# Using SingleR to annotate single-cell RNA-seq data

Aaron Lun*, Jared M. Andrews1, Friederike Dündar2 and Daniel Bunis3

1Washington University in St. Louis, School of Medicine, St. Louis, MO, USA
2Applied Bioinformatics Core, Weill Cornell Medicine
3Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA

*infinite.monkeys.with.keyboards@gmail.com

#### Revised: December 18th, 2019

#### Package

SingleR 1.1.13

# 1 Introduction

SingleR is an automatic annotation method for single-cell RNA sequencing (scRNAseq) data (Aran et al. 2019). Given a reference dataset of samples (single-cell or bulk) with known labels, it labels new cells from a test dataset based on similarity to the reference set. Specifically, for each test cell:

1. We compute the Spearman correlation between its expression profile and that of each reference sample. This is done across the union of marker genes identified between all pairs of labels.
2. We define the per-label score as a fixed quantile (by default, 0.8) of the distribution of correlations.
3. We repeat this for all labels and we take the label with the highest score as the annotation for this cell.
4. We optionally perform a fine-tuning step:

- The reference dataset is subsetted to only include labels with scores close to the maximum.
- Scores are recomputed using only marker genes for the subset of labels.
- This is iterated until one label remains.

Automatic annotation provides a convenient way of transferring biological knowledge across datasets. In this manner, the burden of manually interpreting clusters and defining marker genes only has to be done once, for the reference dataset, and this knowledge can be propagated to new datasets in an automated manner.

# 2 Using the built-in references

SingleR provides several reference datasets (mostly derived from bulk RNA-seq or microarray data) through dedicated data retrieval functions. For example, we obtain reference data from the Human Primary Cell Atlas using the `HumanPrimaryCellAtlasData()` function, which returns a `SummarizedExperiment` object containing matrix of log-expression values with sample-level labels.

    library(SingleR)
    hpca.se <- HumanPrimaryCellAtlasData()
    hpca.se

    ## class: SummarizedExperiment
    ## dim: 19363 713
    ## metadata(0):
    ## assays(1): logcounts
    ## rownames(19363): A1BG A1BG-AS1 ... ZZEF1 ZZZ3
    ## rowData names(0):
    ## colnames(713): GSM112490 GSM112491 ... GSM92233 GSM92234
    ## colData names(3): label.main label.fine label.ont

Our test dataset will is taken from La Manno et al. (2016).
For the sake of speed, we will only label the first 100 cells from this dataset.

    library(scRNAseq)
    hESCs <- LaMannoBrainData('human-es')
    hESCs <- hESCs[,1:100]

    # SingleR() expects log-counts, but the function will also happily take raw
    # counts for the test dataset. The reference, however, must have log-values.
    library(scater)
    hESCs <- logNormCounts(hESCs)

We use our `hpca.se` reference to annotate each cell in `hESCs` via the `SingleR()` function, which uses the algorithm described above. Note that the default marker detection method is to take the genes with the largest positive log-fold changes in the per-label medians for each gene.

    pred.hesc <- SingleR(test = hESCs, ref = hpca.se, labels = hpca.se$label.main)
    pred.hesc

    ## DataFrame with 100 rows and 5 columns
    ##                                          scores         first.labels
    ##                                        <matrix>          <character>
    ## 1772122_301_C02  0.118427:0.179700:0.157326:... Neuroepithelial_cell
    ## 1772122_180_E05  0.129708:0.236277:0.202371:... Neuroepithelial_cell
    ## 1772122_300_H02  0.158201:0.250060:0.211832:... Neuroepithelial_cell
    ## 1772122_180_B09  0.158779:0.277166:0.222681:... Neuroepithelial_cell
    ## 1772122_180_G04  0.138505:0.236659:0.190924:... Neuroepithelial_cell
    ## ...                                         ...                  ...
    ## 1772122_299_E07 0.1459310:0.241154:0.217383:... Neuroepithelial_cell
    ## 1772122_180_D02 0.1229834:0.239181:0.181222:... Neuroepithelial_cell
    ## 1772122_300_D09 0.1297573:0.233775:0.196638:... Neuroepithelial_cell
    ## 1772122_298_F09 0.1431189:0.262267:0.214330:... Neuroepithelial_cell
    ## 1772122_302_A11 0.0912854:0.185945:0.139232:... Neuroepithelial_cell
    ##                       tuning.scores               labels        pruned.labels
    ##                         <DataFrame>          <character>          <character>
    ## 1772122_301_C02 0.1824402:0.0991116 Neuroepithelial_cell Neuroepithelial_cell
    ## 1772122_180_E05 0.1375484:0.0647134              Neurons              Neurons
    ## 1772122_300_H02 0.2757982:0.1369690 Neuroepithelial_cell Neuroepithelial_cell
    ## 1772122_180_B09 0.0851623:0.0819878 Neuroepithelial_cell Neuroepithelial_cell
    ## 1772122_180_G04 0.1988415:0.1016622 Neuroepithelial_cell Neuroepithelial_cell
    ## ...                             ...                  ...                  ...
    ## 1772122_299_E07 0.1760025:0.0922504 Neuroepithelial_cell Neuroepithelial_cell
    ## 1772122_180_D02 0.1967609:0.1124805 Neuroepithelial_cell Neuroepithelial_cell
    ## 1772122_300_D09 0.0816424:0.0221368 Neuroepithelial_cell Neuroepithelial_cell
    ## 1772122_298_F09 0.1872499:0.0671893 Neuroepithelial_cell Neuroepithelial_cell
    ## 1772122_302_A11 0.1560800:0.1051322            Astrocyte            Astrocyte

Each row of the output `DataFrame` contains prediction results for a single cell. Labels are shown before fine-tuning (`first.labels`), after fine-tuning (`labels`) and after pruning (`pruned.labels`), along with the associated scores. We summarize the distribution of labels across our subset of cells:

    table(pred.hesc$labels)

    ##
    ##            Astrocyte Neuroepithelial_cell              Neurons
    ##                   14                   81                    5

At this point, it is worth noting that SingleR is workflow/package agnostic. The above example uses `SummarizedExperiment` objects, but the same functions will accept any (log-)normalized expression matrix.

# 3 Using single-cell references

Here, we will use two human pancreas datasets from the scRNAseq package. The aim is to use one pre-labelled dataset to annotate the other unlabelled dataset. First, we set up the Muraro et al. (2016) dataset to be our reference.

    library(scRNAseq)
    sceM <- MuraroPancreasData()

    # One should normally do cell-based quality control at this point, but for
    # brevity's sake, we will just remove the unlabelled libraries here.
    sceM <- sceM[,!is.na(sceM$label)]

    sceM <- logNormCounts(sceM)

We then set up our test dataset from Grun et al. (2016). To speed up this demonstration, we will subset to the first 100 cells.

    sceG <- GrunPancreasData()
    sceG <- sceG[,colSums(counts(sceG)) > 0] # Remove libraries with no counts.
    sceG <- logNormCounts(sceG)
    sceG <- sceG[,1:100]

We then run `SingleR()` as described previously but with a marker detection mode that considers the variance of expression across cells. Here, we will use the Wilcoxon ranked sum test to identify the top markers for each pairwise comparison between labels. This is slower but more appropriate for single-cell data compared to the default marker detection algorithm (which may fail for low-coverage data where the median is frequently zero).

    pred.grun <- SingleR(test=sceG, ref=sceM, labels=sceM$label, de.method="wilcox")
    table(pred.grun$labels)

    ##
    ## acinar   beta  delta   duct
    ##     53      4      2     41

# 4 Annotation diagnostics

## 4.1 Based on the scores within cells

SingleR provides a few basic yet powerful visualization tools. `plotScoreHeatmap()` displays the scores for all cells across all reference labels, which allows users to inspect the confidence of the predicted labels across the dataset. The actual assigned label for each cell is shown in the color bar at the top; note that this may not be the visually top-scoring label if fine-tuning is applied, as the only the pre-tuned scores are directly comparable across all labels.

    plotScoreHeatmap(pred.grun)

For this plot, the key point is to examine the spread of scores within each cell. Ideally, each cell (i.e., column of the heatmap) should have one score that is obviously larger than the rest, indicating that it is unambiguously assigned to a single label. A spread of similar scores for a given cell indicates that the assignment is uncertain, though this may be acceptable if the uncertainty is distributed across similar cell types that cannot be easily resolved.

We can also display other metadata information for each cell by setting `clusters=` or `annotation_col=`. This is occasionally useful for examining potential batch effects, differences in cell type composition between conditions, relationship to clusters from an unsupervised analysis, etc. In the code below, we display which donor each cell comes from:

    plotScoreHeatmap(pred.grun,
        annotation_col=as.data.frame(colData(sceG)[,"donor",drop=FALSE]))

## 4.2 Based on the deltas across cells

The `pruneScores()` function will remove potentially poor-quality or ambiguous assignments. In particular, ambiguous assignments are identified based on the per-cell “delta”, i.e., the difference between the score for the assigned label and the median across all labels for each cell. Low deltas indicate that the assignment is uncertain, which is especially relevant if the cell’s true label does not exist in the reference. The exact threshold used for pruning is identified using an outlier-based approach that accounts for differences in the scale of the correlations in various contexts.

    to.remove <- pruneScores(pred.grun)
    summary(to.remove)

    ##    Mode   FALSE    TRUE
    ## logical      96       4

By default, `SingleR()` will report pruned labels in the `pruned.labels` field where low-quality assignments are replaced with `NA`. However, the default pruning thresholds may not be appropriate for every dataset - see `?pruneScores` for a more detailed discussion. We provide the `plotScoreDistribution()` to help in determining whether the thresholds are appropriate by using information across cells with the same label. This displays the per-label distribution of the deltas across cells, from which `pruneScores()` defines an appropriate threshold as 3 median absolute deviations (MADs) below the median.

    plotScoreDistribution(pred.grun, show = "delta.med", ncol = 3, show.nmads = 3)

If some tuning parameters must be adjusted, we can simply call `pruneScores()` directly with adjusted parameters. Here, we set labels to `NA` if they are to be discarded, which is also how `SingleR()` marks such labels in `pruned.labels`.

    new.pruned <- pred.grun$labels
    new.pruned[pruneScores(pred.grun, nmads=5)] <- NA
    table(new.pruned, useNA="always")

    ## new.pruned
    ## acinar   beta  delta   duct   <NA>
    ##     53      4      2     41      0

## 4.3 Based on marker gene expression

Another simple yet effective diagnostic is to examine the expression of the marker genes for each label in the test dataset. We extract the identity of the markers from the metadata of the `SingleR()` results and use them in the `plotHeatmap()` function from scater, as shown below for beta cell markers. If a cell in the test dataset is confidently assigned to a particular label, we would expect it to have strong expression of that label’s markers. At the very least, it should exhibit upregulation of those markers relative to cells assigned to other labels.

    all.markers <- metadata(pred.grun)$de.genes
    sceG$labels <- pred.grun$labels

    # Beta cell-related markers
    plotHeatmap(sceG, order_columns_by="labels",
        features=unique(unlist(all.markers$beta)))

We can similarly perform this for all labels by wrapping this code in a loop, as shown below:

    for (lab in unique(pred.grun$labels)) {
        plotHeatmap(sceG, order_columns_by=list(I(pred.grun$labels)),
            features=unique(unlist(all.markers[[lab]])))
    }

Heatmaps are particularly useful because they allow users to check that the genes are actually biologically meaningful to that cell type’s identity. For example, beta cells would be expected to express insulin, and the fact that they do so gives more confidence to the correctness of the assignment. By comparison, the scores and deltas are more abstract and difficult to interpret for diagnostic purposes. If the identified markers are not meaningful or not consistently upregulated, some skepticism towards the quality of the assignments is warranted.

# 5 Available references

The legacy SingleR package provides RDA files that contain normalized expression values and cell types labels based on bulk RNA-seq, microarray and single-cell RNA-seq data from:

- Blueprint (Martens and Stunnenberg 2013) and Encode (The ENCODE Project Consortium 2012),
- the Human Primary Cell Atlas (Mabbott et al. 2013),
- the murine ImmGen (Heng et al. 2008), and
- a collection of mouse data sets downloaded from GEO (Benayoun et al. 2019).

The bulk RNA-seq and microarray data sets of the first three reference data sets were obtained from pre-sorted cell populations, i.e., the cell labels of these samples were mostly derived based on the respective sorting/purification strategy, not via in silico prediction methods.

Three additional reference datasets from bulk RNA-seq and microarray data for immune cells have also been prepared. Each of these datasets were also obtained from pre-sorted cell populations:

- The Database for Immune Cell Expression(/eQTLs/Epigenomics) (Schmiedel et al. 2018),
- Novershtern Hematopoietic Cell Data - GSE24759 - formerly known as Differentiation Map (Novershtern et al. 2011), and
- Monaco Immune Cell Data - GSE107011 (Monaco et al. 2019).

The characteristics of each dataset are summarized below:

Retrieval function | Organism | Samples | Sample types | No. of main labels | No. of fine labels | Cell type focus
--- | --- | --- | --- | --- | --- | ---
`HumanPrimaryCellAtlasData()` | human | 713 | microarrays of sorted cell populations | 37 | 157 | Non-specific
`BlueprintEncodeData()` | human | 259 | RNA-seq | 24 | 43 | Non-specific
`DatabaseImmuneCellExpressionData()` | human | 1561 | RNA-seq | 5 | 15 | Immune
`NovershternHematopoieticData()` | human | 211 | microarrays of sorted cell populations | 17 | 38 | Hematopoietic & Immune
`MonacoImmuneData()` | human | 114 | RNA-seq | 11 | 29 | Immune
`ImmGenData()` | mouse | 830 | microarrays of sorted cell populations | 20 | 253 | Hematopoietic & Immune
`MouseRNAseqData()` | mouse | 358 | RNA-seq | 18 | 28 | Non-specific

Details for each dataset can be viewed on the corresponding help page for its retrieval function (e.g., `?ImmGenData`). The available sample types in each set can be viewed in the collapsible sections below. The cell types in each dataset have also been manually mapped to the Cell Ontology, which provides a standardized vocabulary for comparison of labels across studies.

`BlueprintEncodeData` Labels

| label.main | label.fine | label.ont
--- | --- | --- | ---
mature.neutrophil | Neutrophils | Neutrophils | CL:0000775
CD14.positive..CD16.negative.classical.monocyte | Monocytes | Monocytes | CL:0000576
megakaryocyte.erythroid.progenitor.cell | HSC | MEP | CL:0000050
CD4.positive..alpha.beta.T.cell | CD4+ T-cells | CD4+ T-cells | CL:0000624
regulatory.T.cell | CD4+ T-cells | Tregs | CL:0000815
central.memory.CD4.positive..alpha.beta.T.cell | CD4+ T-cells | CD4+ Tcm | CL:0000904
effector.memory.CD4.positive..alpha.beta.T.cell | CD4+ T-cells | CD4+ Tem | CL:0000905
central.memory.CD8.positive..alpha.beta.T.cell | CD8+ T-cells | CD8+ Tcm | CL:0000907
effector.memory.CD8.positive..alpha.beta.T.cell | CD8+ T-cells | CD8+ Tem | CL:0000913
cytotoxic.CD56.dim.natural.killer.cell | NK cells | NK cells | CL:0000623
CD38.negative.naive.B.cell | B-cells | naive B-cells | CL:0000788
memory.B.cell | B-cells | Memory B-cells | CL:0000787
class.switched.memory.B.cell | B-cells | Class-switched memory B-cells | CL:0000972
hematopoietic.stem.cell | HSC | HSC | CL:0000037
hematopoietic.multipotent.progenitor.cell | HSC | MPP | CL:0000837
common.lymphoid.progenitor | HSC | CLP | CL:0000051
granulocyte.monocyte.progenitor.cell | HSC | GMP | CL:0000557
macrophage | Macrophages | Macrophages | CL:0000235
CD8.positive..alpha.beta.T.cell | CD8+ T-cells | CD8+ T-cells | CL:0000625
erythroblast | Erythrocytes | Erythrocytes | CL:0000232
CD34.negative..CD41.positive..CD42.positive.megakaryocyte.cell | HSC | Megakaryocytes | CL:0000556
common.myeloid.progenitor | HSC | CMP | CL:0000049
inflammatory.macrophage | Macrophages | Macrophages M1 | CL:0000863
alternatively.activated.macrophage | Macrophages | Macrophages M2 | CL:0000890
endothelial.cell.of.umbilical.vein..proliferating. | Endothelial cells | Endothelial cells | CL:0000115
conventional.dendritic.cell | DC | DC | CL:0000451
mature.eosinophil | Eosinophils | Eosinophils | CL:0000771
plasma.cell | B-cells | Plasma cells | CL:0000786
articular.chondrocyte.of.knee.joint | Chondrocytes | Chondrocytes | CL:0000138
pericardium.fibroblast | Fibroblasts | Fibroblasts | CL:0000057
smooth.muscle.cell.of.the.umbilical.artery | Smooth muscle | Smooth muscle | CL:0000192
epithelial.cell.of.proximal.tubule | Epithelial cells | Epithelial cells | CL:0000066
melanocyte.of.skin | Melanocytes | Melanocytes | CL:0000148
skeletal.muscle.tissue | Skeletal muscle | Skeletal muscle | CL:0000188
hair.follicular.keratinocyte | Keratinocytes | Keratinocytes | CL:0000312
lung.microvascular.endothelial.cell | Endothelial cells | mv Endothelial cells | CL:2000008
regular.cardiac.myocyte | Myocytes | Myocytes | CL:0000187
adipose.tissue.of.omentum | Adipocytes | Adipocytes | CL:0000136
Purkinje.cell | Neurons | Neurons | CL:0000540
pericyte.cell | Pericytes | Pericytes | CL:0000669
subcutaneous.preadipocyte | Adipocytes | Preadipocytes | NA
astrocyte | Astrocytes | Astrocytes | CL:0000127
mesangial.cell | Mesangial cells | Mesangial cells | CL:0000650

`HumanPrimaryCellAtlasData` Labels

| label.main | label.fine | label.ont
--- | --- | --- | ---
GSM112490 | DC | DC:monocyte-derived:immature | CL:0000840
GSM112541 | DC | DC:monocyte-derived:Galectin-1 | CL:0000451
GSM112665 | DC | DC:monocyte-derived:LPS | CL:0000451
GSM112668 | DC | DC:monocyte-derived | CL:0000451
GSM116101 | Smooth_muscle_cells | Smooth_muscle_cells:bronchial:vit_D | CL:0002598
GSM116104 | Smooth_muscle_cells | Smooth_muscle_cells:bronchial | CL:0002598
GSM119354 | Epithelial_cells | Epithelial_cells:bronchial | CL:0002328
GSM1209554_HH1763_UI33plus2_201004 | B_cell | B_cell | CL:0000236
GSM1209558_HH1713_u133plus2_011004 | Neutrophils | Neutrophil | CL:0000775
GSM1209561_TW1681_u133plus2_061004 | T_cells | T_cell:CD8+_Central_memory | CL:0000907
GSM1209564_HH1765_UI33plus2_201004 | T_cells | T_cell:CD8+ | CL:0000625
GSM1209565_HH1769_UI33plus2_201004 | T_cells | T_cell:CD4+ | CL:0000624
GSM1209573_TW1678_u133plus2_061004 | T_cells | T_cell:CD8+_effector_memory_RA | CL:0001062
GSM1209577_TW1675_u133plus2_061004 | T_cells | T_cell:CD8+_effector_memory | CL:0000913
GSM1209581_TW1676_u133plus2_061004 | T_cells | T_cell:CD8+_naive | CL:0000900
GSM1209585_HH1762_UI33plus2_201004 | Monocyte | Monocyte | CL:0000576
GSM1209591_HH1719_u133plus2_011004 | Erythroblast | Erythroblast | CL:0000765
GSM1209599_HH1715_u133plus2_011004 | BM & Prog. | BM | NA
GSM132921 | DC | DC:monocyte-derived:rosiglitazone | CL:0000451
GSM132922 | DC | DC:monocyte-derived:AM580 | CL:0000451
GSM132926 | DC | DC:monocyte-derived:rosiglitazone/AGN193109 | CL:0000451
GSM140970 | DC | DC:monocyte-derived:anti-DC-SIGN_2h | CL:0000451
GSM141251 | Endothelial_cells | Endothelial_cells:HUVEC | CL:0002618
GSM141252 | Endothelial_cells | Endothelial_cells:HUVEC:Borrelia_burgdorferi | CL:0002618
GSM141255 | Endothelial_cells | Endothelial_cells:HUVEC:IFNg | CL:0002618
GSM143717 | Endothelial_cells | Endothelial_cells:lymphatic | CL:0002138
GSM143728 | Endothelial_cells | Endothelial_cells:HUVEC:Serum_Amyloid_A | CL:0002618
GSM143907 | Endothelial_cells | Endothelial_cells:lymphatic:TNFa_48h | CL:0002138
GSM153893 | T_cells | T_cell:effector | CL:0000911
GSM154081 | T_cells | T_cell:CCR10+CLA+1,25(OH)2_vit_D3/IL-12 | CL:0000084
GSM154084 | T_cells | T_cell:CCR10-CLA+1,25(OH)2_vit_D3/IL-12 | CL:0000084
GSM158468 | Gametocytes | Gametocytes:spermatocyte | CL:0000017
GSM160532 | DC | DC:monocyte-derived:A._fumigatus_germ_tubes_6h | CL:0000451
GSM172865 | Neurons | Neurons:ES_cell-derived_neural_precursor | CL:0000031
GSM173532 | Keratinocytes | Keratinocytes | CL:0000312
GSM173535 | Keratinocytes | Keratinocytes:IL19 | CL:0000312
GSM173538 | Keratinocytes | Keratinocytes:IL20 | CL:0000312
GSM173541 | Keratinocytes | Keratinocytes:IL22 | CL:0000312
GSM173544 | Keratinocytes | Keratinocytes:IL24 | CL:0000312
GSM173547 | Keratinocytes | Keratinocytes:IL26 | CL:0000312
GSM173550 | Keratinocytes | Keratinocytes:KGF | CL:0000312
GSM173553 | Keratinocytes | Keratinocytes:IFNg | CL:0000312
GSM173555 | Keratinocytes | Keratinocytes:IL1b | CL:0000312
GSM178549 | HSC_-G-CSF | HSC_-G-CSF | CL:0000037
GSM181971 | DC | DC:monocyte-derived:mature | CL:0000841
GSM182001 | Monocyte | Monocyte:anti-FcgRIIB | CL:0000576
GSM183165 | Macrophage | Macrophage:monocyte-derived:IL-4/cntrl | CL:0000235
GSM183217 | Macrophage | Macrophage:monocyte-derived:IL-4/Dex/cntrl | CL:0000235
GSM183392 | Macrophage | Macrophage:monocyte-derived:IL-4/Dex/TGFb | CL:0000235
GSM183483 | Macrophage | Macrophage:monocyte-derived:IL-4/TGFb | CL:0000235
GSM189451 | Monocyte | Monocyte:leukotriene_D4 | CL:0000576
GSM198942 | NK_cell | NK_cell | CL:0000623
GSM198943 | NK_cell | NK_cell:IL2 | CL:0000623
GSM225042 | Embryonic_stem_cells | Embryonic_stem_cells | CL:0002322
GSM239260 | Tissue_stem_cells | Tissue_stem_cells:iliac_MSC | CL:0000134
GSM239606 | Chondrocytes | Chondrocytes:MSC-derived | CL:0000138
GSM239616 | Osteoblasts | Osteoblasts | CL:0000062
GSM250019 | Tissue_stem_cells | Tissue_stem_cells:BM_MSC | CL:0000134
GSM260308 | Osteoblasts | Osteoblasts:BMP2 | CL:0000062
GSM260663 | Tissue_stem_cells | Tissue_stem_cells:BM_MSC:BMP2 | CL:0000134
GSM260675 | Tissue_stem_cells | Tissue_stem_cells:BM_MSC:TGFb3 | CL:0000134
GSM260693 | DC | DC:monocyte-derived:Poly(IC) | CL:0000451
GSM260696 | DC | DC:monocyte-derived:CD40L | CL:0000451
GSM260699 | DC | DC:monocyte-derived:Schuler_treatment | CL:0000451
GSM264757 | DC | DC:monocyte-derived:antiCD40/VAF347 | CL:0000451
GSM265494 | Tissue_stem_cells | Tissue_stem_cells:dental_pulp | CL:0002148
GSM279572 | T_cells | T_cell:CD4+_central_memory | CL:0000904
GSM279577 | T_cells | T_cell:CD4+_effector_memory | CL:0000905
GSM279581 | T_cells | T_cell:CD4+_Naive | CL:0000895
GSM287216 | Smooth_muscle_cells | Smooth_muscle_cells:vascular | CL:0000359
GSM287217 | Smooth_muscle_cells | Smooth_muscle_cells:vascular:IL-17 | CL:0000359
GSM289612 | BM | BM | NA
GSM290414 | Platelets | Platelets | CL:0000233
GSM299095 | Epithelial_cells | Epithelial_cells:bladder | CL:0000066
GSM299556 | Macrophage | Macrophage:monocyte-derived | CL:0000235
GSM299557 | Macrophage | Macrophage:monocyte-derived:M-CSF | CL:0000235
GSM299558 | Macrophage | Macrophage:monocyte-derived:M-CSF/IFNg | CL:0000235
GSM299559 | Macrophage | Macrophage:monocyte-derived:M-CSF/Pam3Cys | CL:0000235
GSM299560 | Macrophage | Macrophage:monocyte-derived:M-CSF/IFNg/Pam3Cys | CL:0000235
GSM300389 | Macrophage | Macrophage:monocyte-derived:IFNa | CL:0000235
GSM304260 | Gametocytes | Gametocytes:oocyte | CL:0000023
GSM305433 | Monocyte | Monocyte:F._tularensis_novicida | CL:0000576
GSM305786 | Endothelial_cells | Endothelial_cells:HUVEC:B._anthracis_LT | CL:0002618
GSM310429 | B_cell | B_cell:Germinal_center | CL:0000844
GSM310432 | B_cell | B_cell:Plasma_cell | CL:0000786
GSM310435 | B_cell | B_cell:Naive | CL:0000788
GSM310438 | B_cell | B_cell:Memory | CL:0000787
GSM320544 | DC | DC:monocyte-derived:AEC-conditioned | CL:0000451
GSM322374 | Tissue_stem_cells | Tissue_stem_cells:lipoma-derived_MSC | CL:0000134
GSM322376 | Tissue_stem_cells | Tissue_stem_cells:adipose-derived_MSC_AM3 | CL:0000134
GSM330314 | Endothelial_cells | Endothelial_cells:HUVEC:FPV-infected | CL:0002618
GSM330315 | Endothelial_cells | Endothelial_cells:HUVEC:PR8-infected | CL:0002618
GSM330316 | Endothelial_cells | Endothelial_cells:HUVEC:H5N1-infected | CL:0002618
GSM343803 | Macrophage | Macrophage:monocyte-derived:S._aureus | CL:0000235
GSM346941 | Fibroblasts | Fibroblasts:foreskin | CL:1001608
GSM347916 | iPS_cells | iPS_cells:skin_fibroblast-derived | NA
GSM347919 | iPS_cells | iPS_cells:skin_fibroblast | NA
GSM349848 | T_cells | T_cell:gamma-delta | CL:0000798
GSM350084 | Monocyte | Monocyte:CD14+ | CL:0001054
GSM359332 | Macrophage | Macrophage:Alveolar | CL:0000583
GSM359758 | Macrophage | Macrophage:Alveolar:B._anthacis_spores | CL:0000583
GSM361272 | Neutrophils | Neutrophil:inflam | CL:0000775
GSM366942 | iPS_cells | iPS_cells:PDB_fibroblasts | NA
GSM367219 | iPS_cells | iPS_cells:PDB_1lox-17Puro-5 | NA
GSM367240 | iPS_cells | iPS_cells:PDB_1lox-17Puro-10 | NA
GSM367241 | iPS_cells | iPS_cells:PDB_1lox-21Puro-20 | NA
GSM367242 | iPS_cells | iPS_cells:PDB_1lox-21Puro-26 | NA
GSM367243 | iPS_cells | iPS_cells:PDB_2lox-5 | NA
GSM367244 | iPS_cells | iPS_cells:PDB_2lox-22 | NA
GSM367245 | iPS_cells | iPS_cells:PDB_2lox-21 | NA
GSM367258 | iPS_cells | iPS_cells:PDB_2lox-17 | NA
GSM372142 | iPS_cells | iPS_cells:CRL2097_foreskin | NA
GSM372154 | iPS_cells | iPS_cells:CRL2097_foreskin-derived:d20_hepatic_diff | NA
GSM372157 | iPS_cells | iPS_cells:CRL2097_foreskin-derived:undiff. | NA
GSM381339 | B_cell | B_cell:CXCR4+_centroblast | CL:0000965
GSM381340 | B_cell | B_cell:CXCR4-_centrocyte | CL:0000966
GSM385338 | Endothelial_cells | Endothelial_cells:HUVEC:VEGF | CL:0002618
GSM402707 | iPS_cells | iPS_cells:fibroblasts | NA
GSM402717 | iPS_cells | iPS_cells:fibroblast-derived:Direct_del._reprog | NA
GSM402806 | iPS_cells | iPS_cells:fibroblast-derived:Retroviral_transf | NA
GSM410672 | Endothelial_cells | Endothelial_cells:lymphatic:KSHV | CL:0002138
GSM410678 | Endothelial_cells | Endothelial_cells:blood_vessel | CL:0000071
GSM422109 | Monocyte | Monocyte:CD16- | CL:0000576
GSM422113 | Monocyte | Monocyte:CD16+ | CL:0000576
GSM451153 | Tissue_stem_cells | Tissue_stem_cells:BM_MSC:osteogenic | CL:0000134
GSM456349 | Hepatocytes | Hepatocytes | CL:0000182
GSM466515 | Neutrophils | Neutrophil:uropathogenic_E._coli_UTI89 | CL:0000775
GSM466516 | Neutrophils | Neutrophil:commensal_E._coli_MG1655 | CL:0000775
GSM469125 | MSC | MSC | CL:0000134
GSM469409 | Neuroepithelial_cell | Neuroepithelial_cell:ESC-derived | CL:0002259
GSM469411 | Astrocyte | Astrocyte:Embryonic_stem_cell-derived | CL:0000127
GSM476783 | Endothelial_cells | Endothelial_cells:HUVEC:IL-1b | CL:0002618
GSM483480 | HSC_CD34+ | HSC_CD34+ | CL:0000037
GSM488968 | CMP | CMP | CL:0000049
GSM488970 | GMP | GMP | CL:0000557
GSM488972 | B_cell | B_cell:immature | CL:0000816
GSM488974 | MEP | MEP | CL:0000050
GSM488976 | Myelocyte | Myelocyte | CL:0002193
GSM488978 | Pre-B_cell_CD34- | Pre-B_cell_CD34- | CL:0000955
GSM488980 | Pro-B_cell_CD34+ | Pro-B_cell_CD34+ | CL:0002048
GSM488982 | Pro-Myelocyte | Pro-Myelocyte | CL:0000836
GSM492834 | Smooth_muscle_cells | Smooth_muscle_cells:umbilical_vein | CL:0002588
GSM500995 | iPS_cells | iPS_cells:foreskin_fibrobasts | NA
GSM500996 | iPS_cells | iPS_cells:iPS:minicircle-derived | NA
GSM501001 | iPS_cells | iPS_cells:adipose_stem_cells | NA
GSM501004 | iPS_cells | iPS_cells:adipose_stem_cell-derived:lentiviral | NA
GSM501007 | iPS_cells | iPS_cells:adipose_stem_cell-derived:minicircle-derived | NA
GSM501890 | Fibroblasts | Fibroblasts:breast | CL:0002555
GSM514669 | Monocyte | Monocyte:MCSF | CL:0000576
GSM514671 | Monocyte | Monocyte:CXCL4 | CL:0000576
GSM53382 | Neurons | Neurons:adrenal_medulla_cell_line | CL:0000540
GSM540714 | Tissue_stem_cells | Tissue_stem_cells:CD326-CD56+ | CL:0000222
GSM542578 | NK_cell | NK_cell:CD56hiCD62L+ | CL:0000623
GSM547998 | T_cells | T_cell:Treg:Naive | CL:0002677
GSM549577 | Neutrophils | Neutrophil:LPS | CL:0000775
GSM549581 | Neutrophils | Neutrophil:GM-CSF_IFNg | CL:0000775
GSM556665 | Monocyte | Monocyte:S._typhimurium_flagellin | CL:0000576
GSM92231 | Neurons | Neurons:Schwann_cell | CL:0002573

`DatabaseImmuneCellExpressionData` Labels

| label.main | label.fine | label.ont
--- | --- | --- | ---
TPM_1 | B cells | B cells, naive | CL:0000788
TPM_1.1 | Monocytes | Monocytes, CD14+ | CL:0002057
TPM_1.2 | Monocytes | Monocytes, CD16+ | CL:0002396
TPM_1.3 | NK cells | NK cells | CL:0000623
TPM_1.4 | T cells, CD4+ | T cells, CD4+, memory TREG | CL:0000792
TPM_1.5 | T cells, CD4+ | T cells, CD4+, naive | CL:0000895
TPM_1.6 | T cells, CD4+ | T cells, CD4+, naive, stimulated | CL:0000896
TPM_1.7 | T cells, CD4+ | T cells, CD4+, naive TREG | CL:0001045
TPM_1.8 | T cells, CD4+ | T cells, CD4+, TFH | CL:0002038
TPM_1.9 | T cells, CD4+ | T cells, CD4+, Th1 | CL:0000545
TPM_1.10 | T cells, CD4+ | T cells, CD4+, Th1_17 | CL:0000492
TPM_1.11 | T cells, CD4+ | T cells, CD4+, Th17 | CL:0000899
TPM_1.12 | T cells, CD4+ | T cells, CD4+, Th2 | CL:0000546
TPM_1.13 | T cells, CD8+ | T cells, CD8+, naive | CL:0000900
TPM_1.14 | T cells, CD8+ | T cells, CD8+, naive, stimulated | CL:0000906

`NovershternHematopoieticData` Labels

| label.main | label.fine | label.ont
--- | --- | --- | ---
GSM609632 | Basophils | Basophils | CL:0000767
GSM609638 | B cells | Naive B cells | CL:0000788
GSM609643 | B cells | Mature B cells class able to switch | CL:0000970
GSM609648 | B cells | Mature B cells | CL:0000785
GSM609653 | B cells | Mature B cells class switched | CL:0000972
GSM609658 | CMPs | Common myeloid progenitors | CL:0000049
GSM609662 | Dendritic cells | Plasmacytoid Dendritic Cells | CL:0000784
GSM609667 | Dendritic cells | Myeloid Dendritic Cells | CL:0000782
GSM609672 | Eosinophils | Eosinophils | CL:0000771
GSM609677 | Erythroid cells | Erythroid_CD34+ CD71+ GlyA- | CL:0002003
GSM609684 | Erythroid cells | Erythroid_CD34- CD71+ GlyA- | CL:0002004
GSM609691 | Erythroid cells | Erythroid_CD34- CD71+ GlyA+ | CL:0002021
GSM609697 | Erythroid cells | Erythroid_CD34- CD71lo GlyA+ | CL:0002016
GSM609704 | Erythroid cells | Erythroid_CD34- CD71- GlyA+ | CL:0002018
GSM609710 | GMPs | Granulocyte/monocyte progenitors | CL:0000557
GSM609714 | Granulocytes | Colony Forming Unit-Granulocytes | CL:0000094
GSM609719 | Granulocytes | Granulocytes (Neutrophilic Metamyelocytes) | CL:0000582
GSM609723 | Granulocytes | Granulocytes (Neutrophils) | CL:0000776
GSM609727 | HSCs | Hematopoietic stem cells_CD133+ CD34dim | CL:0000037
GSM609737 | HSCs | Hematopoietic stem cells_CD38- CD34+ | CL:0001024
GSM609741 | Megakaryocytes | Colony Forming Unit-Megakaryocytic | CL:0000556
GSM609746 | Megakaryocytes | Megakaryocytes | CL:0000556
GSM609753 | MEPs | Megakaryocyte/erythroid progenitors | CL:0000050
GSM609762 | Monocytes | Colony Forming Unit-Monocytes | CL:0000576
GSM609766 | Monocytes | Monocytes | CL:0000576
GSM609771 | NK cells | Mature NK cells_CD56- CD16+ CD3- | CL:0000623
GSM609775 | NK cells | Mature NK cells_CD56+ CD16+ CD3- | CL:0000623
GSM609780 | NK cells | Mature NK cells_CD56- CD16- CD3- | CL:0000623
GSM609785 | NK T cells | NK T cells | CL:0000814
GSM609789 | B cells | Early B cells | CL:0002046
GSM609793 | B cells | Pro B cells | CL:0000826
GSM609798 | CD8+ T cells | CD8+ Effector Memory RA | CL:0001062
GSM609802 | CD8+ T cells | Naive CD8+ T cells | CL:0000900
GSM609809 | CD8+ T cells | CD8+ Effector Memory | CL:0000913
GSM609815 | CD8+ T cells | CD8+ Central Memory | CL:0000907
GSM609822 | CD4+ T cells | Naive CD4+ T cells | CL:0000895
GSM609829 | CD4+ T cells | CD4+ Effector Memory | CL:0000905
GSM609836 | CD4+ T cells | CD4+ Central Memory | CL:0000904

`MonacoImmuneData` Labels

| label.main | label.fine | label.ont
--- | --- | --- | ---
DZQV_CD8_naive | CD8+ T cells | Naive CD8 T cells | CL:0000900
DZQV_CD8_CM | CD8+ T cells | Central memory CD8 T cells | CL:0000907
DZQV_CD8_EM | CD8+ T cells | Effector memory CD8 T cells | CL:0000913
DZQV_CD8_TE | CD8+ T cells | Terminal effector CD8 T cells | CL:0001062
DZQV_MAIT | T cells | MAIT cells | CL:0000940
DZQV_VD2+ | T cells | Vd2 gd T cells | CL:0000798
DZQV_VD2- | T cells | Non-Vd2 gd T cells | CL:0000798
DZQV_TFH | CD4+ T cells | Follicular helper T cells | CL:0002038
DZQV_Treg | CD4+ T cells | T regulatory cells | CL:0000815
DZQV_Th1 | CD4+ T cells | Th1 cells | CL:0000545
DZQV_Th1/Th17 | CD4+ T cells | Th1/Th17 cells | CL:0000912
DZQV_Th17 | CD4+ T cells | Th17 cells | CL:0000899
DZQV_Th2 | CD4+ T cells | Th2 cells | CL:0000546
DZQV_CD4_naive | CD4+ T cells | Naive CD4 T cells | CL:0000895
DZQV_Progenitor | Progenitors | Progenitor cells | CL:0002043
DZQV_B_naive | B cells | Naive B cells | CL:0000788
DZQV_B_NSM | B cells | Non-switched memory B cells | CL:0000970
DZQV_B_Ex | B cells | Exhausted B cells | CL:0000236
DZQV_B_SM | B cells | Switched memory B cells | CL:0000972
DZQV_Plasmablasts | B cells | Plasmablasts | CL:0000980
DZQV_C_mono | Monocytes | Classical monocytes | CL:0000860
DZQV_I_mono | Monocytes | Intermediate monocytes | CL:0002393
DZQV_NC_mono | Monocytes | Non classical monocytes | CL:0000875
DZQV_NK | NK cells | Natural killer cells | CL:0000623
DZQV_pDC | Dendritic cells | Plasmacytoid dendritic cells | CL:0000784
DZQV_mDC | Dendritic cells | Myeloid dendritic cells | CL:0000782
DZQV_Neutrophils | Neutrophils | Low-density neutrophils | CL:0000096
DZQV_Basophils | Basophils | Low-density basophils | CL:0000043
925L_CD4_TE | CD4+ T cells | Terminal effector CD4 T cells | CL:0001044

`ImmGenData` Labels

| label.main | label.fine | label.ont
--- | --- | --- | ---
GSM1136119_EA07068_260297_MOGENE-1_0-ST-V1_MF.11C-11B+.LU_1.CEL | Macrophages | Macrophages (MF.11C-11B+) | CL:0000235
GSM1136122_EA07068_260300_MOGENE-1_0-ST-V1_MF.ALV.LU_1.CEL | Macrophages | Macrophages (MF.ALV) | CL:0000583
GSM1136125_EA07068_260307_MOGENE-1_0-ST-V1_MO.6+I-.BL_1.CEL | Monocytes | Monocytes (MO.6+I-) | CL:0000576
GSM1136126_EA07068_260303_MOGENE-1_0-ST-V1_MO.6+2+.MLN_1.CEL | Monocytes | Monocytes (MO.6+2+) | CL:0000576
GSM1282081_EA07068_147711_MOGENE-1_0-ST-V1_B.MEM.SP_1.CEL | B cells | B cells (B.MEM) | CL:0000787
GSM1282083_EA07068_122841_MOGENE-1_0-ST-V1_B1A.SP_3.CEL | B cells | B cells (B1A) | CL:0000820
GSM1282084_EA07068_267995_MOGENE-1_0-ST-V1_DC.11B+.AT_2.CEL | DC | DC (DC.11B+) | CL:0002465
GSM1282087_EA07068_267991_MOGENE-1_0-ST-V1_DC.11B-.AT_1.CEL | DC | DC (DC.11B-) | CL:0000990
GSM1282089_EA07068_210451_MOGENE-1_0-ST-V1_DN.SLN.CFA.D6_1.CEL | Stromal cells | Stromal cells (DN.CFA) | CL:0000499
GSM1282091_EA07068_210437_MOGENE-1_0-ST-V1_DN.SLN.V2_1.CEL | Stromal cells | Stromal cells (DN) | CL:0000499
GSM1282093_EA07068_267987_MOGENE-1_0-ST-V1_EO.AT_1.CEL | Eosinophils | Eosinophils (EO) | CL:0000771
GSM1282097_EA07068_204064_MOGENE-1_0-ST-V1_FRC.CAD11.WT.CEL | Fibroblasts | Fibroblasts (FRC.CAD11.WT) | CL:0000057
GSM1282098_EA07068_210445_MOGENE-1_0-ST-V1_FRC.SLN.CFA.D6_1.CEL | Fibroblasts | Fibroblasts (FRC.CFA) | CL:0000057
GSM1282100_EA07068_210431_MOGENE-1_0-ST-V1_FRC.SLN.V2_1.CEL | Fibroblasts | Fibroblasts (FRC) | CL:0000057
GSM1282102_EA07068_256271_MOGENE-1_0-ST-V1_GN.BL_4.CEL | Neutrophils | Neutrophils (GN) | CL:0000775
GSM1282106_EA07068_210448_MOGENE-1_0-ST-V1_LEC.SLN.CFA.D6_2.CEL | Endothelial cells | Endothelial cells (LEC.CFA) | CL:0000115
GSM1282107_EA07068_210433_MOGENE-1_0-ST-V1_LEC.SLN.V2_1.CEL | Endothelial cells | Endothelial cells (LEC) | CL:0000115
GSM1282109_EA07068_267983_MOGENE-1_0-ST-V1_MF.AT._1.CEL | Macrophages | Macrophages (MF) | CL:0000235
GSM1282112_EA07068_201211_MOGENE-1_0-ST-V1_T.DP.69-.E17.TH_1.CEL | T cells | T cells (T.DP.69-) | CL:0002427
GSM1282115_EA07068_208625_MOGENE-1_0-ST-V1_T.DP.TH_1.CEL | T cells | T cells (T.DP) | CL:0000809
GSM1282118_EA07068_208628_MOGENE-1_0-ST-V1_T.DP69+.TH_1.CEL | T cells | T cells (T.DP69+) | CL:0002429
GSM1308350_EA07068_256264_MOGENE-1_0-ST-V1_MF.F480HI.GATA6KO.PC_1.CEL | Macrophages | Macrophages (MF.F480HI.GATA6KO) | CL:0000235
GSM1308353_EA07068_256261_MOGENE-1_0-ST-V1_MF.F480HI.CTRL.PC_1.CEL | Macrophages | Macrophages (MF.F480HI.CTRL) | CL:0000235
GSM1358373_EA07068_232185_MOGENE-1_0-ST-V1_CD4.1H_1.CEL | T cells | T cells (T.CD4.1H) | CL:0000624
GSM1358375_EA07068_232189_MOGENE-1_0-ST-V1_CD4.24H_1.CEL | T cells | T cells (T.CD4.24H) | CL:0000624
GSM1358377_EA07068_232191_MOGENE-1_0-ST-V1_CD4.48H_1.CEL | T cells | T cells (T.CD4.48H) | CL:0000624
GSM1358379_EA07068_232187_MOGENE-1_0-ST-V1_CD4.5H_1.CEL | T cells | T cells (T.CD4.5H) | CL:0000624
GSM1358381_EA07068_232193_MOGENE-1_0-ST-V1_CD4.96H_1.CEL | T cells | T cells (T.CD4.96H) | CL:0000624
GSM1358382_EA07068_232183_MOGENE-1_0-ST-V1_CD4.CTR_1.CEL | T cells | T cells (T.CD4.CTR) | CL:0000624
GSM1358384_EA07068_232186_MOGENE-1_0-ST-V1_CD8.1H_1.CEL | T cells | T cells (T.CD8.1H) | CL:0000625
GSM1358386_EA07068_232190_MOGENE-1_0-ST-V1_CD8.24H_1.CEL | T cells | T cells (T.CD8.24H) | CL:0000625
GSM1358388_EA07068_232192_MOGENE-1_0-ST-V1_CD8.48H_1.CEL | T cells | T cells (T.CD8.48H) | CL:0000625
GSM1358390_EA07068_232188_MOGENE-1_0-ST-V1_CD8.5H_1.CEL | T cells | T cells (T.CD8.5H) | CL:0000625
GSM1358392_EA07068_232194_MOGENE-1_0-ST-V1_CD8.96H_1.CEL | T cells | T cells (T.CD8.96H) | CL:0000625
GSM1358393_EA07068_232184_MOGENE-1_0-ST-V1_CD8.CTR_1.CEL | T cells | T cells (T.CD8.CTR) | CL:0000625
GSM1398469_EA07068_117717_MOGENE-1_0-ST-V1_MF.PPAR-.LU_2.CEL | Macrophages | Macrophages (MFAR-) | CL:0000235
GSM1398483_EA07068_260311_MOGENE-1_0-ST-V1_MO.LU_1.CEL | Monocytes | Monocytes (MO) | CL:0000576
GSM1585312_EA07068_339227_MOGENE-1_0-ST-V1_ILC1.CD127+.SP_1.CEL | ILC | ILC (ILC1.CD127+) | CL:0001067
GSM1585315_EA07068_339236_MOGENE-1_0-ST-V1_LIV.ILC1.DX5-_1.CEL | ILC | ILC (LIV.ILC1.DX5-) | CL:0001067
GSM1585318_EA07068_339248_MOGENE-1_0-ST-V1_LPL.NCR+ILC1_1.CEL | ILC | ILC (LPL.NCR+ILC1) | CL:0001067
GSM1585320_EA07068_339234_MOGENE-1_0-ST-V1_ILC2.SI_2.CEL | ILC | ILC (ILC2) | CL:0001069
GSM1585322_EA07068_339251_MOGENE-1_0-ST-V1_LPL.NCR+ILC3_1.CEL | ILC | ILC (LPL.NCR+ILC3) | CL:0001071
GSM1585325_EA07068_305553_MOGENE-1_0-ST-V1_ILC3.LTI.CD4+.SI_4.CEL | ILC | ILC (ILC3.LTI.CD4+) | CL:0001071
GSM1585326_EA07068_305550_MOGENE-1_0-ST-V1_ILC3.LTI.CD4-.SI_4.CEL | ILC | ILC (ILC3.LTI.CD4-) | CL:0001071
GSM1585329_EA07068_267952_MOGENE-1_0-ST-V1_ILC3.LTI.4+.SI_1.CEL | ILC | ILC (ILC3.LTI.4+) | CL:0001071
GSM1585330_EA07068_339254_MOGENE-1_0-ST-V1_NK.CD127-.SP_1.CEL | NK cells | NK cells (NK.CD127-) | CL:0001065
GSM1585333_EA07068_339239_MOGENE-1_0-ST-V1_LIV.NK.DX5+_1.CEL | ILC | ILC (LIV.NK.DX5+) | CL:0001065
GSM1585336_EA07068_339242_MOGENE-1_0-ST-V1_LPL.NCR+CNK_1.CEL | ILC | ILC (LPL.NCR+CNK) | CL:0001065
GSM2112407_EA07068_388554_MOGENE-1_0-ST-V1_BA.BL_1.CEL | Basophils | Basophils (BA) | CL:0000767
GSM2112413_EA07068_397997_MOGENE-1_0-ST-V1_Ep.5wk.MEC.Sca1+.Th_1.CEL | Epithelial cells | Epithelial cells (Ep.5wk.MEC.Sca1+) | CL:0000066
GSM2112415_EA07068_397999_MOGENE-1_0-ST-V1_Ep.5wk.MEChi.Th_2.CEL | Epithelial cells | Epithelial cells (Ep.5wk.MEChi) | CL:0000066
GSM2112416_EA07068_397996_MOGENE-1_0-ST-V1_Ep.5wk.MEClo.Th_1.CEL | Epithelial cells | Epithelial cells (Ep.5wk.MEClo) | CL:0000066
GSM2112418_EA07068_398003_MOGENE-1_0-ST-V1_Ep.8wk.CEC.Sca1+.Th_1.CEL | Epithelial cells | Epithelial cells (Ep.8wk.CEC.Sca1+) | CL:0000066
GSM2112420_EA07068_398002_MOGENE-1_0-ST-V1_Ep.8wk.CEChi.Th_1.CEL | Epithelial cells | Epithelial cells (Ep.8wk.CEChi) | CL:0000066
GSM2112422_EA07068_398004_MOGENE-1_0-ST-V1_Ep.8wk.MEChi.Th_1.CEL | Epithelial cells | Epithelial cells (Ep.8wk.MEChi) | CL:0000066
GSM2112424_EA07068_398005_MOGENE-1_0-ST-V1_Ep.8wk.MEClo.Th_1.CEL | Epithelial cells | Epithelial cells (Ep.8wk.MEClo) | CL:0000066
GSM2112426_EA07068_388553_MOGENE-1_0-ST-V1_MC.ES_1.CEL | Mast cells | Mast cells (MC.ES) | CL:0000097
GSM2112428_EA07068_339312_MOGENE-1_0-ST-V1_MAST.PC_2.CEL | Mast cells | Mast cells (MC) | CL:0000097
GSM2112437_EA07068_354402_MOGENE-1_0-ST-V1_MC.TO_1.CEL | Mast cells | Mast cells (MC.TO) | CL:0000097
GSM2112440_EA07068_388549_MOGENE-1_0-ST-V1_MC.TR_1.CEL | Mast cells | Mast cells (MC.TR) | CL:0000097
GSM2112443_EA07068_449869_MOGENE-1_0-ST-V1_MC.DIGEST.PC_1.CEL | Mast cells | Mast cells (MC.DIGEST) | CL:0000097
GSM2112446_EA07068_201145_MOGENE-1_0-ST-V1_MECHI.GFP+.ADULT_6.CEL | Epithelial cells | Epithelial cells (MECHI.GFP+.ADULT) | CL:0000066
GSM2112449_EA07068_201151_MOGENE-1_0-ST-V1_MECHI.GFP+.ADULT.KO_1.CEL | Epithelial cells | Epithelial cells (MECHI.GFP+.ADULT.KO) | CL:0000066
GSM2112452_EA07068_201148_MOGENE-1_0-ST-V1_MECHI.GFP-.ADULT_6.CEL | Epithelial cells | Epithelial cells (MECHI.GFP-.ADULT) | CL:0000066
GSM2112455_EA07068_307792_MOGENE-1_0-ST-V1_MF.480HI.LV.NAIVE_1.CEL | Macrophages | Macrophages (MF.480HI.NAIVE) | CL:0000235
GSM2112458_EA07068_307793_MOGENE-1_0-ST-V1_MF.480INT.LV.NAIVE_1.CEL | Macrophages | Macrophages (MF.480INT.NAIVE) | CL:0000235
GSM2112461_EA07068_235599_MOGENE-1_0-ST-V1_T.4EFF49D+11A+.SP.D8.LCMV.CEL | T cells | T cells (T.4EFF49D+11A+.D8.LCMV) | CL:0001044
GSM2112463_EA07068_235601_MOGENE-1_0-ST-V1_T.4MEM49D+11A+.SP.D30.LCMV.CEL | T cells | T cells (T.4MEM49D+11A+.D30.LCMV) | CL:0000897
GSM2112465_EA07068_235603_MOGENE-1_0-ST-V1_T.4NVE44-49D-11A-.SP.CEL | T cells | T cells (T.4NVE44-49D-11A-) | CL:0000895
GSM2112467_EA07068_349158_MOGENE-1_0-ST-V1_T.8EFF.TBET+.SP.OT1.D6LISOVA_1.CEL | T cells | T cells (T.8EFF.TBET+.OT1LISOVA) | CL:0001050
GSM2112470_EA07068_349161_MOGENE-1_0-ST-V1_T.8EFF.TBET-.SP.OT1.D6LISOVA_1.CEL | T cells | T cells (T.8EFF.TBET-.OT1LISOVA) | CL:0001050
GSM2112473_EA07068_311873_MOGENE-1_0-ST-V1_T.8EFFKLRG1+CD127-.SP.D8.LISOVA_2.CEL | T cells | T cells (T.8EFFKLRG1+CD127-.D8.LISOVA) | CL:0001050
GSM2112475_EA07068_311875_MOGENE-1_0-ST-V1_T.8MEMKLRG1-CD127+.SP.D8.LISOVA_1.CEL | T cells | T cells (T.8MEMKLRG1-CD127+.D8.LISOVA) | CL:0000909
GSM399362_EA07068_56648_MoGene_T.4+8int.Th_#1.cel | T cells | T cells (T.4+8int) | CL:0002431
GSM399365_EA07068_55678_MoGene_T.4FP3+25+.Sp_#2.cel | T cells | T cells (T.4FP3+25+) | CL:0000792
GSM399367_EA07068_56651_MoGene_T.4int8+.Th_#1.cel | T cells | T cells (T.4int8+) | CL:0002430
GSM399370_EA07068_52774_MoGene_T.4SP24-.Th_#1.cel | T cells | T cells (T.4SP24-) | CL:0000624
GSM399373_EA07068_52777_MoGene_T.4SP24int.Th_#1.cel | T cells | T cells (T.4SP24int) | CL:0000624
GSM399376_EA07068_52768_MoGene_T.4SP69+.Th_#1.cel | T cells | T cells (T.4SP69+) | CL:0000896
GSM399379_EA07068_52780_MoGene_T.8SP24-.Th_#1.cel | T cells | T cells (T.8SP24-) | CL:0000625
GSM399382_EA07068_52783_MoGene_T.8SP24int.Th_#1.cel | T cells | T cells (T.8SP24int) | CL:0000625
GSM399385_EA07068_52771_MoGene_T.8SP69+.Th_#1.cel | T cells | T cells (T.8SP69+) | CL:0000906
GSM399397_EA07068_56645_MoGene_T.DPbl.Th_#1.cel | T cells | T cells (T.DPbl) | CL:0002428
GSM399400_EA07068_56642_MoGene_T.DPsm.Th_#1.cel | T cells | T cells (T.DPsm) | CL:0000809
GSM399403_EA07068_52786_MoGene_T.ISP.Th_#1.cel | T cells | T cells (T.ISP) | CL:0000084
GSM399438_EA07068_54191_MoGene_B.FrE.BM_#2.cel | B cells | B cells (B.FrE) | CL:0002054
GSM399440_EA07068_54192_MoGene_B.FrF.BM_#2.cel | B cells | B cells (B.FrF) | CL:0002056
GSM399448_EA07068_52806_MoGene_preB.FrD.BM_#1.cel | B cells | B cells (preB.FrD) | CL:0002052
GSM399450_EA07068_52803_MoGene_proB.FrBC.BM_#1.cel | B cells | B cells (proB.FrBC) | CL:0002400
GSM399452_EA07068_54189_MoGene_preB.FrC.BM_#2.cel | B cells | B cells (preB.FrC) | CL:0002049
GSM399454_EA07068_80000_MoGene_CD150-CD48-.BM#1.CEL | Stem cells | Stem cells (SC.STSL) | CL:0000034
GSM403986_EA07068_81316_MoGene_CD4+TESTNA.CEL | T cells | T cells (T.CD4+TESTNA) | CL:0000624
GSM403987_EA07068_81315_MoGene_CD4+TESTDB.CEL | T cells | T cells (T.CD4+TESTDB) | CL:0000624
GSM403988_EA07068_54833_MoGene_CD19CONTROL_#2.cel | B cells | B cells (B.CD19CONTROL) | CL:0000236
GSM403994_EA07068_54832_MoGene_CD4CONTROL_#2.cel | T cells | T cells (T.CD4CONTROL) | CL:0000624
GSM404000_EA07068_82676_MoGene_CD4TESTJS#1.CEL | T cells | T cells (T.CD4TESTJS) | CL:0000624
GSM404003_EA07068_82674_MoGene_CD4TESTCJ#2.CEL | T cells | T cells (T.CD4TESTCJ) | CL:0000624
GSM476654_EA07068_80001_MoGene_CD150-CD48-.BM#2.CEL | Stem cells | Stem cells (SC.CD150-CD48-) | CL:0000034
GSM476655_EA07068_54199_MoGene_immTgd.vg2+.Th_#1.cel | Tgd | Tgd (Tgd.imm.vg2+) | CL:0000799
GSM476660_EA07068_56601_MoGene_immTgd.vg2.e17.Th_#2.cel | Tgd | Tgd (Tgd.imm.vg2) | CL:0000799
GSM476664_EA07068_56603_MoGene_matTgd.vg3.e17.Th_#1.cel | Tgd | Tgd (Tgd.mat.vg3) | CL:0000800
GSM476665_EA07068_56604_MoGene_matTgd.vg3.e17.Th.#2.cel | Tgd | Tgd (Tgd.mat.vg3.) | CL:0000800
GSM476672_EA07068_87590_MoGene_TGD.SP#1.CEL | Tgd | Tgd (Tgd) | CL:0000798
GSM476678_EA07068_54193_MoGene_Tgd.vg2+.act.Sp_#1.cel | Tgd | Tgd (Tgd.vg2+.act) | CL:0000798
GSM476681_EA07068_54196_MoGene_Tgd.vg2-.act.Sp_#1.cel | Tgd | Tgd (Tgd.vg2-.act) | CL:0000798
GSM476684_EA07068_54550_MoGene_Tgd.vg2-.Sp_#1.cel | Tgd | Tgd (Tgd.vg2-) | CL:0000798
GSM538198_EA07068_56621_MoGene_B.Fo.PC_#1.CEL | B cells | B cells (B.Fo) | CL:0000843
GSM538204_EA07068_80055_MoGene_B.FRE.FL#1.CEL | B cells | B cells (B.FRE) | CL:0000236
GSM538207_EA07068_80057_MoGene_B.GC.SP#1.CEL | B cells | B cells (B.GC) | CL:0000844
GSM538210_EA07068_56627_MoGene_B.MZ.Sp_#1.CEL | B cells | B cells (B.MZ) | CL:0000845
GSM538213_EA07068_56630_MoGene_B.T1.Sp_#1.CEL | B cells | B cells (B.T1) | CL:0000958
GSM538216_EA07068_56633_MoGene_B.T2.Sp_#1.CEL | B cells | B cells (B.T2) | CL:0000959
GSM538219_EA07068_56636_MoGene_B.T3.Sp_#1.CEL | B cells | B cells (B.T3) | CL:0000960
GSM538222_EA07068_56615_MoGene_B1a.PC_#1.CEL | B cells | B cells (B1a) | CL:0000820
GSM538228_EA07068_56618_MoGene_B1b.PC_#1.CEL | B cells | B cells (B1b) | CL:0000821
GSM538231_EA07068_87581_MoGene_DC2.LU#1.CEL | DC | DC (DC) | CL:0000451
GSM538234_EA07068_96463_MoGene_DC.103+11B-.LV#1.CEL | DC | DC (DC.103+11B-) | CL:0002506
GSM538263_EA07068_96434_MoGene_DC.8-4-11B+.MLN#4.CEL | DC | DC (DC.8-4-11B+) | CL:0002454
GSM538280_EA07068_111375_MoGene_DC.LC.SK#4.CEL | DC | DC (DC.LC) | CL:0000451
GSM538285_EA07068_96472_MoGene_NK.49CI+.SP#1@N2.CEL | NK cells | NK cells (NK.49CI+) | CL:0000623
GSM538288_EA07068_96475_MoGene_NK.49CI-.SP#1@N2.CEL | NK cells | NK cells (NK.49CI-) | CL:0000623
GSM538291_EA07068_96478_MoGene_NK.B2M-.SP#1.CEL | NK cells | NK cells (NK.B2M-) | CL:0000623
GSM538294_EA07068_93784_MoGene_NK.DAP10-.SP#1.CEL | NK cells | NK cells (NK.DAP10-) | CL:0000623
GSM538297_EA07068_99792_MoGene_NK.DAP12-.SP#1.CEL | NK cells | NK cells (NK.DAP12-) | CL:0000623
GSM538300_EA07068_99749_MoGene_NK.H+.MCMV1.SP#1.CEL | NK cells | NK cells (NK.H+.MCMV1) | CL:0000623
GSM538303_EA07068_99755_MoGene_NK.H+.MCMV7.SP#1.CEL | NK cells | NK cells (NK.H+.MCMV7) | CL:0000623
GSM538309_EA07068_87578_MoGene_NK.H+MCMV1#1.CEL | NK cells | NK cells (NK.H+MCMV1) | CL:0000623
GSM538312_EA07068_90292_MoGene_NK.MCMV7#1.CEL | NK cells | NK cells (NK.MCMV7) | CL:0000623
GSM538315_EA07068_86161_MoGene_NK.SP#7.CEL | NK cells | NK cells (NK) | CL:0000623
GSM538318_EA07068_91097_MoGene_NKT.4+.LV#1.CEL | NKT | NKT (NKT.4+) | CL:0000923
GSM538325_EA07068_91101_MoGene_NKT.4-.LV#1.CEL | NKT | NKT (NKT.4-) | CL:0000924
GSM538332_EA07068_91103_MoGene_NKT.44+NK1.1+.TH#1.CEL | NKT | NKT (NKT.44+NK1.1+) | CL:0002438
GSM538335_EA07068_91105_MoGene_NKT.44+NK1.1-.TH#1.CEL | NKT | NKT (NKT.44+NK1.1-) | CL:0002041
GSM538338_EA07068_96453_MoGene_NKT.44-NK1.1-.TH#1.CEL | NKT | NKT (NKT.44-NK1.1-) | CL:0002040
GSM538340_EA07068_80056_MoGene_PREB.FRD.FL#1.CEL | B cells | B cells (preB.FRD) | CL:0000817
GSM538343_EA07068_52801_MoGene_proB.CLP.BM_#1.CEL | B cells | B cells (proB.CLP) | CL:0000051
GSM538346_EA07068_88784_MoGene_CLP#5.CEL | Stem cells | Stem cells (proB.CLP) | CL:0000051
GSM538351_EA07068_52802_MoGene_proB.FrA.BM_#1.CEL | B cells | B cells (proB.FrA) | CL:0002045
GSM538352_EA07068_81297_MoGene_PROB.FRA.BM#4.CEL | B cells | B cells (proB.FRA) | CL:0002045
GSM538353_EA07068_88783_MoGene_FRA#5.CEL | B cells, pro | B cells, pro (proB.FrA) | CL:0002045
GSM538362_EA07068_85523_MoGene_T.4MEM.LN#1.CEL | T cells | T cells (T.4MEM) | CL:0000897
GSM538365_EA07068_58854_MoGene_T.4Mem.Sp_#1.CEL | T cells | T cells (T.4Mem) | CL:0000897
GSM538368_EA07068_96415_MoGene_T.4MEM44H62L.LN#1.CEL | T cells | T cells (T.4MEM44H62L) | CL:0000897
GSM538374_EA07068_52756_MoGene_T.4Nve.LN_#1.CEL | T cells | T cells (T.4Nve) | CL:0000895
GSM538380_EA07068_83933_MoGene_T.4NVE.PP#1.CEL | T cells | T cells (T.4NVE) | CL:0000895
GSM538385_EA07068_80031_MoGene_AG#8.CEL | T cells | T cells (T.8EFF.OT1.D15.VSVOVA) | CL:0001050
GSM538387_EA07068_85512_MoGene_T.8EFF.SP.OT1.D5.VSVOVA#1.CEL | T cells | T cells (T.8EFF.OT1.D5.VSVOVA) | CL:0001050
GSM538389_EA07068_80026_MoGene_AG#1.CEL | T cells | T cells (T.8EFF.OT1.VSVOVA) | CL:0001050
GSM538392_EA07068_80029_MoGene_AG#5.CEL | T cells | T cells (T.8EFF.OT1.D8.VSVOVA) | CL:0001050
GSM538395_EA07068_85520_MoGene_T.8MEM.LN#1.CEL | T cells | T cells (T.8MEM) | CL:0000909
GSM538398_EA07068_58857_MoGene_T.8Mem.Sp_#1.CEL | T cells | T cells (T.8Mem) | CL:0000909
GSM538401_EA07068_85518_MoGene_T.8MEM.SP.OT1.D106.VSVOVA#2.CEL | T cells | T cells (T.8MEM.OT1.D106.VSVOVA) | CL:0000909
GSM538403_EA07068_80032_MoGene_AG#9.CEL | T cells | T cells (T.8EFF.OT1.D45VSV) | CL:0001050
GSM538406_EA07068_52759_MoGene_T.8Nve.LN_#1.CEL | T cells | T cells (T.8Nve) | CL:0000900
GSM538412_EA07068_83936_MoGene_T.8NVE.PP#1.CEL | T cells | T cells (T.8NVE) | CL:0000900
GSM538418_EA07068_81298_MoGene_PROB.FRBC.BM#4.CEL | B cells | B cells (proB.FRBC) | CL:0000826
GSM605753_EA07068_58851_MoGene_T.4.LN.BDC_#2.CEL | T cells | T cells (T.4) | CL:0000624
GSM605756_EA07068_58845_MoGene_T.4.Pa.BDC_#2.CEL | T cells | T cells (T.4.Pa) | CL:0000624
GSM605758_EA07068_58848_MoGene_T.4.PLN.BDC_#1.CEL | T cells | T cells (T.4.PLN) | CL:0000624
GSM605766_EA07068_55683_MoGene_T.4FP3-.Sp_#1.CEL | T cells | T cells (T.4FP3-) | CL:0000624
GSM605787_EA07068_96412_MoGene_TGD.VG2+.SP#4.CEL | Tgd | Tgd (Tgd.VG2+) | CL:0000798
GSM605790_EA07068_54559_MoGene_Tgd.vg2+.Sp.TCRbko_#1.CEL | Tgd | Tgd (Tgd.vg2+.TCRbko) | CL:0000798
GSM605796_EA07068_54202_MoGene_Tgd.vg2-.Sp.TCRbko_#1.CEL | Tgd | Tgd (Tgd.vg2-.TCRbko) | CL:0000798
GSM605802_EA07068_56609_MoGene_Tgd.vg5+.act.IEL_#1.CEL | Tgd | Tgd (Tgd.vg5+.act) | CL:0000798
GSM605804_EA07068_81294_MoGene_TGD.VG5+.ACT.IEL.#4.CEL | Tgd | Tgd (Tgd.VG5+.ACT) | CL:0000798
GSM605805_EA07068_81291_MoGene_TGD.VG5+.IEL.#4.CEL | Tgd | Tgd (Tgd.VG5+) | CL:0000798
GSM605808_EA07068_56606_MoGene_Tgd.vg5-.act.IEL_#1.CEL | Tgd | Tgd (Tgd.vg5-.act) | CL:0000798
GSM605811_EA07068_81288_MoGene_TGD.VG5-.IEL.#4.CEL | Tgd | Tgd (Tgd.VG5-) | CL:0000798
GSM605814_EA07068_108027_MoGene_NK.49H+.SP#1.CEL | NK cells | NK cells (NK.49H+) | CL:0000623
GSM605817_EA07068_108030_MoGene_NK.49H-.SP#1.CEL | NK cells | NK cells (NK.49H-) | CL:0000623
GSM605828_EA07068_108118_MoGene_DC.8+.TH#1.CEL | DC | DC (DC.8+) | CL:0001000
GSM605831_EA07068_108115_MoGene_DC.8-.TH#1.CEL | DC | DC (DC.8-) | CL:0002460
GSM605836_EA07068_96401_MoGene_DC.8-4-11B-.MLN#6@N2.CEL | DC | DC (DC.8-4-11B-) | CL:0000998
GSM605840_EA07068_105309_MoGene_DC.PDC.8+.SP#1.CEL | DC | DC (DC.PDC.8+) | CL:0002456
GSM605843_EA07068_105312_MoGene_DC.PDC.8-.SP#1.CEL | DC | DC (DC.PDC.8-) | CL:0002455
GSM605850_EA07068_105224_MoGene_MF.II-480HI.PC#1.CEL | Macrophages | Macrophages (MF.II-480HI) | CL:0000235
GSM605853_EA07068_105221_MoGene_MF.RP.SP#1.CEL | Macrophages | Macrophages (MF.RP) | CL:0000235
GSM605856_EA07068_105233_MoGene_MF.THIO5.II+480INT.PC#1.CEL | Macrophages | Macrophages (MFIO5.II+480INT) | CL:0000235
GSM605859_EA07068_105242_MoGene_MF.THIO5.II+480LO.PC#1.CEL | Macrophages | Macrophages (MFIO5.II+480LO) | CL:0000235
GSM605862_EA07068_105239_MoGene_MF.THIO5.II-480HI.PC#1.CEL | Macrophages | Macrophages (MFIO5.II-480HI) | CL:0000235
GSM605865_EA07068_105236_MoGene_MF.THIO5.II-480INT.PC#1.CEL | Macrophages | Macrophages (MFIO5.II-480INT) | CL:0000235
GSM605868_EA07068_96442_MoGene_MO.6C+II+.BL#1.CEL | Monocytes | Monocytes (MO.6C+II+) | CL:0002470
GSM605872_EA07068_96439_MoGene_MO.6C+II-.BL#1.CEL | Monocytes | Monocytes (MO.6C+II-) | CL:0002469
GSM605878_EA07068_96448_MoGene_MO.6C-II+.BL#1.CEL | Monocytes | Monocytes (MO.6C-II+) | CL:0002473
GSM605884_EA07068_96447_MoGene_MO.6C-II-.BL#3.CEL | Monocytes | Monocytes (MO.6C-II-) | CL:0002471
GSM605886_EA07068_96450_MoGene_MO.6C-IIINT.BL#1.CEL | Monocytes | Monocytes (MO.6C-IIINT) | CL:0002472
GSM605891_EA07068_82682_MoGene_T.8EFF.SP.OT1.D10LIS.CEL | T cells | T cells (T.8EFF.OT1.D10LIS) | CL:0001050
GSM605892_EA07068_85511_MoGene_T.8EFF.SP.OT1.D10.LISOVA#2.CEL | T cells | T cells (T.8EFF.OT1.D10.LISOVA) | CL:0001050
GSM605894_EA07068_82683_MoGene_T.8EFF.SP.OT1.D15LIS.CEL | T cells | T cells (T.8EFF.OT1.D15LIS) | CL:0001050
GSM605895_EA07068_85510_MoGene_T.8EFF.SP.OT1.D15.LISOVA#2.CEL | T cells | T cells (T.8EFF.OT1.D15.LISOVA) | CL:0001050
GSM605898_EA07068_82680_MoGene_T.8EFF.SP.OT1.D6LISO.CEL | T cells | T cells (T.8EFF.OT1LISO) | CL:0001050
GSM605899_EA07068_85549_MoGene_T.8EFF.SP.OT1.D6.LISOVA#2.CEL | T cells | T cells (T.8EFF.OT1.LISOVA) | CL:0001050
GSM605901_EA07068_82681_MoGene_T.8EFF.SP.OT1.D8LISO.CEL | T cells | T cells (T.8EFF.OT1.D8LISO) | CL:0001050
GSM605902_EA07068_85509_MoGene_T.8EFF.SP.OT1.D8.LISOVA#2.CEL | T cells | T cells (T.8EFF.OT1.D8.LISOVA) | CL:0001050
GSM605904_EA07068_85517_MoGene_T.8MEM.SP.OT1.D100.LISOVA#1.CEL | T cells | T cells (T.8MEM.OT1.D100.LISOVA) | CL:0000909
GSM605907_EA07068_85516_MoGene_T.8MEM.SP.OT1.D45.LISOVA#1.CEL | T cells | T cells (T.8MEM.OT1.D45.LISOVA) | CL:0000909
GSM605909_EA07068_105264_MoGene_T.8NVE.SP.OT1#3.CEL | T cells | T cells (T.8NVE.OT1) | CL:0000900
GSM777019_EA07068_124592_MOGENE-1_0-ST-V1_B.FO.LN_1.CEL | B cells | B cells (B.FO) | CL:0000843
GSM777032_EA07068_108045_MoGene_BEC.MLN_3.CEL | Endothelial cells | Endothelial cells (BEC) | CL:0000115
GSM777041_EA07068_81324_MoGene_EP.MECHI.TH_2.CEL | Epithelial cells | Epithelial cells (EP.MECHI) | CL:0000066
GSM777043_EA07068_81329_MoGene_FI.MTS15+.TH_1.CEL | Fibroblasts | Fibroblasts (FI.MTS15+) | CL:0000057
GSM777046_EA07068_110672_MoGene_FI.SK_1.CEL | Fibroblasts | Fibroblasts (FI) | CL:0000057
GSM777067_EA07068_121816_MOGENE-1_0-ST-V1_ST.31-38-44-.SLN_1.CEL | Stromal cells | Stromal cells (ST.31-38-44-) | CL:0000499
GSM791102_EA07068_142883_MOGENE-1_0-ST-V1_SC.LT34F.BM_1.CEL | Stem cells | Stem cells (SC.LT34F) | CL:0000034
GSM791105_EA07068_140220_MOGENE-1_0-ST-V1_SC.MDP.BM_1.CEL | Stem cells | Stem cells (SC.MDP) | CL:0002009
GSM791108_EA07068_130473_MOGENE-1_0-ST-V1_SC.MEP.BM_1.CEL | Stem cells | Stem cells (SC.MEP) | CL:0000050
GSM791110_EA07068_130475_MOGENE-1_0-ST-V1_SC.MPP34F.BM_1.CEL | Stem cells | Stem cells (SC.MPP34F) | CL:0000837
GSM791112_EA07068_130477_MOGENE-1_0-ST-V1_SC.ST34F.BM_1.CEL | Stem cells | Stem cells (SC.ST34F) | CL:0000034
GSM791114_EA07068_140217_MOGENE-1_0-ST-V1_SC.CDP.BM_1.CEL | Stem cells | Stem cells (SC.CDP) | CL:0000034
GSM791117_EA07068_130471_MOGENE-1_0-ST-V1_SC.CMP.BM.DR_1.CEL | Stem cells | Stem cells (SC.CMP.DR) | CL:0000049
GSM791119_EA07068_111380_MoGene_GMP.BM_1.CEL | Stem cells | Stem cells (GMP) | CL:0000557
GSM791124_EA07068_54184_MoGene_MLP.BM__1.cel | Stem cells | Stem cells (MLP) | CL:0000037
GSM791126_EA07068_80048_MoGene_LTHSC.FL_1.CEL | Stem cells | Stem cells (LTHSC) | CL:0000034
GSM791134_EA07068_110598_MoGene_T.DN2-3.TH_2.CEL | T cells | T cells (T.DN2-3) | CL:0002489
GSM791136_EA07068_110595_MoGene_T.DN2.TH_4.CEL | T cells | T cells (T.DN2) | CL:0000806
GSM791139_EA07068_117726_MOGENE-1_0-ST-V1_T.DN2A.TH_1.CEL | T cells | T cells (T.DN2A) | CL:0000806
GSM791141_EA07068_117728_MOGENE-1_0-ST-V1_T.DN2B.TH_1.CEL | T cells | T cells (T.DN2B) | CL:0000806
GSM791143_EA07068_110601_MoGene_T.DN3-4.TH_1.CEL | T cells | T cells (T.DN3-4) | CL:0002489
GSM791146_EA07068_110599_MoGene_T.DN3A.TH_1.CEL | T cells | T cells (T.DN3A) | CL:0000807
GSM791149_EA07068_110600_MoGene_T.DN3B.TH_1.CEL | T cells | T cells (T.DN3B) | CL:0000807
GSM791152_EA07068_110653_MoGene_T.DN1-2.TH_3.CEL | T cells | T cells (T.DN1-2) | CL:0002489
GSM791154_EA07068_110602_MoGene_T.DN4.TH_4.CEL | T cells | T cells (T.DN4) | CL:0000808
GSM854258_EA07068_116124_MOGENE-1_0-ST-V1_DC.103-11B+.SALM3.SI_1.CEL | Macrophages | Macrophages (MF.103-11B+.SALM3) | CL:0000235
GSM854262_EA07068_105273_MoGene_DC.103-11B+.SI_1.CEL | Macrophages | Macrophages (MF.103-11B+) | CL:0000235
GSM854269_EA07068_142689_MOGENE-1_0-ST-V1_DC.103-11B+24+.LU_1_N2.CEL | DC | DC (DC.103-11B+24+) | CL:0002505
GSM854271_EA07068_142687_MOGENE-1_0-ST-V1_DC.103-11B+24-.LU_1_N2.CEL | Macrophages | Macrophages (MF.103-11B+24-) | CL:0000235
GSM854273_EA07068_140199_MOGENE-1_0-ST-V1_DC.103-11B+F4-80LO.KD_1.CEL | DC | DC (DC.103-11B+F4-80LO.KD) | CL:0002505
GSM854276_EA07068_116128_MOGENE-1_0-ST-V1_DC.11CLOSER.SALM3.SI_1.CEL | Macrophages | Macrophages (MF.11CLOSER.SALM3) | CL:0000235
GSM854280_EA07068_105277_MoGene_DC.11CLOSER.SI_1.CEL | Macrophages | Macrophages (MF.11CLOSER) | CL:0000235
GSM854283_EA07068_108771_MoGene_DC.103CLOSER.SI_4.CEL | Macrophages | Macrophages (MF.103CLOSER) | CL:0000235
GSM854294_EA07068_105226_MoGene_DC.II+480LO.PC_1.CEL | Macrophages | Macrophages (MF.II+480LO) | CL:0000235
GSM854303_EA07068_121819_MOGENE-1_0-ST-V1_GN.ARTH.BM_1.CEL | Neutrophils | Neutrophils (GN.ARTH) | CL:0000775
GSM854309_EA07068_124598_MOGENE-1_0-ST-V1_GN.THIO.PC_1.CEL | Neutrophils | Neutrophils (GN.Thio) | CL:0000775
GSM854312_EA07068_121825_MOGENE-1_0-ST-V1_GN.URAC.PC_1.CEL | Neutrophils | Neutrophils (GN.URAC) | CL:0000775
GSM854315_EA07068_140214_MOGENE-1_0-ST-V1_MF.169+11CHI.SLN_1.CEL | Macrophages | Macrophages (MF.169+11CHI) | CL:0000235
GSM854322_EA07068_140211_MOGENE-1_0-ST-V1_MF.MEDL.SLN_1.CEL | Macrophages | Macrophages (MF.MEDL) | CL:0000235
GSM854324_EA07068_140209_MOGENE-1_0-ST-V1_MF.SBCAPS.SLN_2.CEL | Macrophages | Macrophages (MF.SBCAPS) | CL:0000235
GSM854326_EA07068_111383_MoGene_MICROGLIA.CNS_1.CEL | Microglia | Microglia (Microglia) | CL:0000129
GSM854335_EA07068_110652_MoGene_T.ETP.TH_6.CEL | T cells | T cells (T.ETP) | CL:0002425
GSM920616_EA07068_108089_MoGene_IMMTGD.VG1+.TH.B6_1.CEL | Tgd | Tgd (Tgd.imm.VG1+) | CL:0002414
GSM920619_EA07068_108092_MoGene_IMMTGD.VG1+VD6+.TH.B6_1.CEL | Tgd | Tgd (Tgd.imm.VG1+VD6+) | CL:0002415
GSM920622_EA07068_108084_MoGene_MATTGD.VG1+.TH.B6_1.CEL | Tgd | Tgd (Tgd.mat.VG1+) | CL:0002411
GSM920624_EA07068_108086_MoGene_MATTGD.VG1+VD6+.TH.B6_1.CEL | Tgd | Tgd (Tgd.mat.VG1+VD6+) | CL:0002416
GSM920627_EA07068_114326_MOGENE-1_0-ST-V1_MATTGD.VG2+.TH_1.CEL | Tgd | Tgd (Tgd.mat.VG2+) | CL:0002407
GSM920629_EA07068_140223_MOGENE-1_0-ST-V1_TGD.VG3+24AHI.E17.TH_1.CEL | Tgd | Tgd (Tgd.VG3+24AHI) | CL:0000798
GSM920632_EA07068_142881_MOGENE-1_0-ST-V1_TGD.VG5+24AHI.TH_1.CEL | Tgd | Tgd (Tgd.VG5+24AHI) | CL:0000798
GSM920634_EA07068_130429_MOGENE-1_0-ST-V1_T.8EFF.SP.OT1.12HR.LISOVA_1.CEL | T cells | T cells (T.8EFF.OT1.12HR.LISOVA) | CL:0001050
GSM920637_EA07068_130430_MOGENE-1_0-ST-V1_T.8EFF.SP.OT1.24HR.LISOVA_1.CEL | T cells | T cells (T.8EFF.OT1.24HR.LISOVA) | CL:0001050
GSM920640_EA07068_130432_MOGENE-1_0-ST-V1_T.8EFF.SP.OT1.48HR.LISOVA_1.CEL | T cells | T cells (T.8EFF.OT1.48HR.LISOVA) | CL:0001050
GSM920642_EA07068_105198_MoGene_B614WABDTREG_1.CEL | T cells | T cells (T.Tregs) | CL:0000815
GSM920648_EA07068_201208_MOGENE-1_0-ST-V1_TGD.VG2+24AHI.E17.TH_1.CEL | Tgd | Tgd (Tgd.VG2+24AHI) | CL:0000798
GSM920651_EA07068_201205_MOGENE-1_0-ST-V1_TGD.VG4+24AHI.E17.TH_1.CEL | Tgd | Tgd (Tgd.VG4+24AHI) | CL:0000798
GSM920654_EA07068_201214_MOGENE-1_0-ST-V1_TGD.VG4+24ALO.E17.TH_1.CEL | Tgd | Tgd (Tgd.VG4+24ALO) | CL:0000798

`MouseRNAseqData` Labels

| label.main | label.fine | label.ont
--- | --- | --- | ---
ERR525589Aligned | Adipocytes | Adipocytes | CL_0000136
PGE_young_EAligned | Neurons | aNSCs | CL:0000047
SRR1033783Aligned | Astrocytes | Astrocytes | CL:0000127
SRR2938973Aligned | Astrocytes | Astrocytes activated | CL:0000127
SRR1033795Aligned | Endothelial cells | Endothelial cells | CL:0000115
SRR1536428Aligned | Erythrocytes | Erythrocytes | CL:0000232
SRR1390714Aligned | Fibroblasts | Fibroblasts | CL:0000057
SRR1015752Aligned | Fibroblasts | Fibroblasts activated | CL:0000057
SRR832851Aligned | Fibroblasts | Fibroblasts senescent | CL:0000057
SRR1536401Aligned | Granulocytes | Granulocytes | CL:0000094
SRR1536397Aligned | Macrophages | Macrophages | CL:0000235
SRR1033793Aligned | Microglia | Microglia | CL:0000129
SRR2082382Aligned | Microglia | Microglia activated | CL:0000129
SRR1536407Aligned | Monocytes | Monocytes | CL:0000576
SRR1033785Aligned | Neurons | Neurons | CL:0000540
SRR2938959Aligned | Neurons | Neurons activated | CL:0000540
SRR1536422Aligned | NK cells | NK cells | CL:0000623
E_young_CAligned | Neurons | NPCs | CL:0002319
SRR1033791Aligned | Oligodendrocytes | Oligodendrocytes | CL:0000128
PG_young_DAligned | Neurons | qNSCs | CL:0000047
SRR1536413Aligned | T cells | T cells | CL:0000084
SRR2040609Aligned | Dendritic cells | Dendritic cells | CL:0000451
Cardiomyocyte_pseudo_Bulk | Cardiomyocytes | Cardiomyocytes | CL:0000746
Hepatocyte_pooled_Bulk2 | Hepatocytes | Hepatocytes | CL:0000182
SRR1536411Aligned | B cells | B cells | CL:0000236
Ependymal_Striatum_pseudoBulk_1 | Epithelial cells | Ependymal | CL:0000065
OPCs_pseudoBulk_1 | Oligodendrocytes | OPCs | CL:0002453
SRR1044039Aligned | Macrophages | Macrophages activated | CL:0000890

# 6 Reference options

## 6.1 Pseudo-bulk aggregation

Single-cell reference datasets provide a like-for-like comparison to our test datasets, yielding a more accurate classification of the cells in the latter (hopefully). However, there are frequently many more samples in single-cell references compared to bulk references, increasing the computational work involved in classification. We avoid this by aggregating cells into one “pseudo-bulk” sample per label (e.g., by averaging across log-expression values) and using those as the reference, which allows us to achieve the same efficiency as the use of bulk references.

The obvious cost of this approach is that we discard potentially useful information about the distribution of cells within each label. Cells that belong to a heterogeneous population may not be correctly assigned if they are far from the population center. We attempt to preserve some of this information by using \(k\)-means clustering within each cell to create pseudo-bulk samples that are representative of a particular region of the expression space (i.e., vector quantization). We create \(\sqrt{N}\) clusters given a label with \(N\) cells, which provides a reasonable compromise between reducing computational work and preserving the label’s internal distribution.

This aggregation approach is implemented in the `aggregateReferences` function, which is shown in action below for the Muraro et al. (2016) dataset. The function returns a `SummarizedExperiment` object containing the pseudo-bulk expression profiles and the corresponding labels.

    set.seed(100) # for the k-means step.
    aggr <- aggregateReference(sceM, labels=sceM$label)
    aggr

    ## class: SummarizedExperiment
    ## dim: 19059 116
    ## metadata(0):
    ## assays(1): logcounts
    ## rownames(19059): A1BG-AS1__chr19 A1BG__chr19 ... ZZEF1__chr17
    ##   ZZZ3__chr1
    ## rowData names(0):
    ## colnames(116): alpha.1 alpha.2 ... mesenchymal.8 epsilon.1
    ## colData names(1): label

The resulting `SummarizedExperiment` can then be used as a reference in `SingleR()`.

    pred.aggr <- SingleR(sceG, aggr, labels=aggr$label)
    table(pred.aggr$labels)

    ##
    ## acinar   beta  delta   duct
    ##     53      4      1     42

## 6.2 Using multiple references

In some cases, we may wish to use multiple references for annotation of a test dataset. This yield a more comprehensive set of cell types that are not covered by any individual reference, especially when differences in resolution are also considered. Use of multiple references is supported by simply passing multiple objects to the `ref=` and `label=` argument in `SingleR()`. We demonstrate below by including another reference (from Blueprint-Encode) in our annotation of the La Manno et al. (2016) dataset:

    bp.se <- BlueprintEncodeData()

    pred.combined <- SingleR(test = hESCs,
        ref = list(BP=bp.se, HPCA=hpca.se),
        labels = list(bp.se$label.main, hpca.se$label.main))

The output is the same form as previously described, and we can easily gain access to the combined set of labels:

    table(pred.combined$labels)

    ##
    ##            Astrocyte Neuroepithelial_cell              Neurons
    ##                    6                   64                   30

Our strategy is to perform annotation on each reference separately and then take the highest-scoring label across references. This provides a light-weight approach to combining information from multiple references while avoiding batch effects and the need for up-front harmonization. (Of course, the main practical difficulty of this approach is that the same cell type may have different labels across references, which will require some implicit harmonization during interpretation.) Further comments on the justification behind the choice of this method can be found at `?"combine-predictions"`.

## 6.3 Harmonizing labels

The `matchReferences()` function provides a simple yet elegant approach for label harmonization between two references. Each reference is used to annotate the other and the probability of mutual assignment between each pair of labels is computed. Probabilities close to 1 indicate there is a 1:1 relation between that pair of labels; on the other hand, an all-zero probability vector indicates that a label is unique to a particular reference.

    matched <- matchReferences(bp.se, hpca.se,
        bp.se$label.main, hpca.se$label.main)
    pheatmap::pheatmap(matched, col=viridis::plasma(100))

A heatmap like the one above can be used to guide harmonization to enforce a consistent vocabulary across all labels representing the same cell type or state. The most obvious benefit of harmonization is that interpretation of the results is simplified. However, an even more important effect is that the presence of harmonized labels from multiple references allows the classification machinery to protect against irrelevant batch effects between references. For example, in `SingleR()`’s case, marker genes are favored if they are consistently upregulated across multiple references, improving robustness to technical idiosyncrasies in any test dataset.

We stress that some manual intervention is still required in this process, given the risks posed by differences in biological systems and technologies. For example, neurons are considered unique to each reference while smooth muscle cells in the HPCA data are incorrectly matched to fibroblasts in the Blueprint/ENCODE data. CD4+ and CD8+ T cells are also both assigned to “T cells”, so some decision about the acceptable resolution of the harmonized labels is required here.

As an aside, we can also use this function to identify the matching clusters between two independent scRNA-seq analyses. This is an “off-label” use that involves substituting the cluster assignments as proxies for the labels. We can then match up clusters and integrate conclusions from multiple datasets without the difficulty of batch correction and reclustering.

# 7 Advanced use

## 7.1 Improving efficiency

Advanced users can split the `SingleR()` workflow into two separate training and classification steps. This means that training (e.g., marker detection, assembling of nearest-neighbor indices) only needs to be performed once. The resulting data structures can then be re-used across multiple classifications with different test datasets, provided the test feature set is identical to or a superset of the features in the training set. For example:

    common <- intersect(rownames(hESCs), rownames(hpca.se))
    trained <- trainSingleR(hpca.se[common,], labels=hpca.se$label.main)
    pred.hesc2 <- classifySingleR(hESCs[common,], trained)
    table(pred.hesc$labels, pred.hesc2$labels)

    ##
    ##                        Astrocyte Neuroepithelial_cell Neurons
    ##   Astrocyte                   14                    0       0
    ##   Neuroepithelial_cell         0                   81       0
    ##   Neurons                      0                    0       5

Other efficiency improvements are possible through several arguments:

- Switching to an approximate algorithm for the nearest neighbor search in `trainSingleR()` via the `BNPARAM=` argument from the BiocNeighbors package.
- Parallelizing the fine-tuning step in `classifySingleR()` with the `BPPARAM=` argument from the BiocParallel package.

These arguments can also be specified in the `SingleR()` command.

## 7.2 Defining custom markers

Users can also construct their own marker lists with any DE testing machinery. For example, we can perform pairwise \(t\)-tests using methods from scran and obtain the top 10 marker genes from each pairwise comparison.

    library(scran)
    out <- pairwiseTTests(logcounts(sceM), sceM$label, direction="up")
    markers <- getTopMarkers(out$statistics, out$pairs, n=10)

We then supply these genes to `SingleR()` directly via the `genes=` argument. A more focused gene set also allows annotation to be performed more quickly compared to the default approach.

    pred.grun2 <- SingleR(test=sceG, ref=sceM, labels=sceM$label, genes=markers)
    table(pred.grun2$labels)

    ##
    ##  acinar    beta   delta    duct      pp unclear
    ##      59       4       1      34       1       1

In some cases, markers may only be available for specific labels rather than for pairwise comparisons between labels. This is accommodated by supplying a named list of character vectors to `genes`. Note that this is likely to be less powerful than the list-of-lists approach as information about pairwise differences is discarded.

    label.markers <- lapply(markers, unlist, recursive=FALSE)
    pred.grun3 <- SingleR(test=sceG, ref=sceM, labels=sceM$label, genes=label.markers)
    table(pred.grun$labels, pred.grun3$labels)

    ##
    ##          acinar beta delta duct pp
    ##   acinar     51    0     0    2  0
    ##   beta        0    4     0    0  0
    ##   delta       0    0     1    0  1
    ##   duct        2    0     0   39  0

# 8 FAQs

How can I use this with my `Seurat`, `SingleCellExperiment`, or `cell_data_set` object?

SingleR is workflow agnostic - all it needs is normalized counts. An example showing how to map its results back to common single-cell data objects is available in the README.

Where can I find reference sets appropriate for my data?

scRNAseq contains many single-cell datasets with more continually being added. ArrayExpress and GEOquery can be used to download any of the bulk or single-cell datasets in ArrayExpress or GEO, respectively.

# 9 Session information

    sessionInfo()

    ## R Under development (unstable) (2020-03-10 r77920)
    ## Platform: x86_64-w64-mingw32/x64 (64-bit)
    ## Running under: Windows Server 2012 R2 x64 (build 9600)
    ##
    ## Matrix products: default
    ##
    ## locale:
    ## [1] LC_COLLATE=C
    ## [2] LC_CTYPE=English_United States.1252
    ## [3] LC_MONETARY=English_United States.1252
    ## [4] LC_NUMERIC=C
    ## [5] LC_TIME=English_United States.1252
    ##
    ## attached base packages:
    ## [1] parallel  stats4    stats     graphics  grDevices utils     datasets
    ## [8] methods   base
    ##
    ## other attached packages:
    ##  [1] scran_1.15.24               knitr_1.28
    ##  [3] scater_1.15.29              ggplot2_3.3.0
    ##  [5] scRNAseq_2.1.7              SingleCellExperiment_1.9.2
    ##  [7] SingleR_1.1.13              SummarizedExperiment_1.17.5
    ##  [9] DelayedArray_0.13.8         matrixStats_0.56.0
    ## [11] Biobase_2.47.3              GenomicRanges_1.39.3
    ## [13] GenomeInfoDb_1.23.16        IRanges_2.21.8
    ## [15] S4Vectors_0.25.14           BiocGenerics_0.33.3
    ## [17] BiocStyle_2.15.6
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] bitops_1.0-6                  bit64_0.9-7
    ##  [3] RColorBrewer_1.1-2            httr_1.4.1
    ##  [5] tools_4.0.0                   R6_2.4.1
    ##  [7] irlba_2.3.3                   vipor_0.4.5
    ##  [9] DBI_1.1.0                     colorspace_1.4-1
    ## [11] withr_2.1.2                   tidyselect_1.0.0
    ## [13] gridExtra_2.3                 bit_1.1-15.2
    ## [15] curl_4.3                      compiler_4.0.0
    ## [17] BiocNeighbors_1.5.2           labeling_0.3
    ## [19] bookdown_0.18                 scales_1.1.0
    ## [21] rappdirs_0.3.1                stringr_1.4.0
    ## [23] digest_0.6.25                 rmarkdown_2.1.1
    ## [25] XVector_0.27.2                pkgconfig_2.0.3
    ## [27] htmltools_0.4.0               highr_0.8
    ## [29] limma_3.43.5                  dbplyr_1.4.2
    ## [31] fastmap_1.0.1                 rlang_0.4.5
    ## [33] RSQLite_2.2.0                 shiny_1.4.0.2
    ## [35] DelayedMatrixStats_1.9.0      farver_2.0.3
    ## [37] BiocParallel_1.21.2           dplyr_0.8.5
    ## [39] RCurl_1.98-1.1                magrittr_1.5
    ## [41] BiocSingular_1.3.2            GenomeInfoDbData_1.2.2
    ## [43] Matrix_1.2-18                 Rcpp_1.0.4
    ## [45] ggbeeswarm_0.6.0              munsell_0.5.0
    ## [47] viridis_0.5.1                 lifecycle_0.2.0
    ## [49] edgeR_3.29.1                  stringi_1.4.6
    ## [51] yaml_2.2.1                    zlibbioc_1.33.1
    ## [53] BiocFileCache_1.11.4          AnnotationHub_2.19.7
    ## [55] grid_4.0.0                    blob_1.2.1
    ## [57] dqrng_0.2.1                   promises_1.1.0
    ## [59] ExperimentHub_1.13.5          crayon_1.3.4
    ## [61] lattice_0.20-40               magick_2.3
    ## [63] locfit_1.5-9.4                pillar_1.4.3
    ## [65] igraph_1.2.4.2                glue_1.3.2
    ## [67] BiocVersion_3.11.1            evaluate_0.14
    ## [69] BiocManager_1.30.10           vctrs_0.2.4
    ## [71] httpuv_1.5.2                  gtable_0.3.0
    ## [73] purrr_0.3.3                   assertthat_0.2.1
    ## [75] xfun_0.12                     rsvd_1.0.3
    ## [77] mime_0.9                      xtable_1.8-4
    ## [79] later_1.0.0                   viridisLite_0.3.0
    ## [81] pheatmap_1.0.12               tibble_2.1.3
    ## [83] AnnotationDbi_1.49.1          beeswarm_0.2.3
    ## [85] memoise_1.1.0                 statmod_1.4.34
    ## [87] interactiveDisplayBase_1.25.0

# References

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Benayoun, Bérénice A., Elizabeth A. Pollina, Param Priya Singh, Salah Mahmoudi, Itamar Harel, Kerriann M. Casey, Ben W. Dulken, Anshul Kundaje, and Anne Brunet. 2019. “Remodeling of epigenome and transcriptome landscapes with aging in mice reveals widespread induction of inflammatory responses.” Genome Research 29: 697–709. https://doi.org/10.1101/gr.240093.118.

Grun, D., M. J. Muraro, J. C. Boisset, K. Wiebrands, A. Lyubimova, G. Dharmadhikari, M. van den Born, et al. 2016. “De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data.” Cell Stem Cell 19 (2): 266–77.

Heng, Tracy S. P., Michio W. Painter, Kutlu Elpek, Veronika Lukacs-Kornek, Nora Mauermann, Shannon J. Turley, Daphne Koller, et al. 2008. “The immunological genome project: Networks of gene expression in immune cells.” Nature Immunology 9 (10): 1091–4. https://doi.org/10.1038/ni1008-1091.

La Manno, G., D. Gyllborg, S. Codeluppi, K. Nishimura, C. Salto, A. Zeisel, L. E. Borm, et al. 2016. “Molecular Diversity of Midbrain Development in Mouse, Human, and Stem Cells.” Cell 167 (2): 566–80.

Mabbott, Neil A., J. K. Baillie, Helen Brown, Tom C. Freeman, and David A. Hume. 2013. “An expression atlas of human primary cells: Inference of gene function from coexpression networks.” BMC Genomics 14. https://doi.org/10.1186/1471-2164-14-632.

Martens, Joost H A, and Hendrik G. Stunnenberg. 2013. “BLUEPRINT: Mapping human blood cell epigenomes.” Haematologica 98: 1487–9. https://doi.org/10.3324/haematol.2013.094243.

Monaco, Gianni, Bernett Lee, Weili Xu, Seri Mustafah, You Yi Hwang, Christophe Carré, Nicolas Burdin, et al. 2019. “RNA-Seq Signatures Normalized by mRNA Abundance Allow Absolute Deconvolution of Human Immune Cell Types.” Cell Reports 26 (6): 1627–1640.e7. https://doi.org/10.1016/j.celrep.2019.01.041.

Muraro, M. J., G. Dharmadhikari, D. Grun, N. Groen, T. Dielen, E. Jansen, L. van Gurp, et al. 2016. “A Single-Cell Transcriptome Atlas of the Human Pancreas.” Cell Syst 3 (4): 385–94.

Novershtern, Noa, Aravind Subramanian, Lee N. Lawton, Raymond H. Mak, W. Nicholas Haining, Marie E. McConkey, Naomi Habib, et al. 2011. “Densely Interconnected Transcriptional Circuits Control Cell States in Human Hematopoiesis.” Cell 144 (2): 296–309. https://doi.org/10.1016/j.cell.2011.01.004.

Schmiedel, Benjamin J., Divya Singh, Ariel Madrigal, Alan G. Valdovino-Gonzalez, Brandie M. White, Jose Zapardiel-Gonzalo, Brendan Ha, et al. 2018. “Impact of Genetic Polymorphisms on Human Immune Cell Gene Expression.” Cell 175 (6): 1701–1715.e16. https://doi.org/10.1016/j.cell.2018.10.022.

The ENCODE Project Consortium. 2012. “An integrated encyclopedia of DNA elements in the human genome.” Nature. https://doi.org/10.1038/nature11247.