Update README.md

Author: evelyn-schmidt

README.md

@@ -166,3 +166,145 @@ export SAMPLE="TWJF-10146-0029"
 python3 /opt/scripts/setup_review.py -WB $WORKING_BASE -a ../itb-review-files/*.xlsx -c $WORKING_BASE/../generate_protein_fasta/candidates/annotated_filtered.vcf-pass-51mer.fa.manufacturability.tsv -samp $SAMPLE  -classI $WORKING_BASE/final_results/pVACseq/mhc_i/*.all_epitopes.aggregated.tsv -classII $WORKING_BASE/final_results/pVACseq/mhc_ii/*.all_epitopes.aggregated.tsv 
 ```
 Open colored_peptides51mer.html and copy the table into an excel spreadsheet. The formatting should remain. Utilizing the Annotated.Neoantigen_Candidates and colored Peptides_51-mer for manual review.
+
+# Description of Scripts includes
+
+## Get Basic QC
+
+```bash
+python3  /opt/scripts/get_neoantigen_qc.py --help
+usage: get_neoantigen_qc.py [-h] [-WB WB] [-f FIN_RESULTS] [--n_dna N_DNA] [--t_dna T_DNA] [--t_rna T_RNA] [--concordance CONCORDANCE] [--contam_n CONTAM_N] [--contam_t CONTAM_T]
+                            [--rna_metrics RNA_METRICS] [--strand_check STRAND_CHECK] --yaml YAML [--fin_variants FIN_VARIANTS]
+
+Get the stats for the basic data QC review in the neoantigen final report.
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -WB WB                the path to the gcp_immuno folder of the trial you wish to tun script on, defined as WORKING_BASE in envs.txt
+  -f FIN_RESULTS, --fin_results FIN_RESULTS
+                        Name of the final results folder in gcp immuno
+  --n_dna N_DNA         file path for aligned normal dna FDA report table
+  --t_dna T_DNA         file path for aligned tumor dna FDA report table
+  --t_rna T_RNA         file path for aligned tumor rna FDA report table
+  --concordance CONCORDANCE
+                        file path for Somalier results for sample tumor/normal sample relatedness
+  --contam_n CONTAM_N   file path for VerifyBamID results for contamination the normal sample
+  --contam_t CONTAM_T   file path for VerifyBamID results for contamination the tumor sample
+  --rna_metrics RNA_METRICS
+  --strand_check STRAND_CHECK
+  --yaml YAML
+  --fin_variants FIN_VARIANTS
+```
+
+## GET FDA metrics
+python3  /opt/scripts/get_FDA_thresholds.py --help
+usage: get_FDA_thresholds.py [-h] [-WB WB] [-f FIN_RESULTS] [--n_dna N_DNA] [--t_dna T_DNA] [--t_rna T_RNA] [--una_n_dna UNA_N_DNA] [--una_t_dna UNA_T_DNA] [--una_t_rna UNA_T_RNA]
+                             [--somalier SOMALIER] [--contam_n CONTAM_N] [--contam_t CONTAM_T]
+
+Get FDA qc stats from various files and determine if they pass or fail.
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -WB WB                the path to the gcp_immuno folder of the trial you wish to tun script on, defined as WORKING_BASE in envs.txt
+  -f FIN_RESULTS, --fin_results FIN_RESULTS
+                        Name of the final results folder in gcp immuno
+  --n_dna N_DNA         file path for aligned normal dna FDA report table
+  --t_dna T_DNA         file path for aligned tumor dna FDA report table
+  --t_rna T_RNA         file path for aligned tumor rna FDA report table
+  --una_n_dna UNA_N_DNA
+                        file path for unaligned normal dna FDA report table
+  --una_t_dna UNA_T_DNA
+                        file path for unaligned tumor dna FDA report table
+  --una_t_rna UNA_T_RNA
+                        file path for unaligned tumor rna FDA report table
+  --somalier SOMALIER   file path for Somalier results for sample tumor/normal sample relatedness (concordance.somalier.pairs.tsv)
+  --contam_n CONTAM_N   file path for VerifyBamID results for contamination the normal sample
+  --contam_t CONTAM_T   file path for VerifyBamID results for contamination the tumor dna sample
+
+## Setup Review
+
+The set up review script runs two other scripts: generate_reviews_files.py and color_peptides51mer.py. The first sets up the Annotated.Neoantige.Canidates spreadsheet and the Peptides 51mer spreadsheet. The second script colors the Peptides 51mer sequences and outputs an html table whihc can be copied into a Microsoft spreadsheet.
+
+```bash
+python3  /opt/scripts/setup_review.py --help
+usage: setup_review.py [-h] [-WB WB] [-samp SAMP] [-a A] [-c C] -classI CLASSI -classII CLASSII
+
+Sets up manuel review files
+
+optional arguments:
+  -h, --help        show this help message and exit
+  -WB WB            the path to the gcp_immuno folder of the trial you wish to tun script on, defined as WORKING_BASE in envs.txt
+  -samp SAMP        Name of the sample
+  -a A              Path to ITB Reviewed Candidates
+  -c C              Path to annotated_filtered.vcf-pass-51mer.fa.manufacturability.tsv
+  -classI CLASSI    Path to classI all_epitopes.aggregated.tsv
+  -classII CLASSII  Path to classII all_epitopes.aggregated.tsv
+
+```
+
+## Generate Review Files
+
+```bash
+python3  /opt/scripts/generate_reviews_files.py --help
+usage: generate_reviews_files.py [-h] [-a A] [-c C] [-samp SAMP] [-WB WB] [-f FIN_RESULTS]
+
+Create the file needed for the neoantigen manuel review
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -a A                  The path to the ITB Reviewed Candidates
+  -c C                  The path to annotated_filtered.vcf-pass-51mer.fa.manufacturability.tsv from the generate_protein_fasta script
+  -samp SAMP            The name of the sample
+  -WB WB                the path to the gcp_immuno folder of the trial you wish to tun script on, defined as WORKING_BASE in envs.txt
+  -f FIN_RESULTS, --fin_results FIN_RESULTS
+                        Name of the final results folder in gcp immuno
+```
+
+## Color Peptides 51mer
+
+```
+python3  /opt/scripts/generate_reviews_files.py --help
+usage: generate_reviews_files.py [-h] [-a A] [-c C] [-samp SAMP] [-WB WB] [-f FIN_RESULTS]
+
+Create the file needed for the neoantigen manuel review
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -a A                  The path to the ITB Reviewed Candidates
+  -c C                  The path to annotated_filtered.vcf-pass-51mer.fa.manufacturability.tsv from the generate_protein_fasta script
+  -samp SAMP            The name of the sample
+  -WB WB                the path to the gcp_immuno folder of the trial you wish to tun script on, defined as WORKING_BASE in envs.txt
+  -f FIN_RESULTS, --fin_results FIN_RESULTS
+                        Name of the final results folder in gcp immuno
+root@02d92932f0f8:/# python3  /opt/scripts/color_peptides51mer.py --help
+usage: color_peptides51mer.py [-h] -p P -classI CLASSI -classII CLASSII [-WB WB] [-samp SAMP]
+
+Color the 51mer peptide
+
+optional arguments:
+  -h, --help        show this help message and exit
+  -p P              The path to the Peptides 51 mer
+  -classI CLASSI    The path to the classI all_epitopes.aggregated.tsv used in pVACseq
+  -classII CLASSII  The path to the classII all_epitopes.aggregated.tsv used in pVACseq
+  -WB WB            the path to the gcp_immuno folder of the trial you wish to tun script on, defined as WORKING_BASE in envs.txt
+  -samp SAMP        Name of the sample
+```
+
+## Bold Class II
+
+Bold Class II is not utilized in the current workflow of setting up the manual review. However, it is included as an example of how adding stylization (in this case bold) to certain characters within individual cells of spreadsheets can be accomplished using BeautifulSoup. If you wanted to only bold certain characters (or do any stylzing such as coloring), you can insert a style tag directly into the HTML. However, if you wanted to do formatting inside formatting such as in these review files where there needs to some characters which are red, bold, underlined, or any combination if thise mentioned, BeuaitfulSoup cannot accomplish this. 
+
+```bash
+python3 /opt/scripts/bold_classII.py --help
+usage: bold_classII.py [-h] -p P -classI CLASSI -classII CLASSII -o O
+
+Bold the class II pepetides
+
+optional arguments:
+  -h, --help        show this help message and exit
+  -p P              The path to the Peptides 51 mer
+  -classI CLASSI    The path to the classI all_epitopes.aggregated.tsv used in pVACseq
+  -classII CLASSII  The path to the classII all_epitopes.aggregated.tsv used in pVACseq
+  -o O              Output location
+
+```