Function reference • SMARTTR

All functions

add_mouse(): Add mouse object to an experiment

add_slice(): Add slice to a mouse object

adjust_brain_outline(): Adjust brain outline.

attr2match: attr2match

check_ontology_coding(): Checks the acronyms and full length region names to match with internally stored ontology

check_redundant_parents(): 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.

combine_cell_counts(): Combine cell counts across all mice in an experiment into a single dataframe.

correlation_diff_permutation(): This function performs a permutation analysis to compare the region pairwise correlation coefficients between two different analysis groups.

create_joined_networks(): Create a joined network to visualize overlapping connections with the same outer joined node set.

create_networks(): Create graph objects for plotting different analysis subgroups.

create_perm_diff_network(): 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.

detect_single_slice_regions(): Detect atlas regions that only show up in a single slice object within a mouse.

enough_mice_per_group(): 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.

exclude_anatomy(): exclude_anatomy (generic function)

exclude_by_acronym(): Excluded user chosen regions by entering acronyms

exclude_by_keyword(): Excluded user chosen regions by keywords found in long-form name

exclude_redundant_regions(): Exclude redundant regions

experiment(): Create an experiment object

export_permutation_results(): Export the permutation results as a csv file. This automatically saves into the tables folder.

filter: A filter with parameters for registration used in wholebrain functions.

filter_regions(): Filters to chosen base parent regions and all child subregions

find_outlier_counts(): 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.

get_cell_table(): Get cell tables

get_correlations(): Get regional cross correlations and their p-values in a correlation list object.

get_percent_colabel(): Get the percentage of colabelled cells over either cfos or eyfp channels.

get_registered_volumes(): get_registered_volumes (generic function)

import_mapped_datasets(): Import externally mapped datasets into an experiment

import_segmentation_custom(): import_segmentation (generic function)

import_segmentation_ij(): import_segmentation (generic function)

make_segmentation_object(): make_segmentation_object (generic funciton)

map_cells_to_atlas(): map_cells_to_atlas (generic function)

mouse(): Create a mouse object

normalize_cell_counts(): Normalize cell counts per mm^2^ or by mm^3^ (if multiplying by the stack size).

normalize_colabel_counts(): Normalize colabel counts over a designated denominator channel.

ontology: Ontology

ontology.unified: Unified Kim ontology

parallel_coordinate_plot(): Create a parallel coordinate plot

plot_betweenness_regions(): Plot the betweenness distributions across regions

plot_correlation_heatmaps(): Plot correlation heatmaps

plot_degree_distributions(): Plot the degree distributions

plot_degree_regions(): Plot the degree distributions across regions

plot_joined_networks(): Plot the networks stored in an experiment object

plot_mean_between_centrality(): Plot mean betweenness centrality

plot_mean_clust_coeff(): Plot mean clustering coefficient

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

plot_mean_global_effic(): Plot mean global efficiency

plot_networks(): Plot the networks stored in an experiment object

plot_normalized_counts(): Plot normalized cell counts

plot_percent_colabel(): This function allows for plotting of colabelled cells over either the "cfos" or "eyfp" channels.

plot_perm_diff_network(): Plot a permutation difference network e experiment object

print(<correlation_list>): Print attributes of correlation_list object

print(<experiment>): Print attributes of experiment object

print(<mouse>): Print attributes of mouse object

print(<slice>): Print attributes of slice object

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

register(): Register (generic function)

reset_mouse_root(): Reset the root path for the folder containing the registration and segmentation data.

rewire_network(): Implement rewiring algorithms to current empirical networks to randomize certain network properties.

save_experiment(): Save experiment data

save_mouse(): Save mouse data

segmentation.object: segmentation object compatible with wholebrain package functions

sem(): Standard error function

simplify_by_keywords(): Simplify dataframe by keywords.

simplify_cell_count(): Simplify the combined cell count table

simplify_vec_by_keywords(): Simplify vector of acronyms by keywords.

slice(): Create a slice object

SMARTTR: SMARTTR: A Mapping, Analysis, and Visualization Package for Wholebrain Dual-Ensemble Coronal Datasets.

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

summarize_null_networks(): Summarize the parameters of the rewired null networks generated by rewire_network()

volcano_plot(): Plot the results of the permutation histogram used to determine the p-value of the pairwise region comparison