# 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: 468 × 13
    #>    studyId          name    description publicStudy pmid  citation groups status importDate allSampleCount
    #>    <chr>            <chr>   <chr>       <lgl>       <chr> <chr>    <chr>   <int> <chr>               <int>
    #>  1 acyc_mskcc_2013  Adenoi… Whole-exom… TRUE        2368… Ho et a… "ACYC…      0 2023-12-0…             60
    #>  2 acyc_fmi_2014    Adenoi… Targeted S… TRUE        2441… Ross et… "ACYC…      0 2023-12-0…             28
    #>  3 acyc_jhu_2016    Adenoi… Whole-geno… TRUE        2686… Rettig … "ACYC…      0 2023-12-0…             25
    #>  4 acyc_mda_2015    Adenoi… WGS of 21 … TRUE        2663… Mitani … "ACYC…      0 2023-12-0…            102
    #>  5 acyc_mgh_2016    Adenoi… Whole-geno… TRUE        2682… Drier e… "ACYC"      0 2023-12-0…             10
    #>  6 acyc_sanger_2013 Adenoi… Whole exom… TRUE        2377… Stephen… "ACYC…      0 2023-12-0…             24
    #>  7 bcc_unige_2016   Basal … Whole-exom… TRUE        2695… Bonilla… "PUBL…      0 2023-12-0…            293
    #>  8 all_stjude_2015  Acute … Comprehens… TRUE        2573… Anderss… "PUBL…      0 2023-12-0…             93
    #>  9 ampca_bcm_2016   Ampull… Exome sequ… TRUE        2680… Gingras… "PUBL…      0 2023-12-0…            160
    #> 10 all_stjude_2013  Hypodi… Whole geno… TRUE        2333… Holmfel… ""          0 2023-12-0…             44
    #> # ℹ 458 more rows
    #> # ℹ 3 more variables: readPermission <lgl>, cancerTypeId <chr>, referenceGenome <chr>

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      description publicStudy pmid  citation groups status importDate allSampleCount
    #>   <chr>           <chr>     <chr>       <lgl>       <chr> <chr>    <chr>   <int> <chr>               <int>
    #> 1 blca_nmibc_2017 Nonmuscl… IMPACT seq… TRUE        2858… Pietzak… PUBLIC      0 2023-12-0…            105
    #> 2 prad_msk_2019   Prostate… MSK-IMPACT… TRUE        3156… Granlun… PUBLIC      0 2023-12-1…             18
    #> # ℹ 3 more variables: readPermission <lgl>, cancerTypeId <chr>, referenceGenome <chr>

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                  "2023-12-07 10:15:31"
    #> allSampleCount              "105"
    #> sequencedSampleCount        "105"
    #> cnaSampleCount              "105"
    #> mrnaRnaSeqSampleCount       "0"
    #> mrnaRnaSeqV2SampleCount     "0"
    #> mrnaMicroarraySampleCount   "0"
    #> miRnaSampleCount            "0"
    #> methylationHm27SampleCount  "0"
    #> rppaSampleCount             "0"
    #> massSpectrometrySampleCount "0"
    #> completeSampleCount         "0"
    #> readPermission              "TRUE"
    #> treatmentCount              "0"
    #> structuralVariantCount      "11"
    #> 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                  "2023-12-11 10:54:56"
    #> allSampleCount              "18"
    #> sequencedSampleCount        "18"
    #> cnaSampleCount              "18"
    #> mrnaRnaSeqSampleCount       "0"
    #> mrnaRnaSeqV2SampleCount     "0"
    #> mrnaMicroarraySampleCount   "0"
    #> miRnaSampleCount            "0"
    #> methylationHm27SampleCount  "0"
    #> rppaSampleCount             "0"
    #> massSpectrometrySampleCount "0"
    #> completeSampleCount         "0"
    #> readPermission              "TRUE"
    #> treatmentCount              "0"
    #> structuralVariantCount      "4"
    #> 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
    #>   molecularAlterationT…¹ datatype name  description showProfileInAnalysi…² patientLevel molecularProfileId
    #>   <chr>                  <chr>    <chr> <chr>       <lgl>                  <lgl>        <chr>
    #> 1 COPY_NUMBER_ALTERATION DISCRETE Puta… Copy Numbe… TRUE                   FALSE        blca_nmibc_2017_c…
    #> 2 MUTATION_EXTENDED      MAF      Muta… Mutation d… TRUE                   FALSE        blca_nmibc_2017_m…
    #> 3 STRUCTURAL_VARIANT     SV       Stru… Structural… TRUE                   FALSE        blca_nmibc_2017_s…
    #> # ℹ abbreviated names: ¹​molecularAlterationType, ²​showProfileInAnalysisTab
    #> # ℹ 1 more variable: studyId <chr>

    available_profiles(study_id = "prad_msk_2019")
    #> # A tibble: 3 × 8
    #>   molecularAlterationT…¹ datatype name  description showProfileInAnalysi…² patientLevel molecularProfileId
    #>   <chr>                  <chr>    <chr> <chr>       <lgl>                  <lgl>        <chr>
    #> 1 COPY_NUMBER_ALTERATION DISCRETE Puta… Putative c… TRUE                   FALSE        prad_msk_2019_cna
    #> 2 MUTATION_EXTENDED      MAF      Muta… IMPACT468 … TRUE                   FALSE        prad_msk_2019_mut…
    #> 3 STRUCTURAL_VARIANT     SV       Stru… Structural… TRUE                   FALSE        prad_msk_2019_str…
    #> # ℹ abbreviated names: ¹​molecularAlterationType, ²​showProfileInAnalysisTab
    #> # ℹ 1 more variable: studyId <chr>

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_samples(sample_list_id = <sample list ID>)` to pull sample IDs by a specific sample list ID (see `available_sample_lists()`), or `available_patients()` to 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 with `by = join_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 with `by = join_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 with `by = join_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 with `by = join_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.

Note: some studies also have copy number segments data available that can be pulled by study ID or sample ID:

    seg_blca <- get_segments_by_study("blca_nmibc_2017")
    #> ℹ Returning all "copy number segmentation" data for the "blca_nmibc_2017" study

    # To pull alongside other genomic data types, use the `return_segments` argument
    all_genomic_blca <- get_genetics_by_study("blca_nmibc_2017", return_segments = TRUE)
    #> 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
    #> ℹ Returning all "copy number segmentation" data for the "blca_nmibc_2017" study

#### 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 with `by = join_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 with `by = join_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 with `by = join_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 × 28
    #>   hugoGeneSymbol entrezGeneId uniqueSampleKey       uniquePatientKey molecularProfileId sampleId patientId
    #>   <chr>                 <int> <chr>                 <chr>            <chr>              <chr>    <chr>
    #> 1 TERT                   7015 UC0wMDAxNDUzLVQwMS1J… UC0wMDAxNDUzOmJ… blca_nmibc_2017_m… P-00014… P-0001453
    #> 2 SMAD4                  4089 UC0wMDAxNDUzLVQwMS1J… UC0wMDAxNDUzOmJ… blca_nmibc_2017_m… P-00014… P-0001453
    #> 3 ERBB4                  2066 UC0wMDAxNDUzLVQwMS1J… UC0wMDAxNDUzOmJ… blca_nmibc_2017_m… P-00014… P-0001453
    #> 4 CUL3                   8452 UC0wMDAxNDUzLVQwMS1J… UC0wMDAxNDUzOmJ… blca_nmibc_2017_m… P-00014… P-0001453
    #> 5 PBRM1                 55193 UC0wMDAxNDUzLVQwMS1J… UC0wMDAxNDUzOmJ… blca_nmibc_2017_m… P-00014… P-0001453
    #> 6 APC                     324 UC0wMDAxNDUzLVQwMS1J… UC0wMDAxNDUzOmJ… blca_nmibc_2017_m… P-00014… P-0001453
    #> # ℹ 21 more variables: studyId <chr>, center <chr>, mutationStatus <chr>, validationStatus <chr>,
    #> #   tumorAltCount <int>, tumorRefCount <int>, normalAltCount <int>, normalRefCount <int>,
    #> #   startPosition <int>, endPosition <int>, referenceAllele <chr>, proteinChange <chr>,
    #> #   mutationType <chr>, ncbiBuild <chr>, variantType <chr>, chr <chr>, variantAllele <chr>,
    #> #   refseqMrnaId <chr>, proteinPosStart <int>, proteinPosEnd <int>, keyword <chr>

## 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 datatype patientAttribute priority clinicalAttributeId studyId
    #>    <chr>                        <chr>       <chr>    <lgl>            <chr>    <chr>               <chr>
    #>  1 Cancer Type                  Cancer Type STRING   FALSE            1        CANCER_TYPE         prad_m…
    #>  2 Cancer Type Detailed         Cancer Typ… STRING   FALSE            1        CANCER_TYPE_DETAIL… prad_m…
    #>  3 Fraction Genome Altered      Fraction G… NUMBER   FALSE            20       FRACTION_GENOME_AL… prad_m…
    #>  4 Gene Panel                   Gene Panel. STRING   FALSE            1        GENE_PANEL          prad_m…
    #>  5 Mutation Count               Mutation C… NUMBER   FALSE            30       MUTATION_COUNT      prad_m…
    #>  6 Oncotree Code                Oncotree C… STRING   FALSE            1        ONCOTREE_CODE       prad_m…
    #>  7 Sample Class                 The sample… STRING   FALSE            1        SAMPLE_CLASS        prad_m…
    #>  8 Number of Samples Per Patie… Number of … STRING   TRUE             1        SAMPLE_COUNT        prad_m…
    #>  9 Sample Type                  The type o… STRING   FALSE            1        SAMPLE_TYPE         prad_m…
    #> 10 Sex                          Sex         STRING   TRUE             1        SEX                 prad_m…
    #> 11 Somatic Status               Somatic St… STRING   FALSE            1        SOMATIC_STATUS      prad_m…
    #> 12 Specimen Preservation Type   The method… STRING   FALSE            1        SPECIMEN_PRESERVAT… prad_m…
    #> 13 TMB (nonsynonymous)          TMB (nonsy… NUMBER   FALSE            1        TMB_NONSYNONYMOUS   prad_m…

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 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          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(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                 Male

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
    #> # ℹ 24 more rows