Challenge and solution
This case study arose from a question on the CZI Science Community
Slack. A user asked
Hi! Is it possible to search CELLxGENE and identify all datasets by a
specific author or set of authors?
Unfortunately, this is not possible from the CELLxGENE web site –
authors are only associated with collections, and collections can only
be sorted or filtered by title (or publication / tissue / disease /
organism).
A cellxgenedp
solution uses authors() to discover authors and their
collections, and joins this information to datasets().
author_datasets <- left_join(
authors(),
datasets(),
by = "collection_id",
relationship = "many-to-many"
)
author_datasets
#> # A tibble: 53,628 × 36
#> collection_id family given consortium dataset_id dataset_version_id donor_id
#> <chr> <chr> <chr> <chr> <chr> <chr> <list>
#> 1 db70986c-7d91… Traeu… K. <NA> 72955cdb-… 999a6b92-46ca-498… <chr>
#> 2 db70986c-7d91… Munz M. <NA> 72955cdb-… 999a6b92-46ca-498… <chr>
#> 3 db70986c-7d91… Pauli J. <NA> 72955cdb-… 999a6b92-46ca-498… <chr>
#> 4 db70986c-7d91… Sachs N. <NA> 72955cdb-… 999a6b92-46ca-498… <chr>
#> 5 db70986c-7d91… Vafad… E. <NA> 72955cdb-… 999a6b92-46ca-498… <chr>
#> 6 db70986c-7d91… Carri… T. <NA> 72955cdb-… 999a6b92-46ca-498… <chr>
#> 7 db70986c-7d91… Maegd… L. <NA> 72955cdb-… 999a6b92-46ca-498… <chr>
#> 8 db70986c-7d91… Kastn… P. <NA> 72955cdb-… 999a6b92-46ca-498… <chr>
#> 9 db70986c-7d91… Heinig M. <NA> 72955cdb-… 999a6b92-46ca-498… <chr>
#> 10 2902f08c-f83c… Fan X. <NA> 1f1c5c14-… 2afef4bd-99af-41f… <chr>
#> # ℹ 53,618 more rows
#> # ℹ 29 more variables: assay <list>, batch_condition <list>, cell_count <int>,
#> # cell_type <list>, citation <chr>, default_embedding <chr>,
#> # development_stage <list>, disease <list>, embeddings <list>,
#> # explorer_url <chr>, feature_biotype <list>, feature_count <int>,
#> # feature_reference <list>, is_primary_data <list>,
#> # mean_genes_per_cell <dbl>, organism <list>, primary_cell_count <int>, …
author_datasets provides a convenient point from which
to make basic queries, e.g., finding the authors contributing the most
datasets.
author_datasets |>
count(family, given, sort = TRUE)
#> # A tibble: 4,939 × 3
#> family given n
#> <chr> <chr> <int>
#> 1 Teichmann Sarah A. 286
#> 2 Casper Tamara 256
#> 3 Dee Nick 256
#> 4 Keene C. Dirk 248
#> 5 Hirschstein Daniel 241
#> 6 Macosko Evan Z. 232
#> 7 Travaglini Kyle J. 230
#> 8 Chen Fei 226
#> 9 Ding Song-Lin 226
#> 10 Murray Evan 226
#> # ℹ 4,929 more rows
Perhaps one is interested in the most prolific authors based on
‘collections’, rather than ‘datasets’. The five most prolific authors by
collection are
prolific_authors <-
authors() |>
count(family, given, sort = TRUE) |>
slice(1:5)
prolific_authors
#> # A tibble: 5 × 3
#> family given n
#> <chr> <chr> <int>
#> 1 Teichmann Sarah A. 31
#> 2 Meyer Kerstin B. 16
#> 3 Polanski Krzysztof 15
#> 4 Haniffa Muzlifah 14
#> 5 Regev Aviv 14
The datasets associated with authors are
right_join(
author_datasets,
prolific_authors,
by = c("family", "given")
)
#> # A tibble: 698 × 37
#> collection_id family given consortium dataset_id dataset_version_id donor_id
#> <chr> <chr> <chr> <chr> <chr> <chr> <list>
#> 1 2d2e2acd-dade… Polan… Krzy… <NA> f9846bb4-… 0ffc40ef-2489-46e… <chr>
#> 2 2d2e2acd-dade… Polan… Krzy… <NA> e8a11a27-… 4582aa79-a23d-411… <chr>
#> 3 2d2e2acd-dade… Polan… Krzy… <NA> e887bd14-… da017f05-0843-4a8… <chr>
#> 4 2d2e2acd-dade… Polan… Krzy… <NA> e69d2744-… 94e1aa1d-1715-488… <chr>
#> 5 2d2e2acd-dade… Polan… Krzy… <NA> dc522d04-… 84665531-6e07-4f5… <chr>
#> 6 2d2e2acd-dade… Polan… Krzy… <NA> cdefb878-… 76d0f9ff-da6b-46d… <chr>
#> 7 2d2e2acd-dade… Polan… Krzy… <NA> c932254f-… d026eb58-e981-438… <chr>
#> 8 2d2e2acd-dade… Polan… Krzy… <NA> ae4817c0-… 16998940-19d2-4da… <chr>
#> 9 2d2e2acd-dade… Polan… Krzy… <NA> a6529751-… 2ed31cd2-3a1f-418… <chr>
#> 10 2d2e2acd-dade… Polan… Krzy… <NA> 810ac45f-… cb4c21bc-3149-474… <chr>
#> # ℹ 688 more rows
#> # ℹ 30 more variables: assay <list>, batch_condition <list>, cell_count <int>,
#> # cell_type <list>, citation <chr>, default_embedding <chr>,
#> # development_stage <list>, disease <list>, embeddings <list>,
#> # explorer_url <chr>, feature_biotype <list>, feature_count <int>,
#> # feature_reference <list>, is_primary_data <list>,
#> # mean_genes_per_cell <dbl>, organism <list>, primary_cell_count <int>, …
Alternatively, one might be interested in specific authors. This is
most easily accomplished with a simple filter on
author_datasets, e.g.,
author_datasets |>
filter(
family %in% c("Teichmann", "Regev", "Haniffa")
)
#> # A tibble: 463 × 36
#> collection_id family given consortium dataset_id dataset_version_id donor_id
#> <chr> <chr> <chr> <chr> <chr> <chr> <list>
#> 1 2d2e2acd-dade… Teich… Sara… <NA> f9846bb4-… 0ffc40ef-2489-46e… <chr>
#> 2 2d2e2acd-dade… Teich… Sara… <NA> e8a11a27-… 4582aa79-a23d-411… <chr>
#> 3 2d2e2acd-dade… Teich… Sara… <NA> e887bd14-… da017f05-0843-4a8… <chr>
#> 4 2d2e2acd-dade… Teich… Sara… <NA> e69d2744-… 94e1aa1d-1715-488… <chr>
#> 5 2d2e2acd-dade… Teich… Sara… <NA> dc522d04-… 84665531-6e07-4f5… <chr>
#> 6 2d2e2acd-dade… Teich… Sara… <NA> cdefb878-… 76d0f9ff-da6b-46d… <chr>
#> 7 2d2e2acd-dade… Teich… Sara… <NA> c932254f-… d026eb58-e981-438… <chr>
#> 8 2d2e2acd-dade… Teich… Sara… <NA> ae4817c0-… 16998940-19d2-4da… <chr>
#> 9 2d2e2acd-dade… Teich… Sara… <NA> a6529751-… 2ed31cd2-3a1f-418… <chr>
#> 10 2d2e2acd-dade… Teich… Sara… <NA> 810ac45f-… cb4c21bc-3149-474… <chr>
#> # ℹ 453 more rows
#> # ℹ 29 more variables: assay <list>, batch_condition <list>, cell_count <int>,
#> # cell_type <list>, citation <chr>, default_embedding <chr>,
#> # development_stage <list>, disease <list>, embeddings <list>,
#> # explorer_url <chr>, feature_biotype <list>, feature_count <int>,
#> # feature_reference <list>, is_primary_data <list>,
#> # mean_genes_per_cell <dbl>, organism <list>, primary_cell_count <int>, …
or more carefully by constructing at data.frame of
family and given names, and performing a join with
author_datasets
authors_of_interest <-
tibble(
family = c("Teichmann", "Regev", "Haniffa"),
given = c("Sarah A.", "Aviv", "Muzlifah")
)
right_join(
author_datasets,
authors_of_interest,
by = c("family", "given")
)
#> # A tibble: 427 × 36
#> collection_id family given consortium dataset_id dataset_version_id donor_id
#> <chr> <chr> <chr> <chr> <chr> <chr> <list>
#> 1 2d2e2acd-dade… Teich… Sara… <NA> f9846bb4-… 0ffc40ef-2489-46e… <chr>
#> 2 2d2e2acd-dade… Teich… Sara… <NA> e8a11a27-… 4582aa79-a23d-411… <chr>
#> 3 2d2e2acd-dade… Teich… Sara… <NA> e887bd14-… da017f05-0843-4a8… <chr>
#> 4 2d2e2acd-dade… Teich… Sara… <NA> e69d2744-… 94e1aa1d-1715-488… <chr>
#> 5 2d2e2acd-dade… Teich… Sara… <NA> dc522d04-… 84665531-6e07-4f5… <chr>
#> 6 2d2e2acd-dade… Teich… Sara… <NA> cdefb878-… 76d0f9ff-da6b-46d… <chr>
#> 7 2d2e2acd-dade… Teich… Sara… <NA> c932254f-… d026eb58-e981-438… <chr>
#> 8 2d2e2acd-dade… Teich… Sara… <NA> ae4817c0-… 16998940-19d2-4da… <chr>
#> 9 2d2e2acd-dade… Teich… Sara… <NA> a6529751-… 2ed31cd2-3a1f-418… <chr>
#> 10 2d2e2acd-dade… Teich… Sara… <NA> 810ac45f-… cb4c21bc-3149-474… <chr>
#> # ℹ 417 more rows
#> # ℹ 29 more variables: assay <list>, batch_condition <list>, cell_count <int>,
#> # cell_type <list>, citation <chr>, default_embedding <chr>,
#> # development_stage <list>, disease <list>, embeddings <list>,
#> # explorer_url <chr>, feature_biotype <list>, feature_count <int>,
#> # feature_reference <list>, is_primary_data <list>,
#> # mean_genes_per_cell <dbl>, organism <list>, primary_cell_count <int>, …
Areas of interest
There are several interesting questions that suggest themselves, and
several areas where some additional work is required.
It might be interesting to identify authors working on similar
disease, or other areas of interest. The disease column in
the author_datasets table is a list.
author_datasets |>
select(family, given, dataset_id, disease)
#> # A tibble: 53,628 × 4
#> family given dataset_id disease
#> <chr> <chr> <chr> <list>
#> 1 Traeuble K. 72955cdb-bd92-4135-aa52-21f33f9640db <list [1]>
#> 2 Munz M. 72955cdb-bd92-4135-aa52-21f33f9640db <list [1]>
#> 3 Pauli J. 72955cdb-bd92-4135-aa52-21f33f9640db <list [1]>
#> 4 Sachs N. 72955cdb-bd92-4135-aa52-21f33f9640db <list [1]>
#> 5 Vafadarnejad E. 72955cdb-bd92-4135-aa52-21f33f9640db <list [1]>
#> 6 Carrillo-Roa T. 72955cdb-bd92-4135-aa52-21f33f9640db <list [1]>
#> 7 Maegdefessel L. 72955cdb-bd92-4135-aa52-21f33f9640db <list [1]>
#> 8 Kastner P. 72955cdb-bd92-4135-aa52-21f33f9640db <list [1]>
#> 9 Heinig M. 72955cdb-bd92-4135-aa52-21f33f9640db <list [1]>
#> 10 Fan X. 1f1c5c14-5949-4c81-b28e-b272e271b672 <list [1]>
#> # ℹ 53,618 more rows
This is because a single dataset may involve more than one disease.
Furthermore, each entry in the list contains two elements, the
label and ontology_term_id of the disease.
There are two approaches to working with this data.
One approach to working with this data uses facilities in cellxgenedp as
outlined in an accompanying article. Discover possible diseases.
facets(db(), "disease")
#> # A tibble: 144 × 4
#> facet label ontology_term_id n
#> <chr> <chr> <chr> <int>
#> 1 disease normal PATO:0000461 1354
#> 2 disease COVID-19 MONDO:0100096 66
#> 3 disease dementia MONDO:0001627 50
#> 4 disease breast cancer MONDO:0007254 34
#> 5 disease myocardial infarction MONDO:0005068 28
#> 6 disease diabetic kidney disease MONDO:0005016 26
#> 7 disease autosomal dominant polycystic kidney disease MONDO:0004691 24
#> 8 disease Alzheimer disease MONDO:0004975 21
#> 9 disease nonpapillary renal cell carcinoma MONDO:0007763 19
#> 10 disease colorectal cancer MONDO:0005575 17
#> # ℹ 134 more rows
Focus on COVID-19, and use facets_filter()
to select relevant author-dataset combinations.
author_datasets |>
filter(facets_filter(disease, "label", "COVID-19"))
#> # A tibble: 1,912 × 36
#> collection_id family given consortium dataset_id dataset_version_id donor_id
#> <chr> <chr> <chr> <chr> <chr> <chr> <list>
#> 1 d0e9c47b-4ce7… Wyler Eman… <NA> ca421096-… 2750429a-1fb0-432… <chr>
#> 2 d0e9c47b-4ce7… Wyler Eman… <NA> 4b9e0a15-… e98b604e-c8fa-4df… <chr>
#> 3 d0e9c47b-4ce7… Mösba… Kirs… <NA> ca421096-… 2750429a-1fb0-432… <chr>
#> 4 d0e9c47b-4ce7… Mösba… Kirs… <NA> 4b9e0a15-… e98b604e-c8fa-4df… <chr>
#> 5 d0e9c47b-4ce7… Franke Vedr… <NA> ca421096-… 2750429a-1fb0-432… <chr>
#> 6 d0e9c47b-4ce7… Franke Vedr… <NA> 4b9e0a15-… e98b604e-c8fa-4df… <chr>
#> 7 d0e9c47b-4ce7… Diag Asija <NA> ca421096-… 2750429a-1fb0-432… <chr>
#> 8 d0e9c47b-4ce7… Diag Asija <NA> 4b9e0a15-… e98b604e-c8fa-4df… <chr>
#> 9 d0e9c47b-4ce7… Gottu… Lina… <NA> ca421096-… 2750429a-1fb0-432… <chr>
#> 10 d0e9c47b-4ce7… Gottu… Lina… <NA> 4b9e0a15-… e98b604e-c8fa-4df… <chr>
#> # ℹ 1,902 more rows
#> # ℹ 29 more variables: assay <list>, batch_condition <list>, cell_count <int>,
#> # cell_type <list>, citation <chr>, default_embedding <chr>,
#> # development_stage <list>, disease <list>, embeddings <list>,
#> # explorer_url <chr>, feature_biotype <list>, feature_count <int>,
#> # feature_reference <list>, is_primary_data <list>,
#> # mean_genes_per_cell <dbl>, organism <list>, primary_cell_count <int>, …
Authors contributing to these datasets are
author_datasets |>
filter(facets_filter(disease, "label", "COVID-19")) |>
count(family, given, sort = TRUE)
#> # A tibble: 836 × 3
#> family given n
#> <chr> <chr> <int>
#> 1 Farber Donna L. 29
#> 2 Guo Xinzheng V. 28
#> 3 Saqi Anjali 28
#> 4 Baldwin Matthew R. 27
#> 5 Chait Michael 27
#> 6 Connors Thomas J. 27
#> 7 Davis-Porada Julia 27
#> 8 Dogra Pranay 27
#> 9 Gray Joshua I. 27
#> 10 Idzikowski Emma 27
#> # ℹ 826 more rows
A second approach is to follow the practices in R for Data Science, the
disease column can be ‘unnested’ twice, the first time to
expand the author_datasets table for each disease, and the
second time to separate the two columns of each disease.
author_dataset_diseases <-
author_datasets |>
select(family, given, dataset_id, disease) |>
tidyr::unnest_longer(disease) |>
tidyr::unnest_wider(disease)
author_dataset_diseases
#> # A tibble: 70,040 × 5
#> family given dataset_id label ontology_term_id
#> <chr> <chr> <chr> <chr> <chr>
#> 1 Traeuble K. 72955cdb-bd92-4135-aa52-21f33f9640… athe… MONDO:0005311
#> 2 Munz M. 72955cdb-bd92-4135-aa52-21f33f9640… athe… MONDO:0005311
#> 3 Pauli J. 72955cdb-bd92-4135-aa52-21f33f9640… athe… MONDO:0005311
#> 4 Sachs N. 72955cdb-bd92-4135-aa52-21f33f9640… athe… MONDO:0005311
#> 5 Vafadarnejad E. 72955cdb-bd92-4135-aa52-21f33f9640… athe… MONDO:0005311
#> 6 Carrillo-Roa T. 72955cdb-bd92-4135-aa52-21f33f9640… athe… MONDO:0005311
#> 7 Maegdefessel L. 72955cdb-bd92-4135-aa52-21f33f9640… athe… MONDO:0005311
#> 8 Kastner P. 72955cdb-bd92-4135-aa52-21f33f9640… athe… MONDO:0005311
#> 9 Heinig M. 72955cdb-bd92-4135-aa52-21f33f9640… athe… MONDO:0005311
#> 10 Fan X. 1f1c5c14-5949-4c81-b28e-b272e271b6… norm… PATO:0000461
#> # ℹ 70,030 more rows
Author-dataset combinations associated with COVID-19, and
contributors to these datasets, are
author_dataset_diseases |>
filter(label == "COVID-19")
author_dataset_diseases |>
filter(label == "COVID-19") |>
count(family, given, sort = TRUE)
These computations are the same as the earlier iteration using
functionality in cellxgenedp.
A further resource that might be of interest is the [OSLr][] package
article illustrating how the ontologies used by CELLxGENE can be
manipulated to, e.g., identify studies with terms that derive from a
common term (e.g., all disease terms related to ‘carcinoma’).
Duplicate collection-author combinations
Here are the authors
authors <- authors()
authors
#> # A tibble: 6,475 × 4
#> collection_id family given consortium
#> <chr> <chr> <chr> <chr>
#> 1 db70986c-7d91-49fe-a399-a4730be394ac Traeuble K. <NA>
#> 2 db70986c-7d91-49fe-a399-a4730be394ac Munz M. <NA>
#> 3 db70986c-7d91-49fe-a399-a4730be394ac Pauli J. <NA>
#> 4 db70986c-7d91-49fe-a399-a4730be394ac Sachs N. <NA>
#> 5 db70986c-7d91-49fe-a399-a4730be394ac Vafadarnejad E. <NA>
#> 6 db70986c-7d91-49fe-a399-a4730be394ac Carrillo-Roa T. <NA>
#> 7 db70986c-7d91-49fe-a399-a4730be394ac Maegdefessel L. <NA>
#> 8 db70986c-7d91-49fe-a399-a4730be394ac Kastner P. <NA>
#> 9 db70986c-7d91-49fe-a399-a4730be394ac Heinig M. <NA>
#> 10 2902f08c-f83c-470e-a541-e463e25e5058 Fan X. <NA>
#> # ℹ 6,465 more rows
There are 6475 collection-author combinations. We expect these to be
distinct (each row identifying a unique collection-author combination).
But this is not true
nrow(authors) == nrow(distinct(authors))
#> [1] FALSE
Duplicated data are
authors |>
count(collection_id, family, given, consortium, sort = TRUE) |>
filter(n > 1)
#> # A tibble: 73 × 5
#> collection_id family given consortium n
#> <chr> <chr> <chr> <chr> <int>
#> 1 e5f58829-1a66-40b5-a624-9046778e74f5 Pisco Angela Oliv… <NA> 4
#> 2 e5f58829-1a66-40b5-a624-9046778e74f5 Crasta Sheela <NA> 3
#> 3 e5f58829-1a66-40b5-a624-9046778e74f5 Swift Michael <NA> 3
#> 4 e5f58829-1a66-40b5-a624-9046778e74f5 Travaglini Kyle J. <NA> 3
#> 5 e5f58829-1a66-40b5-a624-9046778e74f5 de Morree Antoine <NA> 3
#> 6 51544e44-293b-4c2b-8c26-560678423380 Betts Michael R. <NA> 2
#> 7 51544e44-293b-4c2b-8c26-560678423380 Faryabi Robert B. <NA> 2
#> 8 51544e44-293b-4c2b-8c26-560678423380 Fasolino Maria <NA> 2
#> 9 51544e44-293b-4c2b-8c26-560678423380 Feldman Michael <NA> 2
#> 10 51544e44-293b-4c2b-8c26-560678423380 Goldman Naomi <NA> 2
#> # ℹ 63 more rows
Discover details of the first duplicated collection,
e5f58829-1a66-40b5-a624-9046778e74f5
duplicate_authors <-
collections() |>
filter(collection_id == "e5f58829-1a66-40b5-a624-9046778e74f5")
duplicate_authors
#> # A tibble: 1 × 18
#> collection_id collection_version_id collection_url consortia contact_email
#> <chr> <chr> <chr> <list> <chr>
#> 1 e5f58829-1a66-40… 464b3442-4323-432b-a… https://cellx… <chr [2]> angela.pisco…
#> # ℹ 13 more variables: contact_name <chr>, curator_name <chr>,
#> # description <chr>, doi <chr>, links <list>, name <chr>,
#> # publisher_metadata <list>, revising_in <lgl>, revision_of <lgl>,
#> # visibility <chr>, created_at <date>, published_at <date>, revised_at <date>
The author information comes from the publisher_metadata
column
publisher_metadata <-
duplicate_authors |>
pull(publisher_metadata)
This is a ‘list-of-lists’, with relevant information as elements in
the first list
names(publisher_metadata[[1]])
#> [1] "authors" "is_preprint" "journal" "published_at"
#> [5] "published_day" "published_month" "published_year"
and relevant information in the authors field, of which
there are 221
length(publisher_metadata[[1]][["authors"]])
#> [1] 221
Inspection shows that there are four authors with family name
Pisco and given name Angela Oliveira: it
appears that the data provided by CZI indeed includes duplicate author
names.
From a pragmatic perspective, it might make sense to remove duplicate
entries from authors before down-stream analysis.
deduplicated_authors <- distinct(authors)
Tools that I have found useful when working with list-of-lists style
data rare listviewer::jsonedit()
for visualization, and rjsoncons for
filtering and querying these data using JSONpointer, JSONpath, or
JMESpath expression (a more R-centric tool is the purrr package).
What is an ‘author’?
The combination of family and given name may refer to two (or more)
different individuals (e.g., two individuals named ‘Martin Morgan’), or
a single individual may be recorded under two different names (e.g.,
given name sometimes ‘Martin’ and sometimes ‘Martin T.’). It is not
clear how this could be resolved; recording ORCID identifiers migth help
with disambiguation.