Wolfram Language Paclet Repository
Community-contributed installable additions to the Wolfram Language
Genomic Data
Example workflow of the use of genomic data from the TCGA Data Tool.
This loads the package.
Needs["JaneShenGunther`TCGADataTool`"]
Genomic Data walkthrough
Summary of the genomic data available for TCGA-CESC project.
Load TCGA-CESC example data structure and its description.
In[84]:=
 | exampleDataTCGA |
Out[84]=
Example data structure for project TCGA-CESC, including 10 randomly sampled patients and full data scope. This example shows how data is stored and organized under the hood by the TCGADataToolUserInterface[]. Files exported in the .m format from the TCGADataToolUserInterface[] will adhere to this format.
In[85]:=
dataStructure=
[{"TCGAProjectData","TCGACESCFullDataScopePatientSample"}];
 | exampleDataTCGA |
Summary
Summary
In[112]:=
Short[#,10]&@dataStructure〚1〛["GenomicData"]["SimpleNucleotideVariation_MaskedSomaticMutation"]
Out[112]//Short=
{HugoGeneSymbolCAMTA1,EntrezGeneID23261,CenterWUGSC,NCBIBuildGRCh38,Chromosomechr1,StartPosition7736540,EndPosition7736540,Strand+,VariantClassificationMissense_Mutation,VariantTypeSNP,ReferenceAlleleG,TumorSeqAllele1G,TumorSeqAllele2A,dbSNP_RSrs1285071931,dbSNP_Val_StatusMissing[],TumorSampleBarcodeTCGA-VS-A8EH-01A-11D-A36J-09,109,HGVSOffsetMissing[],Phenotype0;1,GenePhenotype1,ContextATGGCGGTAAG,TumorBAMUUID9994b31f-bde3-408a-bfb8-626faa375aac,normal_bam_uuid32be9198-0aad-4fd7-ae06-f19cbd44868e,bcr_patient_uuid05026179-b1da-411e-a286-89727b1ae380,GDCFilterMissing[],COSMICMissing[],HotspotN,RNASupportUnknown,RNADepthMissing[],RNARefCountMissing[],RNAAltCountMissing[],Callersmuse;mutect2;varscan2,65,1}
Relevant columns you have access for the masked somatic mutations
In[87]:=
dataStructure〚1〛["GenomicData"]["SimpleNucleotideVariation_MaskedSomaticMutation"]〚1〛//Keys//Multicolumn[#,3]&
Out[87]=
HugoGeneSymbol | Gene | gnomAD_NFE_AF |
EntrezGeneID | Feature | gnomAD_OTH_AF |
Center | FeatureType | gnomAD_SAS_AF |
NCBIBuild | OneConsequence | MAX_AF |
Chromosome | Consequence | MAX_AF_POPS |
StartPosition | cDNAPosition | gnomAD_non_cancer_AF |
EndPosition | CDSPosition | gnomAD_non_cancer_AFR_AF |
Strand | ProteinPosition | gnomAD_non_cancer_AMI_AF |
VariantClassification | AminoAcids | gnomAD_non_cancer_AMR_AF |
VariantType | Codons | gnomAD_non_cancer_ASJ_AF |
ReferenceAllele | ExistingVariation | gnomAD_non_cancer_EAS_AF |
TumorSeqAllele1 | Distance | gnomAD_non_cancer_FIN_AF |
TumorSeqAllele2 | TranscriptStrand | gnomAD_non_cancer_MID_AF |
dbSNP_RS | GeneSymbol | gnomAD_non_cancer_NFE_AF |
dbSNP_Val_Status | SymbolSource | gnomAD_non_cancer_OTH_AF |
TumorSampleBarcode | HGNCGeneID | gnomAD_non_cancer_SAS_AF |
MatchedNormSampleBarcode | Biotype | gnomAD_non_cancer_MAX_AF_adj |
MatchNormSeqAllele1 | Canonical | gnomAD_non_cancer_MAX_AF_POPS_adj |
MatchNormSeqAllele2 | CCDS | ClinicalSignificance |
TumorValidationAllele1 | ENSP | Somatic |
TumorValidationAllele2 | SwissProt | PubmedID |
MatchNormValidationAllele1 | TrEMBL | TranscriptionFactors |
MatchNormValidationAllele2 | UniParc | MotifName |
VerificationStatus | UniProtIsoform | MotifPosition |
ValidationStatus | RefSeq | HighInformationPositionFlag |
MutationStatus | Mane | MotifScoreChange |
SequencingPhase | APPRIS | miRNA |
SequenceSource | Flags | Impact |
ValidationMethod | SIFT | Pick |
Score | PolyPhen | VariantClass |
BAMFile | EXON | TranscriptSupportLevel |
Sequencer | Intron | HGVSOffset |
TumorSampleUUID | Domains | Phenotype |
MatchedNormSampleUUID | 1000G_AF | GenePhenotype |
HGVSc | 1000G_AFR_AF | Context |
HGVSp | 1000G_AMR_AF | TumorBAMUUID |
HGVSpShort | 1000G_EAS_AF | normal_bam_uuid |
TranscriptID | 1000G_EUR_AF | bcr_patient_uuid |
ExonNumber | 1000G_SAS_AF | GDCFilter |
t_depth | ESP_AA_AF | COSMIC |
t_ref_count | ESP_EA_AF | Hotspot |
t_alt_count | gnomAD_AF | RNASupport |
n_depth | gnomAD_AFR_AF | RNADepth |
n_ref_count | gnomAD_AMR_AF | RNARefCount |
n_alt_count | gnomAD_ASJ_AF | RNAAltCount |
AllEffects | gnomAD_EAS_AF | Callers |
Allele | gnomAD_FIN_AF | |
In order to be able to demonstrate the selection of genomic data for different samples we make sure that there are patients who have data relative to multiple aliquots, identified by multiple “TumorSampleBarcode”, resulting from multiple genomic files.
Define variable with patients with multiple sample tested for genomic data
In[88]:=
dataPatientwithmultiplegenomicfiles=Select[Length[Union[Query[All,"TumorSampleBarcode"]@(#["GenomicData"]["SimpleNucleotideVariation_MaskedSomaticMutation"])]]>1&]@dataStructure;Length[dataPatientwithmultiplegenomicfiles]
Out[89]=
1
Extract data based on sample type
Extract data based on sample type
Define example data structure
In[90]:=
dataWithMultiplegenomics=Union[dataStructure〚;;3〛,(*addingapatientknowntohavemultiplefiles*)dataPatientwithmultiplegenomicfiles];
Show different output if restricting to single sample type:
Define sample type
In[91]:=
$sampletype="Primary Tumor";
Example showing how to restrict to a sample type, and displaying differences
In[120]:=
Query[All,"bcr_patient_uuid"#["bcr_patient_uuid"],"Clinical::Patient::weight"Query[First,"weight"]@#["Clinical","Patient"],"Clinical::Patient::height"Query[First,"height"]@#["Clinical","Patient"],(*herethereisnoconstrainonthesampletype*)"Impact"(Query[Counts,"Impact"]@#["GenomicData","SimpleNucleotideVariation_MaskedSomaticMutation"]),"Impact_high"(Query[Counts,"Impact"]@#["GenomicData","SimpleNucleotideVariation_MaskedSomaticMutation"])["HIGH"],(*hereweaddconstrainonthesampletypeby"Select[#["sample_type"]==$sampletype&]"*)"Impact_in_selected_sampletype"Query[Counts,"Impact"]@Query[Select[#["sample_type"]$sampletype&],All]@Query[All,{
"Impact"
,"sample_type"
}]@JoinAcross[(Query[All,{"TumorSampleBarcode","Impact"
}]@#["GenomicData","SimpleNucleotideVariation_MaskedSomaticMutation"]),JoinAcross[(*weneedtofurtherjoinacrossbecauseTumorSampleBarcodeisequivaletto"bcr_aliquot_barcode"andnotsimplyto"bcr_sample_barcode"*)(Query[All,{"bcr_sample_barcode","sample_type"
}]@#["Biospecimen","Sample"]),(Query[All,{"bcr_aliquot_barcode","bcr_sample_barcode"}]@#["Biospecimen","Aliquot"]),"bcr_sample_barcode","Outer"],"TumorSampleBarcode""bcr_aliquot_barcode"],"Impact_high_in_selected_sampletype"(Query[Counts,"Impact"]@Query[Select[#["sample_type"]$sampletype&],All]@Query[All,{"Impact","sample_type"}]@JoinAcross[(Query[All,{"TumorSampleBarcode","Impact"}]@#["GenomicData","SimpleNucleotideVariation_MaskedSomaticMutation"]),JoinAcross[(Query[All,{"bcr_sample_barcode","sample_type"}]@#["Biospecimen","Sample"]),(Query[All,{"bcr_aliquot_barcode","bcr_sample_barcode"}]@#["Biospecimen","Aliquot"]),"bcr_sample_barcode","Outer"],"TumorSampleBarcode""bcr_aliquot_barcode"])["HIGH"]&]@dataWithMultiplegenomicsCreate example data to extend existing design matrix
Create example data to extend existing design matrix
Define sample type
Example 1: Computation based on all columns for a given patient, determining the total count of high impact mutations.
Determine total high impact
Example 2: Computation based on single mutation
Define the mutation of interest
Determine total high impact mutations
Extend a design matrix
Extend a design matrix
Define the variables for design matrix creation
Create a design matrix
DNA methylation download
Load TCGA-CESC example data structure and its description.
Get methylation data
Get methylation data
Workflow functions for methylation data import.
Select project and patients UUID for the example
Select the download folder
Download methylation raw data
Download methylation raw data
Inspect the data
Get human methylation genomic coordinate and merge data
Get human methylation genomic coordinate and merge data
Get data for human methylation genomic coordinates from Wolfram Data Repository.
Merge methylation data with genomic coordinates
Brief data exploration
Brief data exploration
Create an overlapped histogram of beta values from the two patients
Compare distributions for a specific gene name