README.md
In ginnyintifa/GPD:
GPD
A protein-centric approach for exome variant aggregation enables sensitive association analysis with clinical outcomes
1 Installation and Data Formating
GPD can be downloaded and installed in R. Installation of GPD requires devtools as a prerequisite:
install.packages("devtools")
The package 'qvalue' from Bioconductor should also be installed
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("qvalue")
Next, install GPD by:
library("devtools")
devtools::install_github("ginnyintifa/GPD")
library(GPD)
Input files for GPD analysis include
- Mutation data file
- Protein information data file
- Clinical information data file
The mutation data should be formatted similar to Mutation Annotation File (MAF) format:
Hugo_Symbol Gene Chromosome Start_Position End_Position Variant_Classification Variant_Type HGVSc HGVSp Tumor_Sample_Barcode
GFRA1 ENSG00000151892 10 117884831 117884831 Missense_Mutation SNP c.671G>A p.Arg224Gln example-35
LRP6 ENSG00000070018 12 12301919 12301919 Missense_Mutation SNP c.3163G>A p.Glu1055Lys example-35
KIF5A ENSG00000155980 12 57957244 57957244 Missense_Mutation SNP c.152G>A p.Arg51His example-35
BBS10 ENSG00000179941 12 76741456 76741459 Frame_Shift_Del DEL c.306_309delCAGA p.Asp102GlufsTer6 example-35
SUGT1 ENSG00000165416 13 53237236 53237236 Nonsense_Mutation SNP c.484G>T p.Glu162Ter example-35
HECTD1 ENSG00000092148 14 31598273 31598273 Missense_Mutation SNP c.4304T>A p.Val1435Asp example-35
TLN2 ENSG00000171914 15 63017231 63017231 Silent SNP c.3183G>A p.%3D example-35
PDE8A ENSG00000073417 15 85664025 85664025 Intron SNP c.1735-3C>A . example-35
ANKS3 ENSG00000168096 16 4747041 4747041 Missense_Mutation SNP c.1959G>C p.Trp653Cys example-35
CCL11 ENSG00000172156 17 32614719 32614719 3'UTR SNP c.*10A>G . example-35
TP53 ENSG00000141510 17 7578415 7578428 Frame_Shift_Del DEL c.502_515delCACATGACGGAGGT p.His168CysfsTer8 example-35
LAMA3 ENSG00000053747 18 21464756 21464756 Missense_Mutation SNP c.5242G>T p.Val1748Leu example-35
ZNF230 ENSG00000159882 19 44515611 44515611 Missense_Mutation SNP c.1420A>T p.Met474Leu example-35
IZUMO1 ENSG00000182264 19 49245556 49245556 Silent SNP c.510G>A p.%3D example-35
PTCHD2 ENSG00000204624 1 11561189 11561189 Missense_Mutation SNP c.140G>A p.Arg47Gln example-35
IGSF3 ENSG00000143061 1 117150771 117150771 Missense_Mutation SNP c.1015G>A p.Glu339Lys example-35
NEK2 ENSG00000117650 1 211846871 211846871 Missense_Mutation SNP c.509C>T p.Thr170Met example-35
The PIU data format looks as follows:
uniprot_accession start_position end_position center_position unit_name gene_name gene_id unit_label
Q14D04 211 221 216 py VEPH1 ENSG00000197415 PTM
Q14D04 375 385 380 ps VEPH1 ENSG00000197415 PTM
Q14D04 392 402 397 pt VEPH1 ENSG00000197415 PTM
Q14D04 417 427 422 pt VEPH1 ENSG00000197415 PTM
Q14D04 425 435 430 ps VEPH1 ENSG00000197415 PTM
Q14D04 444 454 449 ps VEPH1 ENSG00000197415 PTM
Q14D04 524 534 529 ps VEPH1 ENSG00000197415 PTM
Q14D04 717 819 768 PH VEPH1 ENSG00000197415 Domain
Q14D04 766 776 771 ps VEPH1 ENSG00000197415 PTM
Q14D04 778 788 783 ps VEPH1 ENSG00000197415 PTM
Q14D33 50 150 100 zf-3CxxC RTP5 ENSG00000277949 Domain
The current implementation of GPD requires that user's own mutation data and protein information data follow the formatting shown above, i.e. with exactly the same column names.
Format of clinical information data:
Tumor_Sample_Barcode type age gender race ajcc_pathologic_tumor_stage clinical_stage histological_type histological_grade initial_pathologic_dx_year menopause_status birth_days_to vital_status tumor_status last_contact_days_to death_days_to cause_of_death new_tumor_event_type new_tumor_event_site new_tumor_event_site_other new_tumor_event_dx_days_to treatment_outcome_first_course margin_status residual_tumor OS OS.time DSS DSS.time DFI DFI.time PFI PFI.time Redaction
example-1 example 65 FEMALE WHITE Stage II [Not Applicable] Adrenocortical carcinoma- Usual Type [Not Available] 2011 [Not Available] -24017 Alive WITH TUMOR 383 NA [Not Available] Distant Metastasis Lung #N/A 166 [Not Available] NA NA 0 383 0 383 NA NA 1 166 NA
example-2 example 42 FEMALE WHITE Stage IV [Not Applicable] Adrenocortical carcinoma- Usual Type [Not Available] 1998 [Not Available] -15536 Dead WITH TUMOR NA 436 [Not Available] Distant Metastasis Bone #N/A 61 Progressive Disease NA NA 1 436 1 436 NA NA 1 61 NA
example-3 example 32 FEMALE WHITE Stage III [Not Applicable] Adrenocortical carcinoma- Usual Type [Not Available] 2010 [Not Available] -11970 Dead WITH TUMOR NA 994 [Not Available] Distant Metastasis Lung #N/A 97 Progressive Disease NA NA 1 994 1 994 NA NA 1 97 NA
example-4 example 37 FEMALE [Not Evaluated] Stage II [Not Applicable] Adrenocortical carcinoma- Usual Type [Not Available] 2009 [Not Available] -13574 Alive TUMOR FREE 1857 NA [Not Available] #N/A #N/A #N/A NA Complete Remission/Response NA NA 0 1857 0 1857 0 1857 0 1857 NA
example-5 example 53 FEMALE WHITE Stage IV [Not Applicable] Adrenocortical carcinoma- Usual Type [Not Available] 2011 [Not Available] -19492 Alive WITH TUMOR 1171 NA [Not Available] Distant Metastasis Lung #N/A 351 Stable Disease NA NA 0 1171 0 1171 NA NA 1 351 NA
Clinical information is an input required for survival analysis. Not all the columns in the above file are required, however these columns are compulsory:
Tumor_Sample_Barcode
age
gender
race
OS
OS.time
OS refers to the survival status; OS.time refers to survival time.
In the manuscript, we did survival analysis with adjustment of two additional potential confounders: total mutation count and stage (when available). These are not adjusted in the function provided in the package. However, we provide the script 'survival_model_adjust_totalMutationStage.R' where model is constructed with these additionall covariables, users may refer to the script if needed.
We provide in our R package the mutation data file and clinical data file for a subset of an example cohort. We also provide PIU data file containing the protein modification sites from the PhosphoSitePlus database and pfam domains. User can access these data after installing the package.
sel_example_mutation
ptm_pfam_combine
sel_example_cdr
2 Mutation Extraction
The first step is to read in your mutation data, and identify patients you wish to study by specifying their IDs (barcodes).
library(data.table)
mutation_df = fread("your_path_to_file/user_mutation.tsv",
header = T,
select = c("Hugo_Symbol","Gene","Chromosome","Start_Position","End_Position","Variant_Classification","Variant_Type","HGVSc","HGVSp","Tumor_Sample_Barcode"),
stringsAsFactors = F)
cancer_barcode = unique(mutation_df$Tumor_Sample_Barcode)
These objects will be part of the inputs in the extraction_annotation_posfunction.
An example of running a subset of example somatic mutations is the following:
extraction_annotation_pos(mutation_df = sel_example_mutation,
cancer_type = "example",
cancer_barcode = unique(sel_example_mutation$Tumor_Sample_Barcode),
output_dir = "your_output_dir1/")
This will generate two output files that will be used in the subsequent function:
- "your_output_dir1/example_mutation_npc.tsv"
- "your_output_dir1/example_mutation_pc_pos.tsv"
The first file contains extracted mutations in non-protein coding regions. The second file contains extracted mutations in protein coding regions and their annotated corresponding amino acid positions.
3 Mutation Mapping
GPD maps mutations to PIUs, linker region, and non-coding region. Linker region refers to protein-coding sequences that do not correspond to either protein domains or protein modification sites. Users can load their own PIU data file as follows:
piu_df = fread("your_path_to_file/user_piu.tsv", stringsAsFactors = F)
Mapping to the default PIUs provided in the library can be done with the annotated subset of example somatic mutations:
piu_mapping (piu_df = ptm_pfam_combine,
pc_data_name = "your_output_dir1/example_mutation_pc_pos.tsv",
npc_data_name = "your_output_dir1/example_mutation_npc.tsv",
cancer_barcode = unique(sel_example_mutation$Tumor_Sample_Barcode),
output_dir = "your_output_dir2/")
This function will produce three output files:
- "your_output_dir2/piu_mapping.tsv"
- "your_output_dir2/lu_summarising_count.tsv"
- "your_output_dir2/ncu_summarising_count.tsv"
These files carry mutation mapping results (counts per PIU, linker unit, and non-coding unit), which can be used in subsequent statistical analysis.
4 Survival analysis
After mapping, users can perform survival analysis to study the association between mapped mutation counts on PIU, LU, NCU and patient survival.
Users can upload their clinical data in the following way:
clinical_df = fread("your_path_to_file/user_clinical.tsv", stringsAsFactors = F)
With the subset of example data mapping results and clinical data, we perform the survival analysis.
univariate_cox_model(piu_filename = "your_output_dir2/piu_mapping_count.tsv",
lu_filename = "your_output_dir2/lu_summarising_count.tsv",
ncu_filename = "your_output_dir2/ncu_summarising_count.tsv",
clinical_df = sel_example_cdr,
gender_as_covariate = T,
race_group_min = 6,
min_surv_days = 90,
min_surv_people = 5,
patient_sum_min = 3,
mutation_type = "somatic",
output_dir = "your_output_dir3/")
This function will generate the following output files:
- "your_output_dir3/somatic_piu_cdr_univariate.tsv"
- "your_output_dir3/somatic_lu_cdr_univariate.tsv"
- "your_output_dir3/somatic_ncu_cdr_univariate.tsv"
ginnyintifa/GPD documentation built on Oct. 23, 2019, 1:52 a.m.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
GPD
A protein-centric approach for exome variant aggregation enables sensitive association analysis with clinical outcomes
1 Installation and Data Formating
GPD can be downloaded and installed in R. Installation of GPD requires devtools as a prerequisite:
install.packages("devtools")
The package 'qvalue' from Bioconductor should also be installed
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("qvalue")
Next, install GPD by:
library("devtools")
devtools::install_github("ginnyintifa/GPD")
library(GPD)
Input files for GPD analysis include
- Mutation data file
- Protein information data file
- Clinical information data file
The mutation data should be formatted similar to Mutation Annotation File (MAF) format:
Hugo_Symbol Gene Chromosome Start_Position End_Position Variant_Classification Variant_Type HGVSc HGVSp Tumor_Sample_Barcode
GFRA1 ENSG00000151892 10 117884831 117884831 Missense_Mutation SNP c.671G>A p.Arg224Gln example-35
LRP6 ENSG00000070018 12 12301919 12301919 Missense_Mutation SNP c.3163G>A p.Glu1055Lys example-35
KIF5A ENSG00000155980 12 57957244 57957244 Missense_Mutation SNP c.152G>A p.Arg51His example-35
BBS10 ENSG00000179941 12 76741456 76741459 Frame_Shift_Del DEL c.306_309delCAGA p.Asp102GlufsTer6 example-35
SUGT1 ENSG00000165416 13 53237236 53237236 Nonsense_Mutation SNP c.484G>T p.Glu162Ter example-35
HECTD1 ENSG00000092148 14 31598273 31598273 Missense_Mutation SNP c.4304T>A p.Val1435Asp example-35
TLN2 ENSG00000171914 15 63017231 63017231 Silent SNP c.3183G>A p.%3D example-35
PDE8A ENSG00000073417 15 85664025 85664025 Intron SNP c.1735-3C>A . example-35
ANKS3 ENSG00000168096 16 4747041 4747041 Missense_Mutation SNP c.1959G>C p.Trp653Cys example-35
CCL11 ENSG00000172156 17 32614719 32614719 3'UTR SNP c.*10A>G . example-35
TP53 ENSG00000141510 17 7578415 7578428 Frame_Shift_Del DEL c.502_515delCACATGACGGAGGT p.His168CysfsTer8 example-35
LAMA3 ENSG00000053747 18 21464756 21464756 Missense_Mutation SNP c.5242G>T p.Val1748Leu example-35
ZNF230 ENSG00000159882 19 44515611 44515611 Missense_Mutation SNP c.1420A>T p.Met474Leu example-35
IZUMO1 ENSG00000182264 19 49245556 49245556 Silent SNP c.510G>A p.%3D example-35
PTCHD2 ENSG00000204624 1 11561189 11561189 Missense_Mutation SNP c.140G>A p.Arg47Gln example-35
IGSF3 ENSG00000143061 1 117150771 117150771 Missense_Mutation SNP c.1015G>A p.Glu339Lys example-35
NEK2 ENSG00000117650 1 211846871 211846871 Missense_Mutation SNP c.509C>T p.Thr170Met example-35
The PIU data format looks as follows:
uniprot_accession start_position end_position center_position unit_name gene_name gene_id unit_label
Q14D04 211 221 216 py VEPH1 ENSG00000197415 PTM
Q14D04 375 385 380 ps VEPH1 ENSG00000197415 PTM
Q14D04 392 402 397 pt VEPH1 ENSG00000197415 PTM
Q14D04 417 427 422 pt VEPH1 ENSG00000197415 PTM
Q14D04 425 435 430 ps VEPH1 ENSG00000197415 PTM
Q14D04 444 454 449 ps VEPH1 ENSG00000197415 PTM
Q14D04 524 534 529 ps VEPH1 ENSG00000197415 PTM
Q14D04 717 819 768 PH VEPH1 ENSG00000197415 Domain
Q14D04 766 776 771 ps VEPH1 ENSG00000197415 PTM
Q14D04 778 788 783 ps VEPH1 ENSG00000197415 PTM
Q14D33 50 150 100 zf-3CxxC RTP5 ENSG00000277949 Domain
The current implementation of GPD requires that user's own mutation data and protein information data follow the formatting shown above, i.e. with exactly the same column names.
Format of clinical information data:
Tumor_Sample_Barcode type age gender race ajcc_pathologic_tumor_stage clinical_stage histological_type histological_grade initial_pathologic_dx_year menopause_status birth_days_to vital_status tumor_status last_contact_days_to death_days_to cause_of_death new_tumor_event_type new_tumor_event_site new_tumor_event_site_other new_tumor_event_dx_days_to treatment_outcome_first_course margin_status residual_tumor OS OS.time DSS DSS.time DFI DFI.time PFI PFI.time Redaction
example-1 example 65 FEMALE WHITE Stage II [Not Applicable] Adrenocortical carcinoma- Usual Type [Not Available] 2011 [Not Available] -24017 Alive WITH TUMOR 383 NA [Not Available] Distant Metastasis Lung #N/A 166 [Not Available] NA NA 0 383 0 383 NA NA 1 166 NA
example-2 example 42 FEMALE WHITE Stage IV [Not Applicable] Adrenocortical carcinoma- Usual Type [Not Available] 1998 [Not Available] -15536 Dead WITH TUMOR NA 436 [Not Available] Distant Metastasis Bone #N/A 61 Progressive Disease NA NA 1 436 1 436 NA NA 1 61 NA
example-3 example 32 FEMALE WHITE Stage III [Not Applicable] Adrenocortical carcinoma- Usual Type [Not Available] 2010 [Not Available] -11970 Dead WITH TUMOR NA 994 [Not Available] Distant Metastasis Lung #N/A 97 Progressive Disease NA NA 1 994 1 994 NA NA 1 97 NA
example-4 example 37 FEMALE [Not Evaluated] Stage II [Not Applicable] Adrenocortical carcinoma- Usual Type [Not Available] 2009 [Not Available] -13574 Alive TUMOR FREE 1857 NA [Not Available] #N/A #N/A #N/A NA Complete Remission/Response NA NA 0 1857 0 1857 0 1857 0 1857 NA
example-5 example 53 FEMALE WHITE Stage IV [Not Applicable] Adrenocortical carcinoma- Usual Type [Not Available] 2011 [Not Available] -19492 Alive WITH TUMOR 1171 NA [Not Available] Distant Metastasis Lung #N/A 351 Stable Disease NA NA 0 1171 0 1171 NA NA 1 351 NA
Clinical information is an input required for survival analysis. Not all the columns in the above file are required, however these columns are compulsory:
Tumor_Sample_Barcode
age
gender
race
OS
OS.time
OS refers to the survival status; OS.time refers to survival time.
In the manuscript, we did survival analysis with adjustment of two additional potential confounders: total mutation count and stage (when available). These are not adjusted in the function provided in the package. However, we provide the script 'survival_model_adjust_totalMutationStage.R' where model is constructed with these additionall covariables, users may refer to the script if needed.
We provide in our R package the mutation data file and clinical data file for a subset of an example cohort. We also provide PIU data file containing the protein modification sites from the PhosphoSitePlus database and pfam domains. User can access these data after installing the package.
sel_example_mutation
ptm_pfam_combine
sel_example_cdr
2 Mutation Extraction
The first step is to read in your mutation data, and identify patients you wish to study by specifying their IDs (barcodes).
library(data.table)
mutation_df = fread("your_path_to_file/user_mutation.tsv",
header = T,
select = c("Hugo_Symbol","Gene","Chromosome","Start_Position","End_Position","Variant_Classification","Variant_Type","HGVSc","HGVSp","Tumor_Sample_Barcode"),
stringsAsFactors = F)
cancer_barcode = unique(mutation_df$Tumor_Sample_Barcode)
These objects will be part of the inputs in the extraction_annotation_posfunction.
An example of running a subset of example somatic mutations is the following:
extraction_annotation_pos(mutation_df = sel_example_mutation,
cancer_type = "example",
cancer_barcode = unique(sel_example_mutation$Tumor_Sample_Barcode),
output_dir = "your_output_dir1/")
This will generate two output files that will be used in the subsequent function:
- "your_output_dir1/example_mutation_npc.tsv"
- "your_output_dir1/example_mutation_pc_pos.tsv"
The first file contains extracted mutations in non-protein coding regions. The second file contains extracted mutations in protein coding regions and their annotated corresponding amino acid positions.
3 Mutation Mapping
GPD maps mutations to PIUs, linker region, and non-coding region. Linker region refers to protein-coding sequences that do not correspond to either protein domains or protein modification sites. Users can load their own PIU data file as follows:
piu_df = fread("your_path_to_file/user_piu.tsv", stringsAsFactors = F)
Mapping to the default PIUs provided in the library can be done with the annotated subset of example somatic mutations:
piu_mapping (piu_df = ptm_pfam_combine,
pc_data_name = "your_output_dir1/example_mutation_pc_pos.tsv",
npc_data_name = "your_output_dir1/example_mutation_npc.tsv",
cancer_barcode = unique(sel_example_mutation$Tumor_Sample_Barcode),
output_dir = "your_output_dir2/")
This function will produce three output files:
- "your_output_dir2/piu_mapping.tsv"
- "your_output_dir2/lu_summarising_count.tsv"
- "your_output_dir2/ncu_summarising_count.tsv"
These files carry mutation mapping results (counts per PIU, linker unit, and non-coding unit), which can be used in subsequent statistical analysis.
4 Survival analysis
After mapping, users can perform survival analysis to study the association between mapped mutation counts on PIU, LU, NCU and patient survival.
Users can upload their clinical data in the following way:
clinical_df = fread("your_path_to_file/user_clinical.tsv", stringsAsFactors = F)
With the subset of example data mapping results and clinical data, we perform the survival analysis.
univariate_cox_model(piu_filename = "your_output_dir2/piu_mapping_count.tsv",
lu_filename = "your_output_dir2/lu_summarising_count.tsv",
ncu_filename = "your_output_dir2/ncu_summarising_count.tsv",
clinical_df = sel_example_cdr,
gender_as_covariate = T,
race_group_min = 6,
min_surv_days = 90,
min_surv_people = 5,
patient_sum_min = 3,
mutation_type = "somatic",
output_dir = "your_output_dir3/")
This function will generate the following output files:
- "your_output_dir3/somatic_piu_cdr_univariate.tsv"
- "your_output_dir3/somatic_lu_cdr_univariate.tsv"
- "your_output_dir3/somatic_ncu_cdr_univariate.tsv"
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