From 58ccbf9f3f506ffccd8a26f2957dedd1a912d17a Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Tue, 20 Feb 2024 21:32:06 +0000 Subject: [PATCH] site deploy Auto-generated via {sandpaper} Source : 31f0b1c6dac802fe9a4eb0f8a1569c071317ac72 Branch : md-outputs Author : GitHub Actions Time : 2024-02-20 21:31:45 +0000 Message : markdown source builds Auto-generated via {sandpaper} Source : 6e3a5f99005ccb8f438055b0b6580b654b62dccd Branch : main Author : csmagnano Time : 2024-02-20 21:12:22 +0000 Message : Added celltype annotation lesson --- 404.html | 11 + CODE_OF_CONDUCT.html | 13 +- LICENSE.html | 13 +- aio.html | 1247 ++++++++++++- cell_type_annotation.html | 1533 ++++++++++++++++ eda_qc.html | 39 +- ..._annotation-rendered-anno-vs-preanno-1.png | Bin 0 -> 45949 bytes ...ll_type_annotation-rendered-auc-dist-1.png | Bin 0 -> 118453 bytes ...type_annotation-rendered-cluster-viz-1.png | Bin 0 -> 26362 bytes ...ype_annotation-rendered-plot-markers-1.png | Bin 0 -> 265703 bytes ..._type_annotation-rendered-score-heat-1.png | Bin 0 -> 46285 bytes ..._annotation-rendered-unnamed-chunk-3-1.png | Bin 0 -> 31256 bytes images.html | 27 +- index.html | 9 + instructor-notes.html | 12 + instructor/404.html | 11 + instructor/CODE_OF_CONDUCT.html | 13 +- instructor/LICENSE.html | 13 +- instructor/aio.html | 1248 +++++++++++++- instructor/cell_type_annotation.html | 1535 +++++++++++++++++ instructor/eda_qc.html | 39 +- instructor/images.html | 27 +- instructor/index.html | 18 + instructor/instructor-notes.html | 13 + instructor/intro-sce.html | 25 +- instructor/introduction.html | 13 +- instructor/key-points.html | 16 + instructor/profiles.html | 9 + instructor/reference.html | 13 +- intro-sce.html | 25 +- introduction.html | 13 +- key-points.html | 16 + md5sum.txt | 27 +- pkgdown.yml | 2 +- profiles.html | 9 + reference.html | 13 +- sitemap.xml | 6 + 37 files changed, 5910 insertions(+), 98 deletions(-) create mode 100644 cell_type_annotation.html create mode 100644 fig/cell_type_annotation-rendered-anno-vs-preanno-1.png create mode 100644 fig/cell_type_annotation-rendered-auc-dist-1.png create mode 100644 fig/cell_type_annotation-rendered-cluster-viz-1.png create mode 100644 fig/cell_type_annotation-rendered-plot-markers-1.png create mode 100644 fig/cell_type_annotation-rendered-score-heat-1.png create mode 100644 fig/cell_type_annotation-rendered-unnamed-chunk-3-1.png create mode 100644 instructor/cell_type_annotation.html diff --git a/404.html b/404.html index c0c9d4b..956fb3c 100644 --- a/404.html +++ b/404.html @@ -177,6 +177,17 @@

+ + +
+
+ + + +
+
diff --git a/CODE_OF_CONDUCT.html b/CODE_OF_CONDUCT.html index e134eac..fb195a3 100644 --- a/CODE_OF_CONDUCT.html +++ b/CODE_OF_CONDUCT.html @@ -168,6 +168,15 @@

3. Exploratory data analysis and quality control + + + + @@ -229,7 +238,7 @@

Contributor Code of Conduct

-

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Contributor Code of Conduct

"url": "https://ccb-hms.github.io/osca-carpentries/CODE_OF_CONDUCT.html", "identifier": "https://ccb-hms.github.io/osca-carpentries/CODE_OF_CONDUCT.html", "dateCreated": "2024-01-10", - "dateModified": "2024-02-14", + "dateModified": "2024-02-20", "datePublished": "2024-02-20" } diff --git a/LICENSE.html b/LICENSE.html index fcbc601..845242d 100644 --- a/LICENSE.html +++ b/LICENSE.html @@ -168,6 +168,15 @@

3. Exploratory data analysis and quality control

+ + + + @@ -229,7 +238,7 @@

Licenses

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Licenses

"url": "https://ccb-hms.github.io/osca-carpentries/LICENSE.html", "identifier": "https://ccb-hms.github.io/osca-carpentries/LICENSE.html", "dateCreated": "2024-01-10", - "dateModified": "2024-02-14", + "dateModified": "2024-02-20", "datePublished": "2024-02-20" } diff --git a/aio.html b/aio.html index 52e9989..f8b87b7 100644 --- a/aio.html +++ b/aio.html @@ -212,6 +212,18 @@

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@@ -270,7 +282,7 @@

Content from Using RMarkdown

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Last updated on 2024-02-14 | +

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Key Points

Content from Introduction to Bioconductor and the SingleCellExperiment class

-

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Exercise 1

Content from Exploratory data analysis and quality control

-

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Exercise 1: Normalization

Content from Cell type annotation

+
+

Last updated on 2024-02-20 | + + Edit this page

+
+ +
+
+

Overview

+
+
+
+
+

Questions

+
    +
  • TODO
    • +
+
+
+
+
+
+
+

Objectives

+
    +
  • TODO
    • +
+
+
+
+
+
+
+

Setup +

+
+

R +

+
+library(BiocStyle)
+library(AUCell)
+
+
+

WARNING +

+
Warning: replacing previous import 'S4Arrays::makeNindexFromArrayViewport' by
+'DelayedArray::makeNindexFromArrayViewport' when loading 'SummarizedExperiment'
+
+
+

R +

+
+library(MouseGastrulationData)
+library(SingleR)
+library(bluster)
+library(scater)
+library(scran)
+
+
+
+

Data retrieval +

+
+

R +

+
+sce <- WTChimeraData(samples = 5, type = "processed")
+sce
+
+
+

OUTPUT +

+
class: SingleCellExperiment 
+dim: 29453 2411 
+metadata(0):
+assays(1): counts
+rownames(29453): ENSMUSG00000051951 ENSMUSG00000089699 ...
+  ENSMUSG00000095742 tomato-td
+rowData names(2): ENSEMBL SYMBOL
+colnames(2411): cell_9769 cell_9770 ... cell_12178 cell_12179
+colData names(11): cell barcode ... doub.density sizeFactor
+reducedDimNames(2): pca.corrected.E7.5 pca.corrected.E8.5
+mainExpName: NULL
+altExpNames(0):
+
+

To speed up the computations, we subsample the dataset to 1,000 +cells.

+
+

R +

+
+set.seed(123)
+ind <- sample(ncol(sce), 1000)
+sce <- sce[,ind]
+
+
+
+

Preprocessing +

+
+

R +

+
+sce <- logNormCounts(sce)
+sce <- runPCA(sce)
+
+
+
+

Clustering +

+

Clustering is an unsupervised learning procedure that is used to +empirically define groups of cells with similar expression profiles. Its +primary purpose is to summarize complex scRNA-seq data into a digestible +format for human interpretation. This allows us to describe population +heterogeneity in terms of discrete labels that are easily understood, +rather than attempting to comprehend the high-dimensional manifold on +which the cells truly reside. After annotation based on marker genes, +the clusters can be treated as proxies for more abstract biological +concepts such as cell types or states.

+

Popularized by its use in Seurat, +graph-based clustering is a flexible and scalable technique for +clustering large scRNA-seq datasets. We first build a graph where each +node is a cell that is connected to its nearest neighbors in the +high-dimensional space. Edges are weighted based on the similarity +between the cells involved, with higher weight given to cells that are +more closely related. We then apply algorithms to identify “communities” +of cells that are more connected to cells in the same community than +they are to cells of different communities. Each community represents a +cluster that we can use for downstream interpretation.

+

Here, we use the clusterCells() function from the scran package to +perform graph-based clustering using the Louvain +algorithm for community detection. All calculations are performed +using the top PCs to take advantage of data compression and denoising. +This function returns a vector containing cluster assignments for each +cell in our SingleCellExperiment object.

+
+

R +

+
+colLabels(sce) <- clusterCells(sce, use.dimred = "PCA",
+                               BLUSPARAM = NNGraphParam(cluster.fun = "louvain"))
+table(colLabels(sce))
+
+
+

OUTPUT +

+

+  1   2   3   4   5   6   7   8   9  10  11 
+100 160  99 141  63  93  60 108  44  91  41
+
+

We assign the cluster assignments back into our +SingleCellExperiment object as a factor in the +column metadata. This allows us to conveniently visualize the +distribution of clusters in eg. a t-SNE or a UMAP.

+
+

R +

+
+sce <- runUMAP(sce, dimred = "PCA")
+plotReducedDim(sce, "UMAP", color_by = "label")
+
+
+
+
+

Marker gene detection +

+

To interpret clustering results as obtained in the previous section, +we identify the genes that drive separation between clusters. These +marker genes allow us to assign biological meaning to each cluster based +on their functional annotation. In the simplest case, we have a +priori knowledge of the marker genes associated with particular +cell types, allowing us to treat the clustering as a proxy for cell type +identity.

+

The most straightforward approach to marker gene detection involves +testing for differential expression between clusters. If a gene is +strongly DE between clusters, it is likely to have driven the separation +of cells in the clustering algorithm.

+

Here, we perform a Wilcoxon rank sum test against a log2 fold change +threshold of 1, focusing on up-regulated (positive) markers in one +cluster when compared to another cluster.

+
+

R +

+
+rownames(sce) <- rowData(sce)$SYMBOL
+markers <- findMarkers(sce, test.type = "wilcox", direction = "up", lfc = 1)
+markers
+
+
+

OUTPUT +

+
List of length 11
+names(11): 1 2 3 4 5 6 7 8 9 10 11
+
+

The resulting object contains a sorted marker gene list for each +cluster, in which the top genes are those that contribute the most to +the separation of that cluster from mall other clusters.

+

Here, we inspect the ranked marker gene list for the first +cluster.

+
+

R +

+
+markers[[1]]
+
+
+

OUTPUT +

+
DataFrame with 29453 rows and 14 columns
+                 Top     p.value         FDR summary.AUC     AUC.2     AUC.3
+           <integer>   <numeric>   <numeric>   <numeric> <numeric> <numeric>
+Crabp2             1 7.31206e-35 1.95784e-31    0.938625  0.938625 0.9159596
+Ptn                1 2.26190e-43 6.66197e-39    0.983313  0.983313 0.9812121
+Crabp1             1 1.13915e-32 2.09696e-29    0.926687  0.926687 0.7927273
+Zeb2               2 3.98744e-10 1.38167e-07    0.801500  0.553125 0.3943434
+Mest               2 1.10835e-24 9.60122e-22    0.883000  0.883000 0.0249495
+...              ...         ...         ...         ...       ...       ...
+AC125149.2     29448           1           1           0         0         0
+AC125149.4     29449           1           1           0         0         0
+AC234645.1     29450           1           1           0         0         0
+AC168977.2     29451           1           1           0         0         0
+Vmn2r122       29453           1           1           0         0         0
+               AUC.4     AUC.5     AUC.6     AUC.7     AUC.8     AUC.9
+           <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
+Crabp2      0.861986  0.494444  0.690000  0.763833  0.845370  0.769091
+Ptn         0.911773  0.771429  0.198710  0.830667  0.563333  0.898409
+Crabp1      0.540071  0.714127  0.695591  0.767167  0.620556  0.524773
+Zeb2        0.604184  0.390317  0.337849  0.801500  0.447963  0.341364
+Mest        0.136028  0.476190  0.091828  0.386833  0.314444  0.773864
+...              ...       ...       ...       ...       ...       ...
+AC125149.2         0         0         0         0         0         0
+AC125149.4         0         0         0         0         0         0
+AC234645.1         0         0         0         0         0         0
+AC168977.2         0         0         0         0         0         0
+Vmn2r122           0         0         0         0         0         0
+              AUC.10    AUC.11
+           <numeric> <numeric>
+Crabp2     0.9416484  0.882439
+Ptn        0.9463736  0.967317
+Crabp1     0.8584615  0.714878
+Zeb2       0.6175824  0.501463
+Mest       0.0154945  0.290976
+...              ...       ...
+AC125149.2         0         0
+AC125149.4         0         0
+AC234645.1         0         0
+AC168977.2         0         0
+Vmn2r122           0         0
+
+

The Top field provides the the minimum rank across all +pairwise comparisons. The p.value field provides the +combined p-value across all comparisons, and the +FDR field the BH-adjusted p-value for each gene. +The summary.AUC provides area under the curve (here the +concordance probability) from the comparison with the lowest +p-value, the AUC.n fields provide the AUC for each +pairwise comparison. The AUC is the probability that a randomly selected +cell in cluster A has a greater expression of gene X +than a randomly selected cell in B.

+

We can then inspect the top marker genes for the first cluster using +the plotExpression function from the scater package.

+
+

R +

+
+top.markers <- head(rownames(markers[[1]]))
+plotExpression(sce, features = top.markers, x = "label", color_by = "label")
+
+
+
+
+

Cell type annotation +

+

The most challenging task in scRNA-seq data analysis is arguably the +interpretation of the results. Obtaining clusters of cells is fairly +straightforward, but it is more difficult to determine what biological +state is represented by each of those clusters. Doing so requires us to +bridge the gap between the current dataset and prior biological +knowledge, and the latter is not always available in a consistent and +quantitative manner. Indeed, even the concept of a “cell type” is not clearly +defined, with most practitioners possessing a “I’ll know it when I +see it” intuition that is not amenable to computational analysis. As +such, interpretation of scRNA-seq data is often manual and a common +bottleneck in the analysis workflow.

+

To expedite this step, we can use various computational approaches +that exploit prior information to assign meaning to an uncharacterized +scRNA-seq dataset. The most obvious sources of prior information are the +curated gene sets associated with particular biological processes, e.g., +from the Gene Ontology (GO) or the Kyoto Encyclopedia of Genes and +Genomes (KEGG) collections. Alternatively, we can directly compare our +expression profiles to published reference datasets where each sample or +cell has already been annotated with its putative biological state by +domain experts. Here, we will demonstrate both approaches on the +wild-type chimera dataset.

+
+

Assigning cell labels from reference data +

+

A conceptually straightforward annotation approach is to compare the +single-cell expression profiles with previously annotated reference +datasets. Labels can then be assigned to each cell in our +uncharacterized test dataset based on the most similar reference +sample(s), for some definition of “similar”. This is a standard +classification challenge that can be tackled by standard machine +learning techniques such as random forests and support vector machines. +Any published and labelled RNA-seq dataset (bulk or single-cell) can be +used as a reference, though its reliability depends greatly on the +expertise of the original authors who assigned the labels in the first +place.

+

In this section, we will demonstrate the use of the SingleR +method for cell type annotation Aran et al., +2019. This method assigns labels to cells based on the reference +samples with the highest Spearman rank correlations, using only the +marker genes between pairs of labels to focus on the relevant +differences between cell types. It also performs a fine-tuning step for +each cell where the correlations are recomputed with just the marker +genes for the top-scoring labels. This aims to resolve any ambiguity +between those labels by removing noise from irrelevant markers for other +labels. Further details can be found in the SingleR +book from which most of the examples here are derived.

+
+

R +

+
+ref <- EmbryoAtlasData(samples = 29)
+ref
+
+
+

OUTPUT +

+
class: SingleCellExperiment 
+dim: 29452 7569 
+metadata(0):
+assays(1): counts
+rownames(29452): ENSMUSG00000051951 ENSMUSG00000089699 ...
+  ENSMUSG00000096730 ENSMUSG00000095742
+rowData names(2): ENSEMBL SYMBOL
+colnames(7569): cell_95727 cell_95728 ... cell_103294 cell_103295
+colData names(17): cell barcode ... colour sizeFactor
+reducedDimNames(2): pca.corrected umap
+mainExpName: NULL
+altExpNames(0):
+
+
+

R +

+
+table(ref$stage)
+
+
+

OUTPUT +

+

+E8.5 
+7569
+
+

To speed up the computations, we subsample the dataset to 1,000 +cells.

+
+

R +

+
+set.seed(123)
+ind <- sample(ncol(ref), 1000)
+ref <- ref[,ind]
+
+
+

R +

+
+tab <- sort(table(ref$celltype), decreasing = TRUE)
+tab
+
+
+

OUTPUT +

+

+  Forebrain/Midbrain/Hindbrain                     Erythroid3 
+                           131                             75 
+             Paraxial mesoderm                            NMP 
+                            69                             51 
+                  ExE mesoderm               Surface ectoderm 
+                            49                             47 
+                     Allantois                     Mesenchyme 
+                            46                             45 
+                   Spinal cord            Pharyngeal mesoderm 
+                            45                             41 
+                  ExE endoderm                   Neural crest 
+                            38                             35 
+                           Gut Haematoendothelial progenitors 
+                            30                             27 
+         Intermediate mesoderm                 Cardiomyocytes 
+                            27                             26 
+              Somitic mesoderm                    Endothelium 
+                            25                             23 
+                    Erythroid2                  Def. endoderm 
+                            11                              3 
+                    Erythroid1            Blood progenitors 1 
+                             2                              1 
+           Blood progenitors 2                Caudal Mesoderm 
+                             1                              1 
+                           PGC 
+                             1
+
+
+

R +

+
+ref <- logNormCounts(ref)
+
+

Some cleaning - remove cells of the reference dataset for which the +cell type annotation is missing.

+
+

R +

+
+nna <- !is.na(ref$celltype)
+ref <- ref[,nna]
+
+

Also remove cell types of very low abundance (here less than 10 +cells) to remove noise prior to subsequent annotation tasks.

+
+

R +

+
+abu.ct <- names(tab)[tab >= 10]
+ind <- ref$celltype %in% abu.ct
+ref <- ref[,ind]
+
+

Restrict to genes shared between query and reference dataset.

+
+

R +

+
+rownames(ref) <- rowData(ref)$SYMBOL
+isect <- intersect(rownames(sce), rownames(ref))
+sce <- sce[isect,]
+ref <- ref[isect,]
+
+

Convert sparse assay matrices to regular dense matrices for input to +SingleR.

+
+

R +

+
+sce.mat <- as.matrix(assay(sce, "logcounts"))
+ref.mat <- as.matrix(assay(ref, "logcounts"))
+
+
+

R +

+
+res <- SingleR(test = sce.mat, ref = ref.mat, labels = ref$celltype)
+res
+
+
+

OUTPUT +

+
DataFrame with 1000 rows and 4 columns
+                                   scores                 labels delta.next
+                                 <matrix>            <character>  <numeric>
+cell_11995 0.348586:0.335451:0.314515:... Forebrain/Midbrain/H..  0.1285110
+cell_10294 0.273570:0.260013:0.298932:...             Erythroid3  0.1381951
+cell_9963  0.328538:0.291288:0.475611:...            Endothelium  0.2193295
+cell_11610 0.281161:0.269245:0.299961:...             Erythroid3  0.0359215
+cell_10910 0.422454:0.346897:0.355947:...           ExE mesoderm  0.0984285
+...                                   ...                    ...        ...
+cell_11597 0.323805:0.292967:0.300485:...                    NMP  0.1663369
+cell_9807  0.464466:0.374189:0.381698:...             Mesenchyme  0.0833019
+cell_10095 0.341721:0.288215:0.485324:...            Endothelium  0.0889931
+cell_11706 0.267487:0.240215:0.286012:...             Erythroid2  0.0350557
+cell_11860 0.345786:0.343437:0.313994:... Forebrain/Midbrain/H..  0.0117001
+                    pruned.labels
+                      <character>
+cell_11995 Forebrain/Midbrain/H..
+cell_10294             Erythroid3
+cell_9963             Endothelium
+cell_11610             Erythroid3
+cell_10910           ExE mesoderm
+...                           ...
+cell_11597                    NMP
+cell_9807              Mesenchyme
+cell_10095            Endothelium
+cell_11706             Erythroid2
+cell_11860 Forebrain/Midbrain/H..
+
+

We inspect the results using a heatmap of the per-cell and label +scores. Ideally, each cell should exhibit a high score in one label +relative to all of the others, indicating that the assignment to that +label was unambiguous. This is largely the case for mesenchyme and +endothelial cells, whereas we see expectedly more ambiguity between the +two erythroid cell populations.

+
+

R +

+
+plotScoreHeatmap(res)
+
+

We also compare the cell type assignments with the clustering results +to determine the identity of each cluster. Here, several cell type +classes are nested within the same cluster, indicating that these +clusters are composed of several transcriptomically similar cell +populations (such as cluster 4 and 6). On the other hand, there are also +instances where we have several clusters for the same cell type, +indicating that the clustering represents finer subdivisions within +these cell types.

+
+

R +

+
+library(pheatmap)
+tab <- table(anno = res$pruned.labels, cluster = colLabels(sce))
+pheatmap(log2(tab + 10), color = colorRampPalette(c("white", "blue"))(101))
+
+

As it so happens, we are in the fortunate position where our test +dataset also contains independently defined labels. We see strong +consistency between the two sets of labels, indicating that our +automatic annotation is comparable to that generated manually by domain +experts.

+
+

R +

+
+tab <- table(res$pruned.labels, sce$celltype.mapped)
+pheatmap(log2(tab + 10), color = colorRampPalette(c("white", "blue"))(101))
+
+
+
+
+

Assigning cell labels from gene sets +

+

A related strategy is to explicitly identify sets of marker genes +that are highly expressed in each individual cell. This does not require +matching of individual cells to the expression values of the reference +dataset, which is faster and more convenient when only the identities of +the markers are available. We demonstrate this approach using cell type +markers derived from the mouse embryo atlas dataset.

+
+

R +

+
+library(scran)
+wilcox.z <- pairwiseWilcox(ref, ref$celltype, lfc = 1, direction = "up")
+markers.z <- getTopMarkers(wilcox.z$statistics, wilcox.z$pairs, 
+                           pairwise = FALSE, n = 50)
+lengths(markers.z)
+
+
+

OUTPUT +

+
                     Allantois                 Cardiomyocytes 
+                           106                            106 
+                   Endothelium                     Erythroid2 
+                           103                             54 
+                    Erythroid3                   ExE endoderm 
+                            84                            102 
+                  ExE mesoderm   Forebrain/Midbrain/Hindbrain 
+                            97                             97 
+                           Gut Haematoendothelial progenitors 
+                            90                             71 
+         Intermediate mesoderm                     Mesenchyme 
+                            70                            118 
+                  Neural crest                            NMP 
+                            66                             91 
+             Paraxial mesoderm            Pharyngeal mesoderm 
+                            88                             85 
+              Somitic mesoderm                    Spinal cord 
+                            86                             91 
+              Surface ectoderm 
+                            92
+
+

Our test dataset will be as before the wild-type chimera dataset.

+
+

R +

+
+sce
+
+
+

OUTPUT +

+
class: SingleCellExperiment 
+dim: 29411 1000 
+metadata(0):
+assays(2): counts logcounts
+rownames(29411): Xkr4 Gm1992 ... Vmn2r122 CAAA01147332.1
+rowData names(2): ENSEMBL SYMBOL
+colnames(1000): cell_11995 cell_10294 ... cell_11706 cell_11860
+colData names(12): cell barcode ... sizeFactor label
+reducedDimNames(4): pca.corrected.E7.5 pca.corrected.E8.5 PCA UMAP
+mainExpName: NULL
+altExpNames(0):
+
+

We use the AUCell +package to identify marker sets that are highly expressed in each cell. +This method ranks genes by their expression values within each cell and +constructs a response curve of the number of genes from each marker set +that are present with increasing rank. It then computes the area under +the curve (AUC) for each marker set, quantifying the enrichment of those +markers among the most highly expressed genes in that cell. This is +roughly similar to performing a Wilcoxon rank sum test between genes in +and outside of the set, but involving only the top ranking genes by +expression in each cell.

+
+

R +

+
+library(GSEABase)
+all.sets <- lapply(names(markers.z), 
+                   function(x) GeneSet(markers.z[[x]], setName = x))
+all.sets <- GeneSetCollection(all.sets)
+all.sets
+
+
+

OUTPUT +

+
GeneSetCollection
+  names: Allantois, Cardiomyocytes, ..., Surface ectoderm (19 total)
+  unique identifiers: Prrx2, Spin2c, ..., Igf2bp3 (560 total)
+  types in collection:
+    geneIdType: NullIdentifier (1 total)
+    collectionType: NullCollection (1 total)
+
+
+

R +

+
+library(AUCell)
+rankings <- AUCell_buildRankings(as.matrix(counts(sce)),
+                                 plotStats = FALSE, verbose = FALSE)
+cell.aucs <- AUCell_calcAUC(all.sets, rankings)
+results <- t(assay(cell.aucs))
+head(results)
+
+
+

OUTPUT +

+
            gene sets
+cells         Allantois Cardiomyocytes Endothelium Erythroid2 Erythroid3
+  cell_11995 0.21994609      0.2336827   0.1947563 0.12228508  0.1639749
+  cell_10294 0.10066221      0.1246414   0.1104634 0.60732017  0.6081721
+  cell_9963  0.34273069      0.3158431   0.5407815 0.13588372  0.1797007
+  cell_11610 0.08244651      0.1340388   0.1048735 0.57892981  0.5706619
+  cell_10910 0.31763336      0.2784001   0.2445795 0.08175859  0.1354818
+  cell_11021 0.20549732      0.2320122   0.1869839 0.11351012  0.1769588
+            gene sets
+cells        ExE endoderm ExE mesoderm Forebrain/Midbrain/Hindbrain       Gut
+  cell_11995   0.06053637    0.4371076                    0.6319109 0.3779960
+  cell_10294   0.06369959    0.1900184                    0.2670988 0.1482988
+  cell_9963    0.09466189    0.4165978                    0.4972886 0.3805059
+  cell_11610   0.05720738    0.2060333                    0.2785028 0.1466698
+  cell_10910   0.11787498    0.5770948                    0.5721069 0.4450836
+  cell_11021   0.09285926    0.4736976                    0.6011933 0.3993608
+            gene sets
+cells        Haematoendothelial progenitors Intermediate mesoderm Mesenchyme
+  cell_11995                      0.3329538             0.5396029  0.2456757
+  cell_10294                      0.1621829             0.1781659  0.1078670
+  cell_9963                       0.6010306             0.4623277  0.3566887
+  cell_11610                      0.1526329             0.1968055  0.1118536
+  cell_10910                      0.4333710             0.5481614  0.3512731
+  cell_11021                      0.3336212             0.5197592  0.2403682
+            gene sets
+cells        Neural crest       NMP Paraxial mesoderm Pharyngeal mesoderm
+  cell_11995    0.6535231 0.4841026         0.5510547           0.5076289
+  cell_10294    0.2840264 0.2113283         0.2192031           0.2049349
+  cell_9963     0.5374756 0.3740042         0.5007010           0.4618718
+  cell_11610    0.2949987 0.2235049         0.2348724           0.2134701
+  cell_10910    0.5472885 0.5225294         0.5506325           0.5564014
+  cell_11021    0.5863399 0.5731637         0.4992671           0.4707530
+            gene sets
+cells        Somitic mesoderm Spinal cord Surface ectoderm
+  cell_11995        0.4654095   0.5807133        0.5251957
+  cell_10294        0.2141816   0.2290110        0.1874113
+  cell_9963         0.4307335   0.4373858        0.4173470
+  cell_11610        0.2339673   0.2564642        0.2054802
+  cell_10910        0.5179489   0.5167149        0.5049140
+  cell_11021        0.5186006   0.5526277        0.5052879
+
+

We assign cell type identity to each cell in the test dataset by +taking the marker set with the top AUC as the label for that cell. Our +new labels mostly agree with the original annotation (and, thus, also +with the reference-based annotation). Instances where the original +annotation is divided into several new label groups typically points to +large overlaps in their marker sets. In the absence of prior annotation, +a more general diagnostic check is to compare the assigned labels to +cluster identities, under the expectation that most cells of a single +cluster would have the same label (or, if multiple labels are present, +they should at least represent closely related cell states).

+
+

R +

+
+new.labels <- colnames(results)[max.col(results)]
+tab <- table(new.labels, sce$celltype.mapped)
+tab
+
+
+

OUTPUT +

+
                                
+new.labels                       Allantois Blood progenitors 1
+  Allantois                             28                   0
+  Cardiomyocytes                         0                   0
+  Endothelium                            0                   0
+  Erythroid2                             0                   0
+  Erythroid3                             0                   0
+  ExE mesoderm                           0                   0
+  Forebrain/Midbrain/Hindbrain           1                   0
+  Gut                                    0                   0
+  Haematoendothelial progenitors         1                   0
+  Intermediate mesoderm                  0                   0
+  Mesenchyme                             0                   0
+  Neural crest                          11                   4
+  NMP                                    0                   0
+  Paraxial mesoderm                      5                   0
+  Pharyngeal mesoderm                    0                   0
+  Somitic mesoderm                       0                   0
+  Spinal cord                            0                   0
+  Surface ectoderm                       0                   0
+                                
+new.labels                       Blood progenitors 2 Cardiomyocytes
+  Allantois                                        0              0
+  Cardiomyocytes                                   0             27
+  Endothelium                                      0              0
+  Erythroid2                                       1              0
+  Erythroid3                                       0              0
+  ExE mesoderm                                     0              0
+  Forebrain/Midbrain/Hindbrain                     0              0
+  Gut                                              0              0
+  Haematoendothelial progenitors                   0              0
+  Intermediate mesoderm                            0              0
+  Mesenchyme                                       0              0
+  Neural crest                                     6              1
+  NMP                                              0              0
+  Paraxial mesoderm                                0              4
+  Pharyngeal mesoderm                              0              6
+  Somitic mesoderm                                 0              0
+  Spinal cord                                      0              0
+  Surface ectoderm                                 0              0
+                                
+new.labels                       Caudal epiblast Caudal Mesoderm Def. endoderm
+  Allantois                                    0               0             0
+  Cardiomyocytes                               0               0             0
+  Endothelium                                  0               0             0
+  Erythroid2                                   0               0             0
+  Erythroid3                                   0               0             0
+  ExE mesoderm                                 0               0             0
+  Forebrain/Midbrain/Hindbrain                 1               0             2
+  Gut                                          0               0             0
+  Haematoendothelial progenitors               0               0             0
+  Intermediate mesoderm                        0               0             0
+  Mesenchyme                                   0               0             0
+  Neural crest                                 0               0             1
+  NMP                                          0               0             0
+  Paraxial mesoderm                            0               0             1
+  Pharyngeal mesoderm                          0               0             0
+  Somitic mesoderm                             0               1             1
+  Spinal cord                                  0               0             0
+  Surface ectoderm                             0               0             0
+                                
+new.labels                       Doublet Endothelium Erythroid1 Erythroid2
+  Allantois                            0           0          0          0
+  Cardiomyocytes                       0           0          0          0
+  Endothelium                          0          13          0          0
+  Erythroid2                           0           0         14         21
+  Erythroid3                           0           0          6          5
+  ExE mesoderm                         0           0          0          0
+  Forebrain/Midbrain/Hindbrain         6           0          0          0
+  Gut                                  0           0          0          0
+  Haematoendothelial progenitors       0          19          0          0
+  Intermediate mesoderm               13           0          0          0
+  Mesenchyme                           0           0          0          0
+  Neural crest                        20           1          3          0
+  NMP                                  0           0          0          0
+  Paraxial mesoderm                    5           0          0          0
+  Pharyngeal mesoderm                  1           0          0          0
+  Somitic mesoderm                     1           0          0          0
+  Spinal cord                          1           0          0          0
+  Surface ectoderm                     0           0          0          0
+                                
+new.labels                       Erythroid3 ExE mesoderm
+  Allantois                               0            0
+  Cardiomyocytes                          0            0
+  Endothelium                             0            0
+  Erythroid2                             51            0
+  Erythroid3                             49            0
+  ExE mesoderm                            0           23
+  Forebrain/Midbrain/Hindbrain            0            1
+  Gut                                     0            0
+  Haematoendothelial progenitors          0            0
+  Intermediate mesoderm                   0           16
+  Mesenchyme                              0            0
+  Neural crest                            0           17
+  NMP                                     0            0
+  Paraxial mesoderm                       0            0
+  Pharyngeal mesoderm                     0            0
+  Somitic mesoderm                        0            0
+  Spinal cord                             0            0
+  Surface ectoderm                        0            0
+                                
+new.labels                       Forebrain/Midbrain/Hindbrain Gut
+  Allantois                                                 0   0
+  Cardiomyocytes                                            0   0
+  Endothelium                                               0   0
+  Erythroid2                                                0   0
+  Erythroid3                                                0   0
+  ExE mesoderm                                              0   0
+  Forebrain/Midbrain/Hindbrain                             83   4
+  Gut                                                       0   5
+  Haematoendothelial progenitors                            0   0
+  Intermediate mesoderm                                     0   0
+  Mesenchyme                                                0   0
+  Neural crest                                             20   0
+  NMP                                                       0   0
+  Paraxial mesoderm                                         0   0
+  Pharyngeal mesoderm                                       0   0
+  Somitic mesoderm                                          0   0
+  Spinal cord                                               0   0
+  Surface ectoderm                                          0  12
+                                
+new.labels                       Haematoendothelial progenitors
+  Allantois                                                   0
+  Cardiomyocytes                                              0
+  Endothelium                                                 0
+  Erythroid2                                                  0
+  Erythroid3                                                  0
+  ExE mesoderm                                                0
+  Forebrain/Midbrain/Hindbrain                                0
+  Gut                                                         0
+  Haematoendothelial progenitors                             16
+  Intermediate mesoderm                                       0
+  Mesenchyme                                                  0
+  Neural crest                                                8
+  NMP                                                         0
+  Paraxial mesoderm                                           2
+  Pharyngeal mesoderm                                         0
+  Somitic mesoderm                                            0
+  Spinal cord                                                 0
+  Surface ectoderm                                            0
+                                
+new.labels                       Intermediate mesoderm Mesenchyme Neural crest
+  Allantois                                          0          0            0
+  Cardiomyocytes                                     0          0            0
+  Endothelium                                        0          0            0
+  Erythroid2                                         0          0            0
+  Erythroid3                                         0          0            0
+  ExE mesoderm                                       1          7            0
+  Forebrain/Midbrain/Hindbrain                       0          3            0
+  Gut                                                0          0            0
+  Haematoendothelial progenitors                     0          0            0
+  Intermediate mesoderm                              9         15            0
+  Mesenchyme                                         0         69            0
+  Neural crest                                       4          4           10
+  NMP                                                0          0            0
+  Paraxial mesoderm                                  0          7            0
+  Pharyngeal mesoderm                                0         13            0
+  Somitic mesoderm                                   2          0            0
+  Spinal cord                                        0          0            0
+  Surface ectoderm                                   0          0            0
+                                
+new.labels                       NMP Paraxial mesoderm Pharyngeal mesoderm
+  Allantois                        0                 0                   0
+  Cardiomyocytes                   0                 0                   0
+  Endothelium                      0                 0                   0
+  Erythroid2                       0                 0                   0
+  Erythroid3                       0                 0                   0
+  ExE mesoderm                     0                 0                   0
+  Forebrain/Midbrain/Hindbrain    41                 2                   4
+  Gut                              0                 0                   0
+  Haematoendothelial progenitors   0                 0                   0
+  Intermediate mesoderm            0                 1                   5
+  Mesenchyme                       0                 0                   0
+  Neural crest                     5                20                  10
+  NMP                             16                 0                   0
+  Paraxial mesoderm                0                43                   3
+  Pharyngeal mesoderm              0                 0                  18
+  Somitic mesoderm                 0                 0                   0
+  Spinal cord                      0                 0                   0
+  Surface ectoderm                 0                 0                   0
+                                
+new.labels                       Rostral neurectoderm Somitic mesoderm
+  Allantois                                         0                0
+  Cardiomyocytes                                    0                0
+  Endothelium                                       0                0
+  Erythroid2                                        0                0
+  Erythroid3                                        0                0
+  ExE mesoderm                                      0                0
+  Forebrain/Midbrain/Hindbrain                     11                1
+  Gut                                               0                0
+  Haematoendothelial progenitors                    0                0
+  Intermediate mesoderm                             0                2
+  Mesenchyme                                        0                0
+  Neural crest                                      0                7
+  NMP                                               0                0
+  Paraxial mesoderm                                 0                0
+  Pharyngeal mesoderm                               0                0
+  Somitic mesoderm                                  0               25
+  Spinal cord                                       0                0
+  Surface ectoderm                                  0                0
+                                
+new.labels                       Spinal cord Stripped Surface ectoderm
+  Allantois                                0        0                0
+  Cardiomyocytes                           0        0                0
+  Endothelium                              0        1                0
+  Erythroid2                               0        1                0
+  Erythroid3                               0        1                0
+  ExE mesoderm                             0        0                0
+  Forebrain/Midbrain/Hindbrain            37        0               17
+  Gut                                      0        0                2
+  Haematoendothelial progenitors           0        0                0
+  Intermediate mesoderm                    0        0                0
+  Mesenchyme                               0        0                2
+  Neural crest                             3        5                6
+  NMP                                      0        0                0
+  Paraxial mesoderm                        0        0                0
+  Pharyngeal mesoderm                      0        0                0
+  Somitic mesoderm                         0        0                0
+  Spinal cord                              9        0                0
+  Surface ectoderm                         0        0               20
+
+

As a diagnostic measure, we examine the distribution of AUCs across +cells for each label. In heterogeneous populations, the distribution for +each label should be bimodal with one high-scoring peak containing cells +of that cell type and a low-scoring peak containing cells of other +types. The gap between these two peaks can be used to derive a threshold +for whether a label is “active” for a particular cell. (In this case, we +simply take the single highest-scoring label per cell as the labels +should be mutually exclusive.) In populations where a particular cell +type is expected, lack of clear bimodality for the corresponding label +may indicate that its gene set is not sufficiently informative.

+
+

R +

+
+par(mfrow = c(3,3))
+AUCell_exploreThresholds(cell.aucs[1:9], plotHist = TRUE, assign = TRUE)
+
+

Shown is the distribution of AUCs in the wild-type chimera dataset +for each label in the embryo atlas dataset. The blue curve represents +the density estimate, the red curve represents a fitted two-component +mixture of normals, the pink curve represents a fitted three-component +mixture, and the grey curve represents a fitted normal distribution. +Vertical lines represent threshold estimates corresponding to each +estimate of the distribution.

+
+
+
+

Session Info +

+
+

R +

+
+sessionInfo()
+
+
+

OUTPUT +

+
R version 4.3.2 (2023-10-31)
+Platform: x86_64-pc-linux-gnu (64-bit)
+Running under: Ubuntu 22.04.3 LTS
+
+Matrix products: default
+BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
+LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
+
+locale:
+ [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
+ [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
+ [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
+[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
+
+time zone: UTC
+tzcode source: system (glibc)
+
+attached base packages:
+[1] stats4    stats     graphics  grDevices utils     datasets  methods  
+[8] base     
+
+other attached packages:
+ [1] GSEABase_1.64.0              graph_1.80.0                
+ [3] annotate_1.80.0              XML_3.99-0.16.1             
+ [5] AnnotationDbi_1.64.1         pheatmap_1.0.12             
+ [7] scran_1.30.2                 scater_1.30.1               
+ [9] ggplot2_3.4.4                scuttle_1.12.0              
+[11] bluster_1.12.0               SingleR_2.4.1               
+[13] MouseGastrulationData_1.16.0 SpatialExperiment_1.12.0    
+[15] SingleCellExperiment_1.24.0  SummarizedExperiment_1.32.0 
+[17] Biobase_2.62.0               GenomicRanges_1.54.1        
+[19] GenomeInfoDb_1.38.5          IRanges_2.36.0              
+[21] S4Vectors_0.40.2             BiocGenerics_0.48.1         
+[23] MatrixGenerics_1.14.0        matrixStats_1.2.0           
+[25] AUCell_1.24.0                BiocStyle_2.30.0            
+
+loaded via a namespace (and not attached):
+  [1] RColorBrewer_1.1-3            jsonlite_1.8.8               
+  [3] magrittr_2.0.3                ggbeeswarm_0.7.2             
+  [5] magick_2.8.2                  farver_2.1.1                 
+  [7] rmarkdown_2.25                zlibbioc_1.48.0              
+  [9] vctrs_0.6.5                   memoise_2.0.1                
+ [11] DelayedMatrixStats_1.24.0     RCurl_1.98-1.14              
+ [13] htmltools_0.5.7               S4Arrays_1.2.0               
+ [15] AnnotationHub_3.10.0          curl_5.2.0                   
+ [17] BiocNeighbors_1.20.2          SparseArray_1.2.3            
+ [19] htmlwidgets_1.6.4             plotly_4.10.4                
+ [21] cachem_1.0.8                  igraph_2.0.1.1               
+ [23] mime_0.12                     lifecycle_1.0.4              
+ [25] pkgconfig_2.0.3               rsvd_1.0.5                   
+ [27] Matrix_1.6-5                  R6_2.5.1                     
+ [29] fastmap_1.1.1                 GenomeInfoDbData_1.2.11      
+ [31] shiny_1.8.0                   digest_0.6.34                
+ [33] colorspace_2.1-0              dqrng_0.3.2                  
+ [35] irlba_2.3.5.1                 ExperimentHub_2.10.0         
+ [37] RSQLite_2.3.5                 beachmat_2.18.0              
+ [39] labeling_0.4.3                filelock_1.0.3               
+ [41] fansi_1.0.6                   httr_1.4.7                   
+ [43] abind_1.4-5                   compiler_4.3.2               
+ [45] bit64_4.0.5                   withr_3.0.0                  
+ [47] BiocParallel_1.36.0           viridis_0.6.5                
+ [49] DBI_1.2.1                     highr_0.10                   
+ [51] R.utils_2.12.3                MASS_7.3-60.0.1              
+ [53] rappdirs_0.3.3                DelayedArray_0.28.0          
+ [55] rjson_0.2.21                  tools_4.3.2                  
+ [57] vipor_0.4.7                   beeswarm_0.4.0               
+ [59] interactiveDisplayBase_1.40.0 httpuv_1.6.14                
+ [61] R.oo_1.26.0                   glue_1.7.0                   
+ [63] nlme_3.1-164                  promises_1.2.1               
+ [65] grid_4.3.2                    cluster_2.1.6                
+ [67] generics_0.1.3                gtable_0.3.4                 
+ [69] R.methodsS3_1.8.2             tidyr_1.3.1                  
+ [71] data.table_1.15.0             metapod_1.10.1               
+ [73] BiocSingular_1.18.0           ScaledMatrix_1.10.0          
+ [75] utf8_1.2.4                    XVector_0.42.0               
+ [77] ggrepel_0.9.5                 BiocVersion_3.18.1           
+ [79] pillar_1.9.0                  limma_3.58.1                 
+ [81] BumpyMatrix_1.10.0            later_1.3.2                  
+ [83] splines_4.3.2                 dplyr_1.1.4                  
+ [85] BiocFileCache_2.10.1          lattice_0.22-5               
+ [87] survival_3.5-7                FNN_1.1.4                    
+ [89] renv_1.0.3                    bit_4.0.5                    
+ [91] tidyselect_1.2.0              locfit_1.5-9.8               
+ [93] Biostrings_2.70.2             knitr_1.45                   
+ [95] gridExtra_2.3                 edgeR_4.0.15                 
+ [97] xfun_0.42                     mixtools_2.0.0               
+ [99] statmod_1.5.0                 lazyeval_0.2.2               
+[101] yaml_2.3.8                    evaluate_0.23                
+[103] codetools_0.2-19              kernlab_0.9-32               
+[105] tibble_3.2.1                  BiocManager_1.30.22          
+[107] cli_3.6.2                     uwot_0.1.16                  
+[109] xtable_1.8-4                  segmented_2.0-3              
+[111] munsell_0.5.0                 Rcpp_1.0.12                  
+[113] dbplyr_2.4.0                  png_0.1-8                    
+[115] parallel_4.3.2                ellipsis_0.3.2               
+[117] blob_1.2.4                    sparseMatrixStats_1.14.0     
+[119] bitops_1.0-7                  viridisLite_0.4.2            
+[121] scales_1.3.0                  purrr_1.0.2                  
+[123] crayon_1.5.2                  rlang_1.1.3                  
+[125] KEGGREST_1.42.0
+
+
+
+

Further Reading +

+ +
+
+

Exercises +

+
+
+ +
+
+

Exercise 1: Clustering +

+
+

The Leiden +algorithm is similar to the Louvain algorithm, but it is faster and +has been shown to result in better connected communities. Modify the +above call to clusterCells to carry out the community +detection with the Leiden algorithm instead. Visualize the results in a +UMAP plot.

+
+
+
+
+
+ +
+
+

The NNGraphParam constructor has an argument +cluster.args. This allows to specify arguments passed on to +the cluster_leiden function from the igraph +package. Use the cluster.args argument to parameterize the +clustering to use modularity as the objective function and a resolution +parameter of 0.5.

+
+
+
+
+
+
+ +
+
+

TODO

+
+
+
+
+
+
+ +
+
+

Exercise 2: Cluster annotation +

+
+

Another strategy for annotating the clusters is to perform a gene set +enrichment analysis on the marker genes defining each cluster. This +identifies the pathways and processes that are (relatively) active in +each cluster based on upregulation of the associated genes compared to +other clusters. Focus on the top 100 up-regulated genes in a cluster of +your choice and perform a gene set enrichment analysis of biological +process (BP) gene sets from the Gene Ontology (GO).

+
+
+
+
+
+ +
+
+

Use the goana() function from the limma +package to identify GO BP terms that are overrepresented in the list of +marker genes.

+
+
+
+
+
+
+ +
+
+

TODO

+
+
+
+
+
+
+ +
+
+

Exercise 3: Workflow +

+
+

The scRNAseq +package provides gene-level counts for a collection of public scRNA-seq +datasets, stored as SingleCellExperiment objects with +annotated cell- and gene-level metadata. Consult the vignette of the scRNAseq package +to inspect all available datasets and select a dataset of your choice. +Perform a typical scRNA-seq analysis on this dataset including QC, +normalization, feature selection, dimensionality reduction, clustering, +and marker gene detection.

+
+
+
+
+
+ +
+
+

TODO

+
+
+
+
+
+
+ +
+
+

Key Points +

+
+
    +
  • TODO
    • +
+
+
+
+

diff --git a/cell_type_annotation.html b/cell_type_annotation.html new file mode 100644 index 0000000..40de742 --- /dev/null +++ b/cell_type_annotation.html @@ -0,0 +1,1533 @@ + +Orchestrating Large-Scale Single-Cell Analysis with Bioconductor: Cell type annotation +
+ Orchestrating Large-Scale Single-Cell Analysis with Bioconductor +
+ +
+
+ + + + + +
+
+

Cell type annotation

+

Last updated on 2024-02-20 | + + Edit this page

+ + + +
+ +
+ + + +
+

Overview

+
+
+
+
+

Questions

+
  • TODO
    • +
+
+
+
+
+
+

Objectives

+
  • TODO
    • +
+
+
+
+
+
+

Setup

+
+

R +

+
+library(BiocStyle)
+library(AUCell)
+
+
+

WARNING +

+
Warning: replacing previous import 'S4Arrays::makeNindexFromArrayViewport' by
+'DelayedArray::makeNindexFromArrayViewport' when loading 'SummarizedExperiment'
+
+
+

R +

+
+library(MouseGastrulationData)
+library(SingleR)
+library(bluster)
+library(scater)
+library(scran)
+
+
+
+

Data retrieval

+
+

R +

+
+sce <- WTChimeraData(samples = 5, type = "processed")
+sce
+
+
+

OUTPUT +

+
class: SingleCellExperiment 
+dim: 29453 2411 
+metadata(0):
+assays(1): counts
+rownames(29453): ENSMUSG00000051951 ENSMUSG00000089699 ...
+  ENSMUSG00000095742 tomato-td
+rowData names(2): ENSEMBL SYMBOL
+colnames(2411): cell_9769 cell_9770 ... cell_12178 cell_12179
+colData names(11): cell barcode ... doub.density sizeFactor
+reducedDimNames(2): pca.corrected.E7.5 pca.corrected.E8.5
+mainExpName: NULL
+altExpNames(0):
+
+

To speed up the computations, we subsample the dataset to 1,000 +cells.

+
+

R +

+
+set.seed(123)
+ind <- sample(ncol(sce), 1000)
+sce <- sce[,ind]
+
+
+
+

Preprocessing

+
+

R +

+
+sce <- logNormCounts(sce)
+sce <- runPCA(sce)
+
+
+
+

Clustering

+

Clustering is an unsupervised learning procedure that is used to +empirically define groups of cells with similar expression profiles. Its +primary purpose is to summarize complex scRNA-seq data into a digestible +format for human interpretation. This allows us to describe population +heterogeneity in terms of discrete labels that are easily understood, +rather than attempting to comprehend the high-dimensional manifold on +which the cells truly reside. After annotation based on marker genes, +the clusters can be treated as proxies for more abstract biological +concepts such as cell types or states.

+

Popularized by its use in Seurat, +graph-based clustering is a flexible and scalable technique for +clustering large scRNA-seq datasets. We first build a graph where each +node is a cell that is connected to its nearest neighbors in the +high-dimensional space. Edges are weighted based on the similarity +between the cells involved, with higher weight given to cells that are +more closely related. We then apply algorithms to identify “communities” +of cells that are more connected to cells in the same community than +they are to cells of different communities. Each community represents a +cluster that we can use for downstream interpretation.

+

Here, we use the clusterCells() function from the scran package to +perform graph-based clustering using the Louvain +algorithm for community detection. All calculations are performed +using the top PCs to take advantage of data compression and denoising. +This function returns a vector containing cluster assignments for each +cell in our SingleCellExperiment object.

+
+

R +

+
+colLabels(sce) <- clusterCells(sce, use.dimred = "PCA",
+                               BLUSPARAM = NNGraphParam(cluster.fun = "louvain"))
+table(colLabels(sce))
+
+
+

OUTPUT +

+

+  1   2   3   4   5   6   7   8   9  10  11 
+100 160  99 141  63  93  60 108  44  91  41
+
+

We assign the cluster assignments back into our +SingleCellExperiment object as a factor in the +column metadata. This allows us to conveniently visualize the +distribution of clusters in eg. a t-SNE or a UMAP.

+
+

R +

+
+sce <- runUMAP(sce, dimred = "PCA")
+plotReducedDim(sce, "UMAP", color_by = "label")
+
+
+
+

Marker gene detection

+

To interpret clustering results as obtained in the previous section, +we identify the genes that drive separation between clusters. These +marker genes allow us to assign biological meaning to each cluster based +on their functional annotation. In the simplest case, we have a +priori knowledge of the marker genes associated with particular +cell types, allowing us to treat the clustering as a proxy for cell type +identity.

+

The most straightforward approach to marker gene detection involves +testing for differential expression between clusters. If a gene is +strongly DE between clusters, it is likely to have driven the separation +of cells in the clustering algorithm.

+

Here, we perform a Wilcoxon rank sum test against a log2 fold change +threshold of 1, focusing on up-regulated (positive) markers in one +cluster when compared to another cluster.

+
+

R +

+
+rownames(sce) <- rowData(sce)$SYMBOL
+markers <- findMarkers(sce, test.type = "wilcox", direction = "up", lfc = 1)
+markers
+
+
+

OUTPUT +

+
List of length 11
+names(11): 1 2 3 4 5 6 7 8 9 10 11
+
+

The resulting object contains a sorted marker gene list for each +cluster, in which the top genes are those that contribute the most to +the separation of that cluster from mall other clusters.

+

Here, we inspect the ranked marker gene list for the first +cluster.

+
+

R +

+
+markers[[1]]
+
+
+

OUTPUT +

+
DataFrame with 29453 rows and 14 columns
+                 Top     p.value         FDR summary.AUC     AUC.2     AUC.3
+           <integer>   <numeric>   <numeric>   <numeric> <numeric> <numeric>
+Crabp2             1 7.31206e-35 1.95784e-31    0.938625  0.938625 0.9159596
+Ptn                1 2.26190e-43 6.66197e-39    0.983313  0.983313 0.9812121
+Crabp1             1 1.13915e-32 2.09696e-29    0.926687  0.926687 0.7927273
+Zeb2               2 3.98744e-10 1.38167e-07    0.801500  0.553125 0.3943434
+Mest               2 1.10835e-24 9.60122e-22    0.883000  0.883000 0.0249495
+...              ...         ...         ...         ...       ...       ...
+AC125149.2     29448           1           1           0         0         0
+AC125149.4     29449           1           1           0         0         0
+AC234645.1     29450           1           1           0         0         0
+AC168977.2     29451           1           1           0         0         0
+Vmn2r122       29453           1           1           0         0         0
+               AUC.4     AUC.5     AUC.6     AUC.7     AUC.8     AUC.9
+           <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
+Crabp2      0.861986  0.494444  0.690000  0.763833  0.845370  0.769091
+Ptn         0.911773  0.771429  0.198710  0.830667  0.563333  0.898409
+Crabp1      0.540071  0.714127  0.695591  0.767167  0.620556  0.524773
+Zeb2        0.604184  0.390317  0.337849  0.801500  0.447963  0.341364
+Mest        0.136028  0.476190  0.091828  0.386833  0.314444  0.773864
+...              ...       ...       ...       ...       ...       ...
+AC125149.2         0         0         0         0         0         0
+AC125149.4         0         0         0         0         0         0
+AC234645.1         0         0         0         0         0         0
+AC168977.2         0         0         0         0         0         0
+Vmn2r122           0         0         0         0         0         0
+              AUC.10    AUC.11
+           <numeric> <numeric>
+Crabp2     0.9416484  0.882439
+Ptn        0.9463736  0.967317
+Crabp1     0.8584615  0.714878
+Zeb2       0.6175824  0.501463
+Mest       0.0154945  0.290976
+...              ...       ...
+AC125149.2         0         0
+AC125149.4         0         0
+AC234645.1         0         0
+AC168977.2         0         0
+Vmn2r122           0         0
+
+

The Top field provides the the minimum rank across all +pairwise comparisons. The p.value field provides the +combined p-value across all comparisons, and the +FDR field the BH-adjusted p-value for each gene. +The summary.AUC provides area under the curve (here the +concordance probability) from the comparison with the lowest +p-value, the AUC.n fields provide the AUC for each +pairwise comparison. The AUC is the probability that a randomly selected +cell in cluster A has a greater expression of gene X +than a randomly selected cell in B.

+

We can then inspect the top marker genes for the first cluster using +the plotExpression function from the scater package.

+
+

R +

+
+top.markers <- head(rownames(markers[[1]]))
+plotExpression(sce, features = top.markers, x = "label", color_by = "label")
+
+
+
+

Cell type annotation

+

The most challenging task in scRNA-seq data analysis is arguably the +interpretation of the results. Obtaining clusters of cells is fairly +straightforward, but it is more difficult to determine what biological +state is represented by each of those clusters. Doing so requires us to +bridge the gap between the current dataset and prior biological +knowledge, and the latter is not always available in a consistent and +quantitative manner. Indeed, even the concept of a “cell type” is not clearly +defined, with most practitioners possessing a “I’ll know it when I +see it” intuition that is not amenable to computational analysis. As +such, interpretation of scRNA-seq data is often manual and a common +bottleneck in the analysis workflow.

+

To expedite this step, we can use various computational approaches +that exploit prior information to assign meaning to an uncharacterized +scRNA-seq dataset. The most obvious sources of prior information are the +curated gene sets associated with particular biological processes, e.g., +from the Gene Ontology (GO) or the Kyoto Encyclopedia of Genes and +Genomes (KEGG) collections. Alternatively, we can directly compare our +expression profiles to published reference datasets where each sample or +cell has already been annotated with its putative biological state by +domain experts. Here, we will demonstrate both approaches on the +wild-type chimera dataset.

+
+

Assigning cell labels from reference data

+

A conceptually straightforward annotation approach is to compare the +single-cell expression profiles with previously annotated reference +datasets. Labels can then be assigned to each cell in our +uncharacterized test dataset based on the most similar reference +sample(s), for some definition of “similar”. This is a standard +classification challenge that can be tackled by standard machine +learning techniques such as random forests and support vector machines. +Any published and labelled RNA-seq dataset (bulk or single-cell) can be +used as a reference, though its reliability depends greatly on the +expertise of the original authors who assigned the labels in the first +place.

+

In this section, we will demonstrate the use of the SingleR +method for cell type annotation Aran et al., +2019. This method assigns labels to cells based on the reference +samples with the highest Spearman rank correlations, using only the +marker genes between pairs of labels to focus on the relevant +differences between cell types. It also performs a fine-tuning step for +each cell where the correlations are recomputed with just the marker +genes for the top-scoring labels. This aims to resolve any ambiguity +between those labels by removing noise from irrelevant markers for other +labels. Further details can be found in the SingleR +book from which most of the examples here are derived.

+
+

R +

+
+ref <- EmbryoAtlasData(samples = 29)
+ref
+
+
+

OUTPUT +

+
class: SingleCellExperiment 
+dim: 29452 7569 
+metadata(0):
+assays(1): counts
+rownames(29452): ENSMUSG00000051951 ENSMUSG00000089699 ...
+  ENSMUSG00000096730 ENSMUSG00000095742
+rowData names(2): ENSEMBL SYMBOL
+colnames(7569): cell_95727 cell_95728 ... cell_103294 cell_103295
+colData names(17): cell barcode ... colour sizeFactor
+reducedDimNames(2): pca.corrected umap
+mainExpName: NULL
+altExpNames(0):
+
+
+

R +

+
+table(ref$stage)
+
+
+

OUTPUT +

+

+E8.5 
+7569
+
+

To speed up the computations, we subsample the dataset to 1,000 +cells.

+
+

R +

+
+set.seed(123)
+ind <- sample(ncol(ref), 1000)
+ref <- ref[,ind]
+
+
+

R +

+
+tab <- sort(table(ref$celltype), decreasing = TRUE)
+tab
+
+
+

OUTPUT +

+

+  Forebrain/Midbrain/Hindbrain                     Erythroid3 
+                           131                             75 
+             Paraxial mesoderm                            NMP 
+                            69                             51 
+                  ExE mesoderm               Surface ectoderm 
+                            49                             47 
+                     Allantois                     Mesenchyme 
+                            46                             45 
+                   Spinal cord            Pharyngeal mesoderm 
+                            45                             41 
+                  ExE endoderm                   Neural crest 
+                            38                             35 
+                           Gut Haematoendothelial progenitors 
+                            30                             27 
+         Intermediate mesoderm                 Cardiomyocytes 
+                            27                             26 
+              Somitic mesoderm                    Endothelium 
+                            25                             23 
+                    Erythroid2                  Def. endoderm 
+                            11                              3 
+                    Erythroid1            Blood progenitors 1 
+                             2                              1 
+           Blood progenitors 2                Caudal Mesoderm 
+                             1                              1 
+                           PGC 
+                             1
+
+
+

R +

+
+ref <- logNormCounts(ref)
+
+

Some cleaning - remove cells of the reference dataset for which the +cell type annotation is missing.

+
+

R +

+
+nna <- !is.na(ref$celltype)
+ref <- ref[,nna]
+
+

Also remove cell types of very low abundance (here less than 10 +cells) to remove noise prior to subsequent annotation tasks.

+
+

R +

+
+abu.ct <- names(tab)[tab >= 10]
+ind <- ref$celltype %in% abu.ct
+ref <- ref[,ind]
+
+

Restrict to genes shared between query and reference dataset.

+
+

R +

+
+rownames(ref) <- rowData(ref)$SYMBOL
+isect <- intersect(rownames(sce), rownames(ref))
+sce <- sce[isect,]
+ref <- ref[isect,]
+
+

Convert sparse assay matrices to regular dense matrices for input to +SingleR.

+
+

R +

+
+sce.mat <- as.matrix(assay(sce, "logcounts"))
+ref.mat <- as.matrix(assay(ref, "logcounts"))
+
+
+

R +

+
+res <- SingleR(test = sce.mat, ref = ref.mat, labels = ref$celltype)
+res
+
+
+

OUTPUT +

+
DataFrame with 1000 rows and 4 columns
+                                   scores                 labels delta.next
+                                 <matrix>            <character>  <numeric>
+cell_11995 0.348586:0.335451:0.314515:... Forebrain/Midbrain/H..  0.1285110
+cell_10294 0.273570:0.260013:0.298932:...             Erythroid3  0.1381951
+cell_9963  0.328538:0.291288:0.475611:...            Endothelium  0.2193295
+cell_11610 0.281161:0.269245:0.299961:...             Erythroid3  0.0359215
+cell_10910 0.422454:0.346897:0.355947:...           ExE mesoderm  0.0984285
+...                                   ...                    ...        ...
+cell_11597 0.323805:0.292967:0.300485:...                    NMP  0.1663369
+cell_9807  0.464466:0.374189:0.381698:...             Mesenchyme  0.0833019
+cell_10095 0.341721:0.288215:0.485324:...            Endothelium  0.0889931
+cell_11706 0.267487:0.240215:0.286012:...             Erythroid2  0.0350557
+cell_11860 0.345786:0.343437:0.313994:... Forebrain/Midbrain/H..  0.0117001
+                    pruned.labels
+                      <character>
+cell_11995 Forebrain/Midbrain/H..
+cell_10294             Erythroid3
+cell_9963             Endothelium
+cell_11610             Erythroid3
+cell_10910           ExE mesoderm
+...                           ...
+cell_11597                    NMP
+cell_9807              Mesenchyme
+cell_10095            Endothelium
+cell_11706             Erythroid2
+cell_11860 Forebrain/Midbrain/H..
+
+

We inspect the results using a heatmap of the per-cell and label +scores. Ideally, each cell should exhibit a high score in one label +relative to all of the others, indicating that the assignment to that +label was unambiguous. This is largely the case for mesenchyme and +endothelial cells, whereas we see expectedly more ambiguity between the +two erythroid cell populations.

+
+

R +

+
+plotScoreHeatmap(res)
+
+

We also compare the cell type assignments with the clustering results +to determine the identity of each cluster. Here, several cell type +classes are nested within the same cluster, indicating that these +clusters are composed of several transcriptomically similar cell +populations (such as cluster 4 and 6). On the other hand, there are also +instances where we have several clusters for the same cell type, +indicating that the clustering represents finer subdivisions within +these cell types.

+
+

R +

+
+library(pheatmap)
+tab <- table(anno = res$pruned.labels, cluster = colLabels(sce))
+pheatmap(log2(tab + 10), color = colorRampPalette(c("white", "blue"))(101))
+
+

As it so happens, we are in the fortunate position where our test +dataset also contains independently defined labels. We see strong +consistency between the two sets of labels, indicating that our +automatic annotation is comparable to that generated manually by domain +experts.

+
+

R +

+
+tab <- table(res$pruned.labels, sce$celltype.mapped)
+pheatmap(log2(tab + 10), color = colorRampPalette(c("white", "blue"))(101))
+
+
+
+

Assigning cell labels from gene sets

+

A related strategy is to explicitly identify sets of marker genes +that are highly expressed in each individual cell. This does not require +matching of individual cells to the expression values of the reference +dataset, which is faster and more convenient when only the identities of +the markers are available. We demonstrate this approach using cell type +markers derived from the mouse embryo atlas dataset.

+
+

R +

+
+library(scran)
+wilcox.z <- pairwiseWilcox(ref, ref$celltype, lfc = 1, direction = "up")
+markers.z <- getTopMarkers(wilcox.z$statistics, wilcox.z$pairs, 
+                           pairwise = FALSE, n = 50)
+lengths(markers.z)
+
+
+

OUTPUT +

+
                     Allantois                 Cardiomyocytes 
+                           106                            106 
+                   Endothelium                     Erythroid2 
+                           103                             54 
+                    Erythroid3                   ExE endoderm 
+                            84                            102 
+                  ExE mesoderm   Forebrain/Midbrain/Hindbrain 
+                            97                             97 
+                           Gut Haematoendothelial progenitors 
+                            90                             71 
+         Intermediate mesoderm                     Mesenchyme 
+                            70                            118 
+                  Neural crest                            NMP 
+                            66                             91 
+             Paraxial mesoderm            Pharyngeal mesoderm 
+                            88                             85 
+              Somitic mesoderm                    Spinal cord 
+                            86                             91 
+              Surface ectoderm 
+                            92
+
+

Our test dataset will be as before the wild-type chimera dataset.

+
+

R +

+
+sce
+
+
+

OUTPUT +

+
class: SingleCellExperiment 
+dim: 29411 1000 
+metadata(0):
+assays(2): counts logcounts
+rownames(29411): Xkr4 Gm1992 ... Vmn2r122 CAAA01147332.1
+rowData names(2): ENSEMBL SYMBOL
+colnames(1000): cell_11995 cell_10294 ... cell_11706 cell_11860
+colData names(12): cell barcode ... sizeFactor label
+reducedDimNames(4): pca.corrected.E7.5 pca.corrected.E8.5 PCA UMAP
+mainExpName: NULL
+altExpNames(0):
+
+

We use the AUCell +package to identify marker sets that are highly expressed in each cell. +This method ranks genes by their expression values within each cell and +constructs a response curve of the number of genes from each marker set +that are present with increasing rank. It then computes the area under +the curve (AUC) for each marker set, quantifying the enrichment of those +markers among the most highly expressed genes in that cell. This is +roughly similar to performing a Wilcoxon rank sum test between genes in +and outside of the set, but involving only the top ranking genes by +expression in each cell.

+
+

R +

+
+library(GSEABase)
+all.sets <- lapply(names(markers.z), 
+                   function(x) GeneSet(markers.z[[x]], setName = x))
+all.sets <- GeneSetCollection(all.sets)
+all.sets
+
+
+

OUTPUT +

+
GeneSetCollection
+  names: Allantois, Cardiomyocytes, ..., Surface ectoderm (19 total)
+  unique identifiers: Prrx2, Spin2c, ..., Igf2bp3 (560 total)
+  types in collection:
+    geneIdType: NullIdentifier (1 total)
+    collectionType: NullCollection (1 total)
+
+
+

R +

+
+library(AUCell)
+rankings <- AUCell_buildRankings(as.matrix(counts(sce)),
+                                 plotStats = FALSE, verbose = FALSE)
+cell.aucs <- AUCell_calcAUC(all.sets, rankings)
+results <- t(assay(cell.aucs))
+head(results)
+
+
+

OUTPUT +

+
            gene sets
+cells         Allantois Cardiomyocytes Endothelium Erythroid2 Erythroid3
+  cell_11995 0.21994609      0.2336827   0.1947563 0.12228508  0.1639749
+  cell_10294 0.10066221      0.1246414   0.1104634 0.60732017  0.6081721
+  cell_9963  0.34273069      0.3158431   0.5407815 0.13588372  0.1797007
+  cell_11610 0.08244651      0.1340388   0.1048735 0.57892981  0.5706619
+  cell_10910 0.31763336      0.2784001   0.2445795 0.08175859  0.1354818
+  cell_11021 0.20549732      0.2320122   0.1869839 0.11351012  0.1769588
+            gene sets
+cells        ExE endoderm ExE mesoderm Forebrain/Midbrain/Hindbrain       Gut
+  cell_11995   0.06053637    0.4371076                    0.6319109 0.3779960
+  cell_10294   0.06369959    0.1900184                    0.2670988 0.1482988
+  cell_9963    0.09466189    0.4165978                    0.4972886 0.3805059
+  cell_11610   0.05720738    0.2060333                    0.2785028 0.1466698
+  cell_10910   0.11787498    0.5770948                    0.5721069 0.4450836
+  cell_11021   0.09285926    0.4736976                    0.6011933 0.3993608
+            gene sets
+cells        Haematoendothelial progenitors Intermediate mesoderm Mesenchyme
+  cell_11995                      0.3329538             0.5396029  0.2456757
+  cell_10294                      0.1621829             0.1781659  0.1078670
+  cell_9963                       0.6010306             0.4623277  0.3566887
+  cell_11610                      0.1526329             0.1968055  0.1118536
+  cell_10910                      0.4333710             0.5481614  0.3512731
+  cell_11021                      0.3336212             0.5197592  0.2403682
+            gene sets
+cells        Neural crest       NMP Paraxial mesoderm Pharyngeal mesoderm
+  cell_11995    0.6535231 0.4841026         0.5510547           0.5076289
+  cell_10294    0.2840264 0.2113283         0.2192031           0.2049349
+  cell_9963     0.5374756 0.3740042         0.5007010           0.4618718
+  cell_11610    0.2949987 0.2235049         0.2348724           0.2134701
+  cell_10910    0.5472885 0.5225294         0.5506325           0.5564014
+  cell_11021    0.5863399 0.5731637         0.4992671           0.4707530
+            gene sets
+cells        Somitic mesoderm Spinal cord Surface ectoderm
+  cell_11995        0.4654095   0.5807133        0.5251957
+  cell_10294        0.2141816   0.2290110        0.1874113
+  cell_9963         0.4307335   0.4373858        0.4173470
+  cell_11610        0.2339673   0.2564642        0.2054802
+  cell_10910        0.5179489   0.5167149        0.5049140
+  cell_11021        0.5186006   0.5526277        0.5052879
+
+

We assign cell type identity to each cell in the test dataset by +taking the marker set with the top AUC as the label for that cell. Our +new labels mostly agree with the original annotation (and, thus, also +with the reference-based annotation). Instances where the original +annotation is divided into several new label groups typically points to +large overlaps in their marker sets. In the absence of prior annotation, +a more general diagnostic check is to compare the assigned labels to +cluster identities, under the expectation that most cells of a single +cluster would have the same label (or, if multiple labels are present, +they should at least represent closely related cell states).

+
+

R +

+
+new.labels <- colnames(results)[max.col(results)]
+tab <- table(new.labels, sce$celltype.mapped)
+tab
+
+
+

OUTPUT +

+
                                
+new.labels                       Allantois Blood progenitors 1
+  Allantois                             28                   0
+  Cardiomyocytes                         0                   0
+  Endothelium                            0                   0
+  Erythroid2                             0                   0
+  Erythroid3                             0                   0
+  ExE mesoderm                           0                   0
+  Forebrain/Midbrain/Hindbrain           1                   0
+  Gut                                    0                   0
+  Haematoendothelial progenitors         1                   0
+  Intermediate mesoderm                  0                   0
+  Mesenchyme                             0                   0
+  Neural crest                          11                   4
+  NMP                                    0                   0
+  Paraxial mesoderm                      5                   0
+  Pharyngeal mesoderm                    0                   0
+  Somitic mesoderm                       0                   0
+  Spinal cord                            0                   0
+  Surface ectoderm                       0                   0
+                                
+new.labels                       Blood progenitors 2 Cardiomyocytes
+  Allantois                                        0              0
+  Cardiomyocytes                                   0             27
+  Endothelium                                      0              0
+  Erythroid2                                       1              0
+  Erythroid3                                       0              0
+  ExE mesoderm                                     0              0
+  Forebrain/Midbrain/Hindbrain                     0              0
+  Gut                                              0              0
+  Haematoendothelial progenitors                   0              0
+  Intermediate mesoderm                            0              0
+  Mesenchyme                                       0              0
+  Neural crest                                     6              1
+  NMP                                              0              0
+  Paraxial mesoderm                                0              4
+  Pharyngeal mesoderm                              0              6
+  Somitic mesoderm                                 0              0
+  Spinal cord                                      0              0
+  Surface ectoderm                                 0              0
+                                
+new.labels                       Caudal epiblast Caudal Mesoderm Def. endoderm
+  Allantois                                    0               0             0
+  Cardiomyocytes                               0               0             0
+  Endothelium                                  0               0             0
+  Erythroid2                                   0               0             0
+  Erythroid3                                   0               0             0
+  ExE mesoderm                                 0               0             0
+  Forebrain/Midbrain/Hindbrain                 1               0             2
+  Gut                                          0               0             0
+  Haematoendothelial progenitors               0               0             0
+  Intermediate mesoderm                        0               0             0
+  Mesenchyme                                   0               0             0
+  Neural crest                                 0               0             1
+  NMP                                          0               0             0
+  Paraxial mesoderm                            0               0             1
+  Pharyngeal mesoderm                          0               0             0
+  Somitic mesoderm                             0               1             1
+  Spinal cord                                  0               0             0
+  Surface ectoderm                             0               0             0
+                                
+new.labels                       Doublet Endothelium Erythroid1 Erythroid2
+  Allantois                            0           0          0          0
+  Cardiomyocytes                       0           0          0          0
+  Endothelium                          0          13          0          0
+  Erythroid2                           0           0         14         21
+  Erythroid3                           0           0          6          5
+  ExE mesoderm                         0           0          0          0
+  Forebrain/Midbrain/Hindbrain         6           0          0          0
+  Gut                                  0           0          0          0
+  Haematoendothelial progenitors       0          19          0          0
+  Intermediate mesoderm               13           0          0          0
+  Mesenchyme                           0           0          0          0
+  Neural crest                        20           1          3          0
+  NMP                                  0           0          0          0
+  Paraxial mesoderm                    5           0          0          0
+  Pharyngeal mesoderm                  1           0          0          0
+  Somitic mesoderm                     1           0          0          0
+  Spinal cord                          1           0          0          0
+  Surface ectoderm                     0           0          0          0
+                                
+new.labels                       Erythroid3 ExE mesoderm
+  Allantois                               0            0
+  Cardiomyocytes                          0            0
+  Endothelium                             0            0
+  Erythroid2                             51            0
+  Erythroid3                             49            0
+  ExE mesoderm                            0           23
+  Forebrain/Midbrain/Hindbrain            0            1
+  Gut                                     0            0
+  Haematoendothelial progenitors          0            0
+  Intermediate mesoderm                   0           16
+  Mesenchyme                              0            0
+  Neural crest                            0           17
+  NMP                                     0            0
+  Paraxial mesoderm                       0            0
+  Pharyngeal mesoderm                     0            0
+  Somitic mesoderm                        0            0
+  Spinal cord                             0            0
+  Surface ectoderm                        0            0
+                                
+new.labels                       Forebrain/Midbrain/Hindbrain Gut
+  Allantois                                                 0   0
+  Cardiomyocytes                                            0   0
+  Endothelium                                               0   0
+  Erythroid2                                                0   0
+  Erythroid3                                                0   0
+  ExE mesoderm                                              0   0
+  Forebrain/Midbrain/Hindbrain                             83   4
+  Gut                                                       0   5
+  Haematoendothelial progenitors                            0   0
+  Intermediate mesoderm                                     0   0
+  Mesenchyme                                                0   0
+  Neural crest                                             20   0
+  NMP                                                       0   0
+  Paraxial mesoderm                                         0   0
+  Pharyngeal mesoderm                                       0   0
+  Somitic mesoderm                                          0   0
+  Spinal cord                                               0   0
+  Surface ectoderm                                          0  12
+                                
+new.labels                       Haematoendothelial progenitors
+  Allantois                                                   0
+  Cardiomyocytes                                              0
+  Endothelium                                                 0
+  Erythroid2                                                  0
+  Erythroid3                                                  0
+  ExE mesoderm                                                0
+  Forebrain/Midbrain/Hindbrain                                0
+  Gut                                                         0
+  Haematoendothelial progenitors                             16
+  Intermediate mesoderm                                       0
+  Mesenchyme                                                  0
+  Neural crest                                                8
+  NMP                                                         0
+  Paraxial mesoderm                                           2
+  Pharyngeal mesoderm                                         0
+  Somitic mesoderm                                            0
+  Spinal cord                                                 0
+  Surface ectoderm                                            0
+                                
+new.labels                       Intermediate mesoderm Mesenchyme Neural crest
+  Allantois                                          0          0            0
+  Cardiomyocytes                                     0          0            0
+  Endothelium                                        0          0            0
+  Erythroid2                                         0          0            0
+  Erythroid3                                         0          0            0
+  ExE mesoderm                                       1          7            0
+  Forebrain/Midbrain/Hindbrain                       0          3            0
+  Gut                                                0          0            0
+  Haematoendothelial progenitors                     0          0            0
+  Intermediate mesoderm                              9         15            0
+  Mesenchyme                                         0         69            0
+  Neural crest                                       4          4           10
+  NMP                                                0          0            0
+  Paraxial mesoderm                                  0          7            0
+  Pharyngeal mesoderm                                0         13            0
+  Somitic mesoderm                                   2          0            0
+  Spinal cord                                        0          0            0
+  Surface ectoderm                                   0          0            0
+                                
+new.labels                       NMP Paraxial mesoderm Pharyngeal mesoderm
+  Allantois                        0                 0                   0
+  Cardiomyocytes                   0                 0                   0
+  Endothelium                      0                 0                   0
+  Erythroid2                       0                 0                   0
+  Erythroid3                       0                 0                   0
+  ExE mesoderm                     0                 0                   0
+  Forebrain/Midbrain/Hindbrain    41                 2                   4
+  Gut                              0                 0                   0
+  Haematoendothelial progenitors   0                 0                   0
+  Intermediate mesoderm            0                 1                   5
+  Mesenchyme                       0                 0                   0
+  Neural crest                     5                20                  10
+  NMP                             16                 0                   0
+  Paraxial mesoderm                0                43                   3
+  Pharyngeal mesoderm              0                 0                  18
+  Somitic mesoderm                 0                 0                   0
+  Spinal cord                      0                 0                   0
+  Surface ectoderm                 0                 0                   0
+                                
+new.labels                       Rostral neurectoderm Somitic mesoderm
+  Allantois                                         0                0
+  Cardiomyocytes                                    0                0
+  Endothelium                                       0                0
+  Erythroid2                                        0                0
+  Erythroid3                                        0                0
+  ExE mesoderm                                      0                0
+  Forebrain/Midbrain/Hindbrain                     11                1
+  Gut                                               0                0
+  Haematoendothelial progenitors                    0                0
+  Intermediate mesoderm                             0                2
+  Mesenchyme                                        0                0
+  Neural crest                                      0                7
+  NMP                                               0                0
+  Paraxial mesoderm                                 0                0
+  Pharyngeal mesoderm                               0                0
+  Somitic mesoderm                                  0               25
+  Spinal cord                                       0                0
+  Surface ectoderm                                  0                0
+                                
+new.labels                       Spinal cord Stripped Surface ectoderm
+  Allantois                                0        0                0
+  Cardiomyocytes                           0        0                0
+  Endothelium                              0        1                0
+  Erythroid2                               0        1                0
+  Erythroid3                               0        1                0
+  ExE mesoderm                             0        0                0
+  Forebrain/Midbrain/Hindbrain            37        0               17
+  Gut                                      0        0                2
+  Haematoendothelial progenitors           0        0                0
+  Intermediate mesoderm                    0        0                0
+  Mesenchyme                               0        0                2
+  Neural crest                             3        5                6
+  NMP                                      0        0                0
+  Paraxial mesoderm                        0        0                0
+  Pharyngeal mesoderm                      0        0                0
+  Somitic mesoderm                         0        0                0
+  Spinal cord                              9        0                0
+  Surface ectoderm                         0        0               20
+
+

As a diagnostic measure, we examine the distribution of AUCs across +cells for each label. In heterogeneous populations, the distribution for +each label should be bimodal with one high-scoring peak containing cells +of that cell type and a low-scoring peak containing cells of other +types. The gap between these two peaks can be used to derive a threshold +for whether a label is “active” for a particular cell. (In this case, we +simply take the single highest-scoring label per cell as the labels +should be mutually exclusive.) In populations where a particular cell +type is expected, lack of clear bimodality for the corresponding label +may indicate that its gene set is not sufficiently informative.

+
+

R +

+
+par(mfrow = c(3,3))
+AUCell_exploreThresholds(cell.aucs[1:9], plotHist = TRUE, assign = TRUE)
+
+

Shown is the distribution of AUCs in the wild-type chimera dataset +for each label in the embryo atlas dataset. The blue curve represents +the density estimate, the red curve represents a fitted two-component +mixture of normals, the pink curve represents a fitted three-component +mixture, and the grey curve represents a fitted normal distribution. +Vertical lines represent threshold estimates corresponding to each +estimate of the distribution.

+
+
+
+

Session Info

+
+

R +

+
+sessionInfo()
+
+
+

OUTPUT +

+
R version 4.3.2 (2023-10-31)
+Platform: x86_64-pc-linux-gnu (64-bit)
+Running under: Ubuntu 22.04.3 LTS
+
+Matrix products: default
+BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
+LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
+
+locale:
+ [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
+ [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
+ [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
+[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
+
+time zone: UTC
+tzcode source: system (glibc)
+
+attached base packages:
+[1] stats4    stats     graphics  grDevices utils     datasets  methods  
+[8] base     
+
+other attached packages:
+ [1] GSEABase_1.64.0              graph_1.80.0                
+ [3] annotate_1.80.0              XML_3.99-0.16.1             
+ [5] AnnotationDbi_1.64.1         pheatmap_1.0.12             
+ [7] scran_1.30.2                 scater_1.30.1               
+ [9] ggplot2_3.4.4                scuttle_1.12.0              
+[11] bluster_1.12.0               SingleR_2.4.1               
+[13] MouseGastrulationData_1.16.0 SpatialExperiment_1.12.0    
+[15] SingleCellExperiment_1.24.0  SummarizedExperiment_1.32.0 
+[17] Biobase_2.62.0               GenomicRanges_1.54.1        
+[19] GenomeInfoDb_1.38.5          IRanges_2.36.0              
+[21] S4Vectors_0.40.2             BiocGenerics_0.48.1         
+[23] MatrixGenerics_1.14.0        matrixStats_1.2.0           
+[25] AUCell_1.24.0                BiocStyle_2.30.0            
+
+loaded via a namespace (and not attached):
+  [1] RColorBrewer_1.1-3            jsonlite_1.8.8               
+  [3] magrittr_2.0.3                ggbeeswarm_0.7.2             
+  [5] magick_2.8.2                  farver_2.1.1                 
+  [7] rmarkdown_2.25                zlibbioc_1.48.0              
+  [9] vctrs_0.6.5                   memoise_2.0.1                
+ [11] DelayedMatrixStats_1.24.0     RCurl_1.98-1.14              
+ [13] htmltools_0.5.7               S4Arrays_1.2.0               
+ [15] AnnotationHub_3.10.0          curl_5.2.0                   
+ [17] BiocNeighbors_1.20.2          SparseArray_1.2.3            
+ [19] htmlwidgets_1.6.4             plotly_4.10.4                
+ [21] cachem_1.0.8                  igraph_2.0.1.1               
+ [23] mime_0.12                     lifecycle_1.0.4              
+ [25] pkgconfig_2.0.3               rsvd_1.0.5                   
+ [27] Matrix_1.6-5                  R6_2.5.1                     
+ [29] fastmap_1.1.1                 GenomeInfoDbData_1.2.11      
+ [31] shiny_1.8.0                   digest_0.6.34                
+ [33] colorspace_2.1-0              dqrng_0.3.2                  
+ [35] irlba_2.3.5.1                 ExperimentHub_2.10.0         
+ [37] RSQLite_2.3.5                 beachmat_2.18.0              
+ [39] labeling_0.4.3                filelock_1.0.3               
+ [41] fansi_1.0.6                   httr_1.4.7                   
+ [43] abind_1.4-5                   compiler_4.3.2               
+ [45] bit64_4.0.5                   withr_3.0.0                  
+ [47] BiocParallel_1.36.0           viridis_0.6.5                
+ [49] DBI_1.2.1                     highr_0.10                   
+ [51] R.utils_2.12.3                MASS_7.3-60.0.1              
+ [53] rappdirs_0.3.3                DelayedArray_0.28.0          
+ [55] rjson_0.2.21                  tools_4.3.2                  
+ [57] vipor_0.4.7                   beeswarm_0.4.0               
+ [59] interactiveDisplayBase_1.40.0 httpuv_1.6.14                
+ [61] R.oo_1.26.0                   glue_1.7.0                   
+ [63] nlme_3.1-164                  promises_1.2.1               
+ [65] grid_4.3.2                    cluster_2.1.6                
+ [67] generics_0.1.3                gtable_0.3.4                 
+ [69] R.methodsS3_1.8.2             tidyr_1.3.1                  
+ [71] data.table_1.15.0             metapod_1.10.1               
+ [73] BiocSingular_1.18.0           ScaledMatrix_1.10.0          
+ [75] utf8_1.2.4                    XVector_0.42.0               
+ [77] ggrepel_0.9.5                 BiocVersion_3.18.1           
+ [79] pillar_1.9.0                  limma_3.58.1                 
+ [81] BumpyMatrix_1.10.0            later_1.3.2                  
+ [83] splines_4.3.2                 dplyr_1.1.4                  
+ [85] BiocFileCache_2.10.1          lattice_0.22-5               
+ [87] survival_3.5-7                FNN_1.1.4                    
+ [89] renv_1.0.3                    bit_4.0.5                    
+ [91] tidyselect_1.2.0              locfit_1.5-9.8               
+ [93] Biostrings_2.70.2             knitr_1.45                   
+ [95] gridExtra_2.3                 edgeR_4.0.15                 
+ [97] xfun_0.42                     mixtools_2.0.0               
+ [99] statmod_1.5.0                 lazyeval_0.2.2               
+[101] yaml_2.3.8                    evaluate_0.23                
+[103] codetools_0.2-19              kernlab_0.9-32               
+[105] tibble_3.2.1                  BiocManager_1.30.22          
+[107] cli_3.6.2                     uwot_0.1.16                  
+[109] xtable_1.8-4                  segmented_2.0-3              
+[111] munsell_0.5.0                 Rcpp_1.0.12                  
+[113] dbplyr_2.4.0                  png_0.1-8                    
+[115] parallel_4.3.2                ellipsis_0.3.2               
+[117] blob_1.2.4                    sparseMatrixStats_1.14.0     
+[119] bitops_1.0-7                  viridisLite_0.4.2            
+[121] scales_1.3.0                  purrr_1.0.2                  
+[123] crayon_1.5.2                  rlang_1.1.3                  
+[125] KEGGREST_1.42.0
+
+
+
+

Further Reading

+
+
+

Exercises

+
+
+ +
+
+

Exercise 1: Clustering +

+
+

The Leiden +algorithm is similar to the Louvain algorithm, but it is faster and +has been shown to result in better connected communities. Modify the +above call to clusterCells to carry out the community +detection with the Leiden algorithm instead. Visualize the results in a +UMAP plot.

+
+
+
+
+
+ +
+
+

The NNGraphParam constructor has an argument +cluster.args. This allows to specify arguments passed on to +the cluster_leiden function from the igraph +package. Use the cluster.args argument to parameterize the +clustering to use modularity as the objective function and a resolution +parameter of 0.5.

+
+
+
+
+
+
+ +
+
+

TODO

+
+
+
+
+
+
+ +
+
+

Exercise 2: Cluster annotation +

+
+

Another strategy for annotating the clusters is to perform a gene set +enrichment analysis on the marker genes defining each cluster. This +identifies the pathways and processes that are (relatively) active in +each cluster based on upregulation of the associated genes compared to +other clusters. Focus on the top 100 up-regulated genes in a cluster of +your choice and perform a gene set enrichment analysis of biological +process (BP) gene sets from the Gene Ontology (GO).

+
+
+
+
+
+ +
+
+

Use the goana() function from the limma +package to identify GO BP terms that are overrepresented in the list of +marker genes.

+
+
+
+
+
+
+ +
+
+

TODO

+
+
+
+
+
+
+ +
+
+

Exercise 3: Workflow +

+
+

The scRNAseq +package provides gene-level counts for a collection of public scRNA-seq +datasets, stored as SingleCellExperiment objects with +annotated cell- and gene-level metadata. Consult the vignette of the scRNAseq package +to inspect all available datasets and select a dataset of your choice. +Perform a typical scRNA-seq analysis on this dataset including QC, +normalization, feature selection, dimensionality reduction, clustering, +and marker gene detection.

+
+
+
+
+
+ +
+
+

TODO

+
+
+
+
+
+
+ +
+
+

Key Points +

+
+
  • TODO
    • +
+
+
+ +
+ + + +
+
+ + +
+
+ + + diff --git a/eda_qc.html b/eda_qc.html index 87cea87..095e304 100644 --- a/eda_qc.html +++ b/eda_qc.html @@ -86,11 +86,11 @@ Orchestrating Large-Scale Single-Cell Analysis with Bioconductor -