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7 7  (% class="wikigeneratedid" id="HUseCaseSGA2-SP3-002:IntegratingmultiscaledataA0inareproducibleandadaptablepipeline" %)
8 8  (% style="--darkreader-inline-color:inherit; color:inherit; font-size:24px" %)**Use Case SGA2-SP3-002 KR3.2: Integrating multi-scale data and the output of simulations in a reproducible and adaptable pipeline**
9 9  
10 -Robin Gutzen^^1^^, Giulia De Bonis^^2^^, Elena Pastorelli^^2,3^^, Cristiano Capone^^2^^,
10 +Robin Gutzen^^1,4^^, Giulia De Bonis^^2^^, Elena Pastorelli^^2,3^^, Cristiano Capone^^2^^,
11 11  
12 -Chiara De Luca^^2,3^^, Michael Denker^^1^^, Sonja Grün^^1^^,
12 +Chiara De Luca^^2,3^^, Michael Denker^^1^^, Sonja Grün^^1,4^^,
13 13  
14 -Pier Stanislao Paolucci^^2^^, Andrew Davison^^4^^
14 +Pier Stanislao Paolucci^^2^^, Andrew Davison^^5^^
15 15  
16 -Experiments: Anna Letizia Allegra Mascaro^^5,6^^, Francesco Resta^^5^^, Francesco Saverio Pavone^^5^^, Maria-Victoria Sanchez-Vives^^7,8^^
16 +Experiments: Anna Letizia Allegra Mascaro^^6,7^^, Francesco Resta^^6^^, Francesco Saverio Pavone^^6^^, Maria-Victoria Sanchez-Vives^^8,9^^
17 17  
18 18  ,,1) Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany,,
19 19  
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21 21  
22 22  ,,3) Ph.D. Program in Behavioural Neuroscience, “Sapienza” University of Rome, Rome, Italy,,
23 23  
24 -,,4) Unité de Neurosciences, Information et Complexité, Neuroinformatics Group, CNRS FRE 3693, Gif-sur-Yvette, France,,
24 +,,4) Theoretical Systems Neurobiology, RWTH Aachen University, Aachen, Germany,,
25 25  
26 -,,5) European Laboratory for Non-linear Spectroscopy (LENS), (% style="--darkreader-inline-color:inherit; color:inherit" %)University of Florence, Florence, Italy(%%),,
26 +,,5) Unité de Neurosciences, Information et Complexité, Neuroinformatics Group, CNRS FRE 3693, Gif-sur-Yvette, France,,
27 27  
28 -,,6) Istituto di Neuroscienze, CNR, Pisa, Italy,,
28 +,,6) European Laboratory for Non-linear Spectroscopy (LENS), (% style="--darkreader-inline-color:inherit; color:inherit" %)University of Florence, Florence, Italy(%%),,
29 29  
30 -,,7) Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain,,
30 +,,7) Istituto di Neuroscienze, CNR, Pisa, Italy,,
31 31  
32 -,,8) Institució Catalana de Recerca i Estudis Avanc ̨ats (ICREA), Barcelona, Spain,,
32 +,,8) Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain,,
33 +
34 +,,9) Institució Catalana de Recerca i Estudis Avanc ̨ats (ICREA), Barcelona, Spain,,
33 33  )))
34 34  )))
35 35  
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41 41  
42 42  This collab illustrates the usage of the [[Neo>>https://neo.readthedocs.io/en/stable/]] and [[Elephant>>https://elephant.readthedocs.io/en/latest/]] tools in performing data analysis with regards to the SGA2-SP3-UC002 KR3.2, investigating sleep, anesthesia, and the transition to wakefulness: see Chapter 1 and Figure 2  of SGA2[[ Deliverable D3.2.1.>>https://drive.ebrains.eu/smart-link/17ac0d6e-e050-4a49-8ca2-e223b70a3121/]], for an overview of the scientific motivations and a description of the UseCase workflow; Chapter 2 (same document) for an introduction to KR3.2; Chapter 3, for a description of the mice ECoG data sets; Chapter 5, about the Slow Wave Analysis Pipeline and Chapter 6 for the mice wide-field GECI data). For details on the datasets used in this collab, please see the References below.
43 43  
46 +[[See the introduction video>>https://www.youtube.com/watch?v=uuAiY6HScM0]]
47 +
44 44  == How the pipeline works ==
45 45  
46 46  The design of the pipeline aims at interfacing a variety of general and specific analysis and processing steps in a flexible modular manner. Hence, it enables the pipeline to adapt to diverse types of data (e.g., electrical ECoG, or optical Calcium Imaging recordings) and to different analysis questions. This makes the analyses a) more reproducible and b) comparable amongst each other since they rely on the same stack of algorithms and any differences in the analysis are fully transparent.
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71 71  Each stage has config files (//pipeline/<stage_name>/configs/config_<profile>.yaml//) to specify which analysis/processing blocks to execute and which parameters to use. General and specific information about the blocks and parameters can be found in the README and config files of each stage. There are preset configuration profiles for the benchmark datasets IDIBAPS ([[ECoG, anesthetized mouse>>https://kg.ebrains.eu/search/?facet_type[0]=Dataset&q=sanchez-vives#Dataset/2ead029b-bba5-4611-b957-bb6feb631396]]) and LENS ([[Calcium Imaging, anesthetized mouse>>https://kg.ebrains.eu/search/instances/Dataset/71285966-8381-48f7-bd4d-f7a66afa9d79]]).
72 72  
73 73  * **Run the notebook**
74 -In the jupyter hub, navigate to //drive/My Libraries/My Library/pipeline/showcase_notebooks/run_snakemake_in_collab.ipynb//, or where you copied the //pipeline// folder to.
75 -Follow the notebook to install the required packages into your Python kernel, set the output path, and execute the pipeline with snakemake
78 +In the jupyter hub, navigate to //drive/My Libraries/My Library/run_snakemake_in_collab.ipynb//, or where you copied the file to.
79 +Follow the notebook to install the required packages into your Python kernel, set the output path, and execute the pipeline with snakemake.
76 76  
77 77  === ii) Local execution ===
78 78  
83 +//tested only with Mac OS and Linux!//
84 +
79 79  * **Get the code**
80 80  The source code of the pipeline is available via Github: [[INM-6/wavescalephant>>https://github.com/INM-6/wavescalephant]] and can be cloned to your machine ([[how to get started with Github>>https://guides.github.com/activities/hello-world/]]).
81 81  
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84 84  In the wavescalephant git repository, there is an environment file ([[pipeline/environment.yaml>>https://drive.ebrains.eu/smart-link/1a0b15bb-be87-46ee-b838-4734bc320d20/]]) specifying the required packages and versions. To build the environment, we recommend using conda ([[how to get started with conda>>https://docs.conda.io/projects/conda/en/latest/user-guide/getting-started.html]]).
85 85  ##conda env create ~-~-file environment.yaml
86 86  conda activate wavescalephant_env##
93 +
94 +Make sure that neo and elephant were installed as their Github development version, and if necessary add them manually to the environment.
95 +##pip install git+https:~/~/github.com/NeuralEnsemble/elephant.git
96 +pip install git+https:~/~/github.com/NeuralEnsemble/python-neo.git##
87 87  
88 88  )))
89 89  * **Edit the settings**
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111 111  
112 112  All results are stored in the path specified in the //settings.py// file. The folder structure reflects the structuring of the pipeline into stages and blocks. All intermediate results are stored as //.nix// files using the [[Neo data format>>https://neo.readthedocs.io/en/stable/]] and can be loaded with ##neo.NixIO('/path/to/file.nix').read_block()##. Additionally, most blocks produce a figure, and each stage a report file, to give an overview of the execution log, parameters, intermediate results, and to help with debugging. The final stage (//stage05_wave_characterization//) stores the results as[[ //pandas.DataFrames//>>https://pandas.pydata.org/]] in //.csv// files, separately for each measure as well as in a combined dataframe for all measures.
113 113  
124 +**Examples of the output figures (for IDIBAPS dataset)**
125 +
126 +* Stage 01 - [[example signal traces and metadata>>https://drive.ebrains.eu/smart-link/cf2fa914-260d-4d61-a2da-03ea07b7f9be/]]
127 +* Stage 02 - [[background substraction>>https://drive.ebrains.eu/smart-link/586d2f3c-591b-4dfb-94ee-8c0e28050dc4/]]
128 +* Stage 02 - [[logMUA estimation>>https://drive.ebrains.eu/smart-link/c92e4b0c-0938-44e8-9f8d-00522796b2fd/]]
129 +* Stage 02 - [[processed signal trace>>https://drive.ebrains.eu/smart-link/26ed27c6-de56-4b48-a57b-f70aab629197/]]
130 +* Stage 03 - [[amplitude distribution>>https://drive.ebrains.eu/smart-link/8ba80293-ba75-4a37-8a8f-05d44cf6f65c/]]
131 +* Stage 03 - [[UP state detection>>https://drive.ebrains.eu/smart-link/ab172be0-178e-4153-a3e6-b4bace32dd50/]]
132 +* Stage 04 - [[trigger clustering>>https://drive.ebrains.eu/smart-link/4a1f0169-8b43-49ce-80c8-f2fa0f4d50d3/]]
133 +* Stage 05 - [[planar velocities>>https://drive.ebrains.eu/smart-link/f4de8073-cb40-47a7-bc82-f97d36dbae25/]]
134 +* Stage 05 - [[directionality>>https://drive.ebrains.eu/smart-link/5485032d-0121-4cde-9ea2-3e0af3f12178/]]
135 +
114 114  == Outlook ==
115 115  
116 116  * Using the **KnowledgeGraph API **to insert data directly from the Knowledge Graph into the pipeline and also register and store the corresponding results as Analysis Objects. Such Analysis Objects are to incorporate **Provenance Tracking, **using [[fairgraph>>https://github.com/HumanBrainProject/fairgraph]],** **to record the details of the processing and analysis steps.
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127 127  * [[Sanchez-Vives, M. (2020). "Propagation modes of slow waves in mouse cortex".  //EBRAINS//>>https://doi.org/10.25493/WKA8-Q4T]]
128 128  * [[Sanchez-Vives, M. (2019). "Cortical activity features in transgenic mouse models of cognitive deficits (Fragile X Syndrome).//" EBRAINS//>>https://doi.org/10.25493/ANF9-EG3]]
129 129  
130 -
131 131  Code developed at:
132 132  
133 133  [[image:https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png||height="35" width="35"]][[INM-6/wavescalephant>>https://github.com/INM-6/wavescalephant]]
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