make a wholebrain compatible segmentation object for a slice in a mouse object

Usage

make_segmentation_object(x, ...)

## S3 method for class 'slice'
make_segmentation_object(
  x,
  mouse_ID = NA,
  channels = NULL,
  use_filter = FALSE,
  ...
)

## S3 method for class 'mouse'
make_segmentation_object(
  x,
  slice_ID = NA,
  hemisphere = NULL,
  channels = NULL,
  replace = FALSE,
  use_filter = FALSE,
  ...
)

Arguments

Value

a mouse or slice object

s slice object

Note

If you are processing the colabel channel, the X and Y positions of colabelled cells are the average of the x,y centroid coordinates of the colabelled objects

Examples

## Not run: 
s <- make_segmentation_object(s, mouse_ID = "255",
channels = c("cfos", "eyfp"), use_filter = FALSE)

## End(Not run)
## Not run: 
m <-  make_segmentation_object(m, slice_ID = '1_9', hemisphere = 'left',
channels = c('eyfp', 'cfos', 'colabel'), use_filter = FALSE)

## End(Not run)

Description

Method for forward warping segmentation data to atlas space for a slice object. Requires segmentation objects to be made for channels specified and a registration object.

Method for forward warping segmentation data to atlas space for a slice within a mouse object. Requires segmentation objects to be made for the channels specified and a registration.

Usage

map_cells_to_atlas(x, ...)

## S3 method for class 'slice'
map_cells_to_atlas(
  x,
  channels = NULL,
  clean = TRUE,
  display = TRUE,
  mouse_ID = NULL,
  ...
)

## S3 method for class 'mouse'
map_cells_to_atlas(
  x,
  slice_ID = NA,
  hemisphere = NULL,
  channels = NULL,
  clean = TRUE,
  display = TRUE,
  replace = FALSE,
  ...
)

Arguments

Value

a mouse or slice object

m mouse object

Examples

## Not run: 
s <- map_cells_to_atlas(s, channels c('cfos' , 'eyfp', 'colabel'),
clean = TRUE, display = TRUE, mouse_ID = "255")

## End(Not run)
## Not run: 
 m <- map_cells_to_atlas(m, slice_ID = "1_10", hemisphere = NULL,
 channels = c("cfos", "eyfp", "colabel"), clean = TRUE, replace = FALSE)

## End(Not run)

Description

mouse() constructs an S3 object of class 'mouse'. A mouse object consists of a list of slice objects and attributes stored as a list.

The slice objects are added to a mouse object with the function add_slice(). Each slice is a named element in the mouse object list, with the naming convention dependent on the slice ID and hemisphere attributes of the slice object.

If you are processing either a left or right hemisphere, the slice is named with the convention: "slice_ID" appended with its "hemisphere" If the hemisphere attribute is NULL, i.e. if the whole slice aligns well with a single atlas plate and there is no need to create separate slice objects per hemisphere, then the slice is named with the convention: "slice_ID"

Usage

mouse(
  mouse_ID = "set ID",
  sex = "female",
  age = NULL,
  genotype = NULL,
  reporter = NULL,
  strain = NULL,
  experiment = NULL,
  group = NULL,
  drug = NULL,
  cohort = NULL,
  input_path = "set input path",
  output_path = "set output path",
  ...
)

Arguments

Details

The mouse attributes can be assigned as arguments to the mouse constructor function. See the parameters listed for the default values for these attributes Note that you are able to add custom attributes as keyword pairs, if you would like to keep track of an additional piece of information. However, this will only serve a descriptive purpose and will not be used for analysis. You may not need to use all mouse attributes but fill out as many are applicable to your experiment.

Value

A mouse, a colloquial term for an object of class 'mouse'. A 'mouse' object is also a list, with class list.

See Also

See also slice() for the description of a slice object and it's attributes. See also add_slice() for the description of how to add a slice object to a mouse object.

Examples

mouse_example <- mouse() # initializes a mouse object

Description

Run this function after all the slices that you want to process are finished being added and you have combined your cell counts with get_cell_table(). This functions process all channels where a cell table was made using the latter function.

Usage

normalize_cell_counts(
  m,
  combine_hemispheres = TRUE,
  simplify_regions = TRUE,
  simplify_keywords = c("layer", "part", "stratum", "division", "leaflet",
    "Subgeniculate", "island", "Islands", "Fields of Forel", "Cajal", "Darkschewitsch",
    "Precommissural"),
  split_hipp_DV = TRUE,
  DV_split_AP_thresh = -2.7
)

Arguments

Value

m mouse object

Examples

## Not run: 
m <- normalize_cell_counts(m, combine_hemispheres = TRUE, simplify_regions = TRUE)

## End(Not run)

Description

This function can only be run after running combine_cell_counts(). It divides the colabelled cell counts by a designated normalization channel to provide a normalized ratio. Please note that the areas and volumes cancel out in this operation. This is not designed to work on multiple hemispheres. Please combine cell counts across multiple hemispheres when you run normalize_cell_counts().

Usage

normalize_colabel_counts(e, denominator_channel = "eyfp")

Arguments

Value

e An experiment object with a new dataframe with the normalized ratios of colabelled counts over the designated denominator counts. Because the volumes and region areas cancel out, the values of count, normalized.count.by.area, and normalized.count.by.volume are all the same. This is to provide a consistent input dataframe into the analysis functions.

See Also

combine_cell_counts() & normalize_cell_counts()

Examples

## Not run: 
e <- normalize_colabel_counts(e, denominator_channel = "eyfp")

## End(Not run)

Description

A custom adjustment of the Allen Common Coordinate Framework where the hippocampus (CA1, CA2, CA3, and DG) and it's subregions are split into dorsal and ventral regions and acronyms. They are given unique IDs and parent ids except for the c("dCA1", "dCA2", "dCA3", "dDG") and c("vCA1", "vCA2", "vCA3", "vDG"), whose parents are c(CA1, CA2, CA3, and DG) respectively.

Usage

ontology

Format

A dataframe

Description

The combined ontology created by the Yongsoo Kim lab. Combines nomenclature of the two most-used mouse brain atlases, the Franklin and Paxinos (FP) and the common coordinate framework (CCF) from the Allen Institute for Brain Science.

Usage

ontology.unified

Format

A dataframe

Description

Plot the correlation difference between two comparison groups into a parallel coordinate plot. The function correlation_diff_permutation() must be run first in order to generate results to plot.

Usage

parallel_coordinate_plot(
  e,
  permutation_comparison = "AD_vs_control",
  channels = c("cfos", "eyfp", "colabel"),
  colors = c("#be0000", "#00782e", "#f09b08"),
  x_label_group_1 = NULL,
  x_label_group_2 = NULL,
  height = 10,
  width = 10,
  print_plot = FALSE,
  save_plot = TRUE,
  reverse_group_order = FALSE,
  force = 1,
  plt_theme = NULL,
  label_size = 30,
  image_ext = ".png",
  nudge_x = 2:5
)

Arguments

Value

p_list A list the same length as the number of channels, with each element containing a plot handle for that channel.

Examples

## Not run: 
p_list <- parallel_coordinate_plot(e, permutation_comparison = "Context_vs_Shock",
 channels = "cfos", colors ="#be0000", x_label_group1 = "Context", x_label_group_2 = "Shock")

## End(Not run)

Description

Bar plot of betweenness per region in descending magnitude

Usage

plot_betweenness_regions(
  e,
  channels = c("cfos", "eyfp"),
  colors = c("red", "green"),
  network = "AD",
  title = "",
  height = 10,
  width = 20,
  ylim = c(0, 15),
  filter_isolates = TRUE,
  sort_super_region = FALSE,
  region_label_angle = 60,
  label_text_size = 12,
  image_ext = ".png",
  print_plot = FALSE,
  save_plot = TRUE,
  theme.bar = NULL
)

Arguments

Value

p_list A list the same length as the number of channels, with each element containing a plot handle for that channel.

Examples

## Not run: 
p <- plot_betweenness_regions(e, colors ="#660000", channels = "cfos", region_label_angle = 60,
ylim = c(0, 50), image_ext = ".png")

## End(Not run)

Description

Plot correlation heatmaps

Usage

plot_correlation_heatmaps(
  e,
  correlation_list_name,
  channels = c("cfos", "eyfp", "colabel"),
  colors = c("#be0000", "#00782e", "#f09b08"),
  sig_color = "yellow",
  sig_nudge_y = -0.7,
  sig_size = 7,
  ontology = "allen",
  anatomical.order = c("Isocortex", "OLF", "HPF", "CTXsp", "CNU", "TH", "HY", "MB", "HB",
    "CB"),
  print_plot = FALSE,
  save_plot = TRUE,
  image_ext = ".png",
  plot_title = NULL,
  height = 10,
  width = 10,
  theme.hm = ggplot2::theme(axis.text.x = element_text(hjust = 1, vjust = 0.5, angle =
    90, size = 8), axis.text.y = element_text(vjust = 0.5, size = 8), plot.title =
    element_text(hjust = 0.5, size = 36), axis.title = element_text(size = 18),
    legend.text = element_text(size = 22), legend.key.height = unit(100, "points"),
    legend.title = element_text(size = 22), panel.spacing = unit(0.2, "lines"),
    strip.text.x = element_text(angle = 0, hjust = 0.5, vjust = 0.5, size = 10),
    strip.text.y = element_text(angle = 270, 
     hjust = 0.5, vjust = 0.5, size = 10),
    strip.placement = "outside"),
  strip_background_colors = "lightblue"
)

Arguments

Value

p_list A list the same length as the number of channels, with each element containing a plot handle for that channel.

See Also

get_correlations()

Examples

## Not run: 
plot_correlation_heatmaps(e, correlation_list_name = "female_AD")

## End(Not run)

Description

Plot a stacked bar plot of the degree distributions.

Usage

plot_degree_distributions(
  e,
  channels = c("cfos", "eyfp"),
  color_palettes = c("reds", "greens"),
  colors_manual = NULL,
  labels = c(female_AD = "female_AD_label", female_control = "female_control_label"),
  title = "my_title",
  height = 15,
  width = 15,
  xlim = c(0, 20),
  ylim = c(0, 15),
  image_ext = ".png",
  print_plot = FALSE,
  theme.gg = NULL,
  save_plot = TRUE
)

Arguments

Value

p_list A list the same length as the number of channels, with each element containing a plot handle for that channel.

Examples

## Not run: 
p <- plot_degree_distributions(e, channels = "cfos",
labels = c(female_AD = "female_AD_label", female_control = "female_control_label"),
title = "my title", image_ext = ".png")

## End(Not run)

Description

Bar plot of degree per region in descending magnitude

Usage

plot_degree_regions(
  e,
  channels = c("cfos", "eyfp"),
  colors = c("red", "green"),
  network = "AD",
  title = "",
  height = 10,
  width = 20,
  ylim = c(0, 15),
  sort_super_region = FALSE,
  region_label_angle = 60,
  label_text_size = 12,
  filter_isolates = TRUE,
  image_ext = ".png",
  print_plot = FALSE,
  save_plot = TRUE,
  theme.bar = NULL
)

Arguments

Value

p_list A list the same length as the number of channels, with each element containing a plot handle for that channel.

Examples

## Not run: 
p <- plot_degree_regions(e, colors = c("#660000", "#FF0000"),
channels = "cfos", region_label_angle = 60,
ylim = c(0, 15), image_ext = ".png")

## End(Not run)

Description

Plot the networks stored in an experiment object

Usage

plot_joined_networks(
  e,
  correlation_list_names = c("male_agg", "female_non"),
  title = NULL,
  channels = "cfos",
  absolute_weight = TRUE,
  edge_colors = c(male_agg_pos = "#06537f", male_agg_neg = "#526c7a", female_non_pos =
    "#C70039", female_non_neg = "#71585f"),
  edge_color_labels = c(male_agg_pos = "Positive male", male_agg_neg = "Negative male",
    female_non_pos = "Positive female", female_non_neg = "Negative female"),
  height = 15,
  width = 15,
  region_legend = TRUE,
  degree_scale_limit = c(1, 10),
  correlation_edge_width_limit = c(0.8, 1),
  image_ext = ".png",
  print_plot = FALSE,
  graph_theme = NULL,
  transparent_edge_group1 = TRUE,
  transparent_edge_group2 = FALSE,
  label_size = 5,
  label_offset = 0.15,
  edge_thickness_range = c(1, 5),
  node_size_range = c(1, 8),
  anatomical.colors = NULL,
  save_plot = TRUE
)

Arguments

Value

p_list A list the same length as the number of channels, with each element containing a plot handle for that channel.

Examples

## Not run: 
p_list <- plot_joined_networks(anesthesia, correlation_list_names = c("male_agg", "female_non"),
channels = "cfos", edge_colors = c(male_agg =  "#06537f", female_non = "#C70039"),
edge_color_labels = c(male_agg = "Male aggressor", female_non = "Female non-aggressor"),
degree_scale_limit = c(1,45), correlation_edge_width_limit = c(0.8, 1.0),
height = 30, width = 30, label_size = 13, label_offset = 0.08, image_ext = ".png")

## End(Not run)

Description

Plot the mean betweenness centrality of the networks in a barplot. Error bars are plotted as SEM.

Usage

plot_mean_between_centrality(
  e,
  color_palettes = c("reds", "greens"),
  colors_manual = NULL,
  channels = c("cfos", "eyfp"),
  labels = c(AD = "AD_label", control = "control_label"),
  title = "my_title",
  height = 10,
  width = 10,
  label_angle = 60,
  rev_x_scale = FALSE,
  ylim = c(0, 50),
  theme.gg = NULL,
  image_ext = ".png",
  print_plot = FALSE,
  save_plot = TRUE
)

Arguments

Value

p_list A list the same length as the number of channels, with each element containing a plot handle for that channel.

Examples

## Not run: 
p <- plot_mean_between_centrality(e, colors_manual = c("#660000", "#FF0000"),
channels = "cfos", labels = c("AD" = "AD_label", "control" = "control_label"),
title = "my title", ylim = c(0, 50), image_ext = ".png")

## End(Not run)

Description

Plot the mean clustering coefficients of the networks in a barplot. Error bars are plotted as SEM.

Usage

plot_mean_clust_coeff(
  e,
  color_palettes = c("reds", "greens"),
  colors_manual = NULL,
  channels = c("cfos", "eyfp"),
  labels = c(AD = "AD_label", control = "control_label"),
  title = "my_title",
  height = 10,
  width = 10,
  label_angle = 60,
  rev_x_scale = FALSE,
  ylim = c(0, 0.7),
  theme.gg = NULL,
  image_ext = ".png",
  print_plot = FALSE,
  save_plot = TRUE
)

Arguments

Value

p_list A list the same length as the number of channels, with each element containing a plot handle for that channel.

Examples

## Not run: 
p <- plot_mean_clust_coeff(e, colors_manual = c("#660000", "#FF0000"),
channels = "cfos", labels = c("AD" = "AD_label", "control" = "control_label"),
title = "my title", ylim = c(0, 0.7), image_ext = ".png")

## End(Not run)

Description

Plot the mean degree of the networks in a barplot. Error bars are plotted as SEM.

Usage

plot_mean_degree(
  e,
  color_palettes = c("reds", "greens"),
  colors_manual = NULL,
  channels = c("cfos", "eyfp"),
  labels = c(AD = "AD_label", control = "control_label"),
  title = "my_title",
  height = 10,
  width = 10,
  label_angle = 60,
  rev_x_scale = FALSE,
  ylim = c(0, 70),
  theme.gg = NULL,
  image_ext = ".png",
  print_plot = FALSE,
  save_plot = TRUE
)

Arguments

Value

p_list A list the same length as the number of channels, with each element containing a plot handle for that channel.

Examples

## Not run: 
p <- plot_mean_degree(e, colors_manual = c("#660000", "#FF0000"),
channels = "cfos", labels = c("AD" = "AD_label", "control" = "control_label"),
title = "my title", ylim = c(0,100), image_ext = ".png")

## End(Not run)

Description

Plot the mean global efficiency of the networks in a barplot. Error bars are plotted as SEM.

Usage

plot_mean_global_effic(
  e,
  color_palettes = c("reds", "greens"),
  colors_manual = NULL,
  channels = c("cfos", "eyfp"),
  labels = c(AD = "AD_label", control = "control_label"),
  title = "my_title",
  height = 10,
  width = 10,
  label_angle = 60,
  rev_x_scale = FALSE,
  ylim = c(0, 0.7),
  theme.gg = NULL,
  image_ext = ".png",
  print_plot = FALSE,
  save_plot = TRUE
)

Arguments

Value

p_list A list the same length as the number of channels, with each element containing a plot handle for that channel.

Examples

## Not run: 
p <- plot_mean_global_effic(e, colors_manual = c("#660000", "#FF0000"),
channels = "cfos", labels = c("AD" = "AD_label", "control" = "control_label"),
title = "my title", ylim = c(0, 0.7), image_ext = ".png")

## End(Not run)

Description

Plot the networks stored in an experiment object

Usage

plot_networks(
  e,
  network_name = "AD",
  title = NULL,
  channels = c("cfos", "eyfp", "colabel"),
  edge_color = "firebrick",
  height = 15,
  width = 15,
  edge_type = "arc",
  region_legend = TRUE,
  degree_scale_limit = c(1, 10),
  anatomical.colors = NULL,
  correlation_edge_width_limit = c(0.8, 1),
  image_ext = ".png",
  print_plot = FALSE,
  graph_theme = NULL,
  label_size = 5,
  edge_thickness_range = c(1, 5),
  node_size_range = c(1, 8),
  label_offset = 0.15,
  save_plot = TRUE
)

Arguments

Value

p_list A list the same length as the number of channels, with each element containing a plot handle for that channel.

Examples

## Not run: 
p <- plot_networks(e, network_name = "Shock", channels = "cfos")

## End(Not run)

Description

Plot the cell counts normalized by volume for a given channel

Usage

plot_normalized_counts(
  e,
  channels = c("cfos", "eyfp", "colabel"),
  by = c("sex", "group"),
  values = list(c("female", "non"), c("female", "agg"), c("female", "control"), c("male",
    "agg"), c("male", "control")),
  colors = c("white", "lightblue", "black", "red", "green"),
  ontology = "allen",
  title = NULL,
  unit_label = bquote(`Cell counts `("cells/mm"^3)),
  anatomical.order = c("Isocortex", "OLF", "HPF", "CTXsp", "CNU", "TH", "HY", "MB", "HB",
    "CB"),
  height = 7,
  width = 20,
  print_plot = FALSE,
  save_plot = TRUE,
  flip_axis = FALSE,
  reverse_colors = FALSE,
  limits = c(0, 1e+05),
  facet_background_color = NULL,
  strip_background_colors = "lightblue",
  plot_theme = ggplot2::theme(plot.background = element_blank(), panel.grid.major =
    element_blank(), panel.grid.minor = element_blank(), panel.border = element_blank(),
    axis.line = element_line(color = "black"), legend.justification = c(0, 0),
    legend.position = "inside", legend.position.inside = c(0.05, 0.6), legend.direction =
    "vertical", axis.text.y = element_text(angle = 50, hjust = 1, size = 8, color =
    "black"), axis.text.x = element_text(color = "black"), strip.text.y =
    element_text(angle = 0, margin = ggplot2::margin(t = 5, 
     r = 5, b = 5, l = 5,
    unit = "pt")), strip.placement = "outside", strip.switch.pad.grid = unit(0.1, "in")),
  image_ext = ".pdf"
)

Arguments

Value

p_list A list the same length as the number of channels, with each element containing a plot handle for that channel.

Examples

## Not run: 
p_list <- plot_normalized_counts(e, channels = "cfos", by = c("sex", "group"),
values = list(c("female", "non"), c("female", "agg")), colors = c("white", "lightblue"))

## End(Not run)

Description

Allows for specification of specific brain regions to plot. Two different mouse attributes can be used as categorical variables to map to either the color or pattern aesthetics of the bar plot, e.g. sex and experimental group. The color aesthetic takes precedence over the pattern aesthetic so if you only want to use one mouse attribute, for plotting set it to the color_mapping parameter and set the pattern_mapping parameter to NULL.

Usage

plot_percent_colabel(
  e,
  colabel_channel = "colabel",
  channel = "eyfp",
  rois = c("AAA", "dDG", "HY"),
  color_mapping = "sex",
  colors = c("#952899", "#358a9c"),
  pattern_mapping = NULL,
  patterns = c("gray100", "hs_fdiagonal",
    "hs_horizontal\n                                              ", "gray90",
    "hs_vertical"),
  error_bar = "sem",
  ylim = c(0, 100),
  plot_individual = TRUE,
  height = 8,
  width = 8,
  print_plot = FALSE,
  save_plot = TRUE,
  image_ext = ".png"
)

Arguments

Value

p Plot handle to the figure

Examples

## Not run: 
 plot_percentage_colabel

## End(Not run)

Description

Plot a permutation difference network e experiment object

Usage

plot_perm_diff_network(
  e,
  permutation_group,
  channel = "cfos",
  layout = "stress",
  seed = 30,
  degree_scale_limit = c(1, 15),
  node_size_range = c(3, 15),
  edge_color_pos = "#04C3C3",
  edge_color_neg = "#BF1F1F",
  anatomical.colors = c(Isocortex = "#70ff71", OLF = "#75aadb", HPF = "#7ed04b", CTXsp =
    "#8ada87", CNU = "#98d6f9", TH = "#ff7080", HY = "#e64438", MB = "#ff64ff", HB =
    "#ff9b88"),
  pos_edge_legend_label = "",
  neg_edge_legend_label = "",
  correlation_edge_width_limit = c(0, 2),
  edge_thickness_range = c(0, 6),
  node_text_size = 12,
  graph_theme = NULL,
  mark_hull = TRUE,
  save_plot = TRUE,
  height = 15,
  width = 15,
  image_ext = ".png"
)

Arguments

Value

p A plot handle.

Examples

## Not run: 
p <- plot_perm_diff_network(anesthesia,
permutation_group = "Control_vs_Isoflurane",
channel = "cfos",
pos_edge_legend_label = "Iso > Ctrl",
neg_edge_legend_label = "Iso < Ctrl",
layout = "fr",
mark_hull=TRUE,
node_text_size = 14,
seed = 15,
width = 20,
height = 20,
graph_theme = graph_theme,
anatomical.colors = anatomical.colors,
image_ext = ".png")

## End(Not run)

Description

Print attributes of correlation_list object

Usage

## S3 method for class 'correlation_list'
print(x, ...)

Arguments

Description

Print attributes of experiment object

Usage

## S3 method for class 'experiment'
print(x, ...)

Arguments

Examples

e <- experiment()
print(e)

Description

Print attributes of mouse object

Usage

## S3 method for class 'mouse'
print(x, ...)

Arguments

Examples

m <- mouse()
print(m)

Description

Print attributes of slice object

Usage

## S3 method for class 'slice'
print(x, ...)

Arguments

Examples

s <- slice()
print(s)

Description

Read a csv or excel file as a tibble. Checks first that the file exists, and that it is a csv or xlsx format.

Usage

read_check_file(x, ...)

Arguments

Value

tibble dataframe

Description

Register (generic function)

Register a slice in a slice object

Register a slice in a mouse object. If a slice has been previously registered, the default behavior is to continue modifying the previous registration. Use the replace parameter to change this behavior.

Usage

register(x, ...)

## S3 method for class 'slice'
register(x, filter = NULL, ...)

## S3 method for class 'mouse'
register(
  x,
  slice_ID = NA,
  hemisphere = NULL,
  filter = NULL,
  replace = FALSE,
  ...
)

Arguments

Value

a mouse or slice object

s a slice object

m mouse object

Examples

## Not run: 
s <- register(s)

## End(Not run)
## Not run: 
m <- register(m, slice_ID = '1_10', hemisphere = "left", filter = my_filter)

## End(Not run)

Description

This function takes a mouse object and also a input_path as the root folder for that mouse. It then adjusts all the paths for the registration and segmentation data read to be relative to the root folder. This function is especially useful if you have changed the computers you are analyzing and drive mappings may be different.

Usage

reset_mouse_root(m, input_path = NULL, print = TRUE)

Arguments

Value

m a mouse object

Examples

## Not run: 
m <- reset_mouse_root(m, input_path = "C:/Users/Documents/Mice/mouse_1/", print = TRUE)

## End(Not run)

Description

Not that this keeps other characteristics constant (such as preserved degree sequence). These null networks can them be used to compare against and normalize the empirical networks. Currently this essentially erases edge metrics and treats networks like binary graphs. Edge weights are not used in calculating network topology metrics.

Usage

rewire_network(
  e,
  network_name,
  channels = "cfos",
  method = "ms",
  ontology = "allen",
  n_rewires = 10000,
  n_networks = 100,
  return_graphs = FALSE,
  seed = 5
)

Arguments

Value

Summary table of rewired network properties of all nodes showing the average of all randomized network properties generated.

Examples

## Not run: 
summary_table <- rewire_network(e, network_name = "network1", channels = "cfos",
n_rewire =  igraph::gsize(e$networks$network1$cfos)*100, n_networks = 100)

## End(Not run)

Description

Saves experiment object into it's attribute output path as an RDATA file save_experiment(e)

Usage

save_experiment(..., timestamp = FALSE)

Arguments

Examples

## Not run: 
e <- save_experiment(e, timestamp = TRUE)

## End(Not run)

Description

Saves mouse object into it's attribute output path as an RDATA file save_mouse(m)

Usage

save_mouse(..., timestamp = FALSE)

Arguments

Examples

## Not run: 
m <- save_mouse(m, timestamp = TRUE)

## End(Not run)

Description

segmentation object compatible with wholebrain package functions

Usage

segmentation.object

Format

A

filter

list storing parameter use to segment and get brain contours

soma

list storing cell count data

Description

Standard error function

Usage

sem(x)

Arguments

Value

numeric

Examples

sem(c(3,4,5))

Description

Simplify dataframe by keywords.

Usage

simplify_by_keywords(
  df,
  keywords = c("layer", "part", "stratum", "division", "leaflet", "Subgeniculate",
    "island", "Islands", "Fields of Forel", "Cajal", "Darkschewitsch", "Precommissural"),
  ontology = "allen",
  dont_fold = c("Dorsal part of the lateral geniculate complex",
    "Ventral posterolateral nucleus of the thalamus, parvicellular part",
    "Ventral posteromedial nucleus of the thalamus, parvicellular part",
    "Ventral posterolateral nucleus of the thalamus, parvicellular part",
    "Ventral posteromedial nucleus of the thalamus, parvicellular part",
    "Substantia nigra")
)

Arguments

Value

df

Examples

df <- dplyr::tibble(acronym = c("LGd", "GU4", "dCA1so" ),
name = c("Dorsal part of the lateral geniculate complex",
"Gustatory areas, layer 4", "Field CA2, stratum oriens (dorsal)"))
simplify_by_keywords(df)

df <- dplyr::tibble(acronym = c("LPBD", "MPBE", "CPre"),
name = c("Lateral parabrachial nucleus, dorsal part",
"Medial parabrachial nucleus, external part", "Caudoputamen- rostral extreme"))
simplify_by_keywords(df, keywords = c("dorsal part", "external part",
"Caudoputamen-"), ontology = "unified")

Description

This function is designed to offer flexible simplification of mapped cells counts. This can be applied after running combine_cell_counts(). However, if mapping is being conducted using the SMARTTR package, we recommend simplifying mapped counts earlier, at the level of mouse objects using normalize_cell_counts() because the options offered for simplification are more flexible. The benefit of this function is that it can operate on experiment objects with externally imported combined cell counts tables that are formatted for compatibility. This allows for simplification using other ontologies. See the available atlas options under the ontology parameter.

Usage

simplify_cell_count(
  e,
  ontology = "allen",
  simplify_keywords = c("layer", "part", "stratum", "division", "leaflet",
    "Subgeniculate", "island", "Islands", "Fields of Forel", "Cajal", "Darkschewitsch",
    "Precommissural"),
  dont_fold = c("Dorsal part of the lateral geniculate complex",
    "Ventral posterolateral nucleus of the thalamus, parvicellular part",
    "Ventral posteromedial nucleus of the thalamus, parvicellular part",
    "Ventral posterolateral nucleus of the thalamus, parvicellular part",
    "Ventral posteromedial nucleus of the thalamus, parvicellular part",
    "Substantia nigra")
)

Arguments

Value

e experiment object with simplified keywords

Examples

## Not run: 
e <- simplify_cell_count(e)

## End(Not run)

Description

Simplify vector of acronyms by keywords.

Usage

simplify_vec_by_keywords(
  vec,
  keywords = c("layer", "part", "stratum", "division", "leaflet", "Subgeniculate",
    "island", "Islands", "Fields of Forel", "Cajal", "Darkschewitsch", "Precommissural"),
  ontology = "allen",
  dont_fold = c("Dorsal part of the lateral geniculate complex",
    "Ventral posterolateral nucleus of the thalamus, parvicellular part",
    "Ventral posteromedial nucleus of the thalamus, parvicellular part",
    "Ventral posterolateral nucleus of the thalamus, parvicellular part",
    "Ventral posteromedial nucleus of the thalamus, parvicellular part",
    "Substantia nigra")
)

Arguments

Value

df, dataframe as a tibble with included long name and acronyms that are simplified to parents

Examples

acronyms <- c("LGd", "GU4", "dCA1so" )
simplify_vec_by_keywords(acronyms)

acronyms <- c("LPBD", "MPBE", "CPre")
simplify_vec_by_keywords(acronyms,
keywords = c("dorsal part", "external part", "Caudoputamen-"), ontology = "unified")

Description

slice() constructs an S3 object of class 'slice'. A slice object consists of a list of lists storing information about registration, segmentation, raw mapped data and cleaned mapped data. The object attributes are also stored as a list.

Usage

slice(
  slice_ID = NA,
  coordinate = -1,
  atlas_plate = NA,
  conversion_factor = 1.0833,
  bin = 1,
  z_width = 24,
  hemisphere = NULL,
  channels = c("eyfp", "cfos", "colabel"),
  registration_path = "set registration image path",
  segmentation_path = "set segmentation data path",
  slice_directory = NULL,
  left_regions_excluded = c("fiber tracts", "VS"),
  right_regions_excluded = c("fiber tracts", "VS"),
  left_regions_included = NULL,
  right_regions_included = NULL,
  ...
)

Arguments

Details

The slice attributes can be assigned as arguments to the slice constructor function. See the parameters listed for the default values for these attributes Note that you are able to add custom attributes as keyword pairs, if you would like to keep track of an additional piece of information. However, this will only serve a descriptive purpose and will not be used for analysis. You may not need to use all slice attributes but fill out as many are applicable to your experiment.

Value

A slice, a colloquial term for an object of class 'slice'. A 'slice' object is also a list, with class list.

Examples

slice_example <- slice() # initializes a slice object

Description

SMARTTR. This package allows for the user-friendly pre-processing of segmentation data generated from ImageJ to a be compatible with the wholebrain package to generate region-based cell counts that are normalized by volume. It will also provides tools for data analysis based on experimental groupings.

Details

Object descriptions The data for analysis will be stored in objects that allow for more neat bundling of useful information together.

A slice object will contain all the data related to registration, segmentation for each channel, and cell counts for a particular image. It will also contain “metadata” about your experimental images, such as what the experimenter-assigned slice ID is, which brain atlas AP coordinate matches best with the given image, and what the path to the image used for registration is. These metadata are stored as the object’s attributes.

A mouse object is an object that will store multiple slice objects (and therefore all the information in it), and will eventually store the combined cell data and the region cell counts normalized by volume. Like a slice, it will also contain “metadata” about your mouse stored as attributes. An experimentAn experiment object consists of a list of processed mouse objects with raw data from slices omitted, and experimental attributes stored as a list. It will also contain “metadata” about your experimental personnel and analysis groups stored as attributes.

The package currently allows for easy implementation of the following steps

1. Setting up the pipeline by specifying experimentparameters, and save directories.

2. The interactive registration process.

3. Importing raw segmentation data from .txt files generated from ImageJ for multiple channels.

4. Optionally creating a filter for the 'cfos' and 'eyfp' channels to clean segmented counts.

5. Creating a segmentation object that is compatible with wholebrain functions.

6. Forward warping and mapping the data onto the standardized mouse atlas.

7. Cleaning the mapped data in all the following ways: + Removing cells that map outside the boundaries of the atlas.

   • Omitting regions by a default list of regions to omit.

   • Omitting regions by user specified region acronyms.

   • Removing Layer 1 cells

   • Removing cells from a contralateral hemisphere per slice if the registrations are divided by right and left hemispheres.

8. Obtaining cell counts normalized by region volume (per mm^2^) and region areas (per mm^2^).

Description

Summarize multiple networks. calculate network statistics for each network. This is not meant to summarize networks created using create_joined_networks.

Usage

summarise_networks(
  e,
  network_names,
  channels = c("cfos", "eyfp", "colabel"),
  save_stats = TRUE,
  save_degree_distribution = TRUE,
  save_betweenness_distribution = TRUE,
  save_efficiency_distribution = TRUE
)

Arguments