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From version 75.1
edited by robing
on 2020/05/12 10:25
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To version 66.1
edited by robing
on 2020/04/30 14:23
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9 9  
10 10  Robin Gutzen^^1^^, Giulia De Bonis^^2^^, Elena Pastorelli^^2,3^^, Cristiano Capone^^2^^,
11 11  
12 -Chiara De Luca^^2,3^^, Michael Denker^^1^^, Sonja Grün^^1,4^^,
12 +Chiara De Luca^^2,3^^, Michael Denker^^1^^, Sonja Grün^^1^^,
13 13  
14 -Pier Stanislao Paolucci^^2^^, Andrew Davison^^5^^
14 +Pier Stanislao Paolucci^^2^^, Andrew Davison^^4^^
15 15  
16 -Experiments: Anna Letizia Allegra Mascaro^^6,7^^, Francesco Resta^^6^^, Francesco Saverio Pavone^^6^^, Maria-Victoria Sanchez-Vives^^8,9^^
16 +Experiments: Anna Letizia Allegra Mascaro^^5,6^^, Francesco Resta^^5^^, Francesco Saverio Pavone^^5^^, Maria-Victoria Sanchez-Vives^^7,8^^
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) Theoretical Systems Neurobiology, RWTH Aachen University, Aachen, Germany,,
24 +,,4) Unité de Neurosciences, Information et Complexité, Neuroinformatics Group, CNRS FRE 3693, Gif-sur-Yvette, France,,
25 25  
26 -,,5) Unité de Neurosciences, Information et Complexité, Neuroinformatics Group, CNRS FRE 3693, Gif-sur-Yvette, France,,
26 +,,5) European Laboratory for Non-linear Spectroscopy (LENS), (% style="--darkreader-inline-color:inherit; color:inherit" %)University of Florence, Florence, Italy(%%),,
27 27  
28 -,,6) European Laboratory for Non-linear Spectroscopy (LENS), (% style="--darkreader-inline-color:inherit; color:inherit" %)University of Florence, Florence, Italy(%%),,
28 +,,6) Istituto di Neuroscienze, CNR, Pisa, Italy,,
29 29  
30 -,,7) Istituto di Neuroscienze, CNR, Pisa, Italy,,
30 +,,7) Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain,,
31 31  
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,,
32 +,,8) Institució Catalana de Recerca i Estudis Avanc ̨ats (ICREA), Barcelona, Spain,,
35 35  )))
36 36  )))
37 37  
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41 41  (((
42 42  == Flexible workflows to generate multi-scale analysis scenarios ==
43 43  
44 -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.
42 +This Collab is aimed at experimental and computational neuroscientists interested in the usage of the [[Neo>>https://neo.readthedocs.io/en/stable/]] and [[Elephant>>https://elephant.readthedocs.io/en/latest/]] tools in performing data analysis of spiking data.
43 +Here, the collab illustrates the tool usage 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)
45 45  
45 +[[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]]
46 +
46 46  == How the pipeline works ==
47 47  
48 48  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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78 78  
79 79  === ii) Local execution ===
80 80  
81 -//tested only with Linux!//
82 -
83 83  * **Get the code**
84 84  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/]]).
85 85  
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115 115  
116 116  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.
117 117  
118 -**Examples of the output figures (for IDIBAPS dataset)**
117 +== Outlook ==
119 119  
120 -* Stage 01 - [[example signal traces and metadata>>https://drive.ebrains.eu/smart-link/cf2fa914-260d-4d61-a2da-03ea07b7f9be/]]
121 -* Stage 02 - [[background substraction>>https://drive.ebrains.eu/smart-link/586d2f3c-591b-4dfb-94ee-8c0e28050dc4/]]
122 -* Stage 02 - [[logMUA estimation>>https://drive.ebrains.eu/smart-link/c92e4b0c-0938-44e8-9f8d-00522796b2fd/]]
123 -* Stage 02 - [[processed signal trace>>https://drive.ebrains.eu/smart-link/26ed27c6-de56-4b48-a57b-f70aab629197/]]
124 -* Stage 03 - [[amplitude distribution>>https://drive.ebrains.eu/smart-link/8ba80293-ba75-4a37-8a8f-05d44cf6f65c/]]
125 -* Stage 03 - [[UP state detection>>https://drive.ebrains.eu/smart-link/ab172be0-178e-4153-a3e6-b4bace32dd50/]]
126 -* Stage 04 - [[trigger clustering>>https://drive.ebrains.eu/smart-link/4a1f0169-8b43-49ce-80c8-f2fa0f4d50d3/]]
127 -* Stage 05 - [[planar velocities>>https://drive.ebrains.eu/smart-link/f4de8073-cb40-47a7-bc82-f97d36dbae25/]]
128 -* Stage 05 - [[directionality>>https://drive.ebrains.eu/smart-link/5485032d-0121-4cde-9ea2-3e0af3f12178/]]
129 -
130 -=== Outlook ===
131 -
132 132  * 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.
133 133  * Adding support for the pipeline to make use of **HPC** resources when running on the collab.
134 134  * Further extending the available **methods** to address a wider variety of analysis objectives and support the processing of other datatypes. Additional documentation and guides should also make it easier for non-developers to contribute new method blocks.
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143 143  * [[Sanchez-Vives, M. (2020). "Propagation modes of slow waves in mouse cortex".  //EBRAINS//>>https://doi.org/10.25493/WKA8-Q4T]]
144 144  * [[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]]
145 145  
146 -Code developed at:
133 +== License (to discuss) ==
147 147  
148 -[[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]]
135 +All text and example data in this collab is licensed under Creative Commons CC-BY 4.0 license. Software code is licensed under GNU General Public License v3.0.
149 149  
150 -== License ==
151 -
152 -Text is licensed under the Creative Commons CC-BY 4.0 license. LENS data is licensed under the Creative Commons CC-BY-NC-ND 4.0 license. IDIBAPS data is licensed under the Creative Commons CC-BY-NC-SA 4.0 license. Software code is licensed under GNU General Public License v3.0.
153 -
154 154  [[image:https://i.creativecommons.org/l/by/4.0/88x31.png||style="float:left"]]
155 155  
156 -[[image:https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png||alt="https://i.creativecommons.org/l/by/4.0/88x31.png" style="float:left"]]
139 +== ==
157 157  
158 -[[image:https://licensebuttons.net/l/by-nc-nd/4.0/88x31.png||alt="https://i.creativecommons.org/l/by/4.0/88x31.png" style="float:left"]]
159 -
160 -
161 161  == Acknowledgments ==
162 162  
163 163  This open source software code was developed in part or in whole in the Human Brain Project, funded from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2).
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167 167  )))
168 168  
169 169  
170 -== ==
150 +== Executing the pipeline ==
171 171  
172 172  (% class="col-xs-12 col-sm-4" %)
173 173  (((
174 174  {{box title="**Contents**"}}
175 -{{toc depth="3"/}}
155 +{{toc/}}
176 176  {{/box}}
177 177  
178 178  
Collaboratory.Apps.Collab.Code.CollabClass[0]
Description
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1 -Use Case SGA2-SP3-002 KR3.2: Integrating multi-scale data and the output of simulations in a reproducible and adaptable pipeline
1 +Space for developing and hosting a showcase pipeline for performing reproducible and adaptable analysis with the focus of slow cortical waves.