Journal: bioRxiv

Article Title: NetPyNE: a tool for data-driven multiscale modeling of brain circuits

doi: 10.1101/461137

Figure Lengend Snippet: NetPyNE GUI showing 3D representation of M1 network (background), raster plot and population firing rate statistics (top left), voltage traces (bottom left) and firing rate power spectral density (top right).

Article Snippet: The tool’s GUI is available at https://github.com/MetaCell/NetPyNE-UI and is under active development.

Techniques:

Journal: Current protocols in cytometry

Article Title: Generating Quantitative Cell Identity Labels with Marker Enrichment Modeling (MEM)

doi: 10.1002/cpcy.34

Figure Lengend Snippet: Small heatmaps can be displayed in the R GUI Plots panel. Here the output is shown from build.heatmaps() in RStudio.

Article Snippet: It is therefore best practice to set newWindow.heatmaps = TRUE unless the dataset contains approximately ten populations or fewer. fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window Figure 2 caption a7 caption a8 View output of build.heatmaps() in the RStudio GUI Small heatmaps can be displayed in the R GUI Plots panel.

Techniques:

Journal: Current protocols in cytometry

Article Title: Generating Quantitative Cell Identity Labels with Marker Enrichment Modeling (MEM)

doi: 10.1002/cpcy.34

Figure Lengend Snippet: Heatmaps are shown in new windows when newWindow.heatmaps = TRUE. Output is shown resulting from setting cluster.MEM = “both.” These plots can be saved as PDF files from the dropdown menu in the window’s File tab.

Article Snippet: It is therefore best practice to set newWindow.heatmaps = TRUE unless the dataset contains approximately ten populations or fewer. fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window Figure 2 caption a7 caption a8 View output of build.heatmaps() in the RStudio GUI Small heatmaps can be displayed in the R GUI Plots panel.

Techniques:

Journal: Current protocols in cytometry

Article Title: Generating Quantitative Cell Identity Labels with Marker Enrichment Modeling (MEM)

doi: 10.1002/cpcy.34

Figure Lengend Snippet: Build.heatmaps() creates a new folder called output files as a subdirectory of the working directory and writes output as tab-delimited text files. The files include 1) the population MEM labels (enrichment score row names) that are displayed in the MEM_matrix heatmap, 2) the matrix of population median values, and 3) the matrix of MEM scores.

Article Snippet: It is therefore best practice to set newWindow.heatmaps = TRUE unless the dataset contains approximately ten populations or fewer. fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window Figure 2 caption a7 caption a8 View output of build.heatmaps() in the RStudio GUI Small heatmaps can be displayed in the R GUI Plots panel.

Techniques:

Journal: Current protocols in cytometry

Article Title: Generating Quantitative Cell Identity Labels with Marker Enrichment Modeling (MEM)

doi: 10.1002/cpcy.34

Figure Lengend Snippet: View of the output files folder, located in your working directory, after running build.heatmaps() with the argument output.files=TRUE. If you are unsure of where this folder is located, run the command getwd() at the R command line, and the path to your working directory will print to the console.

Article Snippet: It is therefore best practice to set newWindow.heatmaps = TRUE unless the dataset contains approximately ten populations or fewer. fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window Figure 2 caption a7 caption a8 View output of build.heatmaps() in the RStudio GUI Small heatmaps can be displayed in the R GUI Plots panel.

Techniques: