Shixiang Wang

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This vignette gives users the summary information of API functions provided by UCSCXenaTools for UCSC Xena.

Before using API, user should know some concepts about Xena elements. Following description is copied from xenaPython __init__.py.

Data rows are associated with “sample” IDs.

Sample IDs are unique within a “cohort”. s A “dataset” is a particular assay of a cohort, e.g. gene expression.

Datasets have associated metadata, specifying their data type and cohort.

There are three primary data types: dense matrix (samples by probes), sparse (sample, position, variant), and segmented (sample, position, value).

Dense matrices can be genotypic or phenotypic. Phenotypic matrices have associated field metadata (descriptive names, codes, etc.). Genotypic matricies may have an associated probeMap, which maps probes to genomic locations. If a matrix has hugo probeMap, the probes themselves are gene names. Otherwise, a probeMap is used to map a gene location to a set of probes.

New features

A new series of functions (mostly) starting with fetch_ have been introduced to help fetch a small amount of data by Xena APIs.

Now three are available:

They have similar arguments and all the details can be viewed by running ?fetch in R console after library(UCSCXenaTools).

API categories

API functions can be divided into two classes: lower API functions and higher API functions. They have following difference:

Lower API functions

Lower API functions also have 2 classes:

  • one is generated from .xq files, function names all start with .p_. All .xq files are copied from xenaPython package, which is official Python API for Xena. These functions are dynamicly created when UCSCXenaTools loaded. Their names are given as following:

    #>  [1] ".p_all_cohorts"                     ".p_all_datasets"                   
    #>  [3] ".p_all_datasets_n"                  ".p_all_field_metadata"             
    #>  [5] ".p_cohort_samples"                  ".p_cohort_summary"                 
    #>  [7] ".p_dataset_fetch"                   ".p_dataset_field"                  
    #>  [9] ".p_dataset_field_examples"          ".p_dataset_field_n"                
    #> [11] ".p_dataset_gene_probe_avg"          ".p_dataset_gene_probes_values"     
    #> [13] ".p_dataset_list"                    ".p_dataset_metadata"               
    #> [15] ".p_dataset_probe_signature"         ".p_dataset_probe_values"           
    #> [17] ".p_dataset_samples"                 ".p_dataset_samples_ndense_matrix"  
    #> [19] ".p_datasets_null_rows"              ".p_feature_list"                   
    #> [21] ".p_field_codes"                     ".p_field_metadata"                 
    #> [23] ".p_gene_transcripts"                ".p_match_fields"                   
    #> [25] ".p_probe_count"                     ".p_probemap_list"                  
    #> [27] ".p_ref_gene_exons"                  ".p_ref_gene_position"              
    #> [29] ".p_ref_gene_range"                  ".p_segment_data_examples"          
    #> [31] ".p_segmented_data_range"            ".p_sparse_data"                    
    #> [33] ".p_sparse_data_examples"            ".p_sparse_data_match_field"        
    #> [35] ".p_sparse_data_match_field_slow"    ".p_sparse_data_match_partial_field"
    #> [37] ".p_sparse_data_range"               ".p_transcript_expression"
  • the other one is created in package. The function names all start with ., are given as following:

    #> [1] ".host_cohorts"          ".cohort_datasets"       ".cohort_datasets_count"
    #> [4] ".cohort_samples_each"   ".cohort_samples_any"    ".cohort_samples_all"   
    #> [7] ".dataset_samples_each"  ".dataset_samples_any"   ".dataset_samples_all"

I don’t know how to write these query sentence for Xena Hubs. So here I want to say thanks to authors of xenaPython and xenaR packages.

API report

Of note, I don’t know test all functions generated from .xq files, most of them works. Sometimes functions return you errors or list() may caused by invaild format or bad network, you should try more times. If you make sure there are problems/errors in query procedure, you can check corresponding query variables:

#>  [1] ".xq_all_cohorts"                     ".xq_all_datasets"                   
#>  [3] ".xq_all_datasets_n"                  ".xq_all_field_metadata"             
#>  [5] ".xq_cohort_samples"                  ".xq_cohort_summary"                 
#>  [7] ".xq_dataset_fetch"                   ".xq_dataset_field"                  
#>  [9] ".xq_dataset_field_examples"          ".xq_dataset_field_n"                
#> [11] ".xq_dataset_gene_probe_avg"          ".xq_dataset_gene_probes_values"     
#> [13] ".xq_dataset_list"                    ".xq_dataset_metadata"               
#> [15] ".xq_dataset_probe_signature"         ".xq_dataset_probe_values"           
#> [17] ".xq_dataset_samples"                 ".xq_dataset_samples_ndense_matrix"  
#> [19] ".xq_datasets_null_rows"              ".xq_feature_list"                   
#> [21] ".xq_field_codes"                     ".xq_field_metadata"                 
#> [23] ".xq_gene_transcripts"                ".xq_match_fields"                   
#> [25] ".xq_probe_count"                     ".xq_probemap_list"                  
#> [27] ".xq_ref_gene_exons"                  ".xq_ref_gene_position"              
#> [29] ".xq_ref_gene_range"                  ".xq_segment_data_examples"          
#> [31] ".xq_segmented_data_range"            ".xq_sparse_data"                    
#> [33] ".xq_sparse_data_examples"            ".xq_sparse_data_match_field"        
#> [35] ".xq_sparse_data_match_field_slow"    ".xq_sparse_data_match_partial_field"
#> [37] ".xq_sparse_data_range"               ".xq_transcript_expression"

For example, you’d like to check .p_all_cohorts function, you can take a look at .xq_all_cohorts object.

.xq_all_cohorts
#> [1] ";allCohorts\n(fn [exclude]\n\t(map :cohort\n\t  (query\n\t\t{:select [[#sql/call [:distinct #sql/call [:ifnull :cohort \"(unassigned)\"]] :cohort]]\n\t\t :from [:dataset]\n\t\t :where [:not [:in :type exclude]]})))\n"

cat it may give you more easy-to-read format.

cat(.xq_all_cohorts)
#> ;allCohorts
#> (fn [exclude]
#>  (map :cohort
#>    (query
#>      {:select [[#sql/call [:distinct #sql/call [:ifnull :cohort "(unassigned)"]] :cohort]]
#>       :from [:dataset]
#>       :where [:not [:in :type exclude]]})))

Use cases

Several use cases are modified from README of xenaPython package.

Load package firstly.

library(UCSCXenaTools)

You can find out host id and dataset id from https://xenabrowser.net/datapages/, a more recommened way is use XenaData in UCSCXenaTools.

head(XenaData)[, 1:5]
#> # A tibble: 6 x 5
#>   XenaHosts      XenaHostNames XenaCohorts      XenaDatasets         SampleCount
#>   <chr>          <chr>         <chr>            <chr>                      <int>
#> 1 https://ucscp… publicHub     Breast Cancer C… ucsfNeve_public/ucs…          51
#> 2 https://ucscp… publicHub     Breast Cancer C… ucsfNeve_public/ucs…          57
#> 3 https://ucscp… publicHub     Glioma (Kotliar… kotliarov2006_publi…         194
#> 4 https://ucscp… publicHub     Glioma (Kotliar… kotliarov2006_publi…         194
#> 5 https://ucscp… publicHub     Lung Cancer CGH… weir2007_public/wei…         383
#> 6 https://ucscp… publicHub     Lung Cancer CGH… weir2007_public/wei…         383

The host id is stored at XenaHosts column, and dataset id is stored at XenaDatasets column.

Of note, when you want to query single sample or gene with function starts with .p_, you must transform id of sample or gene into a list

Query four samples and three identifers expression

hub = "https://toil.xenahubs.net"
dataset = "tcga_RSEM_gene_tpm"
samples = c("TCGA-02-0047-01", "TCGA-02-0055-01", "TCGA-02-2483-01", "TCGA-02-2485-01")
probes = c("ENSG00000282740.1", "ENSG00000000005.5", "ENSG00000000419.12")
.p_dataset_probe_values(hub, dataset, samples, probes)
#> [[1]]
#>   chrom chromstart  chromend strand
#> 1  chr1   16739938  16750589      -
#> 2 chr20   50934867  50958555      -
#> 3  chrX  100584802 100599885      +
#> 
#> [[2]]
#>        [,1]   [,2]   [,3]   [,4]
#> [1,] -9.966 -2.826 -9.966 -9.966
#> [2,] -3.171  4.165 -5.574 -3.171
#> [3,]  4.675  6.025  5.826  5.177

Query one probe. As metioned above, one must transform id of proble or sample int a list when he wants to query only one sample/probe.

Bad query:

.p_dataset_probe_values(hub, dataset, samples, "ENSG00000282740.1")
#> [[1]]
#> list()
#> 
#> [[2]]
#>       [,1] [,2] [,3] [,4]
#>  [1,]  NaN  NaN  NaN  NaN
#>  [2,]  NaN  NaN  NaN  NaN
#>  [3,]  NaN  NaN  NaN  NaN
#>  [4,]  NaN  NaN  NaN  NaN
#>  [5,]  NaN  NaN  NaN  NaN
#>  [6,]  NaN  NaN  NaN  NaN
#>  [7,]  NaN  NaN  NaN  NaN
#>  [8,]  NaN  NaN  NaN  NaN
#>  [9,]  NaN  NaN  NaN  NaN
#> [10,]  NaN  NaN  NaN  NaN
#> [11,]  NaN  NaN  NaN  NaN
#> [12,]  NaN  NaN  NaN  NaN
#> [13,]  NaN  NaN  NaN  NaN
#> [14,]  NaN  NaN  NaN  NaN
#> [15,]  NaN  NaN  NaN  NaN
#> [16,]  NaN  NaN  NaN  NaN
#> [17,]  NaN  NaN  NaN  NaN

Good query:

.p_dataset_probe_values(hub, dataset, samples, as.list("ENSG00000282740.1"))
#> [[1]]
#>   chrom chromstart chromend strand
#> 1  chr1   16739938 16750589      -
#> 
#> [[2]]
#>        [,1]   [,2]   [,3]   [,4]
#> [1,] -9.966 -2.826 -9.966 -9.966

Query four samples and three genes expression, when the dataset you want to query has a identifier-to-gene mapping

identifier-to-gene mapping (i.e. xena probeMap)

genes = c("TP53", "RB1", "PIK3CA")
.p_dataset_gene_probe_avg(hub, dataset, samples, genes)
#>     gene                      position                     scores
#> 1   TP53    chr17, 7661779, 7687550, - 5.799, 4.428, 6.515, 6.309
#> 2    RB1  chr13, 48303751, 48481986, + 5.867, 4.700, 4.810, 4.920
#> 3 PIK3CA chr3, 179148114, 179240093, + 3.547, 3.377, 2.789, 2.951

If the dataset does not have id-to-gene mapping, but the dataset used gene names as its identifier

In this situation, you can query gene expression like two ways above will not work.

hub = "https://toil.xenahubs.net"
dataset = "tcga_RSEM_Hugo_norm_count"
samples = c("TCGA-02-0047-01", "TCGA-02-0055-01", "TCGA-02-2483-01", "TCGA-02-2485-01")
probes = c("TP53", "RB1", "PIK3CA")

.p_dataset_probe_values(hub, dataset, samples, probes)
#> [[1]]
#>   chrom chromstart  chromend strand
#> 1 chr13   48303751  48481986      +
#> 2 chr17    7661779   7687550      -
#> 3  chr3  179148114 179240093      +
#> 
#> [[2]]
#>       [,1]  [,2]  [,3]  [,4]
#> [1,] 11.63 10.68 12.65 12.15
#> [2,] 12.04 10.93 11.59 11.41
#> [3,] 10.67 10.90 10.71 10.12

Find out the samples in a dataset

hub = "https://tcga.xenahubs.net"
dataset = "TCGA.BLCA.sampleMap/HiSeqV2"
.p_dataset_samples(hub, dataset, 10)
#>  [1] "TCGA-BT-A20R-11" "TCGA-DK-AA6S-01" "TCGA-DK-A6B2-01" "TCGA-GU-A763-01"
#>  [5] "TCGA-XF-A9T4-01" "TCGA-FD-A5C1-01" "TCGA-GU-A42Q-01" "TCGA-DK-A3IL-01"
#>  [9] "TCGA-XF-AAMH-01" "TCGA-FT-A61P-01"
# obtain all samples
.p_dataset_samples(hub, dataset, NULL) %>% head()
#> [1] "TCGA-BT-A20R-11" "TCGA-DK-AA6S-01" "TCGA-DK-A6B2-01" "TCGA-GU-A763-01"
#> [5] "TCGA-XF-A9T4-01" "TCGA-FD-A5C1-01"

Higher API function samples() has more features. It can be used to do set operation for samples in a host.

xe = XenaHub(cohorts = "Cancer Cell Line Encyclopedia (CCLE)")
# samples in each dataset, first host
x = samples(xe, by = "datasets", how = "each")[[1]]
lengths(x)  # data sets in ccle cohort on first (only) host

Find out the identifiers in a dataset

hub = "https://tcga.xenahubs.net"
dataset = "TCGA.BLCA.sampleMap/HiSeqV2"
.p_dataset_field(hub, dataset) %>% head()
#> [1] "?|100130426" "?|100133144" "?|100134869" "?|10357"     "?|10431"    
#> [6] "?|136542"

Find out the number of identifiers in a dataset

hub = "https://tcga.xenahubs.net"
dataset = "TCGA.BLCA.sampleMap/HiSeqV2"
.p_dataset_field_n(hub, dataset)
#> [1] 20531

LICENSE

GPL-3

Please note, code from XenaR package under Apache 2.0 license.