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Use knit button for building all formats
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‎README.md‎

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@@ -36,6 +36,27 @@ library(CuratedAtlasQueryR)
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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 0.7.1 [unknown@Linux 3.10.0-1160.88.1.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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```
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The `metadata` variable can then be re-used for all subsequent queries.
@@ -93,8 +114,8 @@ single_cell_counts
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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 ... TACAACGTCAGCATTG_SC84_1
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#> CATTCGCTCAATACCG_F02526_1
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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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#> colData names(56): sample_ cell_type ... updated_at_y original_cell_id
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#> reducedDimNames(0):
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#> mainExpName: NULL
@@ -129,8 +150,8 @@ single_cell_counts
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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 ... TACAACGTCAGCATTG_SC84_1
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#> CATTCGCTCAATACCG_F02526_1
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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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#> 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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#> 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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#> colnames(1571): ACAGCCGGTCCGTTAA_F02526_1 GGGAATGAGCCCAGCT_F02526_1 ...
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#> TACAACGTCAGCATTG_SC84_1 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
@@ -287,18 +308,61 @@ data frame.
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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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dplyr::pull(unharmonised) |> head(2)
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#> [[1]]
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#> # Source: SQL [?? x 17]
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#> # Database: DuckDB 0.7.1 [unknown@Linux 3.10.0-1160.88.1.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 0.7.1 [unknown@Linux 3.10.0-1160.88.1.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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\[\[1\]\]
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\[\[2\]\]
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# Cell metadata
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Dataset-specific columns (definitions available at

‎vignettes/Introduction.Rmd‎

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---
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title: "CuratedAtlasQueryR"
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output: rmarkdown::html_vignette
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output: "html_vignette"
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vignette: >
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%\VignetteIndexEntry{CuratedAtlasQueryR}
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%\VignetteEngine{knitr::rmarkdown}
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%\VignetteEncoding{UTF-8}
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%\VignetteEngine{knitr::rmarkdown}
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knit: >
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(function(x, ...){
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proj_root <- rprojroot::find_package_root_file() |> normalizePath()
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rmarkdown::render(
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x,
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output_format = "html_document"
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)
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rmarkdown::render(
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x,
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output_file = "README.md",
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output_format = "github_document",
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output_dir = proj_root,
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knit_root_dir = proj_root
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)
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})
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---
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```{r, include = FALSE}
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)
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root_dir <- knitr::opts_knit$get("root.dir")
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if (!is.null(root_dir)){
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# This hack fixes the relative image paths.
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# See https://github.com/rstudio/rmarkdown/issues/2473
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knitr::opts_knit$set(
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output.dir = root_dir
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)
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}
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proj_root <- rprojroot::find_package_root_file() |> normalizePath()
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```
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```{r, eval=FALSE, echo=FALSE, include=FALSE}
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# Note: knit this to the repo readme file using:
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proj_root <- rprojroot::find_package_root_file() |> normalizePath()
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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 = proj_root,
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# We need to knit in the destination directory, or the relative paths
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# won't work
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knit_root_dir = proj_root
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)
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```
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```{r include=FALSE}
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# Utility function for figures to force them to have the correct path
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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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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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dplyr::pull(unharmonised) |> head(2)
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```
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# Cell metadata

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