Showing 34 of total 34 results (show query)
natverse
nat:NeuroAnatomy Toolbox for Analysis of 3D Image Data
NeuroAnatomy Toolbox (nat) enables analysis and visualisation of 3D biological image data, especially traced neurons. Reads and writes 3D images in NRRD and 'Amira' AmiraMesh formats and reads surfaces in 'Amira' hxsurf format. Traced neurons can be imported from and written to SWC and 'Amira' LineSet and SkeletonGraph formats. These data can then be visualised in 3D via 'rgl', manipulated including applying calculated registrations, e.g. using the 'CMTK' registration suite, and analysed. There is also a simple representation for neurons that have been subjected to 3D skeletonisation but not formally traced; this allows morphological comparison between neurons including searches and clustering (via the 'nat.nblast' extension package).
Maintained by Gregory Jefferis. Last updated 5 months ago.
3dconnectomicsimage-analysisneuroanatomyneuroanatomy-toolboxneuronneuron-morphologyneurosciencevisualisation
171.9 match 67 stars 9.94 score 436 scripts 2 dependentsnatverse
nat.nblast:NeuroAnatomy Toolbox ('nat') Extension for Assessing Neuron Similarity and Clustering
Extends package 'nat' (NeuroAnatomy Toolbox) by providing a collection of NBLAST-related functions for neuronal morphology comparison (Costa et al. (2016) <doi: 10.1016/j.neuron.2016.06.012>).
Maintained by Gregory Jefferis. Last updated 7 months ago.
morphological-analysisnblastneuroanatomyneuroanatomy-toolboxneurons
46.5 match 17 stars 6.69 score 96 scripts
bioc
deltaCaptureC:This Package Discovers Meso-scale Chromatin Remodeling from 3C Data
This package discovers meso-scale chromatin remodelling from 3C data. 3C data is local in nature. It givens interaction counts between restriction enzyme digestion fragments and a preferred 'viewpoint' region. By binning this data and using permutation testing, this package can test whether there are statistically significant changes in the interaction counts between the data from two cell types or two treatments.
Maintained by Michael Shapiro. Last updated 5 months ago.
biologicalquestionstatisticalmethod
20.9 match 3.48 score 1 scripts
jaipizgon
NeuralSens:Sensitivity Analysis of Neural Networks
Analysis functions to quantify inputs importance in neural network models. Functions are available for calculating and plotting the inputs importance and obtaining the activation function of each neuron layer and its derivatives. The importance of a given input is defined as the distribution of the derivatives of the output with respect to that input in each training data point <doi:10.18637/jss.v102.i07>.
Maintained by Jaime Pizarroso Gonzalo. Last updated 6 months ago.
8.0 match 15 stars 5.43 score 24 scripts
spatstat
spatstat.data:Datasets for 'spatstat' Family
Contains all the datasets for the 'spatstat' family of packages.
Maintained by Adrian Baddeley. Last updated 3 hours ago.
kernel-densitypoint-processspatial-analysisspatial-dataspatial-data-analysisspatstatstatistical-analysisstatistical-methodsstatistical-testsstatistics
3.8 match 6 stars 11.02 score 186 scripts 228 dependents
fmmgroupva
FMM:Rhythmic Patterns Modeling by FMM Models
Provides a collection of functions to fit and explore single, multi-component and restricted Frequency Modulated Moebius (FMM) models. 'FMM' is a nonlinear parametric regression model capable of fitting non-sinusoidal shapes in rhythmic patterns. Details about the mathematical formulation of 'FMM' models can be found in Rueda et al. (2019) <doi:10.1038/s41598-019-54569-1>.
Maintained by Itziar Fernandez. Last updated 22 days ago.
6.8 match 2 stars 5.30 score
jrodu
qqboxplot:Implementation of the Q-Q Boxplot
A system to implement the Q-Q boxplot. It is implemented as an extension to 'ggplot2'. The Q-Q boxplot is an amalgam of the boxplot and the Q-Q plot and allows the user to rapidly examine summary statistics and tail behavior for multiple distributions in the same pane. As an extension of the 'ggplot2' implementation of the boxplot, possible modifications to the boxplot extend to the Q-Q boxplot.
Maintained by Jordan Rodu. Last updated 2 years ago.
7.3 match 2 stars 4.76 score 29 scripts
blmayer
deep:A Neural Networks Framework
This package provides a layer oriented way of creating neural networks, the framework is intended to give the user total control of the internals of a net without much effort. Use classes like PerceptronLayer to create a layer of percetron neurons, and specify how many you want. The package does all the tricky stuff internally leaving you focused in what you want. I wrote this package during a neural networks course to help me with the problem set.
Maintained by Brian. Last updated 5 years ago.
machine-learningneural-networks
8.5 match 6 stars 3.95 score 2 scripts
ajpete
scalpel:Processes Calcium Imaging Data
Identifies the locations of neurons, and estimates their calcium concentrations over time using the SCALPEL method proposed in Petersen, Ashley; Simon, Noah; Witten, Daniela. SCALPEL: Extracting neurons from calcium imaging data. Ann. Appl. Stat. 12 (2018), no. 4, 2430--2456. <doi:10.1214/18-AOAS1159>. <https://projecteuclid.org/euclid.aoas/1542078051>.
Maintained by Ashley Petersen. Last updated 4 years ago.
15.1 match 2.20 score 32 scriptsbioc
tenXplore:ontological exploration of scRNA-seq of 1.3 million mouse neurons from 10x genomics
Perform ontological exploration of scRNA-seq of 1.3 million mouse neurons from 10x genomics.
Maintained by VJ Carey. Last updated 5 months ago.
immunooncologydimensionreductionprincipalcomponenttranscriptomicssinglecell
7.2 match 4.18 score 7 scripts
jefferis
nabor:Wraps 'libnabo', a Fast K Nearest Neighbour Library for Low Dimensions
An R wrapper for 'libnabo', an exact or approximate k nearest neighbour library which is optimised for low dimensional spaces (e.g. 3D). 'libnabo' has speed and space advantages over the 'ANN' library wrapped by package 'RANN'. 'nabor' includes a knn function that is designed as a drop-in replacement for 'RANN' function nn2. In addition, objects which include the k-d tree search structure can be returned to speed up repeated queries of the same set of target points.
Maintained by Gregory Jefferis. Last updated 5 years ago.
3.4 match 22 stars 8.21 score 104 scripts 34 dependents
tweedell
motoRneuron:Analyzing Paired Neuron Discharge Times for Time-Domain Synchronization
The temporal relationship between motor neurons can offer explanations for neural strategies. We combined functions to reduce neuron action potential discharge data and analyze it for short-term, time-domain synchronization. Even more so, motoRneuron combines most available methods for the determining cross correlation histogram peaks and most available indices for calculating synchronization into simple functions. See Nordstrom, Fuglevand, and Enoka (1992) <doi:10.1113/jphysiol.1992.sp019244> for a more thorough introduction.
Maintained by Andrew Tweedell. Last updated 6 years ago.
5.3 match 1 stars 3.74 score 11 scriptsbioc
lute:Framework for cell size scale factor normalized bulk transcriptomics deconvolution experiments
Provides a framework for adjustment on cell type size when performing bulk transcripomics deconvolution. The main framework function provides a means of reference normalization using cell size scale factors. It allows for marker selection and deconvolution using non-negative least squares (NNLS) by default. The framework is extensible for other marker selection and deconvolution algorithms, and users may reuse the generics, methods, and classes for these when developing new algorithms.
Maintained by Sean K Maden. Last updated 5 months ago.
rnaseqsequencingsinglecellcoveragetranscriptomicsnormalization
2.3 match 2 stars 5.26 score 3 scripts
mottastefano
SOMMD:Self Organising Maps for the Analysis of Molecular Dynamics Data
Processes data from Molecular Dynamics simulations using Self Organising Maps. Features include the ability to read different input formats. Trajectories can be analysed to identify groups of important frames. Output visualisation can be generated for maps and pathways. Methodological details can be found in Motta S et al (2022) <doi:10.1021/acs.jctc.1c01163>. I/O functions for xtc format files were implemented using the 'xdrfile' library available under open source license. The relevant information can be found in inst/COPYRIGHT.
Maintained by Stefano Motta. Last updated 5 months ago.
6.3 match 1.70 score 4 scripts
ccfang2
nndiagram:Generator of 'LaTeX' Code for Drawing Neural Network Diagrams with 'TikZ'
Generates 'LaTeX' code for drawing well-formatted neural network diagrams with 'TikZ'. Users have to define number of neurons on each layer, and optionally define neuron connections they would like to keep or omit, layers they consider to be oversized and neurons they would like to draw with lighter color. They can also specify the title of diagram, color, opacity of figure, labels of layers, input and output neurons. In addition, this package helps to produce 'LaTeX' code for drawing activation functions which are crucial in neural network analysis. To make the code work in a 'LaTeX' editor, users need to install and import some 'TeX' packages including 'TikZ' in the setting of 'TeX' file.
Maintained by Chencheng Fang. Last updated 2 years ago.
2.7 match 14 stars 3.85 score 3 scripts
uclouvain-cbio
scpdata:Single-Cell Proteomics Data Package
The package disseminates mass spectrometry (MS)-based single-cell proteomics (SCP) datasets. The data were collected from published work and formatted using the `scp` data structure. The data sets contain quantitative information at spectrum, peptide and/or protein level for single cells or minute sample amounts.
Maintained by Christophe Vanderaa. Last updated 8 days ago.
experimentdataexpressiondataexperimenthubreproducibleresearchmassspectrometrydataproteomesinglecelldatapackagetypedata
1.6 match 6 stars 5.58 score 16 scripts
johannes-titz
leabRa:The Artificial Neural Networks Algorithm Leabra
The algorithm Leabra (local error driven and associative biologically realistic algorithm) allows for the construction of artificial neural networks that are biologically realistic and balance supervised and unsupervised learning within a single framework. This package is based on the 'MATLAB' version by Sergio Verduzco-Flores, which in turn was based on the description of the algorithm by Randall O'Reilly (1996) <ftp://grey.colorado.edu/pub/oreilly/thesis/oreilly_thesis.all.pdf>. For more general (not 'R' specific) information on the algorithm Leabra see <https://grey.colorado.edu/emergent/index.php/Leabra>.
Maintained by Johannes Titz. Last updated 7 years ago.
1.9 match 2 stars 4.00 score 5 scripts
oldlipe
ggsom:ggsom
Tool for visualization of SOMs object.
Maintained by Felipe Carvalho. Last updated 5 years ago.
ggplot-extensionplotvisualization
1.8 match 13 stars 3.81 score 5 scripts
rluo
pro:Point-Process Response Model for Optogenetics
Optogenetics is a new tool to study neuronal circuits that have been genetically modified to allow stimulation by flashes of light. This package implements the methodological framework, Point-process Response model for Optogenetics (PRO), for analyzing data from these experiments. This method provides explicit nonlinear transformations to link the flash point-process with the spiking point-process. Such response functions can be used to provide important and interpretable scientific insights into the properties of the biophysical process that governs neural spiking in response to optogenetic stimulation.
Maintained by Xi (Rossi) LUO. Last updated 9 years ago.
2.2 match 3.00 score 4 scripts
cran
NPCirc:Nonparametric Circular Methods
Nonparametric smoothing methods for density and regression estimation involving circular data, including the estimation of the mean regression function and other conditional characteristics.
Maintained by Maria Alonso-Pena. Last updated 2 years ago.
3.8 match 1.78 score 2 dependents
ropensci
DoOR.functions:Integrating Heterogeneous Odorant Response Data into a Common Response Model: A DoOR to the Complete Olfactome
This is a function package providing functions to perform data manipulations and visualizations for DoOR.data. See the URLs for the original and the DoOR 2.0 publication.
Maintained by Daniel Münch. Last updated 1 years ago.
1.2 match 8 stars 5.40 score 52 scriptscran
mixedsde:Estimation Methods for Stochastic Differential Mixed Effects Models
Inference on stochastic differential models Ornstein-Uhlenbeck or Cox-Ingersoll-Ross, with one or two random effects in the drift function.
Maintained by Charlotte Dion. Last updated 6 years ago.
3.5 match 1.70 score 7 scripts
lark-max
DSAM:Data Splitting Algorithms for Model Developments
Providing six different algorithms that can be used to split the available data into training, test and validation subsets with similar distribution for hydrological model developments. The dataSplit() function will help you divide the data according to specific requirements, and you can refer to the par.default() function to set the parameters for data splitting. The getAUC() function will help you measure the similarity of distribution features between the data subsets. For more information about the data splitting algorithms, please refer to: Chen et al. (2022) <doi:10.1016/j.jhydrol.2022.128340>, Zheng et al. (2022) <doi:10.1029/2021WR031818>.
Maintained by Junyi Chen. Last updated 2 months ago.
1.8 match 2 stars 3.00 score 2 scripts
mvt-oviedo
GMDHreg:Regression using GMDH Algorithms
Regression using GMDH algorithms from Prof. Alexey G. Ivakhnenko. Group Method of Data Handling (GMDH), or polynomial neural networks, is a family of inductive algorithms that performs gradually complicated polynomial models and selecting the best solution by an external criterion. In other words, inductive GMDH algorithms give possibility finding automatically interrelations in data, and selecting an optimal structure of model or network. The package includes GMDH Combinatorial, GMDH MIA (Multilayered Iterative Algorithm), GMDH GIA (Generalized Iterative Algorithm) and GMDH Combinatorial with Active Neurons.
Maintained by Manuel Villacorta Tilve. Last updated 1 years ago.
1.6 match 2 stars 2.30 score 6 scripts
andymckenzie
BRETIGEA:Brain Cell Type Specific Gene Expression Analysis
Analysis of relative cell type proportions in bulk gene expression data. Provides a well-validated set of brain cell type-specific marker genes derived from multiple types of experiments, as described in McKenzie (2018) <doi:10.1038/s41598-018-27293-5>. For brain tissue data sets, there are marker genes available for astrocytes, endothelial cells, microglia, neurons, oligodendrocytes, and oligodendrocyte precursor cells, derived from each of human, mice, and combination human/mouse data sets. However, if you have access to your own marker genes, the functions can be applied to bulk gene expression data from any tissue. Also implements multiple options for relative cell type proportion estimation using these marker genes, adapting and expanding on approaches from the 'CellCODE' R package described in Chikina (2015) <doi:10.1093/bioinformatics/btv015>. The number of cell type marker genes used in a given analysis can be increased or decreased based on your preferences and the data set. Finally, provides functions to use the estimates to adjust for variability in the relative proportion of cell types across samples prior to downstream analyses.
Maintained by Andrew McKenzie. Last updated 1 years ago.
cell-typegene-expressiongene-expression-signatures
0.5 match 15 stars 6.20 score 30 scripts
jefferis
IgorR:Read Binary Files Saved by 'Igor Pro' (Including 'Neuromatic' Data)
Provides function to read data from the 'Igor Pro' data analysis program by 'Wavemetrics'. The data formats supported are 'Igor' packed experiment format ('pxp') and 'Igor' binary wave ('ibw'). See: <https://www.wavemetrics.com/> for details. Also includes functions to load special 'pxp' files produced by the 'Igor Pro' 'Neuromatic' and 'Nclamp' packages for recording and analysing neuronal data. See <https://github.com/SilverLabUCL/NeuroMatic> for details.
Maintained by Gregory Jefferis. Last updated 7 months ago.
electrophysiologyigorneuromaticpro
0.5 match 7 stars 4.32 score 6 scriptscran
TeachNet:Fits Neural Networks to Learn About Backpropagation
Can fit neural networks with up to two hidden layer and two different error functions. Also able to handle a weight decay. But just able to compute one output neuron and very slow.
Maintained by Georg Steinbuss. Last updated 6 years ago.
2.2 match 1 stars 1.00 scorenatverse
natcpp:Fast C++ Primitives for the 'NeuroAnatomy Toolbox'
Fast functions implemented in C++ via 'Rcpp' to support the 'NeuroAnatomy Toolbox' ('nat') ecosystem. These functions provide large speed-ups for basic manipulation of neuronal skeletons over pure R functions found in the 'nat' package. The expectation is that end users will not use this package directly, but instead the 'nat' package will automatically use routines from this package when it is available to enable large performance gains.
Maintained by Gregory Jefferis. Last updated 3 years ago.
computational-neuroscienceneuroanatomy-toolboxcpp
0.5 match 2.70 score 4 scripts
andreasdominik
som.nn:Topological k-NN Classifier Based on Self-Organising Maps
A topological version of k-NN: An abstract model is build as 2-dimensional self-organising map. Samples of unknown class are predicted by mapping them on the SOM and analysing class membership of neurons in the neighbourhood.
Maintained by Andreas Dominik. Last updated 12 months ago.
0.5 match 2.40 score 28 scriptscran
Umatrix:Visualization of Structures in High-Dimensional Data
By gaining the property of emergence through self-organization, the enhancement of SOMs(self organizing maps) is called Emergent SOM (ESOM). The result of the projection by ESOM is a grid of neurons which can be visualised as a three dimensional landscape in form of the Umatrix. Further details can be found in the referenced publications (see url). This package offers tools for calculating and visualising the ESOM as well as Umatrix, Pmatrix and UStarMatrix. All the functionality is also available through graphical user interfaces implemented in 'shiny'. Based on the recognized data structures, the method can be used to generate new data.
Maintained by Jorn Lotsch. Last updated 7 months ago.
0.5 match 1 stars 2.16 score 12 scripts
xgdgsc
Rsomoclu:Somoclu
Somoclu is a massively parallel implementation of self-organizing maps. It exploits multicore CPUs and it can be accelerated by CUDA. The topology of the map can be planar or toroid and the grid of neurons can be rectangular or hexagonal . Details refer to (Peter Wittek, et al (2017)) <doi:10.18637/jss.v078.i09>.
Maintained by Shichao Gao. Last updated 2 years ago.
0.5 match 1.00 score 4 scripts
