CStreet Overview

CStreet is a python script (python 3.6 or higher) for cell states trajectory construction by using k-nearest neighbors graph algorithm for time-series single-cell RNA-seq data. It is a developmental version.

Installation

Install CStreet by pip3

CStreet can be installed directly by using pip3 commands :
```
$ pip3 install cstreet
```

Quick Start

Input file: Only expression matrix containing the time-series expression level as reads counts or normalized values for this developmental version.

Output file: An inferenced cell states trajectory.

Add new time-series single cell RNA-seq data.

from cstreet import *
import pandas as pd
# Read single cell data as DataFrame
data_t1=pd.read_table('data_t1.txt',header=0, sep="\t",index_col=0) 
data_t2=pd.read_table('data_t2.txt',header=0, sep="\t",index_col=0)
data_t3=pd.read_table('data_t3.txt',header=0, sep="\t",index_col=0)
# Create a new CStreet object
cdata=CStreetData()
# add data into CStreet object
cdata.add_new_timepoint_scdata(data_t1)
cdata.add_new_timepoint_scdata(data_t2)
cdata.add_new_timepoint_scdata(data_t3)

Customize parameters.

#Step0:basic parameters
cdata.params.output_dir="./"
cdata.params.output_name="cstreet_project"


#Step1:cell cluster
cdata.params.cell_cluster_pca_n=10
cdata.params.cell_cluster_knn_n=15
cdata.params.cell_cluster_resolution=0.1

#Step2:gene and cell filter
cdata.params.filter_dead_cell=True
cdata.params.percent_mito_cutoff=0.2
cdata.params.filter_lowcell_gene=True
cdata.params.min_cells=3
cdata.params.filter_lowgene_cells=True
cdata.params.min_genes=200

#Step3:normalize
cdata.params.normalize=True
cdata.params.normalize_base=10000
cdata.params.log_transform=True

#Step4:get HVG
cdata.params.highly_variable_genes=False

#Step5:get graph
cdata.params.inner_graph_pca_n=10
cdata.params.inner_graph_knn_n=15
cdata.params.link_graph_pca_n=10
cdata.params.link_graph_knn_n=15
cdata.params.max_outgoing=10
cdata.params.min_score=0.1
cdata.params.min_cell_number=50

Run CStreet
```
cdata.run_cstreet()
```

Result

An example of inferenced cell trajectory:

Development Status
- 3 - Alpha
Intended Audience
- Developers
License
- OSI Approved :: MIT License
Programming Language
- Python :: 3.6
Topic
- Software Development :: Build Tools

Release files for cstreet 0.0.6

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for cstreet 0.0.6
File	Size	Uploaded
cstreet-0.0.6.tar.gz	14.1 kB	Oct 14, 2020	Details

Release files / cstreet-0.0.6.tar.gz

Download URL	cstreet-0.0.6.tar.gz
Size	14.1 kB
Tags	Source
SHA-256 checksum How to use checksums	`26c05cd6973ed38aab20bcae916fbe7713aa3e801243fa6728dd993604a0faab`
BLAKE2b-256 checksum How to use checksums	`730e64f303749513e94d0c82a5e53e806afa80ec73b7005644ce25fa606088d3`
Upload date	Oct 14, 2020
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/50.3.0 requests-toolbelt/0.9.1 tqdm/4.50.1 CPython/3.6.8`