Overview of Data Retrieval Workflow
Source:vignettes/overview-of-workflow.Rmd
overview-of-workflow.RmdIntroduction
We will outline the main data retrieval workflow and functions using a case study based on two public sets of data:
- 105 samples in high risk nonmuscle invasive bladder cancer patients (Pietzak et al. 2017).
- 18 samples of 18 prostate cancer patients (Granlund et al. 2020)
Setup
Before accessing data you will need to connect to a cBioPortal database and set your base URL for the R session. In this example we will use data from the public cBioPortal database instance (https://www.cbioportal.org). You do not need a token to access this public website. If you are using a private instance of cBioPortal (like MSK’s institutional database), you will need to acquire a token and save it to your .Renviron file.
Note: If you are a MSK researcher working on IMPACT, you should connect to MSK’s cBioPortal instance to get the most up to date IMPACT data, and you must follow MSK-IMPACT publication guidelines when using the data.
To set the database url for your current R session use the set_cbioportal_db() function. To set it to the public instance you can either provide the full URL to the function, or just public as a shortcut. This function will both check your connection to the database and set the url (www.cbioportal.org/api) as your base url to connect to for all future API calls during your session.
set_cbioportal_db("public")
#> ✔ You are successfully connected!
#> ✔ base_url for this R session is now set to "www.cbioportal.org/api"You can use test_cbioportal_db at any time throughout your session to check your connection. This can be helpful when troubleshooting issues with your API calls.
test_cbioportal_db()
#> ✔ You are successfully connected!Get Study Metadata
Now that we are successfully connected, we may want to view all studies available for our chosen database to find the correct study_id corresponding to the data we want to pull. All studies have a unique identifier in the database. You can view all studies available in your database with the following:
all_studies <- available_studies()
all_studies
#> # A tibble: 353 × 13
#> studyId name descr…¹ publi…² groups status impor…³ allSa…⁴ readP…⁵ cance…⁶ refer…⁷ pmid citat…⁸
#> <chr> <chr> <chr> <lgl> <chr> <int> <chr> <int> <lgl> <chr> <chr> <chr> <chr>
#> 1 acc_tcga Adrenocor… "TCGA … TRUE "PUBL… 0 2022-0… 92 TRUE acc hg19 <NA> <NA>
#> 2 bcc_unige_2016 Basal Cel… "Whole… TRUE "PUBL… 0 2022-0… 293 TRUE bcc hg19 2695… Bonill…
#> 3 ampca_bcm_2016 Ampullary… "Exome… TRUE "PUBL… 0 2022-0… 160 TRUE ampca hg19 2680… Gingra…
#> 4 blca_dfarber_mskcc_2014 Bladder U… "Whole… TRUE "PUBL… 0 2022-0… 50 TRUE blca hg19 2509… Van Al…
#> 5 blca_mskcc_solit_2012 Bladder C… "Compr… TRUE "PUBL… 0 2022-0… 97 TRUE blca hg19 2389… Iyer e…
#> 6 blca_bgi Bladder U… "Whole… TRUE "PUBL… 0 2022-0… 99 TRUE blca hg19 2412… Guo et…
#> 7 blca_mskcc_solit_2014 Bladder C… "Genom… TRUE "PUBL… 0 2022-0… 109 TRUE blca hg19 2509… Kim et…
#> 8 all_stjude_2013 Hypodiplo… "Whole… TRUE "" 0 2022-0… 44 TRUE myeloid hg19 2333… Holmfe…
#> 9 acyc_fmi_2014 Adenoid C… "Targe… TRUE "ACYC… 0 2022-0… 28 TRUE acyc hg19 2441… Ross e…
#> 10 acyc_sanger_2013 Adenoid C… "Whole… TRUE "ACYC… 0 2022-0… 24 TRUE acyc hg19 2377… Stephe…
#> # … with 343 more rows, and abbreviated variable names ¹description, ²publicStudy, ³importDate, ⁴allSampleCount,
#> # ⁵readPermission, ⁶cancerTypeId, ⁷referenceGenome, ⁸citationBy inspecting this data frame, we see the unique study_id for the NMIBC data set is "blca_nmibc_2017" and the unique study_id for the prostate cancer data set is "prad_msk_2019". To get more information on our studies we can do the following:
Note: the transpose function t() is just used here to better view results
all_studies %>%
filter(studyId %in% c("blca_nmibc_2017", "prad_msk_2019"))
#> # A tibble: 2 × 13
#> studyId name descr…¹ publi…² groups status impor…³ allSa…⁴ readP…⁵ cance…⁶ refer…⁷ pmid citat…⁸
#> <chr> <chr> <chr> <lgl> <chr> <int> <chr> <int> <lgl> <chr> <chr> <chr> <chr>
#> 1 blca_nmibc_2017 Nonmuscle Invasive… IMPACT… TRUE PUBLIC 0 2022-0… 105 TRUE blca hg19 2858… Pietza…
#> 2 prad_msk_2019 Prostate Cancer (M… MSK-IM… TRUE PUBLIC 0 2022-0… 18 TRUE prosta… hg19 3156… Granlu…
#> # … with abbreviated variable names ¹description, ²publicStudy, ³importDate, ⁴allSampleCount, ⁵readPermission,
#> # ⁶cancerTypeId, ⁷referenceGenome, ⁸citationMore in-depth information about the study can be found with get_study_info()
get_study_info("blca_nmibc_2017") %>%
t()
#> [,1]
#> name "Nonmuscle Invasive Bladder Cancer (MSK Eur Urol 2017)"
#> description "IMPACT sequencing of 105 High Risk Nonmuscle Invasive Bladder Cancer samples."
#> publicStudy "TRUE"
#> pmid "28583311"
#> citation "Pietzak et al. Eur Urol 2017"
#> groups "PUBLIC"
#> status "0"
#> importDate "2022-08-17 22:59:50"
#> allSampleCount "105"
#> sequencedSampleCount "105"
#> cnaSampleCount "105"
#> mrnaRnaSeqSampleCount "0"
#> mrnaRnaSeqV2SampleCount "0"
#> mrnaMicroarraySampleCount "0"
#> miRnaSampleCount "0"
#> methylationHm27SampleCount "0"
#> rppaSampleCount "0"
#> massSpectrometrySampleCount "0"
#> completeSampleCount "0"
#> readPermission "TRUE"
#> studyId "blca_nmibc_2017"
#> cancerTypeId "blca"
#> cancerType.name "Bladder Urothelial Carcinoma"
#> cancerType.dedicatedColor "Yellow"
#> cancerType.shortName "BLCA"
#> cancerType.parent "bladder"
#> cancerType.cancerTypeId "blca"
#> referenceGenome "hg19"
get_study_info("prad_msk_2019") %>%
t()
#> [,1]
#> name "Prostate Cancer (MSK, Cell Metab 2020)"
#> description "MSK-IMPACT Sequencing of 18 prostate cancer tumor/normal pairs."
#> publicStudy "TRUE"
#> pmid "31564440"
#> citation "Granlund et al. Cell Metab 2020"
#> groups "PUBLIC"
#> status "0"
#> importDate "2022-08-24 10:29:00"
#> allSampleCount "18"
#> sequencedSampleCount "18"
#> cnaSampleCount "18"
#> mrnaRnaSeqSampleCount "0"
#> mrnaRnaSeqV2SampleCount "0"
#> mrnaMicroarraySampleCount "0"
#> miRnaSampleCount "0"
#> methylationHm27SampleCount "0"
#> rppaSampleCount "0"
#> massSpectrometrySampleCount "0"
#> completeSampleCount "0"
#> readPermission "TRUE"
#> studyId "prad_msk_2019"
#> cancerTypeId "prostate"
#> cancerType.name "Prostate"
#> cancerType.dedicatedColor "Cyan"
#> cancerType.shortName "PROSTATE"
#> cancerType.parent "tissue"
#> cancerType.cancerTypeId "prostate"
#> referenceGenome "hg19"Lastly, it is important to know what genomic data is available for our studies. Not all studies in your database will have data available on all types of genomic information. For example, it is common for studies not to provide data on fusions/structural variants.
We can check available genomic data with available_profiles().
available_profiles(study_id = "blca_nmibc_2017")
#> # A tibble: 3 × 8
#> molecularAlterationType datatype name description showP…¹ patie…² molec…³ studyId
#> <chr> <chr> <chr> <chr> <lgl> <lgl> <chr> <chr>
#> 1 COPY_NUMBER_ALTERATION DISCRETE Putative copy-number alterations Copy Number data TRUE FALSE blca_n… blca_n…
#> 2 MUTATION_EXTENDED MAF Mutations Mutation data from n… TRUE FALSE blca_n… blca_n…
#> 3 STRUCTURAL_VARIANT SV Structural variants Structural Variant D… TRUE FALSE blca_n… blca_n…
#> # … with abbreviated variable names ¹showProfileInAnalysisTab, ²patientLevel, ³molecularProfileId
available_profiles(study_id = "prad_msk_2019")
#> # A tibble: 3 × 8
#> molecularAlterationType datatype name descrip…¹ showP…² patie…³ molec…⁴ studyId
#> <chr> <chr> <chr> <chr> <lgl> <lgl> <chr> <chr>
#> 1 COPY_NUMBER_ALTERATION DISCRETE Putative copy-number alterations (IMPACT468) Putative… TRUE FALSE prad_m… prad_m…
#> 2 MUTATION_EXTENDED MAF Mutations IMPACT46… TRUE FALSE prad_m… prad_m…
#> 3 STRUCTURAL_VARIANT SV Structural variants Structur… TRUE FALSE prad_m… prad_m…
#> # … with abbreviated variable names ¹description, ²showProfileInAnalysisTab, ³patientLevel, ⁴molecularProfileIdLuckily, in this example our studies have mutation, copy number alteration and fusion (structural variant) data available. Each of these data types has a unique molecular profile ID. The molecular profile ID usually takes the form of <study_id>_mutations, <study_id>_structural_variants, <study_id>_cna.
available_profiles(study_id = "blca_nmibc_2017") %>%
pull(molecularProfileId)
#> [1] "blca_nmibc_2017_cna" "blca_nmibc_2017_mutations" "blca_nmibc_2017_structural_variants"Pulling Genomic Data
Now that we have inspected our studies and confirmed the genomic data that is available, we will pull the data into our R environment. We will show two ways to do this:
- Using study IDs (
get_genetics_by_study()) - Using sample ID-study ID pairs (
get_genetics_by_sample())
Pulling by study will give us genomic data for all genes/panels included in the study. These functions can only pull data one study ID at a time and will return all genomic data available for that study. Pulling by study ID can be efficient, and a good way to ensure you have all genomic information available in cBioPortal for a particular study.
If you are working across multiple studies, or only need a subset of samples from one or multiple studies, you may chose to pull by sample IDs instead of study ID. When you pull by sample IDs you can pull specific samples across multiple studies, but must also specify the studies they belong to. You may also pass a specific list of genes for which to return information. If you don’t specify a list of genes the function will default to returning all available gene data for each sample.
By Study IDs
To pull by study ID, we can pull each data type individually.
mut_blca <- get_mutations_by_study(study_id = "blca_nmibc_2017")
#> ℹ Returning all data for the "blca_nmibc_2017_mutations" molecular profile in the "blca_nmibc_2017" study
cna_blca<- get_cna_by_study(study_id = "blca_nmibc_2017")
#> ℹ Returning all data for the "blca_nmibc_2017_cna" molecular profile in the "blca_nmibc_2017" study
fus_blca <- get_fusions_by_study(study_id = "blca_nmibc_2017")
#> ℹ Returning all data for the "blca_nmibc_2017_structural_variants" molecular profile in the "blca_nmibc_2017" study
mut_prad <- get_mutations_by_study(study_id = "prad_msk_2019")
#> ℹ Returning all data for the "prad_msk_2019_mutations" molecular profile in the "prad_msk_2019" study
cna_prad <- get_cna_by_study(study_id = "prad_msk_2019")
#> ℹ Returning all data for the "prad_msk_2019_cna" molecular profile in the "prad_msk_2019" study
fus_prad <- get_fusions_by_study(study_id = "prad_msk_2019")
#> ℹ Returning all data for the "prad_msk_2019_structural_variants" molecular profile in the "prad_msk_2019" studyOr we can pull all genomic data at the same time with get_genetics_by_study()
all_genomic_blca <- get_genetics_by_study("blca_nmibc_2017")
#> ℹ Returning all data for the "blca_nmibc_2017_mutations" molecular profile in the "blca_nmibc_2017" study
#> ℹ Returning all data for the "blca_nmibc_2017_cna" molecular profile in the "blca_nmibc_2017" study
#> ℹ Returning all data for the "blca_nmibc_2017_structural_variants" molecular profile in the "blca_nmibc_2017" study
all_genomic_prad <- get_genetics_by_study("prad_msk_2019")
#> ℹ Returning all data for the "prad_msk_2019_mutations" molecular profile in the "prad_msk_2019" study
#> ℹ Returning all data for the "prad_msk_2019_cna" molecular profile in the "prad_msk_2019" study
#> ℹ Returning all data for the "prad_msk_2019_structural_variants" molecular profile in the "prad_msk_2019" study
all_equal(mut_blca, all_genomic_blca$mutation)
#> [1] TRUE
all_equal(cna_blca, all_genomic_blca$cna)
#> [1] TRUE
all_equal(fus_blca, all_genomic_blca$structural_variant)
#> [1] TRUEFinally, we can join the two studies together
By Sample IDs
When we pull by sample IDs, we can pull specific samples across multiple studies. In the above example, we can pull from both studies at the same time for a select set of samples using the sample_study_pairs argument in get_genetics_by_sample().
Let’s pull data for the first 10 samples in each study. We first need to construct our dataframe to pass to the function:
Note: you can also run available_patients() to only pull patient IDs
s1 <- available_samples("blca_nmibc_2017") %>%
select(sampleId, patientId, studyId) %>%
head(10)
s2 <- available_samples("prad_msk_2019") %>%
select(sampleId, patientId, studyId) %>%
head(10)
df_pairs <- bind_rows(s1, s2) %>%
select(-patientId)We need to rename the columns as per the functions documentation.
Now we pass this to get_genetics_by_sample()
all_genomic <- get_genetics_by_sample(sample_study_pairs = df_pairs)
#> Joining, by = "study_id"
#> The following parameters were used in query:
#> Study ID: "blca_nmibc_2017" and "prad_msk_2019"
#> Molecular Profile ID: blca_nmibc_2017_mutations and prad_msk_2019_mutations
#> Genes: "All available genes"
#> Joining, by = "study_id"
#> The following parameters were used in query:
#> Study ID: "blca_nmibc_2017" and "prad_msk_2019"
#> Molecular Profile ID: blca_nmibc_2017_cna and prad_msk_2019_cna
#> Genes: "All available genes"
#> Joining, by = "study_id"
#> The following parameters were used in query:
#> Study ID: "blca_nmibc_2017" and "prad_msk_2019"
#> Molecular Profile ID: blca_nmibc_2017_structural_variants and prad_msk_2019_structural_variants
#> Genes: "All available genes"
mut_sample <- all_genomic$mutationLike with querying by study ID, you can also pull data individually by genomic data type:
mut_only <- get_mutations_by_sample(sample_study_pairs = df_pairs)
#> Joining, by = "study_id"
#> The following parameters were used in query:
#> Study ID: "blca_nmibc_2017" and "prad_msk_2019"
#> Molecular Profile ID: blca_nmibc_2017_mutations and prad_msk_2019_mutations
#> Genes: "All available genes"
identical(mut_only, mut_sample)
#> [1] TRUELet’s compare these results with the ones we got from pulling by study:
# filter to our subset used in sample query
mut_study_subset <- mut_study %>%
filter(sampleId %in% df_pairs$sample_id)
# arrange to compare
mut_study_subset <- mut_study_subset %>%
arrange(desc(sampleId))%>%
arrange(desc(entrezGeneId))
mut_sample <- mut_sample %>%
arrange(desc(sampleId)) %>%
arrange(desc(entrezGeneId)) %>%
# reorder so columns in same order
select(names(mut_study_subset))
all.equal(mut_study_subset, mut_sample)
#> [1] TRUEBoth results are equal.
Limit Results to Specified Genes or Panels
When pulling by sample IDs, we can also limit our results to a specific set of genes by passing a vector of Entrez Gene IDs or Hugo Symbols to the gene argument, or a specified panel by passing a panel ID to the panel argument (see available_gene_panels() for supported panels). This can be useful if, for example, we want to pull all IMPACT gene results for two studies but one of the two uses a much larger panel. In that case, we can limit our query to just the genes for which we want results:
by_hugo <- get_mutations_by_sample(sample_study_pairs = df_pairs, genes = "TP53")
#> Joining, by = "study_id"
#> The following parameters were used in query:
#> Study ID: "blca_nmibc_2017" and "prad_msk_2019"
#> Molecular Profile ID: blca_nmibc_2017_mutations and prad_msk_2019_mutations
#> Genes: "TP53"
by_gene_id <- get_mutations_by_sample(sample_study_pairs = df_pairs, genes = 7157)
#> Joining, by = "study_id"
#> The following parameters were used in query:
#> Study ID: "blca_nmibc_2017" and "prad_msk_2019"
#> Molecular Profile ID: blca_nmibc_2017_mutations and prad_msk_2019_mutations
#> Genes: 7157
identical(by_hugo, by_gene_id)
#> [1] TRUE
get_mutations_by_sample(
sample_study_pairs = df_pairs,
panel = "IMPACT468") %>%
head()
#> Joining, by = "study_id"
#> The following parameters were used in query:
#> Study ID: "blca_nmibc_2017" and "prad_msk_2019"
#> Molecular Profile ID: blca_nmibc_2017_mutations and prad_msk_2019_mutations
#> Genes: "IMPACT468"
#> # A tibble: 6 × 33
#> hugoGe…¹ entre…² uniqu…³ uniqu…⁴ molec…⁵ sampl…⁶ patie…⁷ studyId center mutat…⁸ valid…⁹ tumor…˟ tumor…˟ norma…˟ norma…˟
#> <chr> <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int> <int> <int> <int>
#> 1 TERT 7015 UC0wMD… UC0wMD… blca_n… P-0001… P-0001… blca_n… MSKCC SOMATIC Unknown 130 87 0 72
#> 2 SMAD4 4089 UC0wMD… UC0wMD… blca_n… P-0001… P-0001… blca_n… MSKCC SOMATIC Unknown 43 757 0 314
#> 3 ERBB4 2066 UC0wMD… UC0wMD… blca_n… P-0001… P-0001… blca_n… MSKCC SOMATIC Unknown 182 500 0 402
#> 4 CUL3 8452 UC0wMD… UC0wMD… blca_n… P-0001… P-0001… blca_n… MSKCC SOMATIC Unknown 160 598 0 450
#> 5 PBRM1 55193 UC0wMD… UC0wMD… blca_n… P-0001… P-0001… blca_n… MSKCC SOMATIC Unknown 69 778 0 269
#> 6 APC 324 UC0wMD… UC0wMD… blca_n… P-0001… P-0001… blca_n… MSKCC SOMATIC Unknown 249 373 0 415
#> # … with 18 more variables: startPosition <int>, endPosition <int>, referenceAllele <chr>, proteinChange <chr>,
#> # mutationType <chr>, functionalImpactScore <chr>, fisValue <dbl>, linkXvar <chr>, linkPdb <chr>, linkMsa <chr>,
#> # ncbiBuild <chr>, variantType <chr>, chr <chr>, variantAllele <chr>, refseqMrnaId <chr>, proteinPosStart <int>,
#> # proteinPosEnd <int>, keyword <chr>, and abbreviated variable names ¹hugoGeneSymbol, ²entrezGeneId, ³uniqueSampleKey,
#> # ⁴uniquePatientKey, ⁵molecularProfileId, ⁶sampleId, ⁷patientId, ⁸mutationStatus, ⁹validationStatus, ˟tumorAltCount,
#> # ˟tumorRefCount, ˟normalAltCount, ˟normalRefCountPulling Clinical Data & Sample Metadata
You can also pull clinical data by study ID, sample ID, or patient ID. Pulling by sample ID will pull all sample-level characteristics (e.g. sample site, tumor stage at sampling time and other variables collected at time of sampling that may be available). Pulling by patient ID will pull all patient-level characteristics (e.g. age, sex, etc.). Pulling by study ID will pull all sample and patient-level characteristics at once.
You can explore what clinical data is available a study using:
attr_blca <- available_clinical_attributes("blca_nmibc_2017")
attr_prad <- available_clinical_attributes("prad_msk_2019")
attr_prad
#> # A tibble: 13 × 7
#> displayName description datat…¹ patie…² prior…³ clini…⁴ studyId
#> <chr> <chr> <chr> <lgl> <chr> <chr> <chr>
#> 1 Cancer Type Cancer Type STRING FALSE 1 CANCER… prad_m…
#> 2 Cancer Type Detailed Cancer Type Detailed STRING FALSE 1 CANCER… prad_m…
#> 3 Fraction Genome Altered Fraction Genome Altered NUMBER FALSE 20 FRACTI… prad_m…
#> 4 Gene Panel Gene Panel. STRING FALSE 1 GENE_P… prad_m…
#> 5 Mutation Count Mutation Count NUMBER FALSE 30 MUTATI… prad_m…
#> 6 Oncotree Code Oncotree Code STRING FALSE 1 ONCOTR… prad_m…
#> 7 Sample Class The sample classification (i.e., tumor, celllin… STRING FALSE 1 SAMPLE… prad_m…
#> 8 Number of Samples Per Patient Number of Samples Per Patient STRING TRUE 1 SAMPLE… prad_m…
#> 9 Sample Type The type of sample (i.e., normal, primary, met,… STRING FALSE 1 SAMPLE… prad_m…
#> 10 Sex Sex STRING TRUE 1 SEX prad_m…
#> 11 Somatic Status Somatic Status STRING FALSE 1 SOMATI… prad_m…
#> 12 Specimen Preservation Type The method used for preparing the tissue for ex… STRING FALSE 1 SPECIM… prad_m…
#> 13 TMB (nonsynonymous) TMB (nonsynonymous) NUMBER FALSE 1 TMB_NO… prad_m…
#> # … with abbreviated variable names ¹datatype, ²patientAttribute, ³priority, ⁴clinicalAttributeIdThere are a select set available for both studies:
in_both <- intersect(attr_blca$clinicalAttributeId, attr_prad$clinicalAttributeId)The below pulls data at the sample level:
clinical_blca <- get_clinical_by_sample(sample_id = s1$sampleId,
study_id = "blca_nmibc_2017",
clinical_attribute = in_both)
clinical_prad <- get_clinical_by_sample(sample_id = s2$sampleId,
study_id = "prad_msk_2019",
clinical_attribute = in_both)
all_clinical <- bind_rows(clinical_blca, clinical_prad)
all_clinical %>%
select(-contains("unique")) %>%
head()
#> # A tibble: 6 × 5
#> sampleId patientId studyId clinicalAttributeId value
#> <chr> <chr> <chr> <chr> <chr>
#> 1 P-0001453-T01-IM3 P-0001453 blca_nmibc_2017 CANCER_TYPE Bladder Cancer
#> 2 P-0001453-T01-IM3 P-0001453 blca_nmibc_2017 CANCER_TYPE_DETAILED Bladder Urothelial Carcinoma
#> 3 P-0001453-T01-IM3 P-0001453 blca_nmibc_2017 FRACTION_GENOME_ALTERED 0.4448
#> 4 P-0001453-T01-IM3 P-0001453 blca_nmibc_2017 MUTATION_COUNT 11
#> 5 P-0001453-T01-IM3 P-0001453 blca_nmibc_2017 ONCOTREE_CODE BLCA
#> 6 P-0001453-T01-IM3 P-0001453 blca_nmibc_2017 SOMATIC_STATUS MatchedThe below pulls data at the patient level:
p1 <- available_patients("blca_nmibc_2017")
clinical_blca <- get_clinical_by_patient(patient_id = s1$patientId,
study_id = "blca_nmibc_2017",
clinical_attribute = in_both)
clinical_prad <- get_clinical_by_patient(patient_id = s2$patientId,
study_id = "prad_msk_2019",
clinical_attribute = in_both)
all_clinical <- bind_rows(clinical_blca, clinical_prad)
all_clinical %>%
select(-contains("unique")) %>%
head()
#> # A tibble: 6 × 4
#> patientId studyId clinicalAttributeId value
#> <chr> <chr> <chr> <chr>
#> 1 P-0001453 blca_nmibc_2017 SAMPLE_COUNT 1
#> 2 P-0001453 blca_nmibc_2017 SEX Male
#> 3 P-0002166 blca_nmibc_2017 SAMPLE_COUNT 1
#> 4 P-0002166 blca_nmibc_2017 SEX Male
#> 5 P-0003238 blca_nmibc_2017 SAMPLE_COUNT 1
#> 6 P-0003238 blca_nmibc_2017 SEX MaleLike with the genomic data pull functions, you can also pull clinical data by a data frame of sample ID - study ID pairs, or a data frame of patient ID - study ID pairs. Below, we will pull by patient ID - study ID pairs.
First, we construct the data frame of pairs to pass:
df_pairs <- bind_rows(s1, s2) %>%
select(-sampleId)
df_pairs <- df_pairs %>%
select(patientId, studyId)Now we pass this data frame to get_genetics_by_patient()
all_patient_clinical <- get_clinical_by_patient(patient_study_pairs = df_pairs,
clinical_attribute = in_both)
all_patient_clinical %>%
select(-contains("unique"))
#> # A tibble: 34 × 4
#> patientId studyId clinicalAttributeId value
#> <chr> <chr> <chr> <chr>
#> 1 P-0001453 blca_nmibc_2017 SAMPLE_COUNT 1
#> 2 P-0001453 blca_nmibc_2017 SEX Male
#> 3 P-0002166 blca_nmibc_2017 SAMPLE_COUNT 1
#> 4 P-0002166 blca_nmibc_2017 SEX Male
#> 5 P-0003238 blca_nmibc_2017 SAMPLE_COUNT 1
#> 6 P-0003238 blca_nmibc_2017 SEX Male
#> 7 P-0003257 blca_nmibc_2017 SAMPLE_COUNT 1
#> 8 P-0003257 blca_nmibc_2017 SEX Female
#> 9 P-0003261 blca_nmibc_2017 SAMPLE_COUNT 1
#> 10 P-0003261 blca_nmibc_2017 SEX Male
#> # … with 24 more rows