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ECAUGT-1.0.6
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ECA Client
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | ECAUGT-1.0.6 |
| نام | ECAUGT |
| نسخه کتابخانه | 1.0.6 |
| نگهدارنده | ['Minsheng Hao'] |
| ایمیل نگهدارنده | ['hmsh653@gmail.com'] |
| نویسنده | Yixin Chen & Haiyang Bian |
| ایمیل نویسنده | chenyx19@mails.tsinghua.edu.cn |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/ECAUGT/ |
| مجوز | - |
# ECAUGT
**Caution: This package cannot work on Windows**
ECAUGT is a package designed for customized *in data* cell sorting under the human Ensemble Cell Atalas (hECA). It contains the APIs to search and download data from the hECA's database.
You are welcomed to use our web version at http://eca.xglab.tech/#/cellSorting
You can also find more information at https://github.com/XuegongLab/ECAUGT and http://eca.xglab.tech/ecaugt/index.html
## About hECA
hECA provides a platform for assembling massive scattered single-data into a unified Giant Table (uGT). We keeps exploring information framework and future ways of building and utilizing cell atlas. Here we provide entries for customized in data cell sorting, access to unified Hierarchical Annotation Framework (uHAF) and multifaceted portraits of genes, cell types and organs.
hECA and ECAUGT are designed and developed by [XGlab](http://bioinfo.au.tsinghua.edu.cn/member/xuegonglab/ ) in Tsinghua University.
Visit hECA's homepage at http://eca.xglab.tech/
Read our pre-print paper at https://www.biorxiv.org/content/10.1101/2021.07.21.453289v1
## Install
```
pip install ECAUGT
```
## Tutorial
### 1. Configuration
#### 1.1 Load packages
```python
import sys
import pandas as pd
import ECAUGT
import time
import multiprocessing
import numpy as np
```
#### 1.2 Connect to server
```python
# set parameters
endpoint = "https://HCAd-Datasets.cn-beijing.ots.aliyuncs.com"
access_id = "LTAI5t7t216W9amUD1crMVos" #enter your id and keys
access_key = "ZJPlUbpLCij5qUPjbsU8GnQHm97IxJ"
instance_name = "HCAd-Datasets"
table_name = 'HCA_d'
```
```python
# setup client
ECAUGT.Setup_Client(endpoint, access_id, access_key, instance_name, table_name)
```
#### 1.3 Build index
We should check if the index has been built.
```python
ECAUGT.build_index()
```
### 2. Search cell with metadata condition
Conditions are presented in a structured string which is a combination of several logical expressions.
Each logical expression should be in the following forms:
field_name1 == value1, here '==' means equal
field_name2 <> value2, here '<>' means unequal
Three symbols are used for logical operation between expressions:
logical_expression1 && logical_expression2, here '&&' means AND operation
logical_expression1 || logical_expression2, here '||' means OR operation
! logical_expression1, here '!' means not NOT operation
Brackets are allowed and the priorities of the logical operations are as common. The metadata condition string is also robust to the space character.
```python
# get primary keys
rows_to_get = ECAUGT.query_cells("organ == Lung && cell_type == T cell ")
```
The variable rows_to_get is a list containing their primary keys.
### 3. Download data
We first download three columns of the queried cells and return them in the DataFrame form. (The first column in the result is the primary keys)
For illustration, we only download the first 20 cells.
```python
rows_to_get_2 = rows_to_get[0:20]
```
#### 3.1 Download interested columns
```python
# download data in pandas::DataFrame from
ECAUGT.get_columnsbycell_para(rows_to_get = rows_to_get_2, cols_to_get=['cl_name','uHAF_name','cell_type'], col_filter=None, do_transfer = True, thread_num = multiprocessing.cpu_count()-1)
```
Then we show how the result will look like when we don't do transform.
```python
# download data in list from
ECAUGT.get_columnsbycell_para(rows_to_get = rows_to_get_2, cols_to_get=['cl_name','uHAF_name','cell_type'], col_filter=None, do_transfer = False, thread_num = multiprocessing.cpu_count()-1)
```
#### 3.2 Download all columns
We also compare the time consumption between parallel and unparallel cell download processes for the first 20 cells, and find the parallel process only takes about 1/3 time.
```python
# the parallel version
start_time = time.time()
result = ECAUGT.get_columnsbycell_para(rows_to_get = rows_to_get_2, cols_to_get=None, col_filter=None, do_transfer = False, thread_num = multiprocessing.cpu_count()-1)
time.time()-start_time
```
```python
# the unparallel version
start_time = time.time()
result = ECAUGT.get_columnsbycell(rows_to_get = rows_to_get_2, cols_to_get=None,col_filter=None,do_transfer = False)
time.time()-start_time
```
### 4. Search cell with both metadata condition and gene condition
Now we show hot to add gene conditions when downloading cells. Here we download some genes of the queried cells and select the cells whose expression level on PTPRC is larger than 0.1 and experssion level on CD3D is no less than 0.1
```python
# add col_filter on gene
gene_condition = ECAUGT.seq2filter("PTPRC > 0.1 && CD3D>=0.1")
```
#### 4.1 Download some of the columns
```python
df_result = ECAUGT.get_columnsbycell_para(rows_to_get = rows_to_get, cols_to_get=['CD3D','PTPRC','donor_id','uHAF_name'], col_filter=gene_condition, do_transfer = True, thread_num = multiprocessing.cpu_count()-1)
```
We can find that 7403 cells among the 14870 queried cells has expression levels that satisfy PTPRC > 0.1 && CD3D>=0.1. Then we can download some columns of these cells with the parameter ***cols_to get*** and the genes involved in the condition must be included in the
#### 4.2 Download all columns of these cells
We can get all expression levels and metadatas of these cells by setting the parameter ***cols_to_get*** as None
```python
df_result = ECAUGT.get_columnsbycell_para(rows_to_get = rows_to_get, cols_to_get=None, col_filter=gene_condition, do_transfer = True, thread_num = multiprocessing.cpu_count()-1)
```
نیازمندی
| مقدار | نام |
|---|---|
| >=5.2.1 | tablestore |
| - | numpy |
| - | pandas |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3 | Python |
نحوه نصب
نصب پکیج whl ECAUGT-1.0.6:
pip install ECAUGT-1.0.6.whl
نصب پکیج tar.gz ECAUGT-1.0.6:
pip install ECAUGT-1.0.6.tar.gz