AML-67-001 tutorial

Prepare the parameters

  • -D : AML_67_001
  • -i : ReadCounts_AML_67_001.tsv (Input read count file name , present in the folder Count_file_and_gq_file
  • -g : GQ_AML_67_001.tsv (Quality file name, present in the folder Count_file_and_gq_file
  • -o : AML_67_001_ (Output prefix)

Running PHALCON

  • The output obtained from MissioBio Tapestri pipeline is in the form of loom files. The loom file for AML-67-001 is present in the folder loom file.
  • To convert the loom file in PHALCON-style input use the script loompy_to_readcount_indels.py present in the same folder

Input : Pass the readcount file, genotype quality file and the output prefix name. Run the following command:

python Algorithm_phalcon_indels.py -D AML_67_001 -i ReadCounts_AML_67_001.tsv -g GQ_AML_67_001.tsv -o AML_67_001_

Output : You will get the variants inferred by PHALCON in form of a VCF file.

Subset vcf output:

##fileformat=VCFv4.1
##source=OurAlgoOurAlgo v0.1.0
##FILTER=<ID=LowQual,Description="Low quality">
##INFO=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth; some reads may have been filtered">
##FORMAT=<ID=AD,Number=.,Type=Integer,Description="Allelic depths for alt alleles">
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Approximate read depth (reads with MQ=255 or with bad mates are filtered)">
##FORMAT=<ID=GQ,Number=1,Type=Integer,Description="Genotype Quality">
##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
##FORMAT=<ID=PL,Number=G,Type=Integer,Description="Normalized, Phred-scaled likelihoods for genotypes as defined in the VCF specification">
#CHROM  POS     ID      REF     ALT     QUAL    FILTER  INFO    FORMAT  cell1   cell2   cell3   cell4   cell5   cell6   cell7   cell8   cell9   cell10  cell11  cell12  cell13  cell14  cell15  >
chr1    115258748       *       C       T       *       PASS    DP=809943       GT:AD:DP:GQ:PL  0/1:392:392:99  0/1:227:430:99  0/0:0:444:99    0/0:0:205:99    0/0:0:0:99      0/0:0:211:99    >
chr1    115258749       *       T       G       *       PASS    DP=805084       GT:AD:DP:GQ:PL  0/1:392:392:99  0/1:227:430:99  0/0:0:0:99      0/0:0:207:99    0/0:0:104:99    0/0:0:209:99    >
chr2    25463285        *       GCGGTAGAACTCAAA G       *       PASS    DP=188342       GT:AD:DP:GQ:PL  0/1:0:97:99     0/1:6:23:99     0/1:0:0:12      0/1:26:44:99    0/0:0:124:99    0/1:47:4>
chr2    25469085        *       CG      C       *       PASS    DP=216991       GT:AD:DP:GQ:PL  0/0:0:149:99    0/0:0:63:99     0/1:0:19:57     0/0:0:149:99    0/0:1:124:99    0/1:0:0:99      >
chr2    25469913        *       C       T       *       PASS    DP=128427       GT:AD:DP:GQ:PL  0/1:0:39:99     0/1:39:60:99    0/1:32:32:95    0/1:22:33:99    0/1:66:93:99    0/1:29:33:35    >
chr2    198266943       *       C       T       *       PASS    DP=689574       GT:AD:DP:GQ:PL  0/1:307:313:99  0/1:72:72:99    0/1:149:151:99  0/1:217:217:99  0/1:242:242:99  0/1:153:154:99  >
chr2    198267770       *       G       GAA     *       PASS    DP=257605       GT:AD:DP:GQ:PL  0/1:46:92:99    0/1:0:0:99      0/1:27:59:99    0/1:15:22:99    0/1:56:117:99   0/1:0:0:99      >
chr4    55592244        *       G       A       *       PASS    DP=266536       GT:AD:DP:GQ:PL  0/1:93:325:99   0/1:0:0:99      0/1:0:0:99      0/1:0:0:99      0/1:15:65:99    0/1:0:0:99

Post-processing using dbSNP

The post processing includes removing variants with no mutation across all cells and removing clonal variants present in the dbSNP database 📑

Follow the steps below to generate the final vcf file of post-processed variants:

  • Download the folder post-processing in your local system
  • Open termnal inside the folder and type jupyter-notebook
  • Open the notebook Post-processing_AML-67-001.ipynb
  • Run dbsnp_for_aml_finite_sites.py
  • You will get a final list of variants in form of a vcf file in a file name ending with _post_processed_final.vcf

NOTE: As input to the dbsnp_for_aml_finite_sites.py python file, you only need the vcf file you obtained after running PHALCON. If required, change the location of the vcf file accordingly.

  • Use -i to provide location of the input vcf file
  • Use -o to mention the name of the post processed vcf file

Annotate variants

For variant annotation using ANNOVAR, the necessary files and scripts are present in the annotating_variants link.

Use the following steps to annotate mutations:

  • Download the folder annotating_variants in your local system
  • Open termnal inside the folder and type jupyter-notebook
  • Open the notebook Annotating_the_final_variants.ipynb
  • Run run_annovar_for_annotating_final_variants.py
  • You will get an annotated files of the final variants

NOTE: As input to the run_annovar_for_annotating_final_variants.py python file, you only need the post-processed vcf file. If required, change the location of the vcf file accordingly.

  • Use -i to provide location of the post-processed input vcf file
  • Use -o to mention the output tag of the annotated files

Visualization

The genotyped variants are shown in the form of a heatmap, and the chronological order of mutations is shown in the form of a phylogenetic tree.

The following files are needed to produce the final tree and genotypes (All of these files can be obtained by running the above pipelines):

  • -d : Tag for the dataset (OPTIONAL)(Acts as an identifier)
  • --cluster_labels_file : Inferred cluster labels (Obtained as one of the files after running PHALCON)
  • --lklhd_file : Likelihood file containing mutation likelihood for each cell and variant (Obtained as one of the files after running PHALCON)
  • --final_df_file : Likelihood file containing mutation likelihood for each cell and variant (Obtained as one of the files after running PHALCON)
  • --genotype_config_file : Genotype configuration file (Obtained as one of the files after running PHALCON)
  • --newick_file : Newick format file (Obtained as one of the files after running PHALCON)
  • --vcf_file : Final post-processed VCF file (Post-processed final VCF file, refer to Post-processing using dbSNP)
  • --variant_function_file : Gene-annotated file of the post-processed VCF file, refer to Annotate variants)

Run the following command (all the files used in the command can also be found in the Input_for_visualisation folder for ease):

python phalcon_visualization_pipeline.py -d AML_67_001 --cluster_labels_file aml_67_001_inferred_cluster_labels.txt --lklhd_file aml_67_001_final_lklhds.tsv --final_df_file aml_67_001_final_df.tsv --genotype_config_file aml_67_001_Genotype_configuration.tsv --newick_file aml_67_001_inferred_tree.nw --vcf_file aml_67_001_post_processed_final.vcf --variant_function_file aml_67_001_output.avinput.variant_function

You will obtain the following outputs:

  • Genotype heatmap:

The genotype heatmap shows the genotype profiles of each cell. There is a bar at the top which shows the cluster association of each cell. Heatmap

  • UMAP:

The umap shows the clusters obtained by PHALCON on a 2-d plane.

  • Phylogenetic tree:

The reconstructed mutation history tree shows the order in which the mutations have appeared. Phylogenetic_tree

Co-occurence and mutual exclusivity patterns

Use the R script provided in the cooccurrence and mutual exclusivity script folder to produce the plot.

For generating input for this R script, use the python script provided in the same folder.
  • Odds ratio plot:

Heatmap

The p-value for each association is present in form of a table as the output of the R script.