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‎README.md‎

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CuratedAtlasQueryR
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================
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``` r
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find_figure <- function(names){
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rprojroot::find_package_root_file() |>
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file.path("man", "figures", names)
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}
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```
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<!-- badges: start -->
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[![Lifecycle:maturing](https://img.shields.io/badge/lifecycle-maturing-blue.svg)](https://www.tidyverse.org/lifecycle/#maturing)
@@ -11,11 +18,9 @@ exploration and retrieval of the harmonised, curated and reannotated
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CELLxGENE single-cell human cell atlas. Data can be retrieved at cell,
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sample, or dataset levels based on filtering criteria.
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<img src="man/figures/logo.png" width="120x" height="139px" />
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<img src="man/figures/svcf_logo.jpeg" width="155x" height="58px" /><img src="man/figures/czi_logo.png" width="129px" height="58px" /><img src="man/figures/bioconductor_logo.jpg" width="202px" height="58px" /><img src="man/figures/vca_logo.png" width="219px" height="58px" /><img src="man/figures/nectar_logo.png" width="180px" height="58px" />
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<img src="../man/figures/logo.png" width="120x" height="139px" />
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[website](https://stemangiola.github.io/CuratedAtlasQueryR)
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<img src="../man/figures/svcf_logo.jpeg" width="155x" height="58px" /><img src="../man/figures/czi_logo.png" width="129px" height="58px" /><img src="../man/figures/bioconductor_logo.jpg" width="202px" height="58px" /><img src="../man/figures/vca_logo.png" width="219px" height="58px" /><img src="../man/figures/nectar_logo.png" width="180px" height="58px" />
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# Query interface
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### Load the metadata
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``` r
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metadata = get_metadata()
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metadata
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#> # Source: table</vast/scratch/users/milton.m/cache/R/CuratedAtlasQueryR/metadata.0.2.3.parquet> [?? x 56]
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#> # Database: DuckDB 0.6.2-dev1166 [unknown@Linux 3.10.0-1160.81.1.el7.x86_64:R 4.2.1/:memory:]
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#> cell_ sample_ cell_…¹ cell_…² confi…³ cell_…⁴ cell_…⁵ cell_…⁶ sampl…⁷ _samp…⁸
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#> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr>
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#> 1 AAAC… 689e2f… basal … basal_… 1 <NA> <NA> <NA> f297c7… D17PrP…
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#> 2 AAAC… 689e2f… basal … basal_… 1 <NA> <NA> <NA> f297c7… D17PrP…
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#> 3 AAAC… 689e2f… lumina… lumina… 1 <NA> <NA> <NA> 930938… D17PrP…
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#> 4 AAAC… 689e2f… lumina… lumina… 1 <NA> <NA> <NA> 930938… D17PrP…
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#> 5 AAAC… 689e2f… basal … basal_… 1 <NA> <NA> <NA> f297c7… D17PrP…
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#> 6 AAAC… 689e2f… basal … basal_… 1 <NA> <NA> <NA> f297c7… D17PrP…
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#> 7 AAAC… 689e2f… basal … basal_… 1 <NA> <NA> <NA> f297c7… D17PrP…
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#> 8 AAAC… 689e2f… basal … basal_… 1 <NA> <NA> <NA> f297c7… D17PrP…
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#> 9 AAAC… 689e2f… lumina… lumina… 1 <NA> <NA> <NA> 930938… D17PrP…
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#> 10 AAAC… 689e2f… basal … basal_… 1 <NA> <NA> <NA> f297c7… D17PrP…
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#> # … with more rows, 46 more variables: assay <chr>,
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#> # assay_ontology_term_id <chr>, file_id_db <chr>,
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#> # cell_type_ontology_term_id <chr>, development_stage <chr>,
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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>, experiment___ <chr>, file_id <chr>,
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#> # is_primary_data_x <chr>, organism <chr>, organism_ontology_term_id <chr>, …
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metadata <- get_metadata()
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```
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### Explore the number of datasets per tissue
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The `metadata` variable can then be re-used for all subsequent queries.
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### Explore the tissue
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``` r
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metadata |>
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dplyr::distinct(tissue, dataset_id) |>
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dplyr::count(tissue)
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#> # Source: SQL [?? x 2]
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#> # Database: DuckDB 0.6.2-dev1166 [unknown@Linux 3.10.0-1160.81.1.el7.x86_64:R 4.2.1/:memory:]
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#> tissue n
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#> <chr> <dbl>
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#> 1 cerebellum 3
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#> 2 telencephalon 2
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#> 3 heart 3
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#> 4 intestine 18
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#> 5 kidney 19
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#> 6 liver 24
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#> 7 lung 27
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#> 8 muscle organ 3
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#> 9 pancreas 5
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#> 10 placenta 3
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dplyr::distinct(tissue, file_id)
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```
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``` r
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#> # Source: SQL [?? x 2]
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#> # Database: sqlite 3.40.0 [[email protected]:5432/metadata]
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#> # Ordered by: desc(n)
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#> tissue n
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#> <chr> <int64>
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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 liver 24
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#> 7 middle temporal gyrus 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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### Query raw counts
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``` r
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single_cell_counts =
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metadata |>
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dplyr::filter(
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stringr::str_like(cell_type, "%CD4%")
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) |>
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get_SingleCellExperiment()
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#> ! This function name is deprecated. Please use `get_single_cell_experiment()` instead
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#> ℹ Realising metadata.
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#> ℹ Synchronising files
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#> ℹ Downloading 0 files, totalling 0 GB
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#> ℹ Reading files.
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#> ℹ Compiling Single Cell Experiment.
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#> assays(1): counts
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#> rownames(36229): A1BG A1BG-AS1 ... ZZEF1 ZZZ3
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#> rowData names(0):
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#> colnames(1571): ACAGCCGGTCCGTTAA_F02526_1 GGGAATGAGCCCAGCT_F02526_1 ...
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#> TACAACGTCAGCATTG_SC84_1 CATTCGCTCAATACCG_F02526_1
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#> colnames(1571): ACAGCCGGTCCGTTAA_F02526_1 GGGAATGAGCCCAGCT_F02526_1 ... TACAACGTCAGCATTG_SC84_1
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#> CATTCGCTCAATACCG_F02526_1
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#> colData names(56): sample_ cell_type ... updated_at_y original_cell_id
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#> reducedDimNames(0):
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#> mainExpName: NULL
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stringr::str_like(cell_type, "%CD4%")
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) |>
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get_SingleCellExperiment(assays = "cpm")
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#> ! This function name is deprecated. Please use `get_single_cell_experiment()` instead
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#> ℹ Realising metadata.
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#> ℹ Synchronising files
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#> ℹ Downloading 0 files, totalling 0 GB
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#> ℹ Reading files.
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#> ℹ Compiling Single Cell Experiment.
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#> assays(1): cpm
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#> rownames(36229): A1BG A1BG-AS1 ... ZZEF1 ZZZ3
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#> rowData names(0):
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#> colnames(1571): ACAGCCGGTCCGTTAA_F02526_1 GGGAATGAGCCCAGCT_F02526_1 ...
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#> TACAACGTCAGCATTG_SC84_1 CATTCGCTCAATACCG_F02526_1
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#> colnames(1571): ACAGCCGGTCCGTTAA_F02526_1 GGGAATGAGCCCAGCT_F02526_1 ... TACAACGTCAGCATTG_SC84_1
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#> CATTCGCTCAATACCG_F02526_1
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#> colData names(56): sample_ cell_type ... updated_at_y original_cell_id
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#> reducedDimNames(0):
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#> mainExpName: NULL
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stringr::str_like(cell_type, "%CD4%")
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get_SingleCellExperiment(assays = "cpm", features = "PUM1")
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#> ! This function name is deprecated. Please use `get_single_cell_experiment()` instead
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#> ℹ Realising metadata.
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#> ℹ Synchronising files
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#> ℹ Downloading 0 files, totalling 0 GB
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#> ℹ Reading files.
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#> ℹ Compiling Single Cell Experiment.
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#> assays(1): cpm
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#> rownames(1): PUM1
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#> rowData names(0):
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#> colnames(1571): ACAGCCGGTCCGTTAA_F02526_1 GGGAATGAGCCCAGCT_F02526_1 ...
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#> TACAACGTCAGCATTG_SC84_1 CATTCGCTCAATACCG_F02526_1
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#> colnames(1571): ACAGCCGGTCCGTTAA_F02526_1 GGGAATGAGCCCAGCT_F02526_1 ... TACAACGTCAGCATTG_SC84_1
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#> CATTCGCTCAATACCG_F02526_1
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#> colData names(56): sample_ cell_type ... updated_at_y original_cell_id
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#> reducedDimNames(0):
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#> mainExpName: NULL
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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 Downloading 0 files, totalling 0 GB
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#> ℹ Reading files.
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#> ℹ Compiling Single Cell Experiment.
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geom_jitter(shape=".")
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```
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<img src="man/figures/HLA_A_disease_plot.png" width="525" />
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<img src="../man/figures/HLA_A_disease_plot.png" width="525" />
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``` r
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geom_jitter(shape=".")
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```
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<img src="man/figures/HLA_A_tissue_plot.png" width="525" />
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<img src="../man/figures/HLA_A_tissue_plot.png" width="525" />
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## Obtain Unharmonised Metadata
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harmonised <- get_metadata() |> dplyr::filter(tissue == "kidney blood vessel")
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unharmonised <- get_unharmonised_metadata(harmonised)
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unharmonised
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#> # A tibble: 4 × 2
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#> file_id unharmonised
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#> <chr> <list>
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#> 1 63523aa3-0d04-4fc6-ac59-5cadd3e73a14 <tbl_dck_[,17]>
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#> 2 8fee7b82-178b-4c04-bf23-04689415690d <tbl_dck_[,12]>
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#> 3 dc9d8cdd-29ee-4c44-830c-6559cb3d0af6 <tbl_dck_[,14]>
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#> 4 f7e94dbb-8638-4616-aaf9-16e2212c369f <tbl_dck_[,14]>
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```
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Notice that the columns differ between each dataset’s data frame:
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``` r
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dplyr::pull(unharmonised, unharmonised) |> head(2)
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#> [[1]]
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#> # Source: SQL [?? x 17]
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#> # Database: DuckDB 0.6.2-dev1166 [unknown@Linux 3.10.0-1160.81.1.el7.x86_64:R 4.2.1/:memory:]
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#> cell_ file_id donor…¹ donor…² libra…³ mappe…⁴ sampl…⁵ suspe…⁶ suspe…⁷ autho…⁸
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#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
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#> 1 4602… 63523a… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic…
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#> 2 4602… 63523a… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic…
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#> 3 4602… 63523a… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic…
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#> 4 4602… 63523a… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic…
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#> 5 4602… 63523a… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic…
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#> 6 4602… 63523a… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic…
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#> 7 4602… 63523a… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic…
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#> 8 4602… 63523a… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic…
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#> 9 4602… 63523a… 19 mon… 463181… 671785… GENCOD… 125234… cell c7485e… CD4 T …
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#> 10 4602… 63523a… 27 mon… a8536b… 5ddaea… GENCOD… 61bf84… cell d8a44f… Pelvic…
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#> # … with more rows, 7 more variables: cell_state <chr>,
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#> # reported_diseases <chr>, Short_Sample <chr>, Project <chr>,
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#> # Experiment <chr>, compartment <chr>, broad_celltype <chr>, and abbreviated
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#> # variable names ¹​donor_age, ²​donor_uuid, ³​library_uuid,
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#> # ⁴​mapped_reference_annotation, ⁵​sample_uuid, ⁶​suspension_type,
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#> # ⁷​suspension_uuid, ⁸​author_cell_type
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#>
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#> [[2]]
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#> # Source: SQL [?? x 12]
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#> # Database: DuckDB 0.6.2-dev1166 [unknown@Linux 3.10.0-1160.81.1.el7.x86_64:R 4.2.1/:memory:]
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#> cell_ file_id orig.…¹ nCoun…² nFeat…³ seura…⁴ Project donor…⁵ compa…⁶ broad…⁷
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#> <chr> <chr> <chr> <dbl> <chr> <chr> <chr> <chr> <chr> <chr>
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#> 1 1069 8fee7b… 4602ST… 16082 3997 25 Experi… Wilms3 non_PT Pelvic…
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#> 2 1214 8fee7b… 4602ST… 1037 606 25 Experi… Wilms3 non_PT Pelvic…
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#> 3 2583 8fee7b… 4602ST… 3028 1361 25 Experi… Wilms3 non_PT Pelvic…
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#> 4 2655 8fee7b… 4602ST… 1605 859 25 Experi… Wilms3 non_PT Pelvic…
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#> 5 3609 8fee7b… 4602ST… 1144 682 25 Experi… Wilms3 non_PT Pelvic…
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#> 6 3624 8fee7b… 4602ST… 1874 963 25 Experi… Wilms3 non_PT Pelvic…
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#> 7 3946 8fee7b… 4602ST… 1296 755 25 Experi… Wilms3 non_PT Pelvic…
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#> 8 5163 8fee7b… 4602ST… 11417 3255 25 Experi… Wilms3 non_PT Pelvic…
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#> 9 5446 8fee7b… 4602ST… 1769 946 19 Experi… Wilms2 lympho… CD4 T …
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#> 10 6275 8fee7b… 4602ST… 3750 1559 25 Experi… Wilms3 non_PT Pelvic…
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#> # … with more rows, 2 more variables: author_cell_type <chr>, Sample <chr>, and
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#> # abbreviated variable names ¹​orig.ident, ²​nCount_RNA, ³​nFeature_RNA,
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#> # ⁴​seurat_clusters, ⁵​donor_id, ⁶​compartment, ⁷​broad_celltype
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```
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# Cell metadata
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- `sample_id_db`: Sample subdivision for internal use
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- `file_id_db`: File subdivision for internal use
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- `sample_`: Sample ID
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- `sample_name`: How samples were defined
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- `.sample_name`: How samples were defined
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# RNA abundance
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`x_normalization` column.
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The `cpm` assay includes counts per million.
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# Installation and getting-started problems
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**Problem:** Default R cache path including non-standard characters
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(e.g. dash)
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``` r
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get_metadata()
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# Error in `db_query_fields.DBIConnection()`:
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# ! Can't query fields.
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# Caused by error:
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# ! Parser Error: syntax error at or near "/"
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# LINE 2: FROM /Users/bob/Library/Cach...
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```
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**Solution:** Setup custom cache path (e.g. user home directory)
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``` r
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get_metadata(cache_directory = path.expand('~'))
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```
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**Problem:** namespace ‘dbplyr’ 2.2.1 is being loaded, but \>= 2.3.0 is
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required
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**Solution:** Install new dbplyr
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``` r
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install.packages("dbplyr")
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```
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------------------------------------------------------------------------
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This project has been funded by
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- *Silicon Valley Foundation* CZF2019-002443
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- *Bioconductor core funding* NIH NHGRI 5U24HG004059-18
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- *Victoria Cancer Agency* ECRF21036
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- *Australian National Health and Medical Research Council* 1116955
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- *The Lorenzo and Pamela Galli Medical Research Trust*

‎vignettes/Introduction.Rmd‎

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```{r, eval=FALSE, echo=FALSE}
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# Note: knit this to the repo readme file using:
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rmarkdown::render("Introduction.Rmd", output_format = "github_document", output_dir = getwd() |> dirname())
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rmarkdown::render(
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"Introduction.Rmd",
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output_file = "README.md",
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output_format = "github_document",
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output_dir = getwd() |> dirname()
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)
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```
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```{r}

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