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biocartograph-0.4.2
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تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
Package was renamed from Biocarta v0.2.27 to Biocartograph because of an unintentional name clash
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | biocartograph-0.4.2 |
| نام | biocartograph |
| نسخه کتابخانه | 0.4.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Richard Tjörnhammar |
| ایمیل نویسنده | richard.tjornhammar@gmail.com |
| آدرس صفحه اصلی | https://github.com/rictjo/biocarta |
| آدرس اینترنتی | https://pypi.org/project/biocartograph/ |
| مجوز | - |
# Biocartograph
Creating Cartographic Representations of Biological Data
[](https://zenodo.org/badge/latestdoi/578172132)
# Installation
```
pip install biocartograph
```
# Example code
```
if __name__ == '__main__' :
from biocartograph.quantification import full_mapping
#
adf = pd.read_csv('analytes.tsv',sep='\t',index_col=0)
#
# WE DO NOT WANT TO KEEP POTENTIALLY BAD ENTRIES
adf = adf.iloc[ np.inf != np.abs( 1.0/np.std(adf.values,1) ) ,
np.inf != np.abs( 1.0/np.std(adf.values,0) ) ].copy()
#
# READING IN SAMPLE INFORMATION
# THIS IS NEEDED FOR THE ALIGNED PCA TO WORK
jdf = pd.read_csv('journal.tsv',sep='\t',index_col=0)
jdf = jdf.loc[:,adf.columns.values]
#
alignment_label , sample_label = 'Disease' , None
add_labels = ['Cell-line']
#
cmd = 'max'
# WRITE FILES AND MAKE NOISE
bVerbose = True
# CREATE AN OPTIMIZED REPRESENTATION
bExtreme = True
# WE MIGHT WANT SOME SPECIFIC INTERSECTIONS OF THE HIERARCHY
n_clusters = [20,40,60,80,100]
# USE ALL INFORMATION
n_components = None
umap_dimension = 2
n_neighbors = 20
local_connectivity = 20.
transform_seed = 42
#
print ( adf , jdf )
#
# distance_type = 'correlation,spearman,absolute' # DONT USE THIS
distance_type = 'covariation' # BECOMES CO-EXPRESSION BASED
#
results = full_mapping ( adf , jdf ,
bVerbose = bVerbose ,
bExtreme = bExtreme ,
n_clusters = n_clusters ,
n_components = n_components ,
distance_type = distance_type ,
umap_dimension = umap_dimension ,
umap_n_neighbors = n_neighbors ,
umap_local_connectivity = local_connectivity ,
umap_seed = transform_seed ,
hierarchy_cmd = cmd ,
add_labels = add_labels ,
alignment_label = alignment_label ,
sample_label = None )
#
map_analytes = results[0]
map_samples = results[1]
hierarchy_analytes = results[2]
hierarchy_samples = results[3]
```
or just call it using the default values:
```
import pandas as pd
import numpy as np
if __name__ == '__main__' :
from biocartograph.quantification import full_mapping
#
adf = pd.read_csv('analytes.tsv',sep='\t',index_col=0)
#
adf = adf.iloc[ np.inf != np.abs( 1.0/np.std(adf.values,1) ) ,
np.inf != np.abs( 1.0/np.std(adf.values,0) ) ].copy()
jdf = pd.read_csv('journal.tsv',sep='\t',index_col=0)
jdf = jdf.loc[:,adf.columns.values]
#
alignment_label , sample_label = 'Disease' , None
add_labels = ['Cell-line']
#
results = full_mapping ( adf , jdf ,
bVerbose = True ,
n_clusters = [40,80,120] ,
add_labels = add_labels ,
alignment_label = alignment_label )
#
map_analytes = results[0]
map_samples = results[1]
hierarchy_analytes = results[2]
hierarchy_samples = results[3]
```
and plotting the information of the map analytes yields :
[Cancer Disease Example](https://gist.github.com/rictjo/9cc40579914a51bffe7df442fec140f4)
You can also run an alternative algorithm where the UMAP coordinates are employed directly for clustering by setting
```
results = full_mapping ( adf , jdf ,
bVerbose = True ,
bUseUmap = True ,
n_clusters = [40,80,120] ,
add_labels = add_labels ,
alignment_label = alignment_label )
```
with the following [results](https://rictjo.github.io/?https://gist.githubusercontent.com/rictjo/8be5b5a9cc7f06ea7455d6c6ecc11ad8/raw/e00ea663a1218718f542744a939e0b05c604e8ab/index.html).
Download the zip and open the html index:
```
chromium index.html
```
# Other generated solutions
The clustering visualisations were created using the [Biocartograph](https://pypi.org/project/biocartograph/) and [hvplot](https://pypi.org/project/hvplot/) :
What groupings corresponds to biomarker variance that describe them? Here are two visualisations of that:
Diseases :
[cancers](https://rictjo.github.io/?https://gist.githubusercontent.com/rictjo/870d8cc26ede12d00b7ae60109feebdc/raw/42beb98a82477e9c809f99d3498966fc564846b8/index.html) [biocartograph gfa Reactome enrichments](https://rictjo.github.io/?https://gist.githubusercontent.com/rictjo/afcca63470e5c9398372276f9ab43d42/raw/6e68e1da85fdb6d1b1aeec8c351831a3aad83e9d/index.html) [biocartograph gfa cluster enrichments](https://rictjo.github.io/?https://gist.githubusercontent.com/rictjo/5d83a85537839232f34edccde1cdc8e6/raw/40c49013a55213405a6b6609f9ab31c883668d5d/index.html)
[biocartograph treemap cluster 61](https://rictjo.github.io/?https://gist.githubusercontent.com/rictjo/146ba66109c6554684dc387348d21a82/raw/a32f1e7c80cc6ebe53c33039e2adfb4512e3ce4b/index.html)
Tissues :
[tissues](https://rictjo.github.io/?https://gist.githubusercontent.com/rictjo/5e760b8c4fd3da4842813a4a0cea422c/raw/caa18f0391dc389fb8fc56ae8ac2bc4f7046a939/index.html)
Single Cells:
[single cells](https://rictjo.github.io/?https://gist.githubusercontent.com/rictjo/eb118f70c1d173f2e6d51f06779827d2/raw/c7fd997caf232df3d6bbbd80d607463812d461a1/index.html) [biocartograph gfa enrichment](https://rictjo.github.io/?https://gist.githubusercontent.com/rictjo/64ee6e4d2bacb31715ec46b65c9d441d/raw/a5d91114cc4ab784f865277264efe5f628ea018e/index.html) [biocartograph treemap cluster 47](https://rictjo.github.io/?https://gist.githubusercontent.com/rictjo/34b320ea503b79e29808b063a7266714/raw/eaf39e740eb8baaadf0d08faab521a152c282009/index.html)
Blood Cells:
[blood cells](https://rictjo.github.io/?https://gist.githubusercontent.com/rictjo/200153c58767d8b5162e66688ff4d669/raw/cfb74069d5cc9fc58e3558c753caaa60d4ba5e9b/index.html)
[biocartograph gfa enrichment](https://rictjo.github.io/?https://gist.githubusercontent.com/rictjo/42ec85df088a0c40de339a78322594bd/raw/0725bea467b0c153298655e3a0555670a812e80f/index.html)
[biocartograph treemap cluster 2](https://rictjo.github.io/?https://gist.githubusercontent.com/rictjo/d754528cf594087e509fe44fa071c178/raw/a78a82066e3d6aa2971aba2a64543a4018241372/index.html)
[TCGA-BRCA](https://gdc.cancer.gov/) :
Calculated using the biocartograph and a [TCGA derived data set](https://zenodo.org/record/3407557) with the results for [Breast Cancer mRNA-seq](https://rictjo.github.io/?https://gist.githubusercontent.com/rictjo/ea18ac756d5142ac98219d45960583d4/raw/7cba81abb8af89416d11a682a2e0d19a311c954f/index.html)
نحوه نصب
نصب پکیج whl biocartograph-0.4.2:
pip install biocartograph-0.4.2.whl
نصب پکیج tar.gz biocartograph-0.4.2:
pip install biocartograph-0.4.2.tar.gz