semvec/pypi-packages huggingface dataset:
markitdown-cli 0.1.5
A CLI tool to convert a webpage to markdown
Requires Python: >=3.8

# markitdown-cli

[![PyPI version](https://badge.fury.io/py/markitdown-cli.svg)](https://badge.fury.io/py/markitdown-cli)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

A lightweight CLI wrapper for Microsoft's `markitdown` library. Convert any webpage into a clean Markdown file directly from your terminal.

## Installation

The recommended way to install `markitdown-cli` is using `pipx`:

```bash
pipx install markitdown-cli
```

Alternatively, you can install it with `pip`:

```bash
pip install markitdown-cli
```

## Usage

The primary use case is to provide a URL to the tool. `markitdown-cli` will then download the page, convert it to Markdown, and save it as a file in your current directory.

### Basic Usage

```bash
markitdown-cli https://example.com/article
```

This will create a file named `article.md` in the current directory.

### Interactive Title Suggestion

The tool will suggest a title for the Markdown file based on the URL. You can either accept the suggestion by pressing Enter, or you can type a custom title.

```
$ markitdown-cli https://collabfund.com/blog/the-dumber-side-of-smart-people/
Suggested title [the-dumber-side-of-smart-people]:
Wrote: the-dumber-side-of-smart-people.md
```

### Specifying an Output Directory

You can use the `-o` or `--outdir` option to specify a different directory to save the Markdown file.

```bash
markitdown-cli https://example.com/article -o ./my-articles
```

This will create `article.md` inside the `my-articles` directory.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
sccross 1.0.7.1
Single cell multi-omics cross modal generation, multi-omics simulation and perturbation
Requires Python: >=3.6
Keywords: bioinformatics, deep-learning, single-cell, single-cell-multiomics

# scCross
A Deep Learning-Based Model for the integration, cross-dataset cross-modality generation, self augmentation and matched multi-omics simulation of single-cell multi-omics data. Our model excels at maintaining in-silico perturbations during cross-modality generation and harnessing these perturbations to identify key genes.

For detailed instructions, comprehensive documentation, and helpful tutorials, please visit:
  
* [https://sccross.readthedocs.io](https://sccross.readthedocs.io/en/latest/)


## Overview
<img title="Model Overview" alt="Alt text" src="/figures/main.png">
Single-cell multi-omics provides deep biological insights, but data scarcity and modality integration remain significant challenges. We introduce scCross, harnessing variational autoencoder and generative adversarial network (VAE-GAN) principles, meticulously designed to integrate diverse single-cell multi-omics data. Incorporating biological priors, scCross adeptly aligns modalities with enhanced relevance. Its standout feature is generating cross-modality single-cell data and in-silico perturbations, enabling deeper cellular state examinations and drug explorations. Applied to dual and triple-omics datasets, scCross maps data into a unified latent space, surpassing existing methods. By addressing data limitations and offering novel biological insights, scCross promises to advance single-cell research and therapeutic discovery.

## Key Capabilities

1. Combine more than three single-cell multi-omics datasets, whether they are matched or unmatched, into a unified latent space. This space can be used for downstream analysis, even when dealing with over 4 million cells of varying types.

2. Generate cross-compatible single-cell data between two or more different omics. Trained and tested on independent referenced multi-omics datasets is also feasible.

3. Augment single-cell omics data through self-improvement techniques.

4. Simulate single-cell multi-omics data that match a specific cellular state, irrespective of the type and quantity of omics data involved.

5. Accurately identify key genes by comparing two different cell clusters using in-silico perturbation methods.

6. Maintain genomic integrity during omics perturbations and cross-generations effectively.







## Installation


You may install scCross by the following command:

```
pip install sccross
```

## Example workthroughs
Example workthroughs for each dataset in our study can be found in the [examples](https://github.com/mcgilldinglab/scCross/tree/main/examples) forder.

## Codeocean

We employ [codeocean](https://codeocean.com/capsule/4757520/tree/v1) reproducible platform to help you get into our codes.