# Overview of Data Retrieval Workflow

    library(cbioportalR)
    library(dplyr)

## Introduction

We will outline the main data retrieval workflow and functions using a case study based on two public sets of data:

1. 105 samples in high risk nonmuscle invasive bladder cancer patients (Pietzak et al. 2017).
2. 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
    #>    <chr>          <chr> <chr>   <lgl>   <chr>   <int> <chr>     <int> <lgl>   <chr>
    #>  1 acc_tcga       Adre… "TCGA … TRUE    "PUBL…      0 2022-0…      92 TRUE    acc
    #>  2 bcc_unige_2016 Basa… "Whole… TRUE    "PUBL…      0 2022-0…     293 TRUE    bcc
    #>  3 ampca_bcm_2016 Ampu… "Exome… TRUE    "PUBL…      0 2022-0…     160 TRUE    ampca
    #>  4 blca_dfarber_… Blad… "Whole… TRUE    "PUBL…      0 2022-0…      50 TRUE    blca
    #>  5 blca_mskcc_so… Blad… "Compr… TRUE    "PUBL…      0 2022-0…      97 TRUE    blca
    #>  6 blca_bgi       Blad… "Whole… TRUE    "PUBL…      0 2022-0…      99 TRUE    blca
    #>  7 blca_mskcc_so… Blad… "Genom… TRUE    "PUBL…      0 2022-0…     109 TRUE    blca
    #>  8 all_stjude_20… Hypo… "Whole… TRUE    ""          0 2022-0…      44 TRUE    myeloid
    #>  9 acyc_fmi_2014  Aden… "Targe… TRUE    "ACYC…      0 2022-0…      28 TRUE    acyc
    #> 10 acyc_sanger_2… Aden… "Whole… TRUE    "ACYC…      0 2022-0…      24 TRUE    acyc
    #> # … with 343 more rows, 3 more variables: referenceGenome <chr>, pmid <chr>,
    #> #   citation <chr>, and abbreviated variable names ¹​description, ²​publicStudy,
    #> #   ³​importDate, ⁴​allSampleCount, ⁵​readPermission, ⁶​cancerTypeId

By 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
    #>   <chr>   <chr> <chr>   <lgl>   <chr>   <int> <chr>     <int> <lgl>   <chr>   <chr>
    #> 1 blca_n… Nonm… IMPACT… TRUE    PUBLIC      0 2022-0…     105 TRUE    blca    hg19
    #> 2 prad_m… Pros… MSK-IM… TRUE    PUBLIC      0 2022-0…      18 TRUE    prosta… hg19
    #> # … with 2 more variables: pmid <chr>, citation <chr>, and abbreviated variable
    #> #   names ¹​description, ²​publicStudy, ³​importDate, ⁴​allSampleCount, ⁵​readPermission,
    #> #   ⁶​cancerTypeId, ⁷​referenceGenome

More 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       descr…¹ showP…² patie…³ molec…⁴ studyId
    #>   <chr>                   <chr>    <chr>      <chr>   <lgl>   <lgl>   <chr>   <chr>
    #> 1 COPY_NUMBER_ALTERATION  DISCRETE Putative … Copy N… TRUE    FALSE   blca_n… blca_n…
    #> 2 MUTATION_EXTENDED       MAF      Mutations  Mutati… TRUE    FALSE   blca_n… blca_n…
    #> 3 STRUCTURAL_VARIANT      SV       Structura… Struct… TRUE    FALSE   blca_n… blca_n…
    #> # … with abbreviated variable names ¹​description, ²​showProfileInAnalysisTab,
    #> #   ³​patientLevel, ⁴​molecularProfileId

    available_profiles(study_id = "prad_msk_2019")
    #> # A tibble: 3 × 8
    #>   molecularAlterationType datatype name       descr…¹ showP…² patie…³ molec…⁴ studyId
    #>   <chr>                   <chr>    <chr>      <chr>   <lgl>   <lgl>   <chr>   <chr>
    #> 1 COPY_NUMBER_ALTERATION  DISCRETE Putative … Putati… TRUE    FALSE   prad_m… prad_m…
    #> 2 MUTATION_EXTENDED       MAF      Mutations  IMPACT… TRUE    FALSE   prad_m… prad_m…
    #> 3 STRUCTURAL_VARIANT      SV       Structura… Struct… TRUE    FALSE   prad_m… prad_m…
    #> # … with abbreviated variable names ¹​description, ²​showProfileInAnalysisTab,
    #> #   ³​patientLevel, ⁴​molecularProfileId

Luckily, 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"
    #> [3] "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:

1. Using study IDs (`get_genetics_by_study()`)
2. 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" study

Or 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] TRUE

Finally, we can join the two studies together

    mut_study <- bind_rows(mut_blca, mut_prad)
    cna_study <- bind_rows(cna_blca, cna_prad)
    fus_study <- bind_rows(fus_blca, fus_prad)

### 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.

    df_pairs <- df_pairs %>%
      rename("sample_id" = sampleId,
             "study_id" = studyId)

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$mutation

Like 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] TRUE

Let’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] TRUE

Both 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
    #>   hugoGeneSy…¹ entre…² uniqu…³ uniqu…⁴ molec…⁵ sampl…⁶ patie…⁷ studyId center mutat…⁸
    #>   <chr>          <int> <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>  <chr>
    #> 1 TERT            7015 UC0wMD… UC0wMD… blca_n… P-0001… P-0001… blca_n… MSKCC  SOMATIC
    #> 2 SMAD4           4089 UC0wMD… UC0wMD… blca_n… P-0001… P-0001… blca_n… MSKCC  SOMATIC
    #> 3 ERBB4           2066 UC0wMD… UC0wMD… blca_n… P-0001… P-0001… blca_n… MSKCC  SOMATIC
    #> 4 CUL3            8452 UC0wMD… UC0wMD… blca_n… P-0001… P-0001… blca_n… MSKCC  SOMATIC
    #> 5 PBRM1          55193 UC0wMD… UC0wMD… blca_n… P-0001… P-0001… blca_n… MSKCC  SOMATIC
    #> 6 APC              324 UC0wMD… UC0wMD… blca_n… P-0001… P-0001… blca_n… MSKCC  SOMATIC
    #> # … with 23 more variables: validationStatus <chr>, tumorAltCount <int>,
    #> #   tumorRefCount <int>, normalAltCount <int>, normalRefCount <int>,
    #> #   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 …

## Pulling 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… STRING  FALSE   1       CANCER… prad_m…
    #>  3 Fraction Genome Altered       Fraction Ge… NUMBER  FALSE   20      FRACTI… prad_m…
    #>  4 Gene Panel                    Gene Panel.  STRING  FALSE   1       GENE_P… prad_m…
    #>  5 Mutation Count                Mutation Co… NUMBER  FALSE   30      MUTATI… prad_m…
    #>  6 Oncotree Code                 Oncotree Co… STRING  FALSE   1       ONCOTR… prad_m…
    #>  7 Sample Class                  The sample … STRING  FALSE   1       SAMPLE… prad_m…
    #>  8 Number of Samples Per Patient Number of S… STRING  TRUE    1       SAMPLE… prad_m…
    #>  9 Sample Type                   The type of… STRING  FALSE   1       SAMPLE… prad_m…
    #> 10 Sex                           Sex          STRING  TRUE    1       SEX     prad_m…
    #> 11 Somatic Status                Somatic Sta… STRING  FALSE   1       SOMATI… prad_m…
    #> 12 Specimen Preservation Type    The method … STRING  FALSE   1       SPECIM… prad_m…
    #> 13 TMB (nonsynonymous)           TMB (nonsyn… NUMBER  FALSE   1       TMB_NO… prad_m…
    #> # … with abbreviated variable names ¹​datatype, ²​patientAttribute, ³​priority,
    #> #   ⁴​clinicalAttributeId

There 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 Urothe…
    #> 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          Matched

The 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(sample_id = s2$patientId,
                           study_id = "prad_msk_2019",
                           clinical_attribute = in_both)
    #> Error in get_clinical_by_patient(sample_id = s2$patientId, study_id = "prad_msk_2019", : unused argument (sample_id = s2$patientId)

    all_clinical <- bind_rows(clinical_blca, clinical_prad)

    all_clinical %>%
      select(-contains("unique")) %>%
      head()
    #> # A tibble: 6 × 5
    #>   patientId studyId         clinicalAttributeId value sampleId
    #>   <chr>     <chr>           <chr>               <chr> <chr>
    #> 1 P-0001453 blca_nmibc_2017 SAMPLE_COUNT        1     <NA>
    #> 2 P-0001453 blca_nmibc_2017 SEX                 Male  <NA>
    #> 3 P-0002166 blca_nmibc_2017 SAMPLE_COUNT        1     <NA>
    #> 4 P-0002166 blca_nmibc_2017 SEX                 Male  <NA>
    #> 5 P-0003238 blca_nmibc_2017 SAMPLE_COUNT        1     <NA>
    #> 6 P-0003238 blca_nmibc_2017 SEX                 Male  <NA>

Like 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