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‎README.md‎

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CuratedAtlasQueryR
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================
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#> here() starts at /stornext/Bioinf/data/bioinf-data/Papenfuss_lab/projects/mangiola.s/PostDoc/CuratedAtlasQueryR
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<img src="../inst/logo.png" width="120px" height="139px" />
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## Load the package
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``` r
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library(CuratedAtlasQueryR)
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library(dplyr)
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library(stringr)
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```
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## Load and explore the metadata
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### 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 [/stornext/Home/data/allstaff/m/mangiola.s/.cache/R/CuratedAtlasQueryR/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
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#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
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#> 1 AAACCTGA… 8a0fe0… 5f20d7… D17PrP… 10x … EFO:00… 1e334b… basal … CL:000… 31-yea… HsapDv… normal PATO:0… Europe… HANCES… 00d626…
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#> 2 AAACCTGA… 8a0fe0… 5f20d7… D17PrP… 10x … EFO:00… 1e334b… basal … CL:000… 31-yea… HsapDv… normal PATO:0… Europe… HANCES… 00d626…
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#> 3 AAACCTGC… 02eb2e… 5f20d7… D17PrP… 10x … EFO:00… 30f754… lumina… CL:000… 31-yea… HsapDv… normal PATO:0… Europe… HANCES… 00d626…
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#> 4 AAACCTGC… 02eb2e… 5f20d7… D17PrP… 10x … EFO:00… 30f754… lumina… CL:000… 31-yea… HsapDv… normal PATO:0… Europe… HANCES… 00d626…
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#> 5 AAACCTGG… 8a0fe0… 5f20d7… D17PrP… 10x … EFO:00… 1e334b… basal … CL:000… 31-yea… HsapDv… normal PATO:0… Europe… HANCES… 00d626…
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#> 6 AAACCTGT… 8a0fe0… 5f20d7… D17PrP… 10x … EFO:00… 1e334b… basal … CL:000… 31-yea… HsapDv… normal PATO:0… Europe… HANCES… 00d626…
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#> 7 AAACCTGT… 8a0fe0… 5f20d7… D17PrP… 10x … EFO:00… 1e334b… basal … CL:000… 31-yea… HsapDv… normal PATO:0… Europe… HANCES… 00d626…
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#> 8 AAACGGGA… 8a0fe0… 5f20d7… D17PrP… 10x … EFO:00… 1e334b… basal … CL:000… 31-yea… HsapDv… normal PATO:0… Europe… HANCES… 00d626…
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#> 9 AAACGGGA… 02eb2e… 5f20d7… D17PrP… 10x … EFO:00… 30f754… lumina… CL:000… 31-yea… HsapDv… normal PATO:0… Europe… HANCES… 00d626…
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#> 10 AAACGGGA… 8a0fe0… 5f20d7… D17PrP… 10x … EFO:00… 1e334b… basal … CL:000… 31-yea… HsapDv… normal PATO:0… Europe… HANCES… 00d626…
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#> # … with more rows, 40 more variables: is_primary_data.x <chr>, organism <chr>, organism_ontology_term_id <chr>,
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#> # sample_placeholder <chr>, sex <chr>, sex_ontology_term_id <chr>, tissue <chr>, tissue_ontology_term_id <chr>,
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#> # tissue_harmonised <chr>, age_days <dbl>, dataset_id <chr>, collection_id <chr>, cell_count <int>, dataset_deployments <chr>,
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#> # is_primary_data.y <chr>, is_valid <int>, linked_genesets <int>, mean_genes_per_cell <dbl>, name <chr>, published <int>,
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#> # revision <int>, schema_version <chr>, tombstone <int>, x_normalization <chr>, created_at.x <dbl>, published_at <dbl>,
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#> # revised_at <dbl>, updated_at.x <dbl>, filename <chr>, filetype <chr>, s3_uri <chr>, user_submitted <int>, created_at.y <dbl>,
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#> # updated_at.y <dbl>, cell_type_harmonised <chr>, confidence_class <dbl>, cell_annotation_azimuth_l2 <chr>, …
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```
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### Explore the tissue
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``` r
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get_metadata() |>
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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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## Download single-cell RNA sequencing counts
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### Query raw counts
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``` r
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single_cell_counts =
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get_metadata() |>
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dplyr::filter(
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ethnicity == "African" &
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stringr::str_like(assay, "%10x%") &
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tissue == "lung parenchyma" &
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stringr::str_like(cell_type, "%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 Reading files.
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#> ℹ Compiling Single Cell Experiment.
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single_cell_counts
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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_1 GGGAATGAGCCCAGCT_F02526_1 ... TACAACGTCAGCATTG_SC84_1
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#> CATTCGCTCAATACCG_F02526_1
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#> colData names(56): sample_id_db .sample ... n_tissue_in_cell_type original_cell_id
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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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### Query counts scaled per million
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This is helpful if just few genes are of interest, as they can be
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compared across samples.
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``` r
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single_cell_counts =
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get_metadata() |>
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dplyr::filter(
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ethnicity == "African" &
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stringr::str_like(assay, "%10x%") &
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tissue == "lung parenchyma" &
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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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#> ℹ Realising metadata.
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#> ℹ Synchronising files
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#> ℹ Reading files.
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#> ℹ Compiling Single Cell Experiment.
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single_cell_counts
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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_1 GGGAATGAGCCCAGCT_F02526_1 ... TACAACGTCAGCATTG_SC84_1
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#> CATTCGCTCAATACCG_F02526_1
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#> colData names(56): sample_id_db .sample ... n_tissue_in_cell_type original_cell_id
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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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### Extract only a subset of genes
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``` r
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single_cell_counts =
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get_metadata() |>
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dplyr::filter(
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ethnicity == "African" &
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stringr::str_like(assay, "%10x%") &
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tissue == "lung parenchyma" &
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stringr::str_like(cell_type, "%CD4%")
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) |>
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get_SingleCellExperiment(assays = "cpm", features = "PUM1")
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#> ℹ Realising metadata.
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#> ℹ Synchronising files
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#> ℹ Reading files.
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#> ℹ Compiling Single Cell Experiment.
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single_cell_counts
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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): 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 ... TACAACGTCAGCATTG_SC84_1
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#> CATTCGCTCAATACCG_F02526_1
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#> colData names(56): sample_id_db .sample ... n_tissue_in_cell_type original_cell_id
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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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### Extract the counts as a Seurat object
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This convert the H5 SingleCellExperiment to Seurat so it might take long
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time and occupy a lot of memory dependeing on how many cells you are
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requesting.
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``` r
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single_cell_counts =
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get_metadata() |>
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dplyr::filter(
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ethnicity == "African" &
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stringr::str_like(assay, "%10x%") &
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tissue == "lung parenchyma" &
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stringr::str_like(cell_type, "%CD4%")
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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 Reading files.
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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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single_cell_counts
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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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## Visualise gene transcription
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We can gather all natural killer cells and plot the distribution of CD56
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(NCAM1) across all tissues
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``` r
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library(tidySingleCellExperiment)
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library(ggplot2)
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get_metadata() |>
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# Filter and subset
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filter(cell_type_harmonised=="nk") |>
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select(.cell, file_id_db, disease, file_id, tissue_harmonised) |>
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# Get counts per million for NCAM1 gene
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get_SingleCellExperiment(assays = "cpm", features = "NCAM1") |>
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# Get transcriptional abundance for plotting with `tidySingleCellExperiment`
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join_features("NCAM1", shape = "wide") |>
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# Plot
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ggplot(aes( tissue_harmonised, NCAM1,color = file_id)) +
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geom_jitter(shape=".") +
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# Style
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guides(color="none") +
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scale_y_log10() +
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theme_bw() +
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theme(axis.text.x = element_text(angle = 60, vjust = 1, hjust = 1))
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```
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<img src="../inst/NCAM1_figure.png" width="629" />
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``` r
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sessionInfo()
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#> R version 4.2.0 (2022-04-22)
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#> Platform: x86_64-pc-linux-gnu (64-bit)
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#> Running under: CentOS Linux 7 (Core)
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#>
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#> Matrix products: default
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#> BLAS: /stornext/System/data/apps/R/R-4.2.0/lib64/R/lib/libRblas.so
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#> LAPACK: /stornext/System/data/apps/R/R-4.2.0/lib64/R/lib/libRlapack.so
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#>
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#> locale:
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#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
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#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C
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#> [9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
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#>
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#> attached base packages:
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#> [1] stats graphics grDevices utils datasets methods base
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#>
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#> other attached packages:
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#> [1] stringr_1.5.0 dplyr_1.1.0 CuratedAtlasQueryR_0.1.0 dbplyr_2.3.0 here_1.0.1
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#>
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#> loaded via a namespace (and not attached):
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#> [1] plyr_1.8.8 igraph_1.3.5 lazyeval_0.2.2 sp_1.5-1
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#> [5] splines_4.2.0 listenv_0.9.0 scattermore_0.8 GenomeInfoDb_1.34.7
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#> [9] ggplot2_3.4.0 inline_0.3.19 digest_0.6.31 htmltools_0.5.4
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#> [13] fansi_1.0.4 magrittr_2.0.3 memoise_2.0.1 tensor_1.5
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#> [17] cluster_2.1.4 ROCR_1.0-11 globals_0.16.2 RcppParallel_5.1.6
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#> [21] matrixStats_0.63.0 spatstat.sparse_3.0-0 prettyunits_1.1.1 colorspace_2.1-0
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#> [25] blob_1.2.3 ggrepel_0.9.2 xfun_0.36 callr_3.7.3
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#> [29] crayon_1.5.2 RCurl_1.98-1.9 jsonlite_1.8.4 progressr_0.13.0
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#> [33] spatstat.data_3.0-0 survival_3.5-0 zoo_1.8-11 glue_1.6.2
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#> [37] polyclip_1.10-4 gtable_0.3.1 zlibbioc_1.44.0 XVector_0.38.0
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#> [41] leiden_0.4.3 DelayedArray_0.24.0 V8_4.2.2 pkgbuild_1.4.0
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#> [45] Rhdf5lib_1.20.0 rstan_2.26.6 SingleCellExperiment_1.20.0 future.apply_1.10.0
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#> [49] BiocGenerics_0.44.0 HDF5Array_1.26.0 abind_1.4-5 scales_1.2.1
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#> [53] DBI_1.1.3 spatstat.random_3.0-1 miniUI_0.1.1.1 Rcpp_1.0.10
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#> [57] viridisLite_0.4.1 xtable_1.8-4 reticulate_1.27 bit_4.0.5
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#> [61] stats4_4.2.0 StanHeaders_2.26.6 htmlwidgets_1.6.1 httr_1.4.4
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#> [65] RColorBrewer_1.1-3 ellipsis_0.3.2 Seurat_4.3.0 ica_1.0-3
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#> [69] pkgconfig_2.0.3 loo_2.5.1 uwot_0.1.14 deldir_1.0-6
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#> [73] utf8_1.2.2 tidyselect_1.2.0 rlang_1.0.6 reshape2_1.4.4
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#> [77] later_1.3.0 munsell_0.5.0 tools_4.2.0 cachem_1.0.6
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#> [81] cli_3.6.0 generics_0.1.3 RSQLite_2.2.20 ggridges_0.5.4
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#> [85] evaluate_0.20 fastmap_1.1.0 goftest_1.2-3 yaml_2.3.7
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#> [89] processx_3.8.0 knitr_1.42 bit64_4.0.5 fitdistrplus_1.1-8
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#> [93] purrr_1.0.1 RANN_2.6.1 nlme_3.1-161 pbapply_1.7-0
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#> [97] future_1.30.0 mime_0.12 compiler_4.2.0 rstudioapi_0.14
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#> [101] plotly_4.10.1 curl_5.0.0 png_0.1-8 spatstat.utils_3.0-1
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#> [105] tibble_3.1.8 stringi_1.7.12 highr_0.10 ps_1.7.2
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#> [109] lattice_0.20-45 Matrix_1.5-3 vctrs_0.5.2 pillar_1.8.1
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#> [113] lifecycle_1.0.3 rhdf5filters_1.10.0 spatstat.geom_3.0-3 lmtest_0.9-40
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#> [117] RcppAnnoy_0.0.20 data.table_1.14.6 cowplot_1.1.1 bitops_1.0-7
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#> [121] irlba_2.3.5.1 httpuv_1.6.8 patchwork_1.1.2 GenomicRanges_1.50.2
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#> [125] R6_2.5.1 promises_1.2.0.1 KernSmooth_2.23-20 gridExtra_2.3
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#> [129] IRanges_2.32.0 parallelly_1.34.0 codetools_0.2-18 MASS_7.3-58.1
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#> [133] assertthat_0.2.1 rhdf5_2.42.0 SummarizedExperiment_1.28.0 rprojroot_2.0.3
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#> [137] withr_2.5.0 SeuratObject_4.1.3 sctransform_0.3.5 S4Vectors_0.36.1
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#> [141] GenomeInfoDbData_1.2.9 parallel_4.2.0 grid_4.2.0 tidyr_1.3.0
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#> [145] rmarkdown_2.20 MatrixGenerics_1.10.0 Rtsne_0.16 spatstat.explore_3.0-5
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#> [149] Biobase_2.58.0 shiny_1.7.4
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```

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