Find research datasets worth reusing

opencc-by-4.0Jan 2021View details →

Data set for "Cell-type-specific nicotinic input disinhibits mouse barrel cortex during active sensing"

Data set for: Gasselin C, Hohl B, Vernet A, Crochet C, Petersen CCH (2021) Cell-type-specific nicotinic input disinhibits mouse barrel cortex during active sensing. Neuron doi: 10.1016/j.neuron.2020.12.018 There are 2 files in this upload: 1. The file named "2021_Gasselin_Neuron.pdf" is the Open Access pdf of the online publication in Neuron. 2. The file named "Gasselin_data_code.zip" (~9 GB) is a zipped version of a folder "Gasselin_data_code" (~13 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. To access the data and the code, first unzip the file. Then add the folder with subfolders to the Matlab path and run the different codes. The current folder must be the main folder (‘Gasselin_data_code’). Each code computes and plots the results used in the corresponding figure. Figures and Tables are saved in the subfolder ‘Figures’. The subfolder ‘Functions’ contains functions called by the main codes. The main folder contains the following codes: Gasselin_Figure1: computes and plots the results for the panels D, E and F of figure 1. Gasselin_Figure2: computes and plots the results for the panels B and C of figure 2. Gasselin_Figure3: computes and plots the results for the panels B, C and D of figure 3. Gasselin_Figure4: computes and plots the results for the panels A, B and C of figure 4. Gasselin_FigureS1: computes and plots the results for the panels A, B and C of figure S1. Gasselin_FigureS2: computes and plots the results for the panels A and B of figure S2.   The subfolder ‘Data’ contains the data structures used for the different figures: data_figure1.mat data_figure2.mat data_figure3.mat data_figure4_MECA.mat data_figure4_Activation.mat data_figure4_Inactivation.mat data_figureS2_Activation.mat data_figureS2_Inactivation.mat data_Axon.mat   The data structures contain the following fields: Mouse_Name : name of the mouse. Mouse_DateOfBirth: date of birth of the mouse (YMD). Mouse_Sex: sex of the mouse (F or M). Mouse_Genotype: genotype of the mouse. Mouse_Drug: experimental condition of the recording (control = ‘No Drug’; blockade of glutamatergic transmission = ‘CNQX_DAPV’; blockade of glutamatergic transmission and nicotinic receptors = ‘CNQX_DAPV_MECA’; blockade of nicotinic receptors only = ‘MECA’). Mouse_Virus: virus injected if any. Cell_Counter; cell recorded in a given mouse. Cell_Type: type of the recorded cell based on 2P imaging. (EXC, VIP, PV, SST, 5HT3aR_non_VIP). Cell_Depth: depth of the recorded cell relative to pia (µm). Cell_TargetedBrainArea: cortical area targeted (C2 column of the barrel cortex = C2). Cell_Fluorescence: expression of the genetically encoded fluorophore (FALSE or TRUE). A neuron recorded in a VIP_IRES_Cre x LSL_tdTomato (cf Mouse_Genotype) with Cell_Fluorescence=TRUE is considered as a VIP neuron (cf Cell_Type). Sweep_Counter: number of the sweep recorded for a given neuron (data were acquired across successive continuous sweeps of 30-60 s). Sweep_Type: experimental condition during that sweep (Only spontaneous whisking onset = ‘Onset’; Whisking onset and whisker stimulus = ‘Onset_Whisker_Stim’ ; Optogenetic stimulation = ‘Opto_Stim’;  Optogenetic activation = ‘Opto_Activation’; Optogenetic inactivation = ‘Opto_Inactivation’;  ). Sweep_Start_Time: time at the beginning of the sweep recording (YMDHms). Sweep_WhiskerAngle: C2 whisker angular position extracted from simultaneous high-speed video filming (deg). Sweep_WhiskerAngle_SamplingRate: sampling rate of the whisker angle trace. Sweep_WhiskingOnset_Time: time of identified whisking onset - excluding any whisker stimulus shortly before or after (s). Sweep_MembranePotential: membrane potential recording (mV) after cutting of the APs. Sweep_MembranePotential_SamplingRate: sampling rate of the membrane potential signal (pt.s-1). Sweep_CurrentInjected: current injected into the cell (pA). Sweep_CurrentInjected_SamplingRate: sampling rate of current signal (pt.s-1). Sweep_WhiskerStim_Name: whisker to which the magnetic stimulus was applied to (C2 or B2&C2). Sweep_WhiskerStim_Time: onset times of the whisker stimulus (s). Sweep_OptoStim_Power: light power applied for optogenetic manipulations (% of the max power). Sweep_OptoStim_Time: onset times of the light pulses for optogenetic manipulations (s). Cell_ID: unique cell identifier (= Mouse_Name+Cell_Counter). SpikeThreshold: spike threshold used to detect APs (mV). Trial_WhiskingOnset: data structure containing the cut signals used to compute averaged responses around whisking onset times. Trial_WhiskerStim: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times. Trial_WhiskerStim_QuietTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without whisker movements. Trial_WhiskerStim_WhiskingTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with whisker movements. Trial_Opto: data structure containing the cut signals used to compute averaged responses around optogenetic stimulus onset times. Trial_OptoAndWhisker_QuietTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with optogenetic manipulation and no whisker movements. Trial_OptoAndWhisker_WhiskingTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials with optogenetic manipulation and with whisker movements. Trial_OnlyWhisker_QuietTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without optogenetic manipulation and without whisker movements. Trial_OnlyWhisker_WhiskingTrials: data structure containing the cut signals used to compute averaged responses around whisker stimulus onset times for trials without optogenetic manipulation and with whisker movements. Trial_OnlyOpto: data structure containing the cut signals used to compute averaged responses around optogenetic stimulus onset times in trials without whisker stimulus.

opencc-by-4.0Jan 2022View details →

Data set for "Axonal and dendritic morphology of excitatory neurons in layer 2/3 mouse barrel cortex imaged through whole-brain two-photon tomography and registered to a digital brain atlas"

Data set for: Liu Y, Foustoukos G, Crochet S and Petersen CCH (2022) Axonal and dendritic morphology of excitatory neurons in layer 2/3 mouse barrel cortex imaged through whole-brain two-photon tomography and registered to a digital brain atlas. Front Neuroanat  15: 791015. https://doi.org/10.3389/fnana.2021.791015 There are 2 files in this upload: 1. The file named "2022_Liu_FrontNeuroanat.pdf" is the Open Access pdf of the online publication in Frontiers in Neuroanatomy. 2. The file named "Liu_data_code.zip" (~1 GB) is a zipped version of a folder ‘Liu_data_code’, which contains the data analyzed in the study along with the Python codes used to generate the published figures. The original high resolution image stacks obtained through whole-brain two-photon serial tomography are unfortunately too large for Zenodo, and only highly-downsampled data are included in this upload, which were used for registration with the Allen CCFv3. Instructions on how to view and analyse the anatomical data are provided in the 'README.docx' file, which you will find upon unzipping the folder.

opencc-by-4.0Apr 2018View details →

Data set for "Diverse long-range axonal projections of excitatory layer 2/3 neurons in mouse barrel cortex"

Data set for: Yamashita T, Vavladeli A, Pala A, Galan K, Crochet S, Petersen SSA, Petersen CCH (2018) Diverse long-range axonal projections of excitatory layer 2/3 neurons in mouse barrel cortex. Front Neuroanat 12: 33. https://doi.org/10.3389/fnana.2018.00033 There are 25 files in this data upload: 1. '2018_Yamashita_FrontNeuroanat.pdf' - this a pdf version of the online publication. 2. 'Yamashita_Figure2_Quantification.xlsx' - this is a Microsoft Excel file giving the locations of high density axonal projections from layer 2/3 pyramidal neurons in the mouse C2 barrel column in the coordinate frame of Paxinos & Franklin (2001) The mouse brain in stereotaxic coordinates. Academic Press. The data are plotted in Figure 2 of Yamashita et al., 2018. 3. 'Yamashita_Figure7_Quantification.xlsx' - this is a Microsoft Excel file giving the dendritic length, number of dendrites, number of dendritic nodes and total axonal length, as well as the axonal length in the different projection zones for each reconstructed neuron. The data are plotted in Figure 7 of Yamashita et al., 2018. 4. 'Yamashita_SupMov1_S2P_AP049.mov' - this is a QuickTime video file, showing the 3D structure of neuron AP049 featured in Figure 3 of Yamashita et al., 2018. 5.  'Yamashita_SupMov2_M1P_TY308.mov' - this is a QuickTime video file, showing the 3D structure of neuron TY308 featured in Figure 5 of Yamashita et al., 2018. 6. 'AV198.zip' - this zipped folder contains data relating to mouse AV198: a) 'AV198_stack.tif' the z-stack of whole-brain fluorescence images from expression of tdTomato in layer 2/3 neurons of the C2 barrel column of mouse AV198. b) 'AV198_ROI_Box.zip' can be loaded into FIJI (https://fiji.sc) and indicates projection regions by a box. c) 'AV198_ROI_Point.zip' can be loaded into FIJI (https://fiji.sc) and indicates projection regions by a point. d) 'AV198_Paxinos' is a folder showing the coronal fluorescent brain sections in pdf format overlaid on the equivalent drawing from Paxinos & Franklin (2001) The mouse brain in stereotaxic coordinates. Academic Press. 7. 'AV199.zip' - same as 'AV198.zip' but for mouse AV199. 8. 'AV201.zip' - same as 'AV198.zip' but for mouse AV201. 9. 'AV202.zip' - same as 'AV198.zip' but for mouse AV202. 10. 'AV203.zip' - same as 'AV198.zip' but for mouse AV203. 11. 'AP042.ASC' - Neurolucida (http://www.mbfbioscience.com/neurolucida) data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP042. Brain contours are also traced. 12. 'AP044.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP044. Brain contours are also traced. 13. 'AP046.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP046. Brain contours are also traced. 14. 'AP047.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP047. Brain contours are also traced. 15. 'AP049.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP049. Brain contours are also traced. 16. 'TY220.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY220. Brain contours are also traced. 17. 'TY288.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY288. Brain contours are also traced. 18. 'TY300.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY300. Brain contours are also traced. 19. 'TY302.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY302. Brain contours are also traced. 20. 'TY308.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY308. Brain contours are also traced. 21. 'TY310.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY310. Brain contours are also traced. 22. 'TY337.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY337. Brain contours are also traced. 23. 'TY345.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY345. Brain contours are also traced. 24. 'TY367.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY367. Brain contours are also traced. 25. 'TY369.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY369. Brain contours are also traced.

opencc-by-4.0Oct 2024View details →

MERFISH data of the developing mouse visual cortex under normal- and dark-rearing

MERFISH data for the manuscript "Spatial profiling of the interplay between cell type- and vision-dependent transcriptomic programs in the visual cortex"

opencc-by-4.0Jul 2023View details →

Structural and Molecular Analysis of Adult Mouse Astrocytes and Vascular Connectivity in the Cortex and Hippocampus

After image acquisition (0-RAW_CL230331_E2_serie1) and deconvolution (1-Deconvolved_CL230331_E2_serie1) using confocal microscopy and the SVI Huygens software,respectively, the image processing was conducted using Imaris, Fiji, and Matlab software. This process involved a sequence of manual operations (2-Imaris_surfaces_CL230331_E2_serie1) and custom Groovy scripts (5-Groovy scripts). The dataset analysis (3-Imaris_final_CL230331_E2_serie1_ims) allowed for a deeper investigation of morphological and molecular properties of adult mouse astrocytes (4-Image analysis_CL230331_E2_serie1) in two brain regions, the Isocortex and the Hippocampus, known to be interconnected to support multiple cognitive functions.

opencc-by-4.0Dec 2019View details →

Data set for "Pathway-, layer- and cell-type-specific thalamic input to mouse barrel cortex"

Data set for: Sermet BS, Truschow P, Feyerabend M, Mayrhofer JM, Oram TB, Yizhar O, Staiger JF, Petersen CCH (2019) Pathway-, layer- and cell-type-specific thalamic input to mouse barrel cortex. eLife 8: e52665. https://doi.org/10.7554/eLife.52665 There are 2 files in this upload: 1. The file named "2019_Sermet_eLife.pdf" is the Open Access pdf file of the manuscript published in eLife. 2. The file named "Sermet_data_code.zip" (~5 GB) is a zipped version of a folder "Sermet_data_code" (~5 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. When unzipped, the folder contains 8 Matlab '.m' files with analysis code and one '.mat' data file. In order to run the analysis of the data set, you need to execute 'PopPlot.m'.

opencc-by-4.0May 2021View details →

Dataset: Measuring stimulus-evoked neurophysiological differentiation in distinct populations of neurons in mouse visual cortex

This dataset contains the calcium imaging and behavioral data analyzed in our paper, "Measuring stimulus-evoked neurophysiological differentiation in distinct populations of neurons in mouse visual cortex". These data were obtained at the Allen Brain Observatory as part of the OpenScope project, which is operated by the Allen Institute. Analysis code is available at <a href="https://github.com/wmayner/openscope-differentiation">https://github.com/wmayner/openscope-differentiation</a>.

opencc-by-4.0Nov 2021View details →

Neuropixels recordings from mouse visual cortex for Jia et al (2022)

Neuropixels recordings from mouse visual cortex. The dataset was used in the paper: Multi-regional module-based signal transmission in mouse visual cortex, Jia et al. (2022) Neuron. For information about experimental procedures, see Siegle, Jia et al. (2021) Nature 592, 86-92 (https://www.nature.com/articles/s41586-020-03171-x). For information about file contents, see https://allensdk.readthedocs.io/en/latest/visual_coding_neuropixels.html The NWB 1.0 files can be opened with HDF5 and HDFview.  <table> <tbody> <tr> <td>Mouse ID</td> <td>Genotype</td> <td> </td> </tr> <tr> <td>306046</td> <td>['Sst-IRES-Cre/wt;Ai32/wt']</td> <td> </td> </tr> <tr> <td>388523</td> <td>['Pvalb-Cre',]</td> <td> </td> </tr> <tr> <td>389262</td> <td>['Vip-Cre']</td> <td> </td> </tr> <tr> <td>408153</td> <td>['Sst-IRES-Cre/wt;Ai32/wt']</td> <td> </td> </tr> <tr> <td>410344</td> <td>['Vip-Cre']</td> <td> </td> </tr> <tr> <td>415149</td> <td>['wt/wt']</td> <td> </td> </tr> <tr> <td>412809</td> <td>['wt/wt']</td> <td> </td> </tr> <tr> <td>412804</td> <td>['wt/wt']</td> <td> </td> </tr> <tr> <td>416856</td> <td>['Sst-IRES-Cre/wt;Ai32/wt']</td> <td> </td> </tr> <tr> <td>419114</td> <td>['wt/wt']</td> <td> </td> </tr> <tr> <td>419117</td> <td>['wt/wt']</td> <td> </td> </tr> <tr> <td>419118</td> <td>['wt/wt']</td> <td> </td> </tr> <tr> <td>419119</td> <td>['wt/wt']</td> <td> </td> </tr> <tr> <td>424445</td> <td>['wt/wt']</td> <td> </td> </tr> <tr> <td>415148</td> <td>['wt/wt']</td> <td> </td> </tr> <tr> <td>416356</td> <td>['Sst-IRES-Cre/wt;Ai32/wt']</td> <td> </td> </tr> <tr> <td>416861</td> <td>['Sst-IRES-Cre/wt;Ai32/wt']</td> <td> </td> </tr> <tr> <td>419112</td> <td>['wt/wt']</td> <td> </td> </tr> <tr> <td>419116</td> <td>['wt/wt']</td> <td> </td> </tr> </tbody> </table>

opencc-by-sa-4.0Feb 2018View details →

Dataset of Axon Segmentation and Centerlines, Acquired using a Two-photon Microscope in the Live Mouse Cortex

This dataset contains 20 images of real axons that were published previously in Bass et al (2017). This subset dataset has been added labels of binary images for segmentation of the axons, and of centerline for tracing of the axons. We provide the following: <ul> <li>Images-MAX: 2D images of axons (.png)</li> <li>Images-TIFF: 3D images of axons (.tiff)</li> <li>Labels-binary: Manual segmentation of the axons as binary images (.png)</li> <li>Labels-tracing: Manual tracing of the axons (.swc)</li> </ul> This data was collected in the live mouse cortex, using a two-photon microscope, with a 40x objective, at zoom 4, and with a resolution of 512 × 512 pixels, 0.147 μm per pixel for the x, y planes, and 1 μm for the z plane. .  Please cite the following papers when using this dataset: T. Dai, M. Dubois, K. Arulkumaran, J. Campbell, B. Billot, C. Bass, Z. Uslu, V. De Paola, C. Clopath, and A. A. Bharath. Deep reinforcement learning for subpixel neural tracking. Medical Imaging with Deep Learning. 2019. Bass C, Helkkula P, De Paola V, Clopath C, Bharath AA. Detection of axonal synapses in 3D two-photon images. Giniger E, ed. PLoS ONE. 2017;12(9):e0183309. doi:10.1371/journal.pone.0183309.

opencc-zeroDec 2022View details →

Representations of color and form in mouse visual cortex

Spatial transitions in color can aid any visual perception task, and its neural representation – an "integration of color and form" – is thought to begin at primary visual cortex (V1). Color and form integration is untested in mouse V1, yet studies show that the ventral retina provides the necessary substrate from green-sensitive rods and UV-sensitive cones. Here, we used two-photon imaging in V1 to measure spatial frequency (SF) tuning along four axes of rod and cone contrast space, including luminance and color. We first reveal that V1 has similar responsiveness to luminance and color, yet average SF tuning is significantly shifted lowpass for color. Next, guided by linear models, we used SF tuning along all four color axes to estimate the proportion of neurons that fall into classic models of color opponency – "single-", "double-", and "non-opponent". Few neurons (~6%) fit the criteria for double-opponency, which are uniquely tuned for chromatic borders. Most of the population can be described as a unimodal distribution ranging from strongly single-opponent to non-opponent. Consistent with recent studies of the rodent and primate retina, our V1 data is well-described by a simple model in which ON and OFF channels to V1 sample the photoreceptor mosaic randomly.

opencc-zeroMar 2023View details →

Sound improves neuronal encoding of visual stimuli in mouse primary visual cortex

In everyday life, we integrate visual and auditory information in routine tasks such as navigation and communication. While concurrent sound can improve visual perception, the neuronal correlates of audiovisual integration are not fully understood. Specifically, it remains unclear whether neuronal firing patters in the primary visual cortex (V1) of awake animals demonstrate similar sound-induced improvement in visual discriminability. Furthermore, presentation of sound is associated with movement in the subjects, but little is understood about whether and how sound-associated movement affects audiovisual integration in V1. Here, we investigated how sound and movement interact to modulate V1 visual responses in awake, head-fixed mice and whether this interaction improves neuronal encoding of the visual stimulus. We presented visual drifting gratings with and without simultaneous auditory white noise to awake mice while recording mouse movement and V1 neuronal activity. Sound modulated activity of 80% of light-responsive neurons, with 95% of neurons increasing activity when the auditory stimulus was present. A generalized linear model revealed that sound and movement had distinct and complementary effects of the neuronal visual responses. Furthermore, decoding of the visual stimulus from the neuronal activity was improved with sound, an effect that persisted even when controlling for movement. These results demonstrate that sound and movement modulate visual responses in complementary ways, improving neuronal representation of the visual stimulus. This study clarifies the role of movement as a potential confound in neuronal audiovisual responses and expands our knowledge of how multimodal processing is mediated at a neuronal level in the awake brain.

opencc-by-4.0Jun 2023View details →

Data set for "Membrane potential dynamics of excitatory and inhibitory neurons in mouse barrel cortex during active whisker sensing"

Data set for: Kiritani T, Pala A, Gasselin C, Crochet S, Petersen CCH (2023) Membrane potential dynamics of excitatory and inhibitory neurons in mouse barrel cortex during active whisker sensing. PLOS ONE 18: e0287174. doi: 10.1371/journal.pone.0287174 There are 2 files in this upload: 1. The file named "2023_Kiritani_PLOSONE.pdf" is the Open Access pdf of the online publication in PLOS ONE. 2. The file named "Kiritani_data_code.zip" (~5 GB) is a zipped version of a folder "Kiritani_data_code" (~5 GB), which contains the data analysed in the study along with the Matlab codes used to generate the published figures. To access the data and codes, first unzip the file. You need to install the Matlab 'Signal Processing' and 'Curve Fitting' Toolboxes. In Matlab, add the path of the folder 'Kiritani_data_code' and all subfolders. Directly from this folder, you should first run the codes in the folder 'Data_Analysis_Codes', sequentially executing 'Analysis_1.m' through to 'Analysis_9.m'. Note, execution of 'Analysis_9.m' can take a long time (~1 hour on a good desktop PC). You can then run the codes in the folder 'Figure_Plotting_Codes' to generate the figures published in the journal article. In the folder 'Data', you can also find a DataViewer to visualise the data sets, which you can run by executing 'DataViewer.m' directly from the subfolder ‘Data’.

opencc-by-4.0Aug 2023View details →

Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses

Raw data and code to reproduce figures in the manuscript "Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses" # README ## Introduction This README provides essential information about the codebase for the manuscript titled "Molecularly targetable cell types in mouse visual cortex have distinguishable prediction error responses." The code in this repository is self-contained and is expected to run smoothly given the appropriate versions of the required libraries/packages. ## Directory structure and execution details ### R code - Main Figures 2A-2D, 3A-3C, and 4A-4E, as well as supplemental figures S2A-S2H, S3A-S3E, S4L, and S5A-S5I, were generated using R. Execute the `R_figs_master.r` script located in the `r_code` directory. - All figures will be saved within the `r_code/code_generated_figures` directory. - Note: Exact UMAP representations might vary across different hardware and operating systems, likely due to an issue with the UWOT package ([Reference Issue](https://github.com/satijalab/seurat/issues/5514)). If figures appear outside their designated plot ranges, set "FixAxes" to 'FALSE' in the `single_cell_variables.r` script. ### MATLAB code - Main figures 1B, 1D-1F, and 6A-6H, as well as supplemental figures S1A-S1J and S6A-S6I, were generated using MATLAB (version 9.11.0.1809720 (R2021b) Update 1). Execute the `get_the_figs_matlab.m` script located in the `matlab_code` directory. - All figures will be saved within the `matlab_code/code_generated_figures` directory. - Required: [fca_readfcs, version 2020.06.22](https://ch.mathworks.com/matlabcentral/fileexchange/9608-fca_readfcs). ### Python code - Figures 5B-5F panels were generated using Python (version 3.6.8). Run the `fig_5_analysis_code.py` script located in the `python_code` directory. - All figures will be saved within the `python_code/code_generated_figures` directory. - The preprocessed images located in `python_code/data_repository/Adamts2_processed`, `python_code/data_repository/Agmat_processed`, and `python_code/data_repository/Baz1a_processed` were generated using the ImageJ macro `python_code/cropped_to_processed_macro.ijm` from the raw images in `python_code/data_repository/Adamts2_cropped`, `python_code/data_repository/Agmat_cropped`, and `python_code/data_repository/Baz1a_cropped`. ## Supplementary code (for reference only as raw data is not included) ### Mapping code and genome construction code - Initial processing of Single-cell RNA-sequencing was performed with Cell Ranger, coordinated by the Python script:   `python_code/mapping_and_genome_construction/single_cell_mapping_pipeline.py`. Some components of this script are deprecated and were primarily used to pass .fastq files to Cell Ranger and organize the outputs. - A custom genome was constructed to account for the expression of CaMPARI2 in the single-cell RNA-sequencing dataset:   `python_code/mapping_and_genome_construction/campari2_genome_construction.py`. - Processing of Bulk RNA-sequencing, either single or paired-end, was executed through Python:   `python_code/mapping_and_genome_construction/bulk_single_end_mapping.py` and `python_code/mapping_and_genome_construction/bulk_paired_end_mapping.py`. - A custom genome was constructed to account for the expression of various artificial promoter viruses:   `python_code/mapping_and_genome_construction/bulk_seq_genome_construction.py`.

publicJan 2025View details →

Perirhinal cortex abnormalities impair hippocampal plasticity and learning in Scn2a, Fmr1, and Cdkl5 autism mouse models

Open the record for dataset details and reuse information.

publicMar 2023View details →

Sound improves neuronal encoding of visual stimuli in mouse primary visual cortex

Open the record for dataset details and reuse information.