semvec/pypi-packages huggingface dataset:
topic-cohesion 0.1.1
Cohesion measurement to evaluate partition
Requires Python: ~=3.7

# Topic Cohesion


The Topic-Detection field deals mainly with providing names to given divisions of documents and lacks a quality measurement that provides a rating for the division, that represent a human-subjective score.

Given a division topic_cohesion will calculate the human-subjective score, and the related topic name to each label in a division.

The POC to this attitude can be found in the [colab-notebook](https://colab.research.google.com/drive/1IFWKF3CFfzZWT9WucdISpDbLDI4mtTjX?usp=sharing), or in the ["Topic Cohesion Project- Full report"](https://github.com/TopicCohesion/topic-cohesion/blob/main/Cohesion%20Pipeline%20Project%20-%20Full%20Report.pdf)

The useage example can be also found in the [colab-notebook-usage-example](https://colab.research.google.com/drive/1zAJs5px8HBMo99hPc-MhnlInKi7ze9yI?usp=sharing)


## Installation

```bash
pip install topic-cohesion
```

## Usage Example
The input to the topic cohesion process must be a csv, txt, tsv file with a tab ['\t'] seperator and must have 'label' and 'text' columns.
The 'text' is a list of strings which represents all the corpus senteces while the 'label' is a list of integers that represents the corpus divison.
In the next example, senteces 1-3 are belong to group 1 and senteces 4 and 5 belongs to group 2.

```python
import pandas as pd
from cohesion import topic_cohesion

data = {'text':
            ["we like to play football",
             "I'm playing football better than neymar and cristano ronaldo",
             "I like Fifa more than I like football, My Fav team is #RealMadrid Hala Madrid",
             "Hamburger or Pizza? what would i choose? I will eat both of them, it so tasty!",
             "banana pancakes with syrup maple, thats my favorite meal"],
        'label':
            [1, 1, 1, 2, 2]}
df = pd.DataFrame(data)
score, topic_names  = topic_cohesion.run_df(df)
print("Cohesion Final score is: ", score)
print("Cohesion Topics are: ", topic_names)

```

Expected output
```
Cohesion Final score is: 0.99
Cohesion Topics are: ['like football play ronaldo playing', 'tasty pizza hamburger eat choose']
```
Broad-genepy 1.2.6
A useful module for any CompBio
Requires Python: >=3.5

# genepy

_what is [genepy](https://en.wikipedia.org/wiki/G%C3%A9n%C3%A9pi)?_

A set of awesome functions & tools for Computational Geneticists

![long genome](documentation/genome.jpg)

## Content

- **utils**: where a bunch of helper functions and usefull general scripts are stored
  - **plots**: a set of plotting tools based on [matplotlib]() and [bokeh]() to make volcano plots / CNV maps etc..
  - **helper**: and additional helper functions to save data, do merging of dataframes...
- **terra**: contains a set of functions that uses [dalmatian]() to interact with the [GCP]() powered genomics HPC platform: [Terra](). 
- **sequencing**: contains a set of function to works with bed/bam/fastqs...
- **rna**: contains function to work with RNAseq (and related) data.
  - **pyDESeq2**: it is a python integration of [deseq2]() (the differential expression analyser) with [rpy2]()
- **mutations**: a set of functions to work with maf files, vcf files etc..
- **google**: functions and packages linked to google's apis
  - **google_sheet**: function to upload a df as a google sheet
  - **gcp**: sets of functions to interact with google storage (relies on `gsutil`)
- **epigenetics**: where we have things related to epigenomics
  - **chipseq**: has functions to read, merge, denoise, ChIP seq data.
  - **plot**: has functions to plot ChIP seq data.

### Helper tools

_tools that you do not need to use directly as they have binding functions in genepy._ 

- **epigenetics/rose:**: where an updated version of the rose algorithm is stored (as a git submodule) 
- **cell_line_mapping-master/python/cell_line_mapper**: a set of functions to map cell line ids to other cell line ids based on an up to date google spreadsheet. 


## Install

### with pip

`pip install broad-genepy`

and then use with `from genepy.utils/epigenetics/... import ...` 

### dev mode

```bash
git clone git://github.com/BroadInstitute/genepy.git
pip install -e genepy
```

then you can import files in python with e.g:
```python
from genepy import terra
from genepy.utils import helper as h
from genepy.google import gcp
from genepy.utils import plot
from genepy.epigenetics import chipseq

```

## data:

hg38 genome sizes: from https://github.com/igvteam/igv/blob/master/genomes/sizes/hg38.chrom.sizes


## installation: to get access to all bindings

Install the following tools:
- [htslib/samtools](http://www.htslib.org/)
- [bwa](https://github.com/lh3/bwa)
just used once:
- [bowtie2](http://bowtie-bio.sourceforge.net/bowtie2/index.shtml)

Finaly you can install R packages (GSEABase, erccdashboard, GSVA, DESeq2):

```bash
R -e 'if(!requireNamespace("BiocManager", quietly = TRUE)){install.packages("BiocManager")};BiocManager::install(c("GSEABase", "erccdashboard", "GSVA", "DESeq2"));'
```
## About

please do contribute, we do not have time to fix all issues or work on feature requests

Jeremie Kalfon jkalfon@broadinstitute.org jkobject@gmail.com https://jkobject.com

Javad Noorbakhsh jnoorbak@broadinstitute.org

Apache license 2.0.