Learning about the CZ CELLxGENE Census
Source:vignettes/comp_bio_census_info.Rmd
comp_bio_census_info.RmdThis notebook showcases the Census contents and how to obtain high-level information about it. It covers the organization of data within the Census, what cell and gene metadata are available, and it provides simple demonstrations to summarize cell counts across cell metadata.
Contents
- Opening the census
- Census organization
- Cell metadata
- Gene metadata
- Census summary content tables
- Understanding Census contents beyond the summary tables
Opening the Census
The cellxgene.census R package contains a convenient open_soma() API to open any version of the Census (stable by default).
library("cellxgene.census")
census <- open_soma()You can learn more about the cellxgene.census methods by accessing their corresponding documentation, for example ?cellxgene.census::open_soma.
Census organization
The Census schema defines the structure of the Census. In short, you can think of the Census as a structured collection of items that stores different pieces of information. All of these items and the parent collection are SOMA objects of various types and can all be accessed with the TileDB-SOMA API (documentation).
The cellxgene.census package contains some convenient wrappers of the TileDB-SOMA API. An example of this is the function we used to open the Census: cellxgene_census.open_soma().
Main Census components
With the command above you created census, which is a SOMACollection, an R6 class providing a key-value associative map. Its get() method can access the two top-level collection members, census_info and census_data, each themselves instances of SOMACollection.
Census summary info
-
census$get("census_info"): A collection of data frames providing information of the census as a whole.-
census$get("census_info")$get("summary"): A data frame with high-level information of this Census, e.g. build date, total cell count, etc. -
census$get("census_info")$get("datasets"): A data frame with all datasets from CELLxGENE Discover used to create the Census. -
census$get("census_info")$get("summary_cell_counts"): A data frame with cell counts stratified by relevant cell metadata
-
Census data
Data for each organism is stored in independent SOMAExperiment objects which are a specialized form of a SOMACollection. Each of these store a data matrix (cell by genes), cell metadata, gene metadata, and some other useful components not covered in this notebook.
This is how the data is organized for one organism – Homo sapiens:
-
census$get("census_data")$get("homo_sapiens")$obs: Cell metadata -
census$get("census_data")$get("homo_sapiens")$ms$get("RNA"): Data matrices, currently only… -
census$get("census_data")$get("homo_sapiens")$ms$get("RNA")$X$get("raw"): a matrix of raw counts as aSOMASparseNDArray -
census$get("census_data")$get("homo_sapiens")$ms$get("RNA")$var: Gene Metadata
Cell metadata
You can obtain all cell metadata variables by directly querying the columns of the corresponding SOMADataFrame.
All of these variables can be used for querying the Census in case you want to work with specific cells.
census$get("census_data")$get("homo_sapiens")$obs$colnames()
#> [1] "soma_joinid"
#> [2] "dataset_id"
#> [3] "assay"
#> [4] "assay_ontology_term_id"
#> [5] "cell_type"
#> [6] "cell_type_ontology_term_id"
#> [7] "development_stage"
#> [8] "development_stage_ontology_term_id"
#> [9] "disease"
#> [10] "disease_ontology_term_id"
#> [11] "donor_id"
#> [12] "is_primary_data"
#> [13] "observation_joinid"
#> [14] "self_reported_ethnicity"
#> [15] "self_reported_ethnicity_ontology_term_id"
#> [16] "sex"
#> [17] "sex_ontology_term_id"
#> [18] "suspension_type"
#> [19] "tissue"
#> [20] "tissue_ontology_term_id"
#> [21] "tissue_type"
#> [22] "tissue_general"
#> [23] "tissue_general_ontology_term_id"
#> [24] "raw_sum"
#> [25] "nnz"
#> [26] "raw_mean_nnz"
#> [27] "raw_variance_nnz"
#> [28] "n_measured_vars"All of these variables are defined in the CELLxGENE dataset schema except for the following:
-
soma_joinid: a SOMA-defined value use for join operations. -
dataset_id: the dataset id as encoded incensus$get("census_info")$get("datasets"). -
tissue_generalandtissue_general_ontology_term_id: the high-level tissue mapping.
Gene metadata
Similarly, we can obtain all gene metadata variables by directly querying the columns of the corresponding SOMADataFrame.
These are the variables you can use for querying the Census in case there are specific genes you are interested in.
census$get("census_data")$get("homo_sapiens")$ms$get("RNA")$var$colnames()
#> [1] "soma_joinid" "feature_id" "feature_name" "feature_length" "nnz"
#> [6] "n_measured_obs"All of these variables are defined in the CELLxGENE dataset schema except for the following:
-
soma_joinid: a SOMA-defined value use for join operations. -
feature_length: the length in base pairs of the gene.
Census summary content tables
You can take a quick look at the high-level Census information by looking at census$get("census_info")$get("summary"):
as.data.frame(census$get("census_info")$get("summary")$read()$concat())
#> soma_joinid label value
#> 1 0 census_schema_version 2.0.1
#> 2 1 census_build_date 2024-05-20
#> 3 2 dataset_schema_version 5.0.0
#> 4 3 total_cell_count 115556140
#> 5 4 unique_cell_count 60597966
#> 6 5 number_donors_homo_sapiens 17651
#> 7 6 number_donors_mus_musculus 4216Of special interest are the label-value combinations for:
-
total_cell_countis the total number of cells in the Census. -
unique_cell_countis the number of unique cells, as some cells may be present twice due to meta-analysis or consortia-like data. -
number_donors_homo_sapiensandnumber_donors_mus_musculusare the number of individuals for human and mouse. These are not guaranteed to be unique as one individual ID may be present or identical in different datasets.
Cell counts by cell metadata
By looking at census$get("census_info)$get("summary_cell_counts") you can get a general idea of cell counts stratified by some relevant cell metadata. Not all cell metadata is included in this table, you can take a look at all cell and gene metadata available in the sections below “Cell metadata” and “Gene metadata”.
The line below retrieves this table and casts it into an R data frame:
census_counts <- as.data.frame(census$get("census_info")$get("summary_cell_counts")$read()$concat())
head(census_counts)
#> soma_joinid organism category label ontology_term_id total_cell_count
#> 1 0 Homo sapiens all na na 74322510
#> 2 1 Homo sapiens assay Drop-seq EFO:0008722 336562
#> 3 2 Homo sapiens assay inDrop EFO:0008780 51304
#> 4 3 Homo sapiens assay MARS-seq EFO:0008796 70146
#> 5 4 Homo sapiens assay Seq-Well EFO:0008919 206754
#> 6 5 Homo sapiens assay Smart-seq2 EFO:0008931 190792
#> unique_cell_count
#> 1 44265932
#> 2 292631
#> 3 25652
#> 4 70146
#> 5 89477
#> 6 81294For each combination of organism and values for each category of cell metadata you can take a look at total_cell_count and unique_cell_count for the cell counts of that combination.
The values for each category are specified in ontology_term_id and label, which are the value’s IDs and labels, respectively.
Example: cell metadata included in the summary counts table
To get all the available cell metadata in the summary counts table you can do the following. Remember this is not all the cell metadata available, as some variables were omitted in the creation of this table.
Example: cell counts for each sequencing assay in human data
To get the cell counts for each sequencing assay type in human data, you can perform the following operations:
human_assay_counts <- census_counts[census_counts$organism == "Homo sapiens" & census_counts$category == "assay", ]
human_assay_counts <- human_assay_counts[order(human_assay_counts$total_cell_count, decreasing = TRUE), ]Example: number of microglial cells in the Census
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$label == "microglial cell", ]
#> soma_joinid organism category label ontology_term_id
#> 78 77 Homo sapiens cell_type microglial cell CL:0000129
#> 1237 1236 Mus musculus cell_type microglial cell CL:0000129
#> total_cell_count unique_cell_count
#> 78 587981 406117
#> 1237 119764 76113Understanding Census contents beyond the summary tables
While using the pre-computed tables in census$get("census_info") is an easy and quick way to understand the contents of the Census, it falls short if you want to learn more about certain slices of the Census.
For example, you may want to learn more about:
- What are the cell types available for human liver?
- What are the total number of cells in all lung datasets stratified by sequencing technology?
- What is the sex distribution of all cells from brain in mouse?
- What are the diseases available for T cells?
All of these questions can be answered by directly querying the cell metadata as shown in the examples below.
Example: all cell types available in human
To exemplify the process of accessing and slicing cell metadata for summary stats, let’s start with a trivial example and take a look at all human cell types available in the Census:
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())
#> cell_type is_primary_data
#> 1 plasma cell FALSE
#> 2 mature B cell FALSE
#> 3 plasma cell FALSE
#> 4 mature B cell FALSE
#> 5 mature B cell FALSE
#> 6 mature B cell FALSE
#> 7 mature B cell FALSE
#> 8 plasma cell FALSE
#> 9 mature B cell FALSE
#> 10 plasma cell FALSE
#> 11 mature B cell FALSE
#> 12 mature B cell FALSE
#> 13 plasma cell FALSE
#> 14 mature B cell FALSE
#> 15 plasma cell FALSE
#> 16 mature B cell FALSE
#> 17 plasma cell FALSE
#> 18 plasma cell FALSE
#> 19 mature B cell FALSE
#> 20 mature B cell FALSE
#> 21 plasma cell FALSE
#> 22 plasma cell FALSE
#> 23 plasma cell FALSE
#> 24 plasma cell FALSE
#> 25 mature B cell FALSE
#> 26 plasma cell FALSE
#> 27 mature B cell FALSE
#> 28 plasma cell FALSE
#> 29 plasma cell FALSE
#> 30 plasma cell FALSE
#> 31 plasma cell FALSE
#> 32 mature B cell FALSE
#> 33 mature B cell FALSE
#> 34 mature B cell FALSE
#> 35 mature B cell FALSE
#> 36 mature B cell FALSE
#> 37 mature B cell FALSE
#> 38 plasma cell FALSE
#> 39 mature B cell FALSE
#> 40 mature B cell FALSE
#> 41 mature B cell FALSE
#> 42 plasma cell FALSE
#> 43 plasma cell FALSE
#> 44 mature B cell FALSE
#> 45 plasma cell FALSE
#> 46 plasma cell FALSE
#> 47 mature B cell FALSE
#> 48 mature B cell FALSE
#> 49 plasma cell FALSE
#> 50 mature B cell FALSE
#> 51 mature B cell FALSE
#> 52 plasma cell FALSE
#> 53 mature B cell FALSE
#> 54 mature B cell FALSE
#> 55 plasma cell FALSE
#> 56 plasma cell FALSE
#> 57 mature B cell FALSE
#> 58 plasma cell FALSE
#> 59 mature B cell FALSE
#> 60 plasma cell FALSE
#> 61 plasma cell FALSE
#> 62 mature B cell FALSE
#> 63 mature B cell FALSE
#> 64 mature B cell FALSE
#> 65 plasma cell FALSE
#> 66 mature B cell FALSE
#> 67 plasma cell FALSE
#> 68 plasma cell FALSE
#> 69 mature B cell FALSE
#> 70 plasma cell FALSE
#> 71 mature B cell FALSE
#> 72 plasma cell FALSE
#> 73 plasma cell FALSE
#> 74 mature B cell FALSE
#> 75 plasma cell FALSE
#> 76 plasma cell FALSE
#> 77 plasma cell FALSE
#> 78 mature B cell FALSE
#> 79 plasma cell FALSE
#> 80 plasma cell FALSE
#> 81 plasma cell FALSE
#> 82 mature B cell FALSE
#> 83 plasma cell FALSE
#> 84 mature B cell FALSE
#> 85 mature B cell FALSE
#> 86 plasma cell FALSE
#> 87 mature B cell FALSE
#> 88 mature B cell FALSE
#> 89 mature B cell FALSE
#> 90 mature B cell FALSE
#> 91 plasma cell FALSE
#> 92 mature B cell FALSE
#> 93 plasma cell FALSE
#> 94 mature B cell FALSE
#> 95 mature B cell FALSE
#> 96 mature B cell FALSE
#> 97 mature B cell FALSE
#> 98 mature B cell FALSE
#> 99 mature B cell FALSE
#> 100 mature B cell FALSE
#> 101 plasma cell FALSE
#> 102 plasma cell FALSE
#> 103 plasma cell FALSE
#> 104 mature B cell FALSE
#> 105 plasma cell FALSE
#> 106 mature B cell FALSE
#> 107 mature B cell FALSE
#> 108 mature B cell FALSE
#> 109 plasma cell FALSE
#> 110 plasma cell FALSE
#> 111 plasma cell FALSE
#> 112 mature B cell FALSE
#> 113 mature B cell FALSE
#> 114 mature B cell FALSE
#> 115 mature B cell FALSE
#> 116 mature B cell FALSE
#> 117 mature B cell FALSE
#> 118 mature B cell FALSE
#> 119 plasma cell FALSE
#> 120 mature B cell FALSE
#> 121 mature B cell FALSE
#> 122 plasma cell FALSE
#> 123 plasma cell FALSE
#> 124 mature B cell FALSE
#> 125 mature B cell FALSE
#> 126 plasma cell FALSE
#> 127 plasma cell FALSE
#> 128 plasma cell FALSE
#> [ reached 'max' / getOption("max.print") -- omitted 74322382 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.
obs_df <- census$get("census_data")$get("homo_sapiens")$obs$read(
column_names = "cell_type",
value_filter = "is_primary_data == TRUE"
)
obs_df <- as.data.frame(obs_df$concat())
nrow(obs_df)
#> [1] 44265932This is the number of unique cells. Now let’s look at the counts per cell type:
human_cell_type_counts <- table(obs_df$cell_type)
sort(human_cell_type_counts, decreasing = TRUE)[1:10]
#>
#> neuron
#> 2987968
#> glutamatergic neuron
#> 1604668
#> L2/3-6 intratelencephalic projecting glutamatergic neuron
#> 1570484
#> oligodendrocyte
#> 1373223
#> CD4-positive, alpha-beta T cell
#> 1368136
#> unknown
#> 1322115
#> CD8-positive, alpha-beta T cell
#> 1284243
#> classical monocyte
#> 1059249
#> B cell
#> 1010055
#> natural killer cell
#> 931240This shows you that the most abundant cell types are “glutamatergic neuron”, “CD8-positive, alpha-beta T cell”, and “CD4-positive, alpha-beta T cell”.
Now let’s take a look at the number of unique cell types:
length(human_cell_type_counts)
#> [1] 698That is the total number of different cell types for human.
All the information in this example can be quickly obtained from the summary table at census$get("census-info")$get("summary_cell_counts").
The examples below are more complex and can only be achieved by accessing the cell metadata.
Example: cell types available in human liver
Similar to the example above, we can learn what cell types are available for a specific tissue, e.g. liver.
To achieve this goal we just need to limit our cell metadata to that tissue. We will use the information in the cell metadata variable tissue_general. This variable contains the high-level tissue label for all cells in the Census:
obs_liver_df <- census$get("census_data")$get("homo_sapiens")$obs$read(
column_names = "cell_type",
value_filter = "is_primary_data == TRUE & tissue_general == 'liver'"
)
obs_liver_df <- as.data.frame(obs_liver_df$concat())
sort(table(obs_liver_df$cell_type), decreasing = TRUE)[1:10]
#>
#> periportal region hepatocyte T cell
#> 89999 88351
#> macrophage hepatoblast
#> 72769 58447
#> erythrocyte neoplastic cell
#> 57232 52431
#> natural killer cell monocyte
#> 51789 47013
#> erythroblast centrilobular region hepatocyte
#> 46008 45920These are the cell types and their cell counts in the human liver.
Example: diseased T cells in human tissues
In this example we are going to get the counts for all diseased cells annotated as T cells. For the sake of the example we will focus on “CD8-positive, alpha-beta T cell” and “CD4-positive, alpha-beta T cell”:
obs_t_cells_df <- census$get("census_data")$get("homo_sapiens")$obs$read(
column_names = c("disease", "tissue_general"),
value_filter = "is_primary_data == TRUE & disease != 'normal' & cell_type %in% c('CD8-positive, alpha-beta T cell', 'CD4-positive, alpha-beta T cell')"
)
obs_t_cells_df <- as.data.frame(obs_t_cells_df$concat())
print(table(obs_t_cells_df))
#> tissue_general
#> disease adipose tissue
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease adrenal gland
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease axilla
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease bladder organ
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease blood
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease bone marrow
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease brain
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease breast
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease central nervous system
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease colon
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease digestive system
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease embryo
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease endocrine gland
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease esophagogastric junction
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease esophagus
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease exocrine gland
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease eye
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease fallopian tube
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease gallbladder
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease heart
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease immune system
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease intestine
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease kidney
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease lamina propria
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease large intestine
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease liver
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease lung
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease lymph node
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease mucosa
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease musculature
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease nose
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease omentum
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease ovary
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease pancreas
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease placenta
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease pleura
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease pleural fluid
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease prostate gland
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease reproductive system
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease respiratory system
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease saliva
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease scalp
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease skeletal system
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease skin of body
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease small intestine
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease spinal cord
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease spleen
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease stomach
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease tendon of semitendinosus
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease testis
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease tongue
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease ureter
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease uterus
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease vasculature
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> tissue_general
#> disease yolk sac
#> Alzheimer disease 0
#> B-cell acute lymphoblastic leukemia 0
#> B-cell non-Hodgkin lymphoma 0
#> Barrett esophagus 0
#> [ reached getOption("max.print") -- omitted 105 rows ]These are the cell counts annotated with the indicated disease across human tissues for “CD8-positive, alpha-beta T cell” or “CD4-positive, alpha-beta T cell”.