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Import dplyr in example
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‎R/query.R‎

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@@ -277,6 +277,7 @@ get_seurat <- function(...) {
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#' will not work.
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#' @export
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#' @examples
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#' library(dplyr)
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#' filtered_metadata <- get_metadata() |>
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#' filter(
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#' ethnicity == "African" &

‎README.md‎

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HCAquery
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readme
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================
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Load the package
@@ -16,182 +16,162 @@ Load the metadata
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``` r
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get_metadata()
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#> # Source: table<metadata> [?? x 56]
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#> # Database: sqlite 3.40.0 [/vast/scratch/users/milton.m/cache/hca_harmonised/metadata.sqlite]
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#> .cell sampl…¹ .sample .samp…² assay assay…³ file_…⁴ cell_…⁵ cell_…⁶ devel…⁷ devel…⁸ disease disea…⁹ ethni…˟ ethni…˟ file_id is_pr…˟ organ…˟ organ…˟ sampl…˟ sex sex_o…˟ tissue
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#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
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#> 1 AAACCT… 8a0fe0… 5f20d7… D17PrP… 10x … EFO:00… 1e334b… basal … CL:000… 31-yea… HsapDv… normal PATO:0… Europe… HANCES… 00d626… FALSE Homo s… NCBITa… <NA> male PATO:0… perip…
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#> 2 AAACCT… 8a0fe0… 5f20d7… D17PrP… 10x … EFO:00… 1e334b… basal … CL:000… 31-yea… HsapDv… normal PATO:0… Europe… HANCES… 00d626… FALSE Homo s… NCBITa… <NA> male PATO:0… perip…
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#> 3 AAACCT… 02eb2e… 5f20d7… D17PrP… 10x … EFO:00… 30f754… lumina… CL:000… 31-yea… HsapDv… normal PATO:0… Europe… HANCES… 00d626… FALSE Homo s… NCBITa… <NA> male PATO:0… perip…
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#> 4 AAACCT… 02eb2e… 5f20d7… D17PrP… 10x … EFO:00… 30f754… lumina… CL:000… 31-yea… HsapDv… normal PATO:0… Europe… HANCES… 00d626… FALSE Homo s… NCBITa… <NA> male PATO:0… perip…
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#> 5 AAACCT… 8a0fe0… 5f20d7… D17PrP… 10x … EFO:00… 1e334b… basal … CL:000… 31-yea… HsapDv… normal PATO:0… Europe… HANCES… 00d626… FALSE Homo s… NCBITa… <NA> male PATO:0… perip…
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#> 6 AAACCT… 8a0fe0… 5f20d7… D17PrP… 10x … EFO:00… 1e334b… basal … CL:000… 31-yea… HsapDv… normal PATO:0… Europe… HANCES… 00d626… FALSE Homo s… NCBITa… <NA> male PATO:0… perip…
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#> 7 AAACCT… 8a0fe0… 5f20d7… D17PrP… 10x … EFO:00… 1e334b… basal … CL:000… 31-yea… HsapDv… normal PATO:0… Europe… HANCES… 00d626… FALSE Homo s… NCBITa… <NA> male PATO:0… perip…
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#> 8 AAACGG… 8a0fe0… 5f20d7… D17PrP… 10x … EFO:00… 1e334b… basal … CL:000… 31-yea… HsapDv… normal PATO:0… Europe… HANCES… 00d626… FALSE Homo s… NCBITa… <NA> male PATO:0… perip…
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#> 9 AAACGG… 02eb2e… 5f20d7… D17PrP… 10x … EFO:00… 30f754… lumina… CL:000… 31-yea… HsapDv… normal PATO:0… Europe… HANCES… 00d626… FALSE Homo s… NCBITa… <NA> male PATO:0… perip…
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#> 10 AAACGG… 8a0fe0… 5f20d7… D17PrP… 10x … EFO:00… 1e334b… basal … CL:000… 31-yea… HsapDv… normal PATO:0… Europe… HANCES… 00d626… FALSE Homo s… NCBITa… <NA> male PATO:0… perip…
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#> # … with more rows, 33 more variables: tissue_ontology_term_id <chr>, tissue_harmonised <chr>, age_days <dbl>, dataset_id <chr>, collection_id <chr>, cell_count <int>,
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#> # dataset_deployments <chr>, is_primary_data.y <chr>, is_valid <int>, linked_genesets <int>, mean_genes_per_cell <dbl>, name <chr>, published <int>, revision <int>,
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#> # schema_version <chr>, tombstone <int>, x_normalization <chr>, created_at.x <dbl>, published_at <dbl>, revised_at <dbl>, updated_at.x <dbl>, filename <chr>, filetype <chr>,
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#> # s3_uri <chr>, user_submitted <int>, created_at.y <dbl>, updated_at.y <dbl>, cell_type_harmonised <chr>, confidence_class <dbl>, cell_annotation_azimuth_l2 <chr>,
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#> # cell_annotation_blueprint_singler <chr>, n_cell_type_in_tissue <int>, n_tissue_in_cell_type <int>, and abbreviated variable names ¹​sample_id_db, ²​.sample_name,
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#> # ³​assay_ontology_term_id, ⁴​file_id_db, ⁵​cell_type, ⁶​cell_type_ontology_term_id, ⁷​development_stage, ⁸​development_stage_ontology_term_id, ⁹​disease_ontology_term_id, ˟​ethnicity,
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#> # ˟​ethnicity_ontology_term_id, ˟​is_primary_data.x, ˟​organism, ˟​organism_ontology_term_id, ˟​sample_placeholder, ˟​sex_ontology_term_id
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```
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## # Source: table<metadata> [?? x 56]
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## # Database: sqlite 3.39.3 [/vast/projects/RCP/human_cell_atlas/metadata.sqlite]
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## .cell sampl…¹ .sample .samp…² assay assay…³ file_…⁴ cell_…⁵ cell_…⁶ devel…⁷
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## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
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## 1 AAACCT… 8a0fe0… 5f20d7… D17PrP… 10x … EFO:00… 1e334b… basal … CL:000… 31-yea…
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## 2 AAACCT… 8a0fe0… 5f20d7… D17PrP… 10x … EFO:00… 1e334b… basal … CL:000… 31-yea…
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## 3 AAACCT… 02eb2e… 5f20d7… D17PrP… 10x … EFO:00… 30f754… lumina… CL:000… 31-yea…
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## 4 AAACCT… 02eb2e… 5f20d7… D17PrP… 10x … EFO:00… 30f754… lumina… CL:000… 31-yea…
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## 5 AAACCT… 8a0fe0… 5f20d7… D17PrP… 10x … EFO:00… 1e334b… basal … CL:000… 31-yea…
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## 6 AAACCT… 8a0fe0… 5f20d7… D17PrP… 10x … EFO:00… 1e334b… basal … CL:000… 31-yea…
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## 7 AAACCT… 8a0fe0… 5f20d7… D17PrP… 10x … EFO:00… 1e334b… basal … CL:000… 31-yea…
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## 8 AAACGG… 8a0fe0… 5f20d7… D17PrP… 10x … EFO:00… 1e334b… basal … CL:000… 31-yea…
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## 9 AAACGG… 02eb2e… 5f20d7… D17PrP… 10x … EFO:00… 30f754… lumina… CL:000… 31-yea…
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## 10 AAACGG… 8a0fe0… 5f20d7… D17PrP… 10x … EFO:00… 1e334b… basal … CL:000… 31-yea…
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## # … with more rows, 46 more variables:
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## # development_stage_ontology_term_id <chr>, disease <chr>,
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## # disease_ontology_term_id <chr>, ethnicity <chr>,
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## # ethnicity_ontology_term_id <chr>, file_id <chr>, is_primary_data.x <chr>,
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## # organism <chr>, organism_ontology_term_id <chr>, sample_placeholder <chr>,
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## # sex <chr>, sex_ontology_term_id <chr>, tissue <chr>,
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## # tissue_ontology_term_id <chr>, tissue_harmonised <chr>, age_days <dbl>, …
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Explore the HCA content
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``` r
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get_metadata() |>
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distinct(tissue, file_id) |>
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count(tissue) |>
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arrange(desc(n))
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get_metadata() |>
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distinct(tissue, file_id) |>
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count(tissue) |>
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arrange(desc(n))
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#> # Source: SQL [?? x 2]
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#> # Database: sqlite 3.40.0 [/vast/scratch/users/milton.m/cache/hca_harmonised/metadata.sqlite]
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#> # Ordered by: desc(n)
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#> tissue n
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#> <chr> <int>
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#> 1 blood 47
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#> 2 heart left ventricle 46
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#> 3 cortex of kidney 31
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#> 4 renal medulla 29
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#> 5 lung 27
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#> 6 middle temporal gyrus 24
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#> 7 liver 24
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#> 8 kidney 19
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#> 9 intestine 18
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#> 10 thymus 17
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#> # … with more rows
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```
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## # Source: SQL [?? x 2]
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## # Database: sqlite 3.39.3 [/vast/projects/RCP/human_cell_atlas/metadata.sqlite]
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## # Ordered by: desc(n)
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## tissue n
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## <chr> <int>
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## 1 blood 47
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## 2 heart left ventricle 46
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## 3 cortex of kidney 31
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## 4 renal medulla 29
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## 5 lung 27
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## 6 middle temporal gyrus 24
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## 7 liver 24
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## 8 kidney 19
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## 9 intestine 18
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## 10 thymus 17
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## # … with more rows
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Query raw counts
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``` r
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sce =
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get_metadata() |>
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filter(
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ethnicity == "African" &
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assay %LIKE% "%10x%" &
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tissue == "lung parenchyma" &
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cell_type %LIKE% "%CD4%"
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) |>
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get_SingleCellExperiment()
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```
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## Reading 1 files.
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## .
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sce <-
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get_metadata() |>
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filter(
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ethnicity == "African" &
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assay %LIKE% "%10x%" &
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tissue == "lung parenchyma" &
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cell_type %LIKE% "%CD4%"
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) |>
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get_SingleCellExperiment()
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#> ℹ Realising metadata.
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#> ℹ Synchronising files
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#> ℹ Attaching metadata.
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#> ℹ Compiling Single Cell Experiment.
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``` r
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sce
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#> class: SingleCellExperiment
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#> dim: 60661 1571
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#> metadata(0):
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#> assays(2): counts cpm
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#> rownames(60661): TSPAN6 TNMD ... RP11-175I6.6 PRSS43P
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#> rowData names(0):
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#> colnames(1571): ACAGCCGGTCCGTTAA_F02526 GGGAATGAGCCCAGCT_F02526 ... TACAACGTCAGCATTG_SC84 CATTCGCTCAATACCG_F02526
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#> colData names(55): sample_id_db .sample ... n_cell_type_in_tissue n_tissue_in_cell_type
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#> reducedDimNames(0):
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#> mainExpName: NULL
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#> altExpNames(0):
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```
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## class: SingleCellExperiment
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## dim: 60661 1571
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## metadata(0):
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## assays(1): counts
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## rownames(60661): TSPAN6 TNMD ... RP11-175I6.6 PRSS43P
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## rowData names(0):
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## colnames(1571): ACAGCCGGTCCGTTAA_F02526 GGGAATGAGCCCAGCT_F02526 ...
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## TACAACGTCAGCATTG_SC84 CATTCGCTCAATACCG_F02526
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## colData names(55): sample_id_db .sample ... n_cell_type_in_tissue
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## n_tissue_in_cell_type
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## reducedDimNames(0):
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## mainExpName: NULL
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## altExpNames(0):
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Query counts scaled per million. This is helpful if just few genes are
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of interest
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``` r
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sce =
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get_metadata() |>
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filter(
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ethnicity == "African" &
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assay %LIKE% "%10x%" &
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tissue == "lung parenchyma" &
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cell_type %LIKE% "%CD4%"
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) |>
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get_SingleCellExperiment(assay = "counts_per_million")
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```
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## Reading 1 files.
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## .
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sce <-
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get_metadata() |>
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filter(
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ethnicity == "African" &
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assay %LIKE% "%10x%" &
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tissue == "lung parenchyma" &
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cell_type %LIKE% "%CD4%"
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) |>
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get_SingleCellExperiment(assays = "cpm")
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#> ℹ Realising metadata.
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#> ℹ Synchronising files
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#> ℹ Attaching metadata.
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#> ℹ Compiling Single Cell Experiment.
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``` r
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sce
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#> class: SingleCellExperiment
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#> dim: 60661 1571
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#> metadata(0):
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#> assays(1): cpm
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#> rownames(60661): TSPAN6 TNMD ... RP11-175I6.6 PRSS43P
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#> rowData names(0):
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#> colnames(1571): ACAGCCGGTCCGTTAA_F02526 GGGAATGAGCCCAGCT_F02526 ... TACAACGTCAGCATTG_SC84 CATTCGCTCAATACCG_F02526
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#> colData names(55): sample_id_db .sample ... n_cell_type_in_tissue n_tissue_in_cell_type
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#> reducedDimNames(0):
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#> mainExpName: NULL
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#> altExpNames(0):
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```
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## class: SingleCellExperiment
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## dim: 60661 1571
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## metadata(0):
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## assays(1): counts_per_million
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## rownames(60661): TSPAN6 TNMD ... RP11-175I6.6 PRSS43P
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## rowData names(0):
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## colnames(1571): ACAGCCGGTCCGTTAA_F02526 GGGAATGAGCCCAGCT_F02526 ...
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## TACAACGTCAGCATTG_SC84 CATTCGCTCAATACCG_F02526
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## colData names(55): sample_id_db .sample ... n_cell_type_in_tissue
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## n_tissue_in_cell_type
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## reducedDimNames(0):
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## mainExpName: NULL
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## altExpNames(0):
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Extract only a subset of genes:
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``` r
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get_metadata() |>
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get_metadata() |>
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filter(
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ethnicity == "African" &
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assay %LIKE% "%10x%" &
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tissue == "lung parenchyma" &
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ethnicity == "African" &
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assay %LIKE% "%10x%" &
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tissue == "lung parenchyma" &
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cell_type %LIKE% "%CD4%"
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) |>
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get_SingleCellExperiment(genes = "PUM1")
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) |>
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get_SingleCellExperiment(features = "PUM1")
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#> ℹ Realising metadata.
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#> ℹ Synchronising files
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#> ℹ Attaching metadata.
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#> ℹ Compiling Single Cell Experiment.
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#> class: SingleCellExperiment
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#> dim: 1 1571
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#> metadata(0):
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#> assays(2): counts cpm
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#> rownames(1): PUM1
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#> rowData names(0):
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#> colnames(1571): ACAGCCGGTCCGTTAA_F02526 GGGAATGAGCCCAGCT_F02526 ... TACAACGTCAGCATTG_SC84 CATTCGCTCAATACCG_F02526
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#> colData names(55): sample_id_db .sample ... n_cell_type_in_tissue n_tissue_in_cell_type
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#> reducedDimNames(0):
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#> mainExpName: NULL
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#> altExpNames(0):
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```
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## Reading 1 files.
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## .
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## class: SingleCellExperiment
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## dim: 1 1571
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## metadata(0):
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## assays(1): counts
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## rownames(1): PUM1
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## rowData names(0):
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## colnames(1571): ACAGCCGGTCCGTTAA_F02526 GGGAATGAGCCCAGCT_F02526 ...
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## TACAACGTCAGCATTG_SC84 CATTCGCTCAATACCG_F02526
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## colData names(55): sample_id_db .sample ... n_cell_type_in_tissue
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## n_tissue_in_cell_type
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## reducedDimNames(0):
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## mainExpName: NULL
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## altExpNames(0):
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Extract the counts as a Seurat object:
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``` r
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get_metadata() |>
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get_metadata() |>
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filter(
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ethnicity == "African" &
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assay %LIKE% "%10x%" &
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tissue == "lung parenchyma" &
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ethnicity == "African" &
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assay %LIKE% "%10x%" &
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tissue == "lung parenchyma" &
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cell_type %LIKE% "%CD4%"
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) |>
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get_seurat()
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) |>
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get_seurat()
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#> ℹ Realising metadata.
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#> ℹ Synchronising files
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#> ℹ Attaching metadata.
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#> ℹ Compiling Single Cell Experiment.
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#> Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')
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#> An object of class Seurat
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#> 60661 features across 1571 samples within 1 assay
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#> Active assay: originalexp (60661 features, 0 variable features)
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```
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## Reading 1 files.
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## .
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## Warning: Feature names cannot have underscores ('_'), replacing with dashes
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## ('-')
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## An object of class Seurat
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## 60661 features across 1571 samples within 1 assay
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## Active assay: originalexp (60661 features, 0 variable features)

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