Introduction
GEfetch2R provides functions for users to download
processed single-cell RNA-seq data from Zenodo, CELLxGENE and Human Cell Atlas, including
RDS, RData, h5ad,
h5, loom objects.
## Warning: replacing previous import 'LoomExperiment::import' by
## 'reticulate::import' when loading 'GEfetch2R'Until now, the public resources supported and the returned values:
| Resources | URL | Download Type | Returned values |
|---|---|---|---|
| Zenodo | https://zenodo.org/ | count matrix, rds, rdata, h5ad, et al. | SeuratObject(rds) or failed datasets |
| CELLxGENE | https://cellxgene.cziscience.com/ | rds, h5ad | SeuratObject(rds) or failed datasets |
| Human Cell Atlas | https://www.humancellatlas.org/ | rds, rdata, h5, h5ad, loom | SeuratObject(rds) or failed projects |
Zenodo
Zenodo contains various types of
processed objects, such as SeuratObject which has been
clustered and annotated, AnnData which contains processed
results generated by scanpy.
Extract metadata
GEfetch2R provides ExtractZenodoMeta to
extract dataset metadata, including dataset title, description,
available files and corresponding md5. Please note that when the dataset
is restricted access, the returned dataframe will be empty.
# library
library(GEfetch2R)
# single doi
zebrafish.df <- ExtractZenodoMeta(doi = "10.5281/zenodo.7243603")
# vector dois
multi.dois <- ExtractZenodoMeta(doi = c("1111", "10.5281/zenodo.7243603", "10.5281/zenodo.7244441"))Show the metadata:
# single doi
zebrafish.df## title
## 1 zebrafish scRNA data set objects
## 2 zebrafish scRNA data set objects
## description
## 1 <p>Combined and converted scRNA data from http://tome.gs.washington.edu/ (Qiu et al. 2022), see a detailed description of the study here: https://www.nature.com/articles/s41588-022-01018-x</p>\n\n<p>Data were downloaded from http://tome.gs.washington.edu/ as R rds files, combined into a single Seurat object and converted into loom and AnnData (h5ad) files to be able to analyse with e.g. python scanpy package.</p>\n\n<p>If you use this data, please cite Farrel et al. 2018, Wagner et al. 2018 and Qiu et al. 2022.</p>
## 2 <p>Combined and converted scRNA data from http://tome.gs.washington.edu/ (Qiu et al. 2022), see a detailed description of the study here: https://www.nature.com/articles/s41588-022-01018-x</p>\n\n<p>Data were downloaded from http://tome.gs.washington.edu/ as R rds files, combined into a single Seurat object and converted into loom and AnnData (h5ad) files to be able to analyse with e.g. python scanpy package.</p>\n\n<p>If you use this data, please cite Farrel et al. 2018, Wagner et al. 2018 and Qiu et al. 2022.</p>
## url
## 1 https://zenodo.org/api/records/7243603/files/zebrafish_data.h5ad/content
## 2 https://zenodo.org/api/records/7243603/files/zebrafish_data.RData/content
## filename md5 license
## 1 zebrafish_data.h5ad 124f2229128918b411a7dc7931558f97 cc-by-4.0
## 2 zebrafish_data.RData a08c3ebd285b370fcf34cf2f8f9bdb59 cc-by-4.0
# vector dois
multi.dois## title
## 1 zebrafish scRNA data set objects
## 2 zebrafish scRNA data set objects
## 3 frog scRNA data set objects
## 4 frog scRNA data set objects
## description
## 1 <p>Combined and converted scRNA data from http://tome.gs.washington.edu/ (Qiu et al. 2022), see a detailed description of the study here: https://www.nature.com/articles/s41588-022-01018-x</p>\n\n<p>Data were downloaded from http://tome.gs.washington.edu/ as R rds files, combined into a single Seurat object and converted into loom and AnnData (h5ad) files to be able to analyse with e.g. python scanpy package.</p>\n\n<p>If you use this data, please cite Farrel et al. 2018, Wagner et al. 2018 and Qiu et al. 2022.</p>
## 2 <p>Combined and converted scRNA data from http://tome.gs.washington.edu/ (Qiu et al. 2022), see a detailed description of the study here: https://www.nature.com/articles/s41588-022-01018-x</p>\n\n<p>Data were downloaded from http://tome.gs.washington.edu/ as R rds files, combined into a single Seurat object and converted into loom and AnnData (h5ad) files to be able to analyse with e.g. python scanpy package.</p>\n\n<p>If you use this data, please cite Farrel et al. 2018, Wagner et al. 2018 and Qiu et al. 2022.</p>
## 3 <p>Combined and converted scRNA data from http://tome.gs.washington.edu/ (Qiu et al. 2022), see a detailed description of the study here: https://www.nature.com/articles/s41588-022-01018-x</p>\n\n<p>Data were downloaded from http://tome.gs.washington.edu/ as R rds files, combined into a single Seurat object and converted into loom and AnnData (h5ad) files to be able to analyse with e.g. python scanpy package.</p>\n\n<p>If you use this data, please cite Briggs et al. 2018 and Qiu et al. 2022.</p>
## 4 <p>Combined and converted scRNA data from http://tome.gs.washington.edu/ (Qiu et al. 2022), see a detailed description of the study here: https://www.nature.com/articles/s41588-022-01018-x</p>\n\n<p>Data were downloaded from http://tome.gs.washington.edu/ as R rds files, combined into a single Seurat object and converted into loom and AnnData (h5ad) files to be able to analyse with e.g. python scanpy package.</p>\n\n<p>If you use this data, please cite Briggs et al. 2018 and Qiu et al. 2022.</p>
## url
## 1 https://zenodo.org/api/records/7243603/files/zebrafish_data.h5ad/content
## 2 https://zenodo.org/api/records/7243603/files/zebrafish_data.RData/content
## 3 https://zenodo.org/api/records/7244441/files/frog_data.h5ad/content
## 4 https://zenodo.org/api/records/7244441/files/frog_data.RData/content
## filename md5 license
## 1 zebrafish_data.h5ad 124f2229128918b411a7dc7931558f97 cc-by-4.0
## 2 zebrafish_data.RData a08c3ebd285b370fcf34cf2f8f9bdb59 cc-by-4.0
## 3 frog_data.h5ad 7be7d6ff024ab2c8579b4d0edb2428e3 cc-by-4.0
## 4 frog_data.RData c80f46320c0cff9e341bed195f12c3b1 cc-by-4.0Download object and load to R
After manually check the extracted metadata, users can
download the specified objects with
ParseZenodo. The downloaded objects are controlled by
file.ext and the provided object formats should be
in lower case (e.g. rds/rdata/h5ad).
The returned value is a dataframe containing failed objects or a
SeuratObject (if file.ext is
rds). If dataframe, users can re-run
ParseZenodo by setting doi.df to the returned
value.
# download objects
multi.dois.parse <- ParseZenodo(
doi = c("1111", "10.5281/zenodo.7243603", "10.5281/zenodo.7244441"),
file.ext = c("rdata"),
out.folder = "/Volumes/soyabean/GEfetch2R/download_zenodo"
)
# return SeuratObject
sinle.doi.parse.seu <- ParseZenodo(
doi = "10.5281/zenodo.8011282",
file.ext = c("rds"), return.seu = TRUE,
out.folder = "/Volumes/soyabean/GEfetch2R/download_zenodo"
)Show the returned SeuratObject:
sinle.doi.parse.seu## An object of class Seurat
## 19594 features across 9219 samples within 2 assays
## Active assay: RNA (17594 features, 0 variable features)
## 1 other assay present: integrated
## 2 dimensional reductions calculated: pca, umapThe structure of downloaded objects:
tree /Volumes/soyabean/GEfetch2R/download_zenodo## /Volumes/soyabean/GEfetch2R/download_zenodo
## ├── PyMTM_immune_scRNA.rds
## ├── frog_data.RData
## └── zebrafish_data.RData
##
## 1 directory, 3 filesCELLxGENE
The CELLxGENE is a
web server contains 1598 single-cell datasets, users
can explore, download and upload own datasets. The downloaded datasets
provided by CELLxGENE
have two formats: h5ad (AnnData v0.8) and
rds (Seurat v4).
CELLxGENE provides an
R package (cellxgene.census)
to access the data, but sometimes it’s not timely updated.
GEfetch2R also supports users to access CELLxGENE via cellxgene.census
(use.census = TRUE).
Show available datasets
GEfetch2R provides ShowCELLxGENEDatasets to
extract dataset metadata, including dataset title, description, contact,
organism, ethnicity, sex, tissue, disease, assay, suspension type, cell
type, et al.
# all available datasets
all.cellxgene.datasets <- ShowCELLxGENEDatasets()Show the metadata:
head(all.cellxgene.datasets)## title
## 1 Plaque Atlas
## 2 Single-cell reconstruction of follicular remodeling in the human adult ovary
## 3 Molecular characterization of selectively vulnerable neurons in Alzheimer's Disease
## 4 Molecular characterization of selectively vulnerable neurons in Alzheimer's Disease
## 5 Molecular characterization of selectively vulnerable neurons in Alzheimer's Disease
## 6 Molecular characterization of selectively vulnerable neurons in Alzheimer's Disease
## description
## 1 A curation of publicly available atherosclerotic plaque scRNAseq datasets including annotation and validation.
## 2 The ovary is perhaps the most dynamic organ in the human body, only rivaled by the uterus. The molecular mechanisms that regulate follicular growth and regression, ensuring ovarian tissue homeostasis, remain elusive. We have performed single-cell RNA-sequencing using human adult ovaries to provide a map of the molecular signature of growing and regressing follicular populations. We have identified different types of granulosa and theca cells and detected local production of components of the complement system by (atretic) theca cells and stromal cells. We also have detected a mixture of adaptive and innate immune cells, as well as several types of endothelial and smooth muscle cells to aid the remodeling process. Our results highlight the relevance of mapping whole adult organs at the single-cell level and reflect ongoing efforts to map the human body. The association between complement system and follicular remodeling may provide key insights in reproductive biology and (in)fertility.
## 3 Single-nuclei RNA sequencing of caudal entorhinal cortex and superior frontal gyrus from individuals spanning the neuropathological progression of AD
## 4 Single-nuclei RNA sequencing of caudal entorhinal cortex and superior frontal gyrus from individuals spanning the neuropathological progression of AD
## 5 Single-nuclei RNA sequencing of caudal entorhinal cortex and superior frontal gyrus from individuals spanning the neuropathological progression of AD
## 6 Single-nuclei RNA sequencing of caudal entorhinal cortex and superior frontal gyrus from individuals spanning the neuropathological progression of AD
## doi contact
## 1 10.1101/2024.09.11.612431 Korbinian Träuble
## 2 10.1038/s41467-019-11036-9 S. M. Chuva de Sousa Lopes
## 3 10.1038/s41593-020-00764-7 Martin Kampmann
## 4 10.1038/s41593-020-00764-7 Martin Kampmann
## 5 10.1038/s41593-020-00764-7 Martin Kampmann
## 6 10.1038/s41593-020-00764-7 Martin Kampmann
## contact_email collection_id
## 1 korbinian.traeuble@helmholtz-munich.de db70986c-7d91-49fe-a399-a4730be394ac
## 2 lopes@lumc.nl 2902f08c-f83c-470e-a541-e463e25e5058
## 3 Martin.Kampmann@ucsf.edu 180bff9c-c8a5-4539-b13b-ddbc00d643e6
## 4 Martin.Kampmann@ucsf.edu 180bff9c-c8a5-4539-b13b-ddbc00d643e6
## 5 Martin.Kampmann@ucsf.edu 180bff9c-c8a5-4539-b13b-ddbc00d643e6
## 6 Martin.Kampmann@ucsf.edu 180bff9c-c8a5-4539-b13b-ddbc00d643e6
## collection_url
## 1 https://cellxgene.cziscience.com/collections/db70986c-7d91-49fe-a399-a4730be394ac
## 2 https://cellxgene.cziscience.com/collections/2902f08c-f83c-470e-a541-e463e25e5058
## 3 https://cellxgene.cziscience.com/collections/180bff9c-c8a5-4539-b13b-ddbc00d643e6
## 4 https://cellxgene.cziscience.com/collections/180bff9c-c8a5-4539-b13b-ddbc00d643e6
## 5 https://cellxgene.cziscience.com/collections/180bff9c-c8a5-4539-b13b-ddbc00d643e6
## 6 https://cellxgene.cziscience.com/collections/180bff9c-c8a5-4539-b13b-ddbc00d643e6
## consortia curator_name visibility assay
## 1 James Chaffer PUBLIC 10x 3' v2, 10x 3' v3
## 2 Jennifer Yu-Sheng Chien PUBLIC 10x 3' v2
## 3 Jennifer Yu-Sheng Chien PUBLIC 10x 3' v2
## 4 Jennifer Yu-Sheng Chien PUBLIC 10x 3' v2
## 5 Jennifer Yu-Sheng Chien PUBLIC 10x 3' v2
## 6 Jennifer Yu-Sheng Chien PUBLIC 10x 3' v2
## assets
## 1 4221495897, 1216838411, H5AD, RDS, https://datasets.cellxgene.cziscience.com/999a6b92-46ca-498e-b1ee-5fc43b6988ef.h5ad, https://datasets.cellxgene.cziscience.com/999a6b92-46ca-498e-b1ee-5fc43b6988ef.rds
## 2 105862325, 96479066, H5AD, RDS, https://datasets.cellxgene.cziscience.com/2afef4bd-99af-41f4-b507-f80718b6a8ef.h5ad, https://datasets.cellxgene.cziscience.com/2afef4bd-99af-41f4-b507-f80718b6a8ef.rds
## 3 17724244, 14805677, H5AD, RDS, https://datasets.cellxgene.cziscience.com/d9db936c-41c6-4398-a8c4-a9dca34a225e.h5ad, https://datasets.cellxgene.cziscience.com/d9db936c-41c6-4398-a8c4-a9dca34a225e.rds
## 4 47491494, 46270555, H5AD, RDS, https://datasets.cellxgene.cziscience.com/000198ac-27c7-4b9f-9fd4-6362a5b91596.h5ad, https://datasets.cellxgene.cziscience.com/000198ac-27c7-4b9f-9fd4-6362a5b91596.rds
## 5 9460591, 7134383, H5AD, RDS, https://datasets.cellxgene.cziscience.com/017837df-b8be-4a4f-bc08-c5f14bd8815a.h5ad, https://datasets.cellxgene.cziscience.com/017837df-b8be-4a4f-bc08-c5f14bd8815a.rds
## 6 16025334, 13487453, H5AD, RDS, https://datasets.cellxgene.cziscience.com/3512034b-9a5c-4dba-9dfb-cead9f9840dc.h5ad, https://datasets.cellxgene.cziscience.com/3512034b-9a5c-4dba-9dfb-cead9f9840dc.rds
## cell_count
## 1 184623
## 2 20676
## 3 8168
## 4 8362
## 5 3799
## 6 5970
## cell_type
## 1 B cell, T cell, dendritic cell, endothelial cell, fibroblast, macrophage, mast cell, monocyte, natural killer cell, plasma cell, smooth muscle cell
## 2 B cell, T cell, endothelial cell, granulosa cell, innate lymphoid cell, natural killer cell, smooth muscle cell, stromal cell of ovary, theca cell
## 3 oligodendrocyte
## 4 glutamatergic neuron
## 5 mature microglial cell
## 6 mature astrocyte
## citation
## 1 Publication: https://doi.org/10.1101/2024.09.11.612431 Dataset Version: https://datasets.cellxgene.cziscience.com/999a6b92-46ca-498e-b1ee-5fc43b6988ef.h5ad curated and distributed by CZ CELLxGENE Discover in Collection: https://cellxgene.cziscience.com/collections/db70986c-7d91-49fe-a399-a4730be394ac
## 2 Publication: https://doi.org/10.1038/s41467-019-11036-9 Dataset Version: https://datasets.cellxgene.cziscience.com/2afef4bd-99af-41f4-b507-f80718b6a8ef.h5ad curated and distributed by CZ CELLxGENE Discover in Collection: https://cellxgene.cziscience.com/collections/2902f08c-f83c-470e-a541-e463e25e5058
## 3 Publication: https://doi.org/10.1038/s41593-020-00764-7 Dataset Version: https://datasets.cellxgene.cziscience.com/d9db936c-41c6-4398-a8c4-a9dca34a225e.h5ad curated and distributed by CZ CELLxGENE Discover in Collection: https://cellxgene.cziscience.com/collections/180bff9c-c8a5-4539-b13b-ddbc00d643e6
## 4 Publication: https://doi.org/10.1038/s41593-020-00764-7 Dataset Version: https://datasets.cellxgene.cziscience.com/000198ac-27c7-4b9f-9fd4-6362a5b91596.h5ad curated and distributed by CZ CELLxGENE Discover in Collection: https://cellxgene.cziscience.com/collections/180bff9c-c8a5-4539-b13b-ddbc00d643e6
## 5 Publication: https://doi.org/10.1038/s41593-020-00764-7 Dataset Version: https://datasets.cellxgene.cziscience.com/017837df-b8be-4a4f-bc08-c5f14bd8815a.h5ad curated and distributed by CZ CELLxGENE Discover in Collection: https://cellxgene.cziscience.com/collections/180bff9c-c8a5-4539-b13b-ddbc00d643e6
## 6 Publication: https://doi.org/10.1038/s41593-020-00764-7 Dataset Version: https://datasets.cellxgene.cziscience.com/3512034b-9a5c-4dba-9dfb-cead9f9840dc.h5ad curated and distributed by CZ CELLxGENE Discover in Collection: https://cellxgene.cziscience.com/collections/180bff9c-c8a5-4539-b13b-ddbc00d643e6
## dataset_id dataset_version_id
## 1 72955cdb-bd92-4135-aa52-21f33f9640db 999a6b92-46ca-498e-b1ee-5fc43b6988ef
## 2 1f1c5c14-5949-4c81-b28e-b272e271b672 2afef4bd-99af-41f4-b507-f80718b6a8ef
## 3 f9ad5649-f372-43e1-a3a8-423383e5a8a2 d9db936c-41c6-4398-a8c4-a9dca34a225e
## 4 cd77258f-b08b-4c89-b93f-6e6f146b1a4d 000198ac-27c7-4b9f-9fd4-6362a5b91596
## 5 bdacc907-7c26-419f-8808-969eab3ca2e8 017837df-b8be-4a4f-bc08-c5f14bd8815a
## 6 b94e3bdf-a385-49cc-b312-7a63cc28b77a 3512034b-9a5c-4dba-9dfb-cead9f9840dc
## development_stage
## 1 54-year-old stage, 58-year-old stage, 65-year-old stage, 67-year-old stage, 74-year-old stage, 76-year-old stage, 77-year-old stage, 82-year-old stage, 83-year-old stage, 87-year-old stage, unknown
## 2 adult stage
## 3 50-year-old stage, 60-year-old stage, 71-year-old stage, 72-year-old stage, 77-year-old stage, 80 year-old and over stage, 82-year-old stage, 87-year-old stage
## 4 50-year-old stage, 60-year-old stage, 71-year-old stage, 72-year-old stage, 77-year-old stage, 80 year-old and over stage, 82-year-old stage, 87-year-old stage
## 5 50-year-old stage, 60-year-old stage, 71-year-old stage, 72-year-old stage, 77-year-old stage, 80 year-old and over stage, 82-year-old stage, 87-year-old stage
## 6 50-year-old stage, 60-year-old stage, 71-year-old stage, 72-year-old stage, 77-year-old stage, 80 year-old and over stage, 82-year-old stage, 87-year-old stage
## disease
## 1 atherosclerosis
## 2 normal
## 3 Alzheimer disease, normal
## 4 Alzheimer disease, normal
## 5 Alzheimer disease, normal
## 6 Alzheimer disease, normal
## donor_id
## 1 2_Pauli, 3_Pauli, 4_Pauli, 5_Pauli, 6_Pauli, 7_Pauli, 8_Pauli, 9_Pauli, 1_Alsaigh, 2_Alsaigh, 3_Alsaigh, 1_Fernandez, 2_Fernandez, 3_Fernandez, 3A_Fernandez, 4_Fernandez, 5_Fernandez, 6_Fernandez, 1_Wirka, 2_Wirka, 3_Wirka, 4_Wirka, 1_Emoto_ACS, 2_Emoto_SAP, 3_Chowdhury, 6_Chowdhury, 10_Chowdhury, 4_Chowdhury, 5_Chowdhury, 12_Chowdhury, 11_Chowdhury, 1_Chowdhury, 2_Chowdhury, 7_Chowdhury, 9_Chowdhury, 8_Chowdhury, 1_Pan, 2_Pan, 3_Pan, 1_Dib, 2_Dib, 3_Dib, 4_Dib, 5_Dib, 6_Dib, 1_2_Slysz, 3_Slysz, 4_Slysz, 1_2_3_Slysz_femoral, 4_Slysz_femoral, 5_Slysz_femoral, 6_Slysz_femoral, 7_Slysz_femoral, 8_Slysz_femoral, 9_Slysz_femoral, 1_Jaiswal, 2_Jaiswal
## 2 pat9, pat7, pat0, pat3, pat2
## 3 3, 1, 2, 5, 6, 7, 4, 8, 9, 10
## 4 3, 1, 2, 5, 6, 7, 4, 8, 9, 10
## 5 3, 1, 2, 5, 6, 7, 4, 8, 9, 10
## 6 3, 1, 2, 5, 6, 7, 4, 8, 9, 10
## embeddings
## 1 X_scpoli, X_umap
## 2 X_mnn.correct, X_pca, X_tsne, X_umap
## 3 X_cca, X_cca.aligned, X_tsne
## 4 X_cca, X_cca.aligned, X_tsne
## 5 X_cca, X_cca.aligned, X_tsne
## 6 X_cca, X_cca.aligned, X_tsne
## explorer_url
## 1 https://cellxgene.cziscience.com/e/72955cdb-bd92-4135-aa52-21f33f9640db.cxg/
## 2 https://cellxgene.cziscience.com/e/1f1c5c14-5949-4c81-b28e-b272e271b672.cxg/
## 3 https://cellxgene.cziscience.com/e/f9ad5649-f372-43e1-a3a8-423383e5a8a2.cxg/
## 4 https://cellxgene.cziscience.com/e/cd77258f-b08b-4c89-b93f-6e6f146b1a4d.cxg/
## 5 https://cellxgene.cziscience.com/e/bdacc907-7c26-419f-8808-969eab3ca2e8.cxg/
## 6 https://cellxgene.cziscience.com/e/b94e3bdf-a385-49cc-b312-7a63cc28b77a.cxg/
## feature_biotype feature_count feature_reference is_primary_data
## 1 gene 28033 NCBITaxon:9606 TRUE
## 2 gene 32839 NCBITaxon:9606 TRUE
## 3 gene 32743 NCBITaxon:9606 FALSE
## 4 gene 32743 NCBITaxon:9606 FALSE
## 5 gene 32743 NCBITaxon:9606 FALSE
## 6 gene 32743 NCBITaxon:9606 FALSE
## mean_genes_per_cell organism primary_cell_count published_at
## 1 1390.0576 Homo sapiens 184623 2024-09-24T15:53:59+00:00
## 2 942.2772 Homo sapiens 20676 2022-06-20T09:32:24+00:00
## 3 599.5956 Homo sapiens 0 2020-11-20T13:39:38+00:00
## 4 2165.4574 Homo sapiens 0 2020-11-20T13:39:40+00:00
## 5 585.7452 Homo sapiens 0 2020-11-20T13:39:43+00:00
## 6 777.2630 Homo sapiens 0 2020-11-20T13:39:41+00:00
## raw_data_location revised_at schema_version
## 1 raw.X 2024-10-10T20:20:32+00:00 5.2.0
## 2 raw.X 2024-10-10T20:20:32+00:00 5.2.0
## 3 X 2024-10-10T20:20:33+00:00 5.2.0
## 4 X 2024-10-10T20:20:33+00:00 5.2.0
## 5 X 2024-10-10T20:20:33+00:00 5.2.0
## 6 X 2024-10-10T20:20:33+00:00 5.2.0
## self_reported_ethnicity sex spatial suspension_type
## 1 European, unknown female, male, unknown NA cell
## 2 unknown female NA cell
## 3 unknown male NA nucleus
## 4 unknown male NA nucleus
## 5 unknown male NA nucleus
## 6 unknown male NA nucleus
## tissue
## 1 carotid artery segment, coronary artery, femoral artery
## 2 ovary
## 3 entorhinal cortex
## 4 entorhinal cortex
## 5 superior frontal gyrus
## 6 superior frontal gyrus
## dataset_description
## 1 plaque atlas
## 2 Single-cell reconstruction of follicular remodeling in the human adult ovary
## 3 Molecular characterization of selectively vulnerable neurons in Alzheimer’s Disease: EC oligodendrocyte
## 4 Molecular characterization of selectively vulnerable neurons in Alzheimer’s Disease: EC excitatory neurons
## 5 Molecular characterization of selectively vulnerable neurons in Alzheimer’s Disease: SFG microglia
## 6 Molecular characterization of selectively vulnerable neurons in Alzheimer’s Disease: SFG astrocytes
## tombstone x_approximate_distribution spatial.has_fullres spatial.is_single
## 1 FALSE <NA> NA NA
## 2 FALSE <NA> NA NA
## 3 FALSE <NA> NA NA
## 4 FALSE <NA> NA NA
## 5 FALSE <NA> NA NA
## 6 FALSE <NA> NA NA
## batch_condition default_embedding
## 1 <NA>
## 2 <NA>
## 3 <NA>
## 4 <NA>
## 5 <NA>
## 6 <NA>
## rds_id
## 1 https://datasets.cellxgene.cziscience.com/999a6b92-46ca-498e-b1ee-5fc43b6988ef.rds
## 2 https://datasets.cellxgene.cziscience.com/2afef4bd-99af-41f4-b507-f80718b6a8ef.rds
## 3 https://datasets.cellxgene.cziscience.com/d9db936c-41c6-4398-a8c4-a9dca34a225e.rds
## 4 https://datasets.cellxgene.cziscience.com/000198ac-27c7-4b9f-9fd4-6362a5b91596.rds
## 5 https://datasets.cellxgene.cziscience.com/017837df-b8be-4a4f-bc08-c5f14bd8815a.rds
## 6 https://datasets.cellxgene.cziscience.com/3512034b-9a5c-4dba-9dfb-cead9f9840dc.rds
## h5ad_id
## 1 https://datasets.cellxgene.cziscience.com/999a6b92-46ca-498e-b1ee-5fc43b6988ef.h5ad
## 2 https://datasets.cellxgene.cziscience.com/2afef4bd-99af-41f4-b507-f80718b6a8ef.h5ad
## 3 https://datasets.cellxgene.cziscience.com/d9db936c-41c6-4398-a8c4-a9dca34a225e.h5ad
## 4 https://datasets.cellxgene.cziscience.com/000198ac-27c7-4b9f-9fd4-6362a5b91596.h5ad
## 5 https://datasets.cellxgene.cziscience.com/017837df-b8be-4a4f-bc08-c5f14bd8815a.h5ad
## 6 https://datasets.cellxgene.cziscience.com/3512034b-9a5c-4dba-9dfb-cead9f9840dc.h5adSummary attributes
GEfetch2R provides StatDBAttribute to
summary attributes of CELLxGENE:
StatDBAttribute(
df = all.cellxgene.datasets, filter = c("organism", "sex", "disease"),
database = "CELLxGENE", combine = TRUE
)## # A tibble: 343 × 4
## # Groups: organism, sex [18]
## organism sex disease Num
## <chr> <chr> <chr> <int>
## 1 homo sapiens male normal 856
## 2 homo sapiens female normal 691
## 3 mus musculus male normal 191
## 4 homo sapiens unknown normal 146
## 5 mus musculus female normal 143
## 6 mus musculus unknown normal 123
## 7 homo sapiens female covid-19 50
## 8 homo sapiens female dementia 50
## 9 homo sapiens male dementia 50
## 10 homo sapiens male covid-19 42
## # ℹ 333 more rows
# # use cellxgene.census
# StatDBAttribute(filter = c("disease", "tissue", "cell_type"), database = "CELLxGENE",
# use.census = TRUE, organism = "homo_sapiens")Extract metadata
GEfetch2R provides ExtractCELLxGENEMeta to
filter dataset metadata, the available values of attributes can be
obtained with StatDBAttribute except cell
number:
# human 10x v2 and v3 datasets
human.10x.cellxgene.meta <- ExtractCELLxGENEMeta(
all.samples.df = all.cellxgene.datasets,
assay = c("10x 3' v2", "10x 3' v3"), organism = "Homo sapiens"
)
# subset
cellxgene.down.meta <- human.10x.cellxgene.meta[human.10x.cellxgene.meta$cell_type == "oligodendrocyte" &
human.10x.cellxgene.meta$tissue == "entorhinal cortex", ]Show the metadata:
head(cellxgene.down.meta)## title
## 3 Molecular characterization of selectively vulnerable neurons in Alzheimer's Disease
## description
## 3 Single-nuclei RNA sequencing of caudal entorhinal cortex and superior frontal gyrus from individuals spanning the neuropathological progression of AD
## doi contact contact_email
## 3 10.1038/s41593-020-00764-7 Martin Kampmann Martin.Kampmann@ucsf.edu
## collection_id
## 3 180bff9c-c8a5-4539-b13b-ddbc00d643e6
## collection_url
## 3 https://cellxgene.cziscience.com/collections/180bff9c-c8a5-4539-b13b-ddbc00d643e6
## consortia curator_name visibility assay
## 3 Jennifer Yu-Sheng Chien PUBLIC 10x 3' v2
## assets
## 3 17724244, 14805677, H5AD, RDS, https://datasets.cellxgene.cziscience.com/d9db936c-41c6-4398-a8c4-a9dca34a225e.h5ad, https://datasets.cellxgene.cziscience.com/d9db936c-41c6-4398-a8c4-a9dca34a225e.rds
## cell_count cell_type
## 3 8168 oligodendrocyte
## citation
## 3 Publication: https://doi.org/10.1038/s41593-020-00764-7 Dataset Version: https://datasets.cellxgene.cziscience.com/d9db936c-41c6-4398-a8c4-a9dca34a225e.h5ad curated and distributed by CZ CELLxGENE Discover in Collection: https://cellxgene.cziscience.com/collections/180bff9c-c8a5-4539-b13b-ddbc00d643e6
## dataset_id dataset_version_id
## 3 f9ad5649-f372-43e1-a3a8-423383e5a8a2 d9db936c-41c6-4398-a8c4-a9dca34a225e
## development_stage
## 3 50-year-old stage, 60-year-old stage, 71-year-old stage, 72-year-old stage, 77-year-old stage, 80 year-old and over stage, 82-year-old stage, 87-year-old stage
## disease donor_id
## 3 Alzheimer disease, normal 3, 1, 2, 5, 6, 7, 4, 8, 9, 10
## embeddings
## 3 X_cca, X_cca.aligned, X_tsne
## explorer_url
## 3 https://cellxgene.cziscience.com/e/f9ad5649-f372-43e1-a3a8-423383e5a8a2.cxg/
## feature_biotype feature_count feature_reference is_primary_data
## 3 gene 32743 NCBITaxon:9606 FALSE
## mean_genes_per_cell organism primary_cell_count published_at
## 3 599.5956 Homo sapiens 0 2020-11-20T13:39:38+00:00
## raw_data_location revised_at schema_version
## 3 X 2024-10-10T20:20:33+00:00 5.2.0
## self_reported_ethnicity sex spatial suspension_type tissue
## 3 unknown male NA nucleus entorhinal cortex
## dataset_description
## 3 Molecular characterization of selectively vulnerable neurons in Alzheimer’s Disease: EC oligodendrocyte
## tombstone x_approximate_distribution spatial.has_fullres spatial.is_single
## 3 FALSE <NA> NA NA
## batch_condition default_embedding
## 3 <NA>
## rds_id
## 3 https://datasets.cellxgene.cziscience.com/d9db936c-41c6-4398-a8c4-a9dca34a225e.rds
## h5ad_id
## 3 https://datasets.cellxgene.cziscience.com/d9db936c-41c6-4398-a8c4-a9dca34a225e.h5adDownload object and load to R
After manually check the extracted metadata, users can
download the specified objects with
ParseCELLxGENE. The downloaded objects are controlled by
file.ext (choose from "rds" and
"h5ad").
The returned value is a dataframe containing failed objects or a
SeuratObject (if file.ext is
rds). If dataframe, users can re-run
ParseCELLxGENE by setting meta to the returned
value.
When using cellxgene.census, users can subset
metadata and gene.
# download objects
cellxgene.down <- ParseCELLxGENE(
meta = cellxgene.down.meta, file.ext = "rds",
out.folder = "/Volumes/soyabean/GEfetch2R/download_cellxgene"
)
# retuen SeuratObject
cellxgene.down.seu <- ParseCELLxGENE(
meta = cellxgene.down.meta, file.ext = "rds", return.seu = TRUE,
obs.value.filter = "cell_type == 'oligodendrocyte' & disease == 'Alzheimer disease'",
obs.keys = c("cell_type", "disease", "sex", "suspension_type", "development_stage"),
out.folder = "/Volumes/soyabean/GEfetch2R/download_cellxgene"
)
# use cellxgene.census (support subset, but update is not timely)
cellxgene.down.census <- ParseCELLxGENE(
use.census = TRUE, organism = "Homo sapiens",
obs.value.filter = "cell_type == 'B cell' & tissue_general == 'lung' & disease == 'COVID-19'",
obs.keys = c("cell_type", "tissue_general", "disease", "sex"),
include.genes = c("ENSG00000161798", "ENSG00000188229")
)Show the returned SeuratObject:
cellxgene.down.seu## An object of class Seurat
## 32743 features across 6873 samples within 1 assay
## Active assay: RNA (32743 features, 0 variable features)
## 3 dimensional reductions calculated: cca, cca.aligned, tsneShow the returned SeuratObject (cellxgene.census):
cellxgene.down.census## An object of class Seurat
## 2 features across 2729 samples within 1 assay
## Active assay: RNA (2 features, 0 variable features)The structure of downloaded objects:
tree /Volumes/soyabean/GEfetch2R/download_cellxgene## /Volumes/soyabean/GEfetch2R/download_cellxgene
## └── Molecular.characterization.of.selectively.vulnerable.neurons.in.Alzheimer.s.Disease..EC.oligodendrocyte.rds
##
## 1 directory, 1 fileHuman Cell Atlas
The Human Cell Atlas
aims to map every cell type in the human body, it contains 484
projects, most of which are from Homo sapiens
(also includes projects from Mus musculus,
Macaca mulatta and
canis lupus familiaris).
Show available datasets
GEfetch2R provides ShowHCAProjects to
extract detailed project metadata, including project title, description,
organism, sex, organ/organPart, disease, assay, preservation method,
sample type, suspension type, cell type, development stage, et al.
There are 484 unique projects under five
different catalogs (dcp29, dcp30,
dcp1, lm2, lm3):
all.hca.projects <- ShowHCAProjects()Show the metadata:
head(all.hca.projects)## projectTitle
## 1 1.3 Million Brain Cells from E18 Mice
## 2 A Cellular Anatomy of the Normal Adult Human Prostate and Prostatic Urethra
## 3 A Cellular Atlas of Pitx2-Dependent Cardiac Development.
## 4 A Human Liver Cell Atlas reveals Heterogeneity and Epithelial Progenitors
## 5 A Partial Picture of the Single-Cell Transcriptomics of Human IgA Nephropathy
## 6 A Protocol for Revealing Oral Neutrophil Heterogeneity by Single-Cell Immune Profiling in Human Saliva
## projectId projectShortname
## 1 74b6d569-3b11-42ef-b6b1-a0454522b4a0 1M Neurons
## 2 53c53cd4-8127-4e12-bc7f-8fe1610a715c ProstateCellAtlas
## 3 7027adc6-c9c9-46f3-84ee-9badc3a4f53b Pitx2DevelopingHeart
## 4 94e4ee09-9b4b-410a-84dc-a751ad36d0df LiverCellAtlasHeterogeneity
## 5 c5b475f2-76b3-4a8e-8465-f3b69828fec3 Tang-Human-IgAN-GEXSCOPE
## 6 60ea42e1-af49-42f5-8164-d641fdb696bc PRJNA640427_human_neutrophils
## projectDescription
## 1 Cortex, hippocampus, and subventricular zone were purchased from BrainBits (C57EHCV). They were from 2 E18 C57BL/6 mice dissected on the same day, shipped overnight on ice, and stored at 4C until being prepared for scRNA-Seq. Brain tissues were dissociated following the Demonstrated Protocol for Mouse Embryonic Neural Tissue (https://support.10xgenomics.com/single-cell/sample-prep/doc/demonstrated-protocol-dissociation-of-mouse-embryonic-neural-tissue-for-single-cell-rna-sequencing). 69 scRNA-Seq libraries were made from first mouse brain 2 days after the dissection. Another 64 scRNA-Seq libraries were made from second mouse brain 6 days after the dissection.
## 2 A comprehensive cellular anatomy of normal human prostate is essential for solving the cellular origins of benign prostatic hyperplasia and prostate cancer. The tools used to analyze the contribution of individual cell types are not robust. We provide a cellular atlas of the young adult human prostate and prostatic urethra using an iterative process of single-cell RNA sequencing (scRNA-seq) and flow cytometry on ∼98,000 cells taken from different anatomical regions. Immunohistochemistry with newly derived cell type-specific markers revealed the distribution of each epithelial and stromal cell type on whole mounts, revising our understanding of zonal anatomy. Based on discovered cell surface markers, flow cytometry antibody panels were designed to improve the purification of each cell type, with each gate confirmed by scRNA-seq. The molecular classification, anatomical distribution, and purification tools for each cell type in the human prostate create a powerful resource for experimental design in human prostate disease.
## 3 Single-cell RNA sequencing was applied to study the role of Pitx2 expression in heart development in mice. Over 75,000 single cardiac cell transcriptomes between two key developmental timepoints in control and Pitx2-null embryos were amplified and sequenced.
## 4 We perfomed single-cell RNA-sequnecing of around 10,000 cells from normal human liver tissue to construct a human liver cell atlas. We reveal previously unknown subtypes in different cell type compartments. Overall design: Single cells were isolated from human liver resection specimens and then sorted by FACS into 384 well plates in a unbiased way and on the basis of cell surface markers for distinct cell types. ScRNA-seq was done using the mCelSeq2 protocol.
## 5 Aa comprehensive scRNA-seq analysis of human renal biopsies from IgAN. We showed for the first time that IgAN mesangial cells displayed increased expression of several novel genes including MALAT1, GADD45B, SOX4, and EDIL3, which were related to cell proliferation and matrix accumulation. The overexpressed genes in tubule cells of IgAN were mainly enriched in inflammatory pathways including TNF signaling, IL-17 signaling, and NOD-like receptor signaling. Furthermore, we compared the results of 4 IgAN patients with the published scRNA-Seq data of healthy kidney tissues of three human donors in order to further validate the findings in our study. The results also verified that the overexpressed genes in tubule cells from IgAN patients were mainly enriched in inflammatory pathways including TNF signaling, IL-17 signaling, and NOD-like receptor signaling. The receptor-ligand crosstalk analysis revealed potential interactions between mesangial cells and other cells in IgAN. IgAN patients with overt proteinuria displayed elevated genes participating in several signaling pathways compared with microproteinuria group. It needs to be mentioned that based on number of mesangial cells and other kidney cells analyzed in this study, the results of our study are preliminary and needs to be confirmed on larger number of cells from larger number of patients and controls in future studies. Therefore, these results offer new insight into pathogenesis and identify new therapeutic targets for IgAN.
## 6 Neutrophils are the most abundant white blood cells in the human body responsible for fighting viral, bacterial and fungi infections. Out of the 100 billion neutrophils produced daily, it is estimated that 10 % of these cells end up in oral biofluids. Because saliva is a fluid accessible through non-invasive techniques, it is an optimal source of cells and molecule surveillance in health and disease. While neutrophils are abundant in saliva, scientific advancements in neutrophil biology have been hampered likely due to their short life span, inability to divide once terminally differentiated, sensitivity to physical stress, and low RNA content. Here, we devise a protocol aiming to understand neutrophil heterogeneity by improving isolation methods, single-cell RNA extraction, sequencing and bioinformatic pipelines. Advanced flow cytometry 3D analysis, and machine learning validated our gating system model, by including positive neutrophil markers and excluding other immune cells and uncovered neutrophil heterogeneity. Considering specific cell markers, unique mitochondrial content, stringent and less stringent filtering strategies, our transcriptome single cell findings unraveled novel neutrophil subpopulations. Collectively, this methodology accelerates the discovery of salivary immune landscapes, with the promise of improving the understanding of diversification mechanisms, clinical diagnostics in health and disease, and guide targeted therapies.
## publications
## 1 Massively parallel digital transcriptional profiling of single cells
## 2 A Cellular Anatomy of the Normal Adult Human Prostate and Prostatic Urethra
## 3 A cellular atlas of Pit2x-dependent cardiac development.
## 4 A human liver cell atlas reveals heterogeneity and epithelial progenitors.
## 5 A Partial Picture of the Single-Cell Transcriptomics of Human IgA Nephropathy
## 6 A Protocol for Revealing Oral Neutrophil Heterogeneity by Single-Cell Immune Profiling in Human Saliva
## laboratory
## 1 Human Cell Atlas Data Coordination Platform
## 2 Human Cell Atlas Data Coordination Platform|Strand Lab
## 3 Department of Molecular Physiology and Biophysics|Human Cell Atlas|Program in Developmental Biology
## 4 NA
## 5 Centre for Inflammatory Diseases|Department of Hematology|Department of Medical Records & Information|Department of Nephrology|Department of Nephrology;Centre for Inflammatory Diseases|Department of Organ Transplantation|Department of Pathology|Department of Ultrasound|Human Cell Atlas Data Coordination Platform
## 6 Human Cell Atlas
## accessions accessible estimatedCellCount
## 1 GSE93421, SRP096558, PRJNA360949 TRUE 1330000
## 2 GSE117403 TRUE 108700
## 3 SRP198380, GSE131181, PRJNA542873 TRUE 75000
## 4 SRP174502, GSE124395, PRJNA511895 TRUE 10000
## 5 GSE171314, SRP313266, PRJNA719108 TRUE 20570
## 6 SRP271375, PRJNA640427 TRUE 1145
## sampleEntityType organ
## 1 specimens brain
## 2 specimens prostate gland
## 3 specimens heart
## 4 organoids, specimens liver, liver
## 5 specimens kidney
## 6 specimens oral cavity
## organPart
## 1 cortex
## 2 peripheral zone of prostate|transition zone of prostate
## 3 NA
## 4 , NA
## 5 NA
## 6 saliva
## sampleID
## 1 E18_20160930_Brain|E18_20161004_Brain
## 2 D17PrPz|D17PrTz|D27PrPz|D27PrTz|D35PrPz|D35PrTz
## 3 SAMN11640779_het|SAMN11640779_wt|SAMN11640780|SAMN11640781_het|SAMN11640781_wt|SAMN11640782_het|SAMN11640782_wt|SAMN11640783|SAMN11640784|SAMN11640785_het|SAMN11640785_wt|SAMN11640786_het|SAMN11640786_wt|SAMN11640787|SAMN11640788|SAMN11640789|SAMN11640790
## 4 SAMN10645790|SAMN10645791|SAMN10645843|SAMN10645847|SAMN10645915|SAMN10645916|SAMN10645917|SAMN10645918|SAMN10645919|SAMN10645920|SAMN10645921|SAMN10645922|SAMN10645923|SAMN10645924, SAMN10645729|SAMN10645734|SAMN10645735|SAMN10645736|SAMN10645737|SAMN10645738|SAMN10645739|SAMN10645741|SAMN10645744|SAMN10645745|SAMN10645746|SAMN10645747|SAMN10645748|SAMN10645749|SAMN10645750|SAMN10645751|SAMN10645756|SAMN10645758|SAMN10645761|SAMN10645773|SAMN10645782|SAMN10645810|SAMN10645830|SAMN10645831|SAMN10645832|SAMN10645833|SAMN10645834|SAMN10645835|SAMN10645836|SAMN10645837|SAMN10645838|SAMN10645839|SAMN10645840|SAMN10645848|SAMN10645849|SAMN10645850|SAMN10645851|SAMN10645852|SAMN10645853|SAMN10645854|SAMN10645855|SAMN10645856|SAMN10645857|SAMN10645858|SAMN10645860|SAMN10645861|SAMN10645862|SAMN10645863|SAMN10645864|SAMN10645873|SAMN10645880|SAMN10645881|SAMN10645882|SAMN10645883|SAMN10645884|SAMN10645885|SAMN10645886|SAMN10645888|SAMN10645892|SAMN10645911|SAMN10645935|SAMN10645945|SAMN10645954|SAMN10645955|SAMN10645956|SAMN10645962|SAMN10645963|SAMN10645964|SAMN10645965|SAMN10645966|SAMN10645967|SAMN10645968|SAMN10645969|SAMN10645970|SAMN10645971|SAMN10645972|SAMN10645973|SAMN10645974|SAMN10645977|SAMN10645978|SAMN10645979|SAMN10645980|SAMN10645989|SAMN10646005|SAMN10646007|SAMN10646011|SAMN10646013|SAMN10646019|SAMN10646032|SAMN10646036|SAMN10646037|SAMN10646038|SAMN10646039|SAMN10646040|SAMN10646041|SAMN10646042|SAMN10646043|SAMN10646044|SAMN10646045|SAMN10646046
## 5 kidney_1_igan|kidney_2_igan|kidney_3_igan|kidney_4_igan|kidney_5_healthy
## 6 saliva_sample1|saliva_sample2|saliva_sample3
## disease preservationMethod donorCount
## 1 normal fresh 2
## 2 normal fresh 3
## 3 normal NA 17
## 4 , hepatocellular carcinoma|normal , NA 23
## 5 IgA glomerulonephritis|normal NA 5
## 6 normal NA 3
## developmentStage genusSpecies biologicalSex
## 1 mouse embryo stage Mus musculus unknown
## 2 human adult stage Homo sapiens male
## 3 Theiler stage 17|Theiler stage 21 Mus musculus unknown
## 4 human adult stage Homo sapiens unknown
## 5 human adult stage Homo sapiens female|male
## 6 human adult stage Homo sapiens male
## selectedCellType
## 1 neuron
## 2 basal cell of prostate epithelium|epithelial cell of prostate|fibroblast of connective tissue of prostate|luminal cell of prostate epithelium|prostate epithelial cell|prostate stromal cell|smooth muscle cell of prostate
## 3 NA
## 4 NA
## 5 NA
## 6 neutrophil
## catalog entryId
## 1 dcp43 74b6d569-3b11-42ef-b6b1-a0454522b4a0
## 2 dcp43 53c53cd4-8127-4e12-bc7f-8fe1610a715c
## 3 dcp43 7027adc6-c9c9-46f3-84ee-9badc3a4f53b
## 4 dcp43 94e4ee09-9b4b-410a-84dc-a751ad36d0df
## 5 dcp43 c5b475f2-76b3-4a8e-8465-f3b69828fec3
## 6 dcp43 60ea42e1-af49-42f5-8164-d641fdb696bc
## sourceId
## 1 53e00b4b-4351-4131-b873-7931f3d4037f
## 2 1d18ed01-dce0-4f9d-8ff0-61a56e63854b
## 3 aae2cbe1-2c21-4abe-a611-71d1c778657d
## 4 c95bf55d-7f4e-47b0-af97-198823f21aaa
## 5 be6cf599-d084-4c5e-a89f-95308dbac4cd
## 6 6abbe02d-b89d-49b2-b639-b45f21d5baa7
## sourceSpec
## 1 tdr:bigquery:gcp:datarepo-aa6a9210:hca_prod_74b6d5693b1142efb6b1a0454522b4a0__20220117_dcp2_20220307_dcp14:/0
## 2 tdr:bigquery:gcp:datarepo-d13e36e7:hca_prod_53c53cd481274e12bc7f8fe1610a715c__20220117_dcp2_20231002_dcp32:/0
## 3 tdr:bigquery:gcp:datarepo-1840929b:hca_prod_7027adc6c9c946f384ee9badc3a4f53b__20220117_dcp2_20230815_dcp30:/0
## 4 tdr:bigquery:gcp:datarepo-071fb08c:hca_prod_94e4ee099b4b410a84dca751ad36d0df__20220519_dcp2_20220804_dcp19:/0
## 5 tdr:bigquery:gcp:datarepo-b3b1e92f:hca_prod_c5b475f276b34a8e8465f3b69828fec3__20230331_dcp2_20230331_dcp26:/0
## 6 tdr:bigquery:gcp:datarepo-41cca7ce:hca_prod_60ea42e1af4942f58164d641fdb696bc__20220117_dcp2_20230314_dcp25:/0
## workflow
## 1
## 2 optimus_post_processing_v1.0.0|optimus_v4.2.3, ,
## 3 optimus_post_processing_v1.0.0|optimus_v4.2.2, ,
## 4 analysis_protocol_normalization|analysis_protocol_quantification, ,
## 5 analysis_protocol, ,
## 6
## libraryConstructionApproach nucleicAcidSource instrumentManufacturerModel
## 1 10x 3' v2, single cell, , Illumina HiSeq 4000
## 2 , 10X v2 sequencing, , single cell, , , Illumina NextSeq 500
## 3 , 10X 3' v2 sequencing, , single cell, , , Illumina NextSeq 500
## 4 , CEL-seq2, , single cell, , , Illumina HiSeq 2500
## 5 , GEXSCOPE technology, , single cell, , , Illumina HiSeq X
## 6 Smart-seq2, single cell, , Illumina NovaSeq 6000
## pairedEnd cellLineID cellLineType cellLinemodelOrgan
## 1 , FALSE
## 2 , , FALSE
## 3 , , FALSE
## 4 , , FALSE
## 5 , , TRUE
## 6 , TRUE
## organoidsID
## 1
## 2
## 3
## 4 SAMN10645790|SAMN10645791|SAMN10645843|SAMN10645847|SAMN10645915|SAMN10645916|SAMN10645917|SAMN10645918|SAMN10645919|SAMN10645920|SAMN10645921|SAMN10645922|SAMN10645923|SAMN10645924
## 5
## 6
## organoidsmodelOrgan organoidsmodelOrganPart lastModifiedDate
## 1 2021-10-20T09:03:20.617000Z
## 2 2022-08-31T14:47:54.785000Z
## 3 2022-08-31T14:47:30.838000Z
## 4 liver NA 2022-04-21T15:03:35.422000Z
## 5 2023-03-20T11:06:31.139000Z
## 6 2021-09-30T17:38:34.675000ZSummary attributes
GEfetch2R provides StatDBAttribute to
summary attributes of Human
Cell Atlas:
StatDBAttribute(df = all.hca.projects, filter = c("organism", "sex"), database = "HCA")## $organism
## Value Num Key
## 1 homo sapiens 464 organism
## 2 mus musculus 56 organism
## 3 canis lupus familiaris 1 organism
## 4 macaca mulatta 1 organism
##
## $sex
## Value Num Key
## 1 female 353 sex
## 2 male 348 sex
## 3 unknown 151 sex
## 4 mixed 5 sexExtract metadata
GEfetch2R provides ExtractHCAMeta to filter
projects metadata, the available values of attributes can be obtained
with StatDBAttribute except cell
number:
# human 10x v2 and v3 datasets
hca.human.10x.projects <- ExtractHCAMeta(
all.projects.df = all.hca.projects, organism = "Homo sapiens",
protocol = c("10x 3' v2", "10x 3' v3")
)Show the metadata:
head(hca.human.10x.projects)## projectTitle
## 1 A Single-Cell Atlas of the Human Healthy Airways
## 2 A Single-Cell Transcriptomic Atlas of Human Skin Aging.
## 3 A human breast atlas integrating single-cell proteomics and transcriptomics
## 4 A human cell atlas of the pressure-induced hypertrophic heart
## 5 A human embryonic limb cell atlas resolved in space and time
## 6 A human fetal lung cell atlas uncovers proximal-distal gradients of differentiation and key regulators of epithelial fates
## projectId projectShortname
## 1 ef1e3497-515e-4bbe-8d4c-10161854b699 healthyAirwaysAtlas
## 2 923d3231-7295-4184-b3f6-c3082766a8c7 AgingSkinAtlas
## 3 9b876d31-0739-4e96-9846-f76e6a427279 breastTranscriptomeAtlas
## 4 902dc043-7091-445c-9442-d72e163b9879 PressureInducedHypertrophicHeart
## 5 b176d756-62d8-4933-83a4-8b026380262f EmbryonicHindlimb
## 6 2fe3c60b-ac1a-4c61-9b59-f6556c0fce63 FetalLungImmune
## projectDescription
## 1 Rationale: The respiratory tract constitutes an elaborated line of defense that is based on a unique cellular ecosystem. Single-cell profiling methods enable the investigation of cell population distributions and transcriptional changes along the airways.Methods: We have explored the cellular heterogeneity of the human airway epithelium in 10 healthy living volunteers by single-cell RNA profiling. 77,969 cells were collected at 35 distinct locations, from the nose to the 12th division of the airway tree.Results: The resulting atlas is composed of a high percentage of epithelial cells (89.1%), but also immune (6.2%) and stromal (4.7%) cells with distinct cellular proportions in different regions of the airways. It reveals differential gene expression between identical cell types (suprabasal, secretory, and multiciliated cells) from the nose (MUC4, PI3, SIX3) and tracheobronchial (SCGB1A1, TFF3) airways. By contrast, cell-type specific gene expression is stable across all tracheobronchial samples. Our atlas improves the description of ionocytes, pulmonary neuro-endocrine (PNEC) and brush cells, and identifies a related population of NREP-positive cells. We also report the association of KRT13 with dividing cells that are reminiscent of previously described mouse “hillock” cells, and with squamous cells expressing SCEL, SPRR1A/B.Conclusions: Robust characterization of a single-cell cohort in healthy airways establishes a valuable resource for future investigations. The precise description of the continuum existing\nfrom the nasal epithelium to successive divisions of the airways and the stable gene expression profile of these regions better defines conditions under which relevant tracheobronchial proxies of human respiratory diseases can be developed.
## 2 Skin undergoes constant self-renewal, and its functional decline is a visible consequence of aging. Understanding human skin aging requires in-depth knowledge of the molecular and functional properties of various skin cell types. We performed single-cell RNA sequencing of human eyelid skin from healthy individuals across different ages and identified eleven canonical cell types, as well as six subpopulations of basal cells. Further analysis revealed progressive accumulation of photoaging-related changes and increased chronic inflammation with age. Transcriptional factors involved in the developmental process underwent early-onset decline during aging. Furthermore, inhibition of key transcription factors HES1 in fibroblasts and KLF6 in keratinocytes not only compromised cell proliferation, but also increased inflammation and cellular senescence during aging. Lastly, we found that genetic activation of HES1 or pharmacological treatment with quercetin alleviated cellular senescence of dermal fibroblasts. These findings provide a single-cell molecular framework of human skin aging, providing a rich resource for developing therapeutic strategies against aging-related skin disorders.
## 3 The breast is a dynamic organ whose response to physiological and pathophysiological conditions alters its disease susceptibility, yet the specific effects of these clinical variables on cell state remain poorly annotated. We present a unified, high-resolution breast atlas by integrating single-cell RNA-seq, mass cytometry, and cyclic immunofluorescence, encompassing a myriad of states. We define cell subtypes within the alveolar, hormone-sensing, and basal epithelial lineages, delineating associations of several subtypes with cancer risk factors, including age, parity, and BRCA2 germline mutation. Of particular interest is a subset of alveolar cells termed basal-luminal (BL) cells, which exhibit poor transcriptional lineage fidelity, accumulate with age, and carry a gene signature associated with basal-like breast cancer. We further utilize a medium-depletion approach to identify molecular factors regulating cell-subtype proportion in organoids. Together, these data are a rich resource to elucidate diverse mammary cell states. Overall design: A total of 16 breast samples were assayed (4 samples from reductive mammoplasties and 12 from prophylactic mastectomies).
## 4 Pathological cardiac hypertrophy is a leading cause of heart failure, but knowledge of the full repertoire of cardiac cells and their gene expression profiles in the human hypertrophic heart is missing. Here, by using large-scale single-nucleus transcriptomics, we present the transcriptional response of human cardiomyocytes to pressure overload caused by aortic valve stenosis and describe major alterations in cardiac cellular crosstalk. Hypertrophied cardiomyocytes had reduced input from endothelial cells and fibroblasts. Genes encoding Eph receptor tyrosine kinases, particularly EPHB1, were significantly downregulated in cardiomyocytes of the hypertrophied heart. Consequently, EPHB1 activation by its ligand ephrin (EFN)B2, which is mainly expressed by endothelial cells, was reduced. EFNB2 inhibited cardiomyocyte hypertrophy in vitro, while silencing its expression in endothelial cells induced hypertrophy in co-cultured cardiomyocytes. Our human cell atlas of the hypertrophied heart highlights the importance of intercellular crosstalk in disease pathogenesis and provides a valuable resource.
## 5 Human limbs emerge during the fourth post-conception week as mesenchymal buds, which develop into fully formed limbs over the subsequent months. This process is orchestrated by numerous temporally and spatially restricted gene expression programmes, making congenital alterations in phenotype common. Decades of work with model organisms have defined the fundamental mechanisms underlying vertebrate limb development, but an in-depth characterization of this process in humans has yet to be performed. Here we detail human embryonic limb development across space and time using single-cell and spatial transcriptomics. We demonstrate extensive diversification of cells from a few multipotent progenitors to myriad differentiated cell states, including several novel cell populations. We uncover two waves of human muscle development, each characterized by different cell states regulated by separate gene expression programmes, and identify musculin (MSC) as a key transcriptional repressor maintaining muscle stem cell identity. Through assembly of multiple anatomically continuous spatial transcriptomic samples using VisiumStitcher, we map cells across a sagittal section of a whole fetal hindlimb. We reveal a clear anatomical segregation between genes linked to brachydactyly and polysyndactyly, and uncover transcriptionally and spatially distinct populations of the mesenchyme in the autopod. Finally, we perform single-cell RNA sequencing on mouse embryonic limbs to facilitate cross-species developmental comparison, finding substantial homology between the two species.
## 6 We present a multiomic cell atlas of human lung development that combines single cell RNA and ATAC sequencing, high throughput spatial transcriptomics and single cell imaging. Coupling single cell methods with spatial analysis has allowed a comprehensive cellular survey of the epithelial, mesenchymal, endothelial and erythrocyte/leukocyte compartments from 5-22 post conception weeks. We identify new cell states in all compartments. These include developmental-specific secretory progenitors that resemble cells in adult fibrotic lungs and a new subtype of neuroendocrine cell related to human small cell lung cancer; observations which strengthen the connections between development and disease/regeneration. Our datasets are available for the community to download and interact with through our web interface ( https://fetal-lung.cellgeni.sanger.ac.uk ). Finally, to illustrate its general utility, we use our cell atlas to generate predictions about cell-cell signalling and transcription factor hierarchies which we test using organoid models. Highlights Spatiotemporal atlas of human lung development from 5-22 post conception weeks identifies 147 cell types/states. Tracking the developmental origins of multiple cell compartments, including new progenitor states. Functional diversity of fibroblasts in distinct anatomical signalling niches. Resource applied to interrogate and experimentally test the transcription factor code controlling neuroendocrine cell heterogeneity and the origins of small cell lung cancer.
## publications
## 1 A Single-Cell Atlas of the Human Healthy Airways.
## 2 A Single-Cell Transcriptomic Atlas of Human Skin Aging.
## 3 A human breast atlas integrating single-cell proteomics and transcriptomics
## 4 A human cell atlas of the pressure-induced hypertrophic heart
## 5 A human embryonic limb cell atlas resolved in space and time
## 6 A human fetal lung cell atlas uncovers proximal-distal gradients of differentiation and key regulators of epithelial fates
## laboratory
## 1 Memorial Sloan Kettering Cancer Center, New York, New York|Université Côte d'Azur, CNRS, Institut Pharmacologie Moléculaire et Cellulaire, Sophia-Antipolis, France.|Université Côte d'Azur, Centre Hospitalier Universitaire de Nice, Fédération Hospitalo-Universitaire OncoAge, CNRS, Inserm, Institute for Research on Cancer and Aging Nice Team 3, Pulmonology Department, Nice, France.
## 2 NA
## 3 NA
## 4 Cardiopulmonary Institute; Cardiac Metabolism Group, Department of Cardiology|Department of Cardiovascular Surgery|Institute for Cardiovascular Regeneration|Institute for Cardiovascular Regeneration; Cardiopulmonary Institute
## 5 Department of Clinical Neurosciences|Division of Biology and Biological Engineering|INSERM, CNRS, Institut de la Vision|John van Geest Centre for Brain Repair, Department of Clinical Neurosciences; Wellcome-MRC Cambridge Stem Cell Institute|Key Laboratory of Tropical Disease Control of Ministry of Education, Zhongshan School of Medicine, Institute of Human Virology|MRC Human Genetics Unit, IGC, WGH|The Key Laboratory for Stem Cells and Tissue Engineering, Zhongshan School of Medicine|Wellcome-MRC Cambridge Stem Cell Institute
## 6 NA
## accessions
## 1 EGAS00001004082
## 2
## 3 SRP329970, GSE180878, PRJNA749859
## 4 E-MTAB-11268
## 5 ERP119958, ERP129376, ERP160032, E-MTAB-8813, E-MTAB-10514, E-MTAB-10367, PRJEB36736, PRJEB45293, S-SUBS5
## 6 E-MTAB-11265, E-MTAB-11278, E-MTAB-11267, E-MTAB-11266
## accessible estimatedCellCount sampleEntityType organ
## 1 TRUE 77969 specimens bronchus|nose|trachea
## 2 TRUE 35678 specimens skin of body
## 3 TRUE 52682 specimens breast
## 4 TRUE 88536 specimens heart
## 5 TRUE 125955 specimens forelimb|hindlimb
## 6 TRUE NA organoids, specimens lung, heart|lung
## organPart
## 1 epithelium of bronchus|epithelium of trachea|inferior nasal concha|terminal bronchus epithelium
## 2 NA
## 3 NA
## 4 interventricular septum muscular part
## 5 forelimb stylopod|forelimb zeugopod|hindlimb bud|hindlimb stylopod|hindlimb zeugopod|manus|pes|right hindlimb|NA
## 6 , heart atrium|lung|lung epithelium
## sampleID
## 1 D322_Biop_Int1|D322_Biop_Nas1|D322_Biop_Pro1|D326_Biop_Int1|D326_Biop_Pro1|D326_Brus_Dis1|D337_Brus_Dis1|D339_Biop_Int1|D339_Biop_Nas1|D339_Biop_Pro1|D339_Brus_Dis1|D344_Biop_Int1|D344_Biop_Nas1|D344_Biop_Pro1|D344_Brus_Dis1|D353_Biop_Int2|D353_Biop_Pro1|D353_Brus_Dis1|D353_Brus_Nas1|D354_Biop_Int2|D354_Biop_Pro1|D354_Brus_Dis1|D363_Biop_Int2|D363_Biop_Pro1|D363_Brus_Dis1|D363_Brus_Nas1|D367_Biop_Int1|D367_Biop_Pro1|D367_Brus_Dis1|D367_Brus_Nas1|D372_Biop_Int1|D372_Biop_Int2|D372_Biop_Pro1|D372_Brus_Dis1|D372_Brus_Nas1
## 2 HRS118996|HRS118997|HRS118998|HRS118999|HRS119000|HRS119001|HRS119002|HRS119003|HRS119004
## 3 SAMN20422990|SAMN20422991|SAMN20422992|SAMN20422993|SAMN20422994|SAMN20422995|SAMN20422996|SAMN20422997|SAMN20422998|SAMN20422999|SAMN20423000|SAMN20423001|SAMN20423002|SAMN20423003|SAMN20423004|SAMN20423005
## 4 AS1_specimen|AS2_specimen|AS3_specimen|AS4_specimen|AS5_specimen
## 5 10_e15_mid|11_e15_dist|12_e13|13_e14|1_e13_5|3_e11|4_e12|5_e13|6_e15|7_e10_5|8_e15_whole|9_e15_prox|C1-FSM-0-SC-1|C13-FSM-0-SC-1|C3-FSM-0-SC-1|C34-FSM-0-SC-1|C35-FSM-0-SC-1|C36-FSM--SC-1|C36-FSM--SC-2|C4-FSM-0-SC-1|C42-FLEG-0-FO-1|C5-FSM-0-SC-1|C58-FLEG-0-FO-2-S15-D1-ii|C58-FLEG-0-FO-2-S16-D1-iii|C59-FLEG-0-FO-1-S11-C1-ii|C59-FLEG-0-FO-1-S18-D1-i|C59-FLEG-0-FO-1-S5-C1-i|C68-FARM-1-SC-1|C68-FARM-2-SC-1|C68-FARM-3-SC-1|C68-FLEG-1-SC-1|C68-FLEG-2-SC-1|C68-FLEG-3-SC-1|C70-FLEG-0-FO-2-S10-A1-ii|C70-FLEG-0-FO-2-S11-B1-i|C70-FLEG-0-FO-2-S20-B1-ii|C70-FLEG-0-FO-2-S7-A1-i|C72-FARM-0-SC-1|C72-FARM-0-SC-2|C72-FARM-0-SC-3|C72-FLEG-0-SC-1|C72-FLEG-0-SC-2|C72-FLEG-0-SC-3|C9-FSM-0-SC-1|GSM4227224|GSM4227225|GSM4227226|GSM4227227|GSM4498677|GSM4498678|RFL-D_1|RFL-D_2|RFL-M_1|RFL-M_2|RFL-P_1|RFL-P_2|RHL-D_1|RHL-D_2|RHL-M_1|RHL-M_2|RHL-P_1|RHL-P_2|RHL-P_3
## 6 HDBR-N13393_ASCL1_OE|HDBR-N13393_Ctrl_OE|HDBR-N13393_NEUROD1_OE|HDBR-N13393_NEUROG3_OE|HDBR-N13393_PAX9_OE|HDBR-N13393_RFX6_OE|HDBR-N13393_TFAP2A_OE|HDBR-N13393_deltaNp63alpha_OE|HDBR1915_ASCL1_OE|HDBR1915_Ctrl_OE|HDBR1915_NEUROD1_OE|HDBR1915_NEUROG3_OE|HDBR1915_PAX9_OE|HDBR1915_RFX6_OE|HDBR1915_TFAP2A_OE|HDBR1915_deltaNp63alpha_OE|HDBR2174_ASCL1_OE|HDBR2174_Ctrl_OE|HDBR2174_NEUROD1_OE|HDBR2174_NEUROG3_OE|HDBR2174_PAX9_OE|HDBR2174_RFX6_OE|HDBR2174_TFAP2A_OE|HDBR2174_deltaNp63alpha_OE, 15413-LNG--FO-3_specimen|15415-LNG--FO-2_specimen|15417-LNG-0-FO-4_specimen|15424-LNG-0-FO-3_specimen|15428-LNG-0-FO-2_specimen|15737-FLNG-1(distal)_specimen|15739-FLNG-1(distal)_specimen|15739-FLNG-3(proximal)_specimen|15773-FLNG-3(proximal)_specimen|5478STDY7698210specimen|5698STDY7839908specimen|5698STDY7839910specimen|5698STDY7839918specimen|5891STDY8062349specimen|5891STDY8062350specimen|5891STDY8062351specimen|5891STDY8062352specimen|5891STDY8062353specimen|5891STDY8062354specimen|5891STDY8062355specimen|5891STDY8062356specimen|5891STDY9030806_8specimen|5891STDY9030807_9specimen|Hst4-LNG--FO-1_specimen|Hst5-LNG--FO-1_specimen|Hst7-LNG--FO-2_specimen|SIGAA10specimen|SIGAB10specimen|SIGAC10specimen|SIGAE6specimen|SIGAF6specimen|SIGAG12specimen|SIGAG6specimen|SIGAH10specimen|SIGAH12specimen|SIGAH4specimen|WSSS8011222specimen|WSSS8012016specimen|WSSS_F_LNG8713176specimen|WSSS_F_LNG8713177specimen|WSSS_F_LNG8713178specimen_15168proximal|WSSS_F_LNG8713179specimen_15168distal|WSSS_F_LNG8713184specimen_15233proximal|WSSS_F_LNG8713185specimen_15233distal|WSSS_F_LNG8713186specimen_proximal|WSSS_F_LNG8713187specimen_distal
## disease preservationMethod donorCount
## 1 normal NA 10
## 2 normal NA 9
## 3 normal NA 16
## 4 aortic valve stenosis cryopreservation, other 5
## 5 normal cryopreservation, other|NA 36
## 6 , normal , NA 38
## developmentStage
## 1 human adult stage
## 2 adolescent stage|human adult stage
## 3 human adult stage
## 4 human adult stage
## 5 10th week post-fertilization human stage|9th week post-fertilization human stage|Carnegie stage 21|Carnegie stage 22|Carnegie stage 23|embryo stage|embryonic day 10.5|embryonic day 11.5|embryonic day 12|embryonic day 12.5|embryonic day 13|embryonic day 13.5|embryonic day 14.5|embryonic day 15.5|embryonic day 16.5|embryonic day 18.5|embryonic day 9.5
## 6 11th week post-fertilization human stage|12th week post-fertilization human stage|13th week post-fertilization human stage|14th week post-fertilization human stage|15th week post-fertilization human stage|16th week post-fertilization human stage|17th week post-fertilization human stage|18th week post-fertilization human stage|19th week post-fertilization human stage|20th week post-fertilization human stage|21st week post-fertilization human stage|22nd week post-fertilization human stage|9th week post-fertilization human stage|Carnegie stage 14|Carnegie stage 17|Carnegie stage 22|Carnegie stage 23
## genusSpecies biologicalSex selectedCellType catalog
## 1 Homo sapiens female|male NA, NA, NA dcp43
## 2 Homo sapiens female NA dcp43
## 3 Homo sapiens female NA dcp43
## 4 Homo sapiens female|male NA dcp43
## 5 Homo sapiens|Mus musculus female|male|unknown NA, NA dcp43
## 6 Homo sapiens female|male|unknown NA, NA dcp43
## entryId sourceId
## 1 ef1e3497-515e-4bbe-8d4c-10161854b699 949febcd-69f0-4c61-aaf8-52252d0ae5cd
## 2 923d3231-7295-4184-b3f6-c3082766a8c7 445d8c04-718b-4367-9616-38d106e4431b
## 3 9b876d31-0739-4e96-9846-f76e6a427279 d5994701-3915-4357-a3f7-d1b3d8ebb0ee
## 4 902dc043-7091-445c-9442-d72e163b9879 52a17083-f001-4e0f-b207-6b1d8fa32579
## 5 b176d756-62d8-4933-83a4-8b026380262f 124b11a6-66a9-4e3e-b14a-255d21521b53
## 6 2fe3c60b-ac1a-4c61-9b59-f6556c0fce63 f52e9f35-cbbb-43f9-bb87-94f9d2788bf6
## sourceSpec
## 1 tdr:bigquery:gcp:datarepo-145a904d:hca_prod_ef1e3497515e4bbe8d4c10161854b699__20220118_dcp2_20231213_dcp34:/0
## 2 tdr:bigquery:gcp:datarepo-64e86c6c:hca_prod_923d323172954184b3f6c3082766a8c7__20220906_dcp2_20230314_dcp25:/0
## 3 tdr:bigquery:gcp:datarepo-9f5be9ac:hca_prod_9b876d3107394e969846f76e6a427279__20220906_dcp2_20230314_dcp25:/0
## 4 tdr:bigquery:gcp:datarepo-f0498b78:hca_prod_902dc0437091445c9442d72e163b9879__20240201_dcp2_20240328_dcp37:/0
## 5 tdr:bigquery:gcp:datarepo-582bf509:hca_prod_b176d75662d8493383a48b026380262f__20240903_dcp2_20240904_dcp42:/0
## 6 tdr:bigquery:gcp:datarepo-573f4ced:hca_prod_2fe3c60bac1a4c619b59f6556c0fce63__20220606_dcp2_20230314_dcp25:/0
## workflow
## 1 ap_matrixNormalization|ap_rawMatrixGeneration_hg19|ap_rawMatrixGeneration_hg38, ,
## 2
## 3 analysis_protocol_1|analysis_protocol_2, ,
## 4 raw_matrix_generation, ,
## 5 scRNA_analysis|visium_analysis, , ,
## 6 processed_matrix_ap|raw_matrix_ap|visium_matrix_ap, , ,
## libraryConstructionApproach
## 1 , 10x 3' v2,
## 2 10x 3' v2,
## 3 , 10x 3' v2,
## 4 , 10x 3' v3,
## 5 , , 10x 3' v2|10x 3' v3|10x 5' v1|Visium Spatial Gene Expression,
## 6 , , 10x 3' v2|10x 3' v3|10x 5' v1|Visium Spatial Gene Expression|scATAC-seq,
## nucleicAcidSource instrumentManufacturerModel
## 1 , single cell, , , Illumina NextSeq 500|Illumina NextSeq 550
## 2 single cell, , Illumina NovaSeq 6000
## 3 , single cell, , , Illumina HiSeq X
## 4 , single nucleus, , , Illumina NovaSeq 6000
## 5 , , bulk cell|single cell, , , , Illumina HiSeq 4000|Illumina NovaSeq 6000
## 6 , , single cell, , , , Illumina HiSeq 4000|Illumina NovaSeq 6000
## pairedEnd cellLineID cellLineType cellLinemodelOrgan
## 1 , , FALSE
## 2 , FALSE
## 3 , , FALSE
## 4 , , FALSE
## 5 , , , FALSE
## 6 , , , FALSE
## organoidsID
## 1
## 2
## 3
## 4
## 5
## 6 HDBR-N13393_ASCL1_OE|HDBR-N13393_Ctrl_OE|HDBR-N13393_NEUROD1_OE|HDBR-N13393_NEUROG3_OE|HDBR-N13393_PAX9_OE|HDBR-N13393_RFX6_OE|HDBR-N13393_TFAP2A_OE|HDBR-N13393_deltaNp63alpha_OE|HDBR-N13393_organoid|HDBR1915_ASCL1_OE|HDBR1915_Ctrl_OE|HDBR1915_NEUROD1_OE|HDBR1915_NEUROG3_OE|HDBR1915_PAX9_OE|HDBR1915_RFX6_OE|HDBR1915_TFAP2A_OE|HDBR1915_deltaNp63alpha_OE|HDBR1915_organoid|HDBR2174_ASCL1_OE|HDBR2174_Ctrl_OE|HDBR2174_NEUROD1_OE|HDBR2174_NEUROG3_OE|HDBR2174_PAX9_OE|HDBR2174_RFX6_OE|HDBR2174_TFAP2A_OE|HDBR2174_deltaNp63alpha_OE|HDBR2174_organoid
## organoidsmodelOrgan organoidsmodelOrganPart lastModifiedDate
## 1 2023-11-02T12:30:26.777000Z
## 2 2022-08-08T11:11:06.854000Z
## 3 2022-08-23T12:48:58.239000Z
## 4 2024-03-06T13:09:41.289000Z
## 5 2024-07-03T12:56:39.509000Z
## 6 lung epithelial cell of lung 2022-05-20T09:34:37.057000ZDownload object and load to R
After manually check the extracted metadata, users can
download the specified objects with
ParseHCA. The downloaded objects are controlled by
file.ext (choose from "rds",
"rdata", "h5", "h5ad" and
"loom").
The returned value is a dataframe containing failed objects or a
SeuratObject (if file.ext is
rds). If dataframe, users can re-run ParseHCA
by setting meta to the returned value.
# download objects
hca.down <- ParseHCA(
meta = hca.human.10x.projects[1:4, ], out.folder = "/Volumes/soyabean/GEfetch2R/download_hca",
file.ext = c("h5ad", "rds")
)
# return SeuratObject
hca.down.seu <- ParseHCA(
meta = hca.human.10x.projects[1:4, ], out.folder = "/Volumes/soyabean/GEfetch2R/download_hca",
file.ext = c("h5ad", "rds"), return.seu = TRUE
)Show the returned SeuratObject:
hca.down.seu## An object of class Seurat
## 33555 features across 88536 samples within 1 assay
## Active assay: RNA (33555 features, 2000 variable features)
## 3 dimensional reductions calculated: pca, harmony, umapThe example structure of downloaded objects:
tree /Volumes/soyabean/GEfetch2R/download_hca## /Volumes/soyabean/GEfetch2R/download_hca
## └── seurat_object_hca_as_harmonized_AS_SP_nuc_refined_cells.rds
##
## 1 directory, 1 file