For BICCN data access in FireCloud, a quick registration (< 1 minute) is required.
NeMO - Broad Transcriptome Analysis
A main goal of the BCDC is bringing BICCN data to the community and to facilitate its use and analysis. To help users get started we have prepared a sample analysis workflow whereby you can
Moving forward, we will automate and increase the integration of the NeMO and FireCloud/Single Cell Portal Environments. Currently, this demo focuses on running single cell transcriptomics data (specifically 10X v2).
NeMO Archive (NeMOarchive, RRID:SCR_016152)
L-Measure is a software tool designed to extract a wide variety of quantitative morphological measurements from neuromorphological reconstructions. Both local parameters (e.g. bifurcation angles) and global descriptors (e.g. total arbor length) can be extracted and combined in many useful analyses, including the popular distributions of surface area as a function of path distance from the soma. Users can specify the target of the analysis by structural domains (e.g. axons vs dendrites) or by morphological features (e.g. terminal branches) . The tool has built-in capability to search for neurons with specific morphological characteristics from a large collection or to compare two neuronal populations with parametric and non-parametric statistical tests. The user-friendly graphical user interface is written in JAVA and can run remotely through a web browser or locally on Linux, Windows, or Mac. The number-crunching engine is written in C++ and can be called from batch scripts for faster execution of large-scale computations. (L-Measure, RRID:SCR_003487)
Access L-Measure here: http://cng.gmu.edu:8080/Lm/
Related Nature Protocols publication: https://www.nature.com/articles/nprot.2008.51
G. Ascoli Lab (http://krasnow1.gmu.edu/cn3/ascoli/), George Mason University
TreePersVec - Feature quantification of morphology data is an important part of determining cell type identity. TreePersVec is a topological based analysis tool used to generate 1D persistence feature vectors of neuron trees. TreePersVec first uses a descriptor function mapping nodes in the neuron tree to positive real values. The default descriptor function uses geodesic distance from any tree node to the root. With function values assigned, TreePersVec will then calculate corresponding persistence diagram and output to a file consisting of a set of 2D points. Coordinates represent the birth and death times of features, and the difference between a feature’s birth and death time shows the importance of the feature. Finally, TreePersVec converts the persistence diagram summary into a 1D persistence feature vector and outputs persistence vector files.. The function for calculating descriptor function values is written in Java, the other functions are written in C++. Source code and instruction are available on GitHub.
Access TreepersVec here: https://github.com/wangdingkang/NeuronVectorization
Y. Wang Lab (https://web.cse.ohio-state.edu/~wang.1016/), Ohio State University
P. Mitra (https://www.cshl.edu/research/faculty-staff/partha-mitra/), Cold Spring Harbor Lab
MetaNeighbor quantifies the degree to which cell types replicate across datasets, and enables rapid identification of clusters with high similarity. MetaNeighbor first measures the replicability of neuronal identity, comparing results across eight technically and biologically diverse datasets to define best practices for more complex assessments. By taking the correlations between all pairs of cells a network is built where every node is a cell and the edges represent how similar each cell is to each other cell. This network can be extended to include data from multiple experiments (multiple datasets). To assess cell-type identity across experiments neighbor voting is used for cross-validation, systematically hiding the labels from one dataset at a time for testing. Cells within the test set are predicted as similar to the cell types from other training sets using a neighbor-voting formalism. Whether these scores prioritize cells as the correct type within the dataset determines the performance, expressed as the AUROC. Comparative assessment of cells occurs only within a dataset, but is based only on training information from outside that dataset. (MetaNeighbor, RRID:SCR_016727)
Access MetaNeighbor here: https://www.bioconductor.org/packages/release/bioc/html/MetaNeighbor.html
J. Gillis Lab (http://gillislab.labsites.cshl.edu/), Cold Spring Harbor Laboratory
ModelDB, Biophysical Models - The advanced cognitive capabilities of the human brain are often attributed to our recently evolved neocortex. However, it is not known whether the basic building blocks of human neocortex, the pyramidal neurons, possess unique biophysical properties that might impact on cortical computations. The Segev group has shown that layer 2/3 pyramidal neurons from human temporal cortex (HL2/3 PCs) have a specific membrane capacitance (Cm) of ~0.5 µF/cm2, half of the commonly accepted “universal” value (~1 µF/cm2) for biological membranes. This finding was predicted by fitting in vitro voltage transients to theoretical transients then validated by direct measurement of Cm in nucleated patch experiments. This is the first demonstration that human cortical neurons have distinctive membrane properties, suggesting important implications for signal processing in human neocortex. They also have developed detailed models of pyramidal cells from human neocortex, including models on their excitatory synapses, dendritic spines, dendritic NMDA- and somatic/axonal Na+ spikes that provided new insights into signal processing and computational capabilities of these principal cells. (ModelDB, RRID:SCR_007271)
Access the related ModelDB models and codes here:
I. Segev Lab (https://elsc.huji.ac.il/segev/home), Hebrew University