bitcometz/test0

just use to get used to github and read the docs

Gene scores, annotations and disease heritability analysis results for Jagadeesh*, Dey* et al (2020) bioRxiv titled "Identifying disease-critical cell types and cellular processes across the human body by integration of single-cell profiles and human genetics"

############################################################################################## Description of each folder and subfolder ##############################################################################################

|-- scdata All Processsed scRNA-seq data with annotated cell types

• singlecell_datasets.xlsx: Description of all scRNA-seq datasets used to construct gene programs
• *.h5ad: HDF5 files corresponding to each scRNA-seq with annotated cell types

|-- gene_scores - Contains all gene scores data for the three types of programs |-- cell_type_programs : All cell type programs from different tissues annotated_celltypes.xlsc: Annotation of each cell type program

|-- disease_progression_programs: All diseae progression programs from UC, MS, Alzheimers, Asthma, COVID-19 and Fibrosis 
|-- cellular_process_programs:
annotated_nmf.xlsx: Annotation of each NMF program using top pathway from pathway enrichment analysis.
	|-- nmf : All cellular process programs from different tissues 
    	  (*_annotated represents subsets of * which have distinct annotation)
|-- nmf_disease_healthy: All cellular process programs from disease + healthy tissues 
     	  (*_annotated represents subsets of * which have distinct annotation) 

|-- annotations

- Contains all annotations linking genes in gene score with RoadmapUABC enhancer-gene linking strategy.
|-- cell_type_programs : Annotations for all cell type gene programs
|-- disease_progression_programs: Annotations for all disease progression programs 
|-- cellular_process_programs 
	|-- nmf: Annotations for all cellular process programs for a tissue 
|-- nmf_disease_healthy: Annotations for all cellular process programs for disease +healthy tissue

|-- sclinker_results

• Contains heritability analysis results for each annotation in the 'annotations' directory.

|-- cell_type_programs : Heritability analysis results for annotations in annotations/cell_type_programs |-- disease_progression_programs: Heritability analysis results for annotations in annotations/disease_progression_programs |-- cellular_process_programs

   |-- nmf: Heritability analysis results for annotations in annotations/cellular_process_programs/nmf
   |-- nmf_disease_healthy: Heritability analysis results for annotations in annotations/cellular_process_programs/nmf_disease_healthy

############################################################################################## How to use these annotations? ##############################################################################################

How to use it?

1. Download the LDSC from git (https://github.com/bulik/ldsc/wiki/Partitioned-Heritability)
2. Download the baselineLD_v2.1 annotations from Broad webpage (https://data.broadinstitute.org/alkesgroup/LDSCORE/)
3. Download all sc-linker annotations from (see above) from https://data.broadinstitute.org/alkesgroup/LDSCORE/Jagadeesh_Dey_sclinker
4. Use your GWAS summary statistics formatted in LDSC details is available (https://github.com/bulik/ldsc/wiki/Summary-Statistics-File-Format)
5. Download the baseline frq file and weights for 1000G available (https://data.broadinstitute.org/alkesgroup/LDSCORE/)
6. Run S-LDSC with these annotations conditional on baselineLD_v2.1 (see https://github.com/bulik/ldsc/)

################### ANNOT FILE header (*.annot): ##################

################### ANNOT FILE header (*.annot): ##################

CHR -- chromosome BP -- physical position (base pairs) SNP -- SNP identifier (rs number) CM -- genetic position (centimorgans) all additional columns -- Annotations

NOTE: Although one would expect the genetic position to be non-negative for all 1000G SNPs, we have checked that in fact the genetic position is negative for a handful of 1000G SNPs that have a physical position that is smaller than the smallest physical position in the genetic map. The genetic positions were obtained by running PLINK on the Oxford genetic map (http://www.shapeit.fr/files/genetic_map_b37.tar.gz).

MORE DETAIL CAN BE OBTAINED FROM https://github.com/bulik/ldsc/wiki/LD-File-Formats

################### LD SCORE FILE header (*.l2.ldscore): ##################

CHR -- chromosome BP -- physical position (base pairs) SNP -- SNP identifier (rs number) all additional columns -- LD Scores

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