Name: (Kallist**O** » Jupyte**R** » BUStool**S** » Rstudi**O** » 10**X**)

#### 10Xv2 single cell RNA seq analysis using the following tools:
* Kallisto for: 
   + Indexing . 
   + Pseudomapping.
* BUStools for: 
   + Correct.  
   + Sort.  
   + Count.  
* RStudio for:
   + transforming SYMBOLS → ENTREZgeneID. 
   + Over Representation Analysis (ORA) annotation of the genes for each cluster.  



Find the jupyter notebooks for the analysis steps followed with Kallisto and ScanPy, divided in the following folders:

   * 1st, getting ready up to the gene matrix, using the first part of the pipeline → **obtain_from_fastq_bus_and_gene_matrix**
   
   * 1.5th: get the geneID,transcripts,symbol list:
         + Using python: **get_transcripts_with_GTF_file_and_python**   →     t2g.py and the GTF ensembl file(ensembl).
         + Or get it with R: **get_transcripts_with_R_biomaRt**         →     get_transcripts_to_genes_dr.R
   
   
   * 2nd, analysis of the data using ScanPy for each sample, in my case Control and Experimental using → **analyzing_the_gene_matrix**
      
         +  For control sample            →        ControlLeiden.ipynb
         +  For experimental sample       →        ExperimentalLeiden.ipynb
   
   * 3rd, once generated the clusters and obtained the genes list associated, annotate them.  
         +  For the enrichment of the clusters use the following R scripts:
                  Control                  →           v3_clusterAnnotation_controlPath.R
                  Experimental             →           v3_clusterAnnotation_experimentalPath.R
         
         +  or with scientific expertise.