معرفی شرکت ها


exoTras-0.4


Card image cap
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
Card image cap
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
Card image cap
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
Card image cap
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
Card image cap
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

توضیحات

exosome-containing droplet identification and source tracking in scRNA-seq data
ویژگی مقدار
سیستم عامل -
نام فایل exoTras-0.4
نام exoTras
نسخه کتابخانه 0.4
نگهدارنده []
ایمیل نگهدارنده []
نویسنده Ruiqiao He
ایمیل نویسنده ruiqiaohe@gmail.com
آدرس صفحه اصلی http://pypi.python.org/pypi/exoTras/
آدرس اینترنتی https://pypi.org/project/exoTras/
مجوز GPL
## exoTras delineates exosome profiles at droplet resolution from single-cell transcriptomes exoTras /ˈɛkoʊˌtɹeɪs/ stands for <ins>exo</ins>some-containing droplet identification and source <ins>tra</ins>cking in <ins>s</ins>cRNA-seq data. You can freely use exoTras to explore exosome heterogeneity at single droplet, characterize cell type dynamics in light of exosome activity and unlock diagnostic potential of exosomes in concert with cells. <p align="center"> <img src='./docs/exoTras_overview.png'> </p> <p align="center"> Overview of exoTras. </p> ### Prerequisites "numpy", "pandas", "scipy", "scanpy", "statsmodels", "gseapy" ### Installation ```bash pip install exoTras ``` We also suggest to use a separate conda environment for installing exoTras. ```bash conda create -y -n exoTras_env python=3.7 conda activate exoTras_env pip install exoTras ``` ### Basic Usage Input for exoTras is a cell-by-gene matrix. In the case of scRNA-seq dataset using 10X Genomics, we used `raw_feature_bc_matrix` directory generated by Cell Ranger as input. Output of exoTras consists of the score of exosome signals and classification for each droplet. Such exosome information will be used for downstream analysis and as basis for the construction of the exosome secretion activity index (ESAI) and source tracking functions. We implemented four functions for exosomes recognizing and functional analyses. In exoTras, `exosomes_recognizer` recognizes exosome-containing droplets in the raw scRNA-seq data; `source_tracker` and `ESAI_celltype` trace these droplets to their original cell type and estimited corresponding exosome secretion activity; and `cellfree_simulator` simulates transcriptional profile of cell free droplets in scRNA-seq. ### Examples For a study with multiple scRNA-seq samples, we support two input ways. ```bash ### first if samples locate in one directory exoTras.exosomes_recognizer(input_path='directory_path', sample_file='sample_name', out_path='output_path', species='Homo') ### second if samples locate in differet directories exoTras.exosomes_recognizer(sample_file='sample_path', out_path='output_path', species='Homo') ``` In the first way, `directory_path` is the path for the directory that contains all sample; and `sample_name` is the file that list each sample in th directory row by row. Every sample should contain directory structure like 'sample/outs/raw_feature_bc_matrix/', and exoTras would automatically recognize for each sample. The second way supports one sample file with abosulte path for each sample, if they are not in one directory. And `out_path` defines the output of exoTras that is one h5ad file, named 'raw_exoTras.h5ad', with exoTras score and exosome classification in the 'obs' for all droplets, and one named 'exosomes_exoTras.h5ad' with only exosome-containing droplets. Then, we could source these recoginzed exosomes to original cell type and calculate the exosome secretion activity. ```bash celltype_exosome_number, adata_exo, adata_combined = exoTras.source_tracker(adata_exo, adata_cell, OBSsample='batch', OBScelltype='celltype') exo_activity_dat = exoTras.ESAI_celltype(adata_exo, adata_cell, OBSsample='batch', OBScelltype='celltype') ``` In the parameters, the two adata variable represent the exosome- and cell- anndata objects. And `batch` and `celltype` are the indexes of sample names and cell types in the anndata of cells. The output of `source_tracker` is in the 'obsm' of 'adata_exo' indexed as 'source'. Further tutorials please refer to https://exoTras.readthedocs.io/.


نیازمندی

مقدار نام
>=1.6.0 scanpy
>=1.21.5 numpy
>=1.1.2 pandas
>=1.5.4 scipy
>=0.12.1 statsmodels
- gseapy


زبان مورد نیاز

مقدار نام
>=3.7.1 Python


نحوه نصب


نصب پکیج whl exoTras-0.4:

    pip install exoTras-0.4.whl


نصب پکیج tar.gz exoTras-0.4:

    pip install exoTras-0.4.tar.gz