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fcvisualization-0.0.0.6
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FeatureCloud Visualization
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | fcvisualization-0.0.0.6 |
| نام | fcvisualization |
| نسخه کتابخانه | 0.0.0.6 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | FeatureCloud |
| ایمیل نویسنده | balazs.orban@gnd.ro |
| آدرس صفحه اصلی | https://github.com/FeatureCloud/fc-cluster-visualization-app |
| آدرس اینترنتی | https://pypi.org/project/fcvisualization/ |
| مجوز | MIT |
# Featurecloud Cluster Visualization app
This is an interactive cluster visualization app implemented in Dash and Plotly.
## App usage
This app is intended to be used in [FeatureCloud](https://featurecloud.ai) environment. It requires input data in order to generate the interactive visualization interface.
The data must be in the place and in the format specified by this documentation.
The app has a tabular interface consisting of:
- Confounders
- Distances
- Clustering Quality
- Scree plot
- Volcano plot
- Help
### Confounders tab
Main features:
- Cluster or client id field based clustering display
- K number selector
- Cluster selector
- X and Y axes selector
- Use pie or bar chart selector for discrete data type visualization
- Confounding factors filter
- Scatter plot with confidence ellipsis
- Linear or logarithmic scale
- Point/Lasso/Box selection
- Export diagrams to png
- Confounding factors diagrams
- Visualize and download selected points
### Distances tab
Main features:
- K number selector
- Cluster selector
- Confounding factors filter
- Clustergram
### Clustering Quality tab
Main features:
- K number selector
- Silhouette plot
- K number selector
### Scree plot tab
Main features:
- Display components' eigenvalue
### Volcano plot tab
Main features:
- Set effect sizes thresholds (vertical)
- Set genome wide line threshold (horizontal)
### Help
It displays this documentation.
## Input data requirements
### Expected folder structure for visual representation
```
data
└───results
│ └───K2
│ │ clustering.csv
│ │ silhouette.csv
│ └───K3
│ │ clustering.csv
│ │ silhouette.csv
│ └───...
│ └───K<n>
│ │ clustering.csv
│ │ silhouette.csv
│ confoundingData.csv
│ confoundingData.meta
│ localData.csv
│ distanceMatrix.csv
│ varianceExplained.csv
| volcano_data.csv
```
#### Download
For a better understanding an example data set can be downloaded by clicking [here](./assets/data.zip).
#### Tip for running
When running the app in a workflow, one can upload a zip file containing the config file and data files. It will be automatically unzipped and copied to the input directory of the app. To test this, just upload the example data set mentioned above.
When running the app in a workflow, one can trigger the app to finish, by clicking the Finished button in the top right corner. This makes possible starting the next app in the workflow, if any, or stopping the workflow.
Notes:
- localData.csv file is mandatory. The rest of files are optional.
- All files under K<n> folders (if the folder exists) are mandatory
### Delimiter
The default delimiter is the ";" character. It can be overwritten in the config.yml file.
### Expected file structure
#### confoundingData.csv
This file contains all confounding factors related to local data.
First column is the id (mandatory), followed by a maximum of 5 of columns of confounders. The confounders column names are arbitrary and must not match reserved column names: id, cluster, client_id
##### Example
```csv
id;age;sex;race;height;sugar-level
1;38;F;Caucasian;159;low
2;17;F;Asian;175;low
3;40;F;African-American;162;medium
4;32;F;Indian;183;high
5;18;F;Indian;193;low
```
#### confoundingData.meta
This file contains meta information about confounding factors
##### Supported data and value types:
Columns:
- name: the name of the confounding factor
- data_type:
- continous: arbitrary values
- discrete: values from a predefined value set
- ordinal: values from a predefined value set in ordered manner
- value_type
- integer
- string
- enumeration values in ordered manner
##### Example
```csv
name;data_type;value_type
age;continuous;integer
sex;discrete;string
race;discrete;string
height;continuous;integer
sugar-level;ordinal;low,medium,high
```
#### localData.csv
This file contains the base values.
Columns:
- id: sample id (mandatory)
- client_id: optional field, the app supports display of clustering on this field as well
- data columns: at least 2 data columns need to be present. More than 2 data columns are supported. The column names are arbitrary and must not match reserved column names: id, cluster, client_id
##### Example
```csv
id;client_id;x;y;z
1;1;-0.115257648318211;0.289555823437292;0.333954194475931
2;1;-0.226069897739012;0.293898393621215;0.130668954544708
3;1;0.0606059327164007;0.0297344961039227;0.112959671444335
4;1;0.0398616396572761;-0.37563056412847;-0.35560909629883
5;1;-0.21084222999711;0.592948181336414;-0.368794747648271
```
#### distanceMatrix.csv
This file contains distances between samples. It is of n x n dimension, where n is the number of sample data.
##### Example
```csv
1;2;3;4;5
1;0;0.53851648071345;0.509901951359278;0.648074069840786;0.141421356237309
2;0.53851648071345;0;0.3;0.331662479035541;0.608276253029822
3;0.509901951359278;0.3;0;0.244948974278318;0.509901951359278
4;0.648074069840786;0.331662479035541;0.244948974278318;0;0.648074069840786
5;0.141421356237309;0.608276253029822;0.509901951359278;0.648074069840786;0
```
#### varianceExplained.csv
This file contains the eigenvalues for components.
Columns:
- component: mandatory field, it contains the name of the component
- eigenvalue: mandatory field, it contains the eigenvalue of the component
##### Example
```csv
component;eigenvalue
x;0.729624454
y;0.408507618
z;0.228507618
```
#### clustering.csv
This file contains the cluster distribution of the samples.
Columns:
- id: mandatory, sample id
- cluster: mandatory, cluster id
##### Example
```csv
id;cluster
1;1
2;1
3;1
4;1
5;1
```
#### silhouette.csv
This file contains data used to display the clusters silhouette plot.
Columns:
- index column, mandatory
- y: mandatory, contains the value to be plotted
- cluster: mandatory, contains the cluster id
##### Example
```csv
x;y;cluster
1;0.369499266613275;1
2;0.783307729521766;1
3;0.0627545099705458;1
4;0.205028521828353;1
5;0.915254552382976;1
```
#### volcano_data.csv
This file contains data used to display the volcano plot.
The columns are the default columns used in [Dash Bio Volcano plot library](https://dash.plotly.com/dash-bio/volcanoplot).
##### Example
```csv
CHR;BP;P;SNP;ZSCORE;EFFECTSIZE;GENE;DISTANCE
1;937641;0.335343792801723;rs9697358;0.9634;-0.0946;ISG15;1068
1;1136887;0.245857131900266;rs34945898;1.1605;-0.0947;TNFRSF4;0
1;2116240;0.823285880265757;rs12034613;0.2233;-0.0741;FP7162;0
```
### General requirements for input data
- the number of samples has to be the same in all files
- the sample ids must be persistent
## Config file support
The app supports setting all data file and directory paths from config file. The config.yml file should be placed in the default data directory (mnt/input/data)
Example:
```yml
fc-cluster-visualization-app:
delimiter: ';'
data-dir: 'data/exampleData'
local-data-path: 'data/exampleData/localData.csv'
distance-matrix-path: 'data/exampleData/distanceMatrix.csv'
confounding-meta-path: 'data/exampleData/confoundingData.meta'
confounding-data-path: 'data/exampleData/confoundingData.csv'
variance-explained-path: 'data/exampleData/varianceExplained.csv'
k-values-clustering-result-dir: 'data/exampleData/results'
k-values-clustering-file-name: 'clustering.csv'
k-values-silhouette-file-name: 'silhouette.csv'
volcano-data-path: 'exampleData/volcano_data.csv'
# all files downloaded from the browser will end up here too
download-dir: 'data/exampleData/downloads'
```
If config file is not present, the app will search for data in the default folder (/mnt/input/data).
Any key from the config file can be omitted, in that case the app will search in the default data directory.
Keys should not be left with blank values.
## Limitations
- the app supports displaying 5 confounding factors simultaneously
- if more than 5 confounding factors are present in the confoundingMeta.csv file, it will display the first 5
## Workflow
When the app runs in a FeatureCloud workflow, a Finished button will be displayed in the upper right corner. Clicking on the button terminates the application, while the controller shuts down the Docker container. Also, input folder content will be copied to the output folder.
## Screenshots
### Confounders tab
Confounding factors filter with scatter plot
Scatter plot with confounding factors diagrams
View selected data from scatter plot
### Distances tab
Clustergram
### Clustering Quality tab
Silhouette diagram
### Scree plot
### Volcano plot
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl fcvisualization-0.0.0.6:
pip install fcvisualization-0.0.0.6.whl
نصب پکیج tar.gz fcvisualization-0.0.0.6:
pip install fcvisualization-0.0.0.6.tar.gz