Metadata-Version: 2.1
Name: scimotf
Version: 0.1.0
Summary: UNKNOWN
Home-page: https://github.com/ArianeMora/scimotf
Author: Ariane Mora
Author-email: ariane.n.mora@gmail.com
License: GPL3
Project-URL: Bug Tracker, https://github.com/ArianeMora/scimotf/issues
Project-URL: Documentation, https://github.com/ArianeMora/scimotf
Project-URL: Source Code, https://github.com/ArianeMora/scimotf
Description: # sci-moTF
        [![codecov.io](https://codecov.io/github/ArianeMora/scimotf/coverage.svg?branch=main)](https://codecov.io/github/ArianeMora/scimotf?branch=main)
        [![PyPI](https://img.shields.io/pypi/v/scimotf)](https://pypi.org/project/scitf/)
        
        
        sci-moTF is a very simple package to help with finding motifs that are enriched in different clusters, that are also 
        expressed in your dataset and make it easier to draw inferences on which TFs may be driving the observed changes.
        
        The input to scimotf is: 1) the output of [FIMO](https://meme-suite.org/meme/doc/fimo.html?man_type=web>)  , fimo.tsv, 2) a csv file with gene identifier (e.g. name), cluster, log2FC,
         and p-value.
        
        ### Example format for fimo.tsv
        ``` 
        motif_id        motif_alt_id    sequence_name   start   stop    strand  score   p-value q-value matched_sequence
        SP5_MOUSE.H11MO.0.C             Gh      1668    1691    -       32.7879 9.78e-16        4e-09   GGGGGGGAGGGGGAGGGGGAGGGG
        ```
        
        ### Example format for cluster.csv
        ``` 
        gene_name,cluster,log2FC,padj
        Hoxa9,hindbrain,-2.8,0.00031
        ```
        
        sci-TF will output two files, 1) scitf_detailed.csv, and 2) scitf_summary.csv. 
        
        ### sictf_motif_merged_fp-0.05_cp-1.0.csv
        
        This gives a detailed output of each TF that was potentially able to bind to genes in a cluster.
        ``` 
        cluster,motif,p-value,q-value,odds-ratio,count-genes-in-cluster,count-genes-bg,remainder-cluster,remainder-bg,tf-log2FC,tf-padj,tf-cluster,%-coverage,genes
        ```
        
        ### Overview
        1) Filter fimo.tsv and remove any motifs that don't meet the p or qvalue threshold
        2) Filter any motifs in fimo.tsv that don't exist in the users input data (have a 0 logFC)
        3) For each TF for each cluster, count how many genes exist and perform a FET w.r.t the background
        4) adjust p-values
        5) summarise the identified TFs
        
        Please post questions and issues related to sci-moTF on the [Issues page](https://github.com/ArianeMora/scimotf/issues)_  
        
        section of the GitHub repository.
        
        
Keywords: motif,TF,transcription-factor
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.6
Description-Content-Type: text/markdown
