census_counts <- as.data.frame(census$get("census_info")$get("summary_cell_counts")$read()$concat())
head(census_counts)
-#> # A tibble: 6 × 7
-#> soma_joinid organism category ontology_term_id unique_cell_count total_cell_count
-#> <int> <chr> <chr> <chr> <int> <int>
-#> 1 0 Homo sapiens all na 33364242 56400873
-#> 2 1 Homo sapiens assay EFO:0008722 264166 279635
-#> 3 2 Homo sapiens assay EFO:0008780 25652 51304
-#> 4 3 Homo sapiens assay EFO:0008919 89477 206754
-#> 5 4 Homo sapiens assay EFO:0008931 78750 188248
-#> 6 5 Homo sapiens assay EFO:0008953 4693 9386
-#> # ℹ 1 more variable: label <chr>
+#> soma_joinid organism category ontology_term_id unique_cell_count total_cell_count
+#> 1 0 Homo sapiens all na 33364242 56400873
+#> 2 1 Homo sapiens assay EFO:0008722 264166 279635
+#> 3 2 Homo sapiens assay EFO:0008780 25652 51304
+#> 4 3 Homo sapiens assay EFO:0008919 89477 206754
+#> 5 4 Homo sapiens assay EFO:0008931 78750 188248
+#> 6 5 Homo sapiens assay EFO:0008953 4693 9386
+#> label
+#> 1 na
+#> 2 Drop-seq
+#> 3 inDrop
+#> 4 Seq-Well
+#> 5 Smart-seq2
+#> 6 STRT-seq
For each combination of organism and values for each category of cell metadata you can take a look at total_cell_count and unique_cell_count for the cell counts of that combination.
The values for each category are specified in ontology_term_id and label, which are the value’s IDs and labels, respectively.
@@ -288,12 +290,12 @@
Example: number of mic
If you have a specific term from any of the categories shown above you can directly find out the number of cells for that term.
census_counts[census_counts$label == "microglial cell", ]
-#> # A tibble: 2 × 7
-#> soma_joinid organism category ontology_term_id unique_cell_count total_cell_count
-#> <int> <chr> <chr> <chr> <int> <int>
-#> 1 69 Homo sapiens cell_type CL:0000129 268114 370771
-#> 2 1038 Mus musculus cell_type CL:0000129 48998 62617
-#> # ℹ 1 more variable: label <chr>
+
#> soma_joinid organism category ontology_term_id unique_cell_count
+
#> 70 69 Homo sapiens cell_type CL:0000129 268114
+
#> 1039 1038 Mus musculus cell_type CL:0000129 48998
+
#> total_cell_count label
+
#> 70 370771 microglial cell
+
#> 1039 62617 microglial cellExample: all cell types avail
obs_df <- census$get("census_data")$get("homo_sapiens")$obs$read(column_names = c("cell_type", "is_primary_data"))
as.data.frame(obs_df$concat())
-#> # A tibble: 56,400,873 × 2
-#> cell_type is_primary_data
-#> <chr> <lgl>
-#> 1 syncytiotrophoblast cell FALSE
-#> 2 placental villous trophoblast FALSE
-#> 3 syncytiotrophoblast cell FALSE
-#> 4 syncytiotrophoblast cell FALSE
-#> 5 extravillous trophoblast FALSE
-#> 6 placental villous trophoblast FALSE
-#> 7 syncytiotrophoblast cell FALSE
-#> 8 extravillous trophoblast FALSE
-#> 9 placental villous trophoblast FALSE
-#> 10 syncytiotrophoblast cell FALSE
-#> # ℹ 56,400,863 more rows
The number of rows is the total number of cells for humans. Now, if you wish to get the cell counts per cell type we can work with this data frame.
In addition, we will only focus on cells that are marked with is_primary_data=TRUE as this ensures we de-duplicate cells that appear more than once in CELLxGENE Discover.
diff --git a/api/r/cellxgene.census/docs/articles/comp_bio_data_integration.html b/api/r/cellxgene.census/docs/articles/comp_bio_data_integration.html
index 2a93a17e3..2f51223df 100644
--- a/api/r/cellxgene.census/docs/articles/comp_bio_data_integration.html
+++ b/api/r/cellxgene.census/docs/articles/comp_bio_data_integration.html
@@ -32,7 +32,7 @@
cellxgene.census
- 1.6.0
+ 1.7.0
Now we can use the values from dataset_id to query and load a Seurat object with all the cells from those datasets.
tabula_muris_liver_ids <- c("4546e757-34d0-4d17-be06-538318925fcd", "6202a243-b713-4e12-9ced-c387f8483dea")
@@ -293,20 +298,20 @@ # Run the standard workflow for visualization and clustering
seurat_obj.combined <- RunPCA(seurat_obj.combined, npcs = 30, verbose = FALSE)
seurat_obj.combined <- RunUMAP(seurat_obj.combined, reduction = "pca", dims = 1:30)
-#> 13:30:46 UMAP embedding parameters a = 0.9922 b = 1.112
-#> 13:30:46 Read 10153 rows and found 30 numeric columns
-#> 13:30:46 Using Annoy for neighbor search, n_neighbors = 30
-#> 13:30:46 Building Annoy index with metric = cosine, n_trees = 50
+#> 10:26:29 UMAP embedding parameters a = 0.9922 b = 1.112
+#> 10:26:29 Read 10153 rows and found 30 numeric columns
+#> 10:26:29 Using Annoy for neighbor search, n_neighbors = 30
+#> 10:26:29 Building Annoy index with metric = cosine, n_trees = 50
#> 0% 10 20 30 40 50 60 70 80 90 100%
#> [----|----|----|----|----|----|----|----|----|----|
#> **************************************************|
-#> 13:30:47 Writing NN index file to temp file /var/folders/9q/j_1zpqns7vz_s5n9l6_b943r0000gr/T//RtmpwHKO6w/file16d2113ba6e8
-#> 13:30:47 Searching Annoy index using 1 thread, search_k = 3000
-#> 13:30:51 Annoy recall = 100%
-#> 13:30:51 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
-#> 13:30:52 Initializing from normalized Laplacian + noise (using irlba)
-#> 13:30:53 Commencing optimization for 200 epochs, with 410804 positive edges
-#> 13:30:59 Optimization finished
Plot the UMAP.
# By assay
diff --git a/api/r/cellxgene.census/docs/articles/comp_bio_normalizing_full_gene_sequencing.html b/api/r/cellxgene.census/docs/articles/comp_bio_normalizing_full_gene_sequencing.html
index c91232c4e..782e325c1 100644
--- a/api/r/cellxgene.census/docs/articles/comp_bio_normalizing_full_gene_sequencing.html
+++ b/api/r/cellxgene.census/docs/articles/comp_bio_normalizing_full_gene_sequencing.html
@@ -32,7 +32,7 @@
cellxgene.census
- 1.6.0
+ 1.7.0
Now we can use this id to fetch the data.
liver_dataset_id <- liver_dataset[1,"dataset_id"]
diff --git a/api/r/cellxgene.census/docs/articles/comp_bio_summarize_axis_query.html b/api/r/cellxgene.census/docs/articles/comp_bio_summarize_axis_query.html
index 6c4a6ddab..422b086eb 100644
--- a/api/r/cellxgene.census/docs/articles/comp_bio_summarize_axis_query.html
+++ b/api/r/cellxgene.census/docs/articles/comp_bio_summarize_axis_query.html
@@ -32,7 +32,7 @@
cellxgene.census
- 1.6.0
+ 1.7.0