Outlines

Merge multiple data sets
Cell Type Annotation
Differential Analysis
CITE-seq data processing

A more advanced scRNAseq workflow

overview

A more advanced scRNAseq workflow

Full image here of workflow is here: overview

scRNA seq is a very variable process and each dataset has unique QC problems. Though our simplified approach often works, we also need many more tools in our toolbox to handle more complex datasets. Often it is a case of trial and error to see what approaches work best for your data.

Create some functions

We are going to try out a few tools and approaches. We want to wrap some of our analysis steps into a function to simplify rerunning things.

Normalization: * Log normalization with scale factor = 10,000 * Find Variable features with vst, select top 2000 variable features

data_proc <- function(seu) {
    seu <- NormalizeData(seu, normalization.method = "LogNormalize", scale.factor = 10000)
    seu <- FindVariableFeatures(seu, select.method = "vst", nfeatures = 2000)
    return(seu)
}

Create some functions

Make clusters: * Scale data with ScaleData() * Principle Component Analysis by using RunPCA() with npcs=30 PCs * Make non-linear dimensional reduction in UMAP by using RunUMAP() with dims=1:10 * Estimate Neighbors by using FindNeighbors() with dims=1:10 * Identify clusters with FindClusters() by using resolution=0.5

quick_clust <- function(seu) {
    set.seed(42)
    seu <- ScaleData(seu, verbose = FALSE)
    seu <- RunPCA(seu, npcs = 30, verbose = FALSE)
    seu <- RunUMAP(seu, reduction = "pca", dims = 1:10, verbose = FALSE)
    seu <- FindNeighbors(seu, reduction = "pca", dims = 1:10, verbose = FALSE)
    seu <- FindClusters(seu, resolution = 0.5, verbose = FALSE)
    return(seu)
}

Merging Datasets

An advanced scRNAseq workflow

overview

Merge multiple datasets

We will discuss a number of approaches and methods we may need to use to merge datasets.

No corrections
Reciprocal Principle Component Analysis (rPCA)
Harmony

Example dataset

We will be using a dataset containing 4 snRNAseq samples. This is from a preprint studying Alzheimers disease. Though this dataset has 10 samples, we will be using 4 for our example. There are 2 AD samples and 2 Control samples from the entorhinal cortex taht have been collected post-mortem.

You could access the raw data from GE0: GSE287652. But we have it here already loaded into a Seurat object and after some preprocessing.

my_seu_list <- readRDS("data/to_integrate.rds")

Example dataset

Each dataset has been individually processed, QC’d and reviewed prior to combining into a list. Sotring everything in a list will make our life easier later.

Load into Seurat object.
Log Normalized and scaled.
Filtered based on MT levels (2.5%).
Filtered based on doublets (Scrublet).
Regressed to MT.
PCA.
Clusters.
UMAP.

Simple Merge

Concatenate the datasets

We can use merge() function to integrate multiple data sets, as they are. We need to provide a single seurat object as our first argument, and a list containing the rest of our Seurat objects as the second. We also need to provide Group information and a name for the project as arguments.

seu_merge <- merge(my_seu_list[[1]], my_seu_list[2:4], add.cell.ids = c("AD2b", "AD4",
    "C1", "C3"), project = "Merge")
head(seu_merge, 4)

##                               orig.ident nCount_RNA nFeature_RNA percent.mt
## AD2b_AAACCCACAGCTGAAG-1_C1 SeuratProject        763          390 0.39318480
## AD2b_AAACGAAAGGTCGTAG-1_C1 SeuratProject       1030          501 0.00000000
## AD2b_AAACGAAGTCTTGAGT-1_C1 SeuratProject       3556         2272 0.61797753
## AD2b_AAACGAATCACCTCGT-1_C1 SeuratProject       3091         1863 0.03234153
##                            scrubletScore sample_id paper_cluster group
## AD2b_AAACCCACAGCTGAAG-1_C1    0.03906250        C1             0     C
## AD2b_AAACGAAAGGTCGTAG-1_C1    0.08771930        C1             0     C
## AD2b_AAACGAAGTCTTGAGT-1_C1    0.06688963        C1            12     C
## AD2b_AAACGAATCACCTCGT-1_C1    0.04857143        C1            29     C
##                                   paper_annot
## AD2b_AAACCCACAGCTGAAG-1_C1 Excitatory Neurons
## AD2b_AAACGAAAGGTCGTAG-1_C1 Excitatory Neurons
## AD2b_AAACGAAGTCTTGAGT-1_C1 Inhibitory Neurons
## AD2b_AAACGAATCACCTCGT-1_C1 Inhibitory Neurons

Process and make clusters

Now that our data is merged we need to reprocess it. This ensures nromalization and scaling is done in a globally context. We can use our processing and clustering functions to analyse our merged dataset.

seu_merge <- data_proc(seu_merge)
seu_merge <- quick_clust(seu_merge)

UMAP

Our UMAP shows our cells are quite similar. Bu there are a few regions that are distinct depending on the condition.

DimPlot(seu_merge, group.by = "sample_id", pt.size = 0.2)

Our samples don’t group

This result indicates there is a difference between our 4 samples groups.
Is this a true biological phenomena or is it a batch effect?

Our samples don’t group

(Slowikowski, 2019)

Merge with reciprocal PCA

Reciprocal PCA minimizes the batch effects while merging different data sets.

The steps involved in rPCA:

Each dataset’s PCA embeddings are reciprocally projected onto the other dataset’s principal components.
Then nearest neighbors are found mutually across datasets.
These nearest neighbors serve as anchors, which are used to align datasets.

Merge with reciprocal PCA

(Stuart et al, 2019)

Merge with reciprocal PCA

(Stuart et al, 2019)

RPCA workflow

Normalize data sets. We can use our data_proc().
Select features for integration by using SelectIntegrationFeatures().
Scale data and process PCA by using the features identified for integration
Identify Anchors for integration by using FindIntegrationAnchors()
Integrate data by using IntegratedData()
Process quick_cluster() and evaluate results with UMAP

Prepare for RPCA merge

First we need to identify features for integration. We do this on our list of Seurat objects. This is similar to the VariableFeatures function we ran on a single dataset, but works across all 4 datasets. We will then run scaling and PCA, using these features.

feats <- SelectIntegrationFeatures(my_seu_list)

my_seu_list_rpca <- lapply(my_seu_list, function(seu, feats) {
    seu <- ScaleData(seu, features = feats, verbose = FALSE)
    seu <- RunPCA(seu, features = feats, verbose = FALSE)
    return(seu)
}, feats)

Integrating data in RPCA merge

We can then identify anchors. These are the features through which we will integrate our data. Once we have these features, we can then integrate our data sets together.

anchors <- FindIntegrationAnchors(my_seu_list_rpca, anchor.features = feats, reduction = "rpca")

my_seu_merge_rpca <- IntegrateData(anchorset = anchors)

my_seu_merge_rpca

## An object of class Seurat 
## 28110 features across 10018 samples within 2 assays 
## Active assay: integrated (2000 features, 2000 variable features)
##  1 layer present: data
##  1 other assay present: RNA

Evaluating RPCA using clusters

To evaluate how well the merge has worked we must check the clustering. Again we must scale, and then use our quick_clust function.

my_seu_merge_rpca <- ScaleData(my_seu_merge_rpca)
my_seu_merge_rpca <- quick_clust(my_seu_merge_rpca)

Assesing Integration

To assess the integration we can use similar metrics to assessing the QC of the datasets in general.

Are there discrete clusters?
Are there distinct markers (and do they make sense)?

We also check the overlap of our datasets. Generally we expect most cells between samples to overlap, butt this can be very experiment dependent.

Evaluating RPCA using clusters

We can see that our data sets overlay with each other. It looks slightly better than before. The dataset looked pretty good before, but now the overlap is tighter.

DimPlot(my_seu_merge_rpca, group.by = "sample_id", pt.size = 0.2)

UMAP - Clusters

Our UMAP shows our cell clusters are fairly distinct, though not perfect (and the clustering itself could do with some optimization).

DimPlot(my_seu_merge_rpca, group.by = "seurat_clusters", pt.size = 0.2)

Heatmap - clusters

We can check the numbers in each cluster. Broadly, there are similar numbers per cluster now. To do this we make a heatmap and then scale it along the row to accunt for the different number of cells in each sample.

library(pheatmap)
tbl <- table(my_seu_merge_rpca$sample_id, my_seu_merge_rpca$seurat_clusters)
pheatmap(tbl, scale = "row")

UMAP - Markers

A given cell type should often be clustered together. This means marker genes should be specific. This oligodendrocyte marker has quite specific distribution.

FeaturePlot(my_seu_merge_rpca, "MOBP", pt.size = 0.2)

UMAP - Annotation

This dataset also has some annotation in the paper_annot slot. We can check the distribution of the labels. Our UMAP shows our cell types are distinct.

DimPlot(my_seu_merge_rpca, group.by = "paper_annot", pt.size = 0.2)

Evaluating RPCA using cell types

Using heatmaps we can also check how specific each cluster is to each cell type.

library(pheatmap)

tbl <- table(my_seu_merge_rpca$seurat_clusters, my_seu_merge_rpca$paper_annot)
pheatmap(tbl, scale = "row")

Merge data with Harmony

Harmony

Harmony is an R package for single-cell data integration liike.
There is also a python implementation of this package.
Harmony works well with Seurat objects to directly so we can easily add it into our workflow.

Harmony

Harmony works in a different way to rPCA.

Fuzzy clustering - It will assign clusters, but allow cells to belong to multiple clusters, and caluclate the strength of assingmetn to each cluster.
During clustering there is a penalty term, to maintain diversity of groups i.e. a cluster with just one sample is penalized. This means any structure due to a batch effect is not hard.
The centroid of each cluster is calculated for all cells and each individual sample, then a correction factor for each sample based on these centroids.
Cells are then moved based on the dataset correction factor, weighted by their individual assignment scores for each clsuter
Repeat iteratviely until convergence.

Harmony

(Korsunksy et al, 2020)

Prepare data for Harmony

We can prepare for Harmony in much the same way as we prepare for the simple Seurat merge: merge, normalize, scale, PCA and UMAP.

seu_merge <- merge(my_seu_list[[1]], my_seu_list[2:4], add.cell.ids = c("C1", "C3",
    "AD2b", "AD4"), project = "Merge")
seu_merge <- data_proc(seu_merge)
seu_merge <- ScaleData(seu_merge)
seu_merge <- RunPCA(seu_merge)
seu_merge <- RunUMAP(seu_merge, reduction = "pca", dims = 1:10, reduction.name = "umap")

Prepare data for Harmony

As you can see we are back with our completely separate groups.

DimPlot(seu_merge, group.by = "sample_id")

Merge data with Harmony

We can use the RunHarmony() function to implement the Harmony correction.

library(harmony)
seu_merge_harmony <- RunHarmony(seu_merge, group.by.vars = "sample_id", assay.use = "RNA")
seu_merge_harmony <- RunUMAP(seu_merge_harmony, reduction = "harmony", dims = 1:10,
    reduction.name = "umap_harmony")
seu_merge_harmony <- FindNeighbors(seu_merge_harmony, reduction = "harmony")
seu_merge_harmony <- FindClusters(seu_merge_harmony)

## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 10018
## Number of edges: 338251
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9148
## Number of communities: 21
## Elapsed time: 1 seconds

Evaluating Harmony

We can see that our data sets overlay with each other. Again, it looks slightly better than before.

DimPlot(seu_merge_harmony, group.by = "sample_id", reduction = "umap_harmony", pt.size = 0.2)

UMAP - Clusters

Our UMAP shows our cell clusters are fairly distinct, though not perfect (and the clustering itself could do with some optimization).

DimPlot(seu_merge_harmony, group.by = "seurat_clusters", reduction = "umap_harmony",
    pt.size = 0.2)

Heatmap - clusters

We can check the numbers in each cluster. Broadly, there are similar numbers per cluster now. To do this we make a heatmap and then scale it along the row to account for the different number of cells in each sample.

library(pheatmap)
tbl <- table(seu_merge_harmony$sample_id, seu_merge_harmony$seurat_clusters)
pheatmap(tbl, scale = "row")

UMAP - Markers

A given cell type should often be clustered together. This means marker genes should be specific. This oligodendrocyte marker has quite specific distribution.

FeaturePlot(seu_merge_harmony, "MOBP", pt.size = 0.2)

UMAP - Annotation

This dataset also has some annotation in the paper_annot slot. We can check the distribution of the labels. Our UMAP shows our cell types are distinct.

DimPlot(seu_merge_harmony, group.by = "paper_annot", pt.size = 0.2)

Evaluating RPCA using cell types

Using heatmaps we can also check how specific each cluster is to each cell type.

tbl <- table(seu_merge_harmony$seurat_clusters, seu_merge_harmony$paper_annot)
pheatmap(tbl, scale = "row")

Comparing our results

Broadly it seems like rPCA may be the best option in this case. Why?:

The integration in general is subtle and rPCA is considered more conservative
Sample UMAP looks cleanest
The number of cells per cluster seems more equal
The annotation seems cleaner

Compare

Data Integration Compared

Harmony
Works at the cluster level
Iterative nature pushes to convergence so can be a heavy handed
Allows for more complex model terms for batch correction
Fast and scalable
rPCA
- Working at the single cell level
- Preserves biological structure, by being More lenient and allowing unique groups

Data Integration Compared

Though Harmony and rPCA are our main workhorses for integration. There are many other alternatives too.

MNN - Like rPCA it uses MNN across datasets and corrects batch effects while preserving structure (we have code for this in prior version of course)
Liger - Factorizes gene expression into shared and dataset-specific components, so very god at maintaining dataset specific patterns, but it will need some tuning.
Scanorama - Also uses a MNN based approach. Good for datasets with little overlap, but it is computationally expensive.
ComBat (from sva package) Batch Correction - Uses a more traditional linear modeling approach that has been adapted from bulk methods. Good for systematic differences, but less nuanced and tendency to over correct.
BBKNN (Batch-Balanced kNN) - Works on neighbor graphs, not counts/PCA. Simple, fast, good for clustering and visual, but there’s no depper integration/correction of data.
scVI (Single-cell Variational Inference) - Uses a deep generative model (VAE) to learn a representation without batch effects. Powerful, scalable, handles missing data but requires GPU for efficiency.

For more systematic comparison check out this paper.

An advanced scRNAseq workflow

overview

##             used   (Mb) gc trigger    (Mb)   max used    (Mb)
## Ncells  14143263  755.4   24674155  1317.8   24674155  1317.8
## Vcells 775953080 5920.1 1739313404 13270.0 1739313404 13270.0

Cell type annotation

Manual Annotation

As mentioned before this dataset has been manually annotated.

DimPlot(my_seu_merge_rpca, group.by = "paper_annot")

Manual Annotation

This was done by careful assessment of the marker genes. Here we are look at Oligodendrocyte markers.

FeaturePlot(my_seu_merge_rpca, c("MOBP", "MAG"))

Automated annotation

To annotate the Single-cell data sets, we can evaluate the gene expression pattern of well known cell-type specific marker genes and make a manual annotation. This is time consuming and not systematic.

Here, we will introduce two more strategies to make cell type annotations automatically: 1. Mapping and annotating query datasets with Seurat using a reference data set. link 2. Make annotation with SingleR link

Cell type annotation

For this we need a reference dataset and a query dataset. Our annotation ends up being only as good as our reference dataset. There are many sources for reference data.

R packages:
- celldex
- ExperimentHub
Consortium data i.e. Allen Brain Map
Papers (though this can be variable) - Adipose or Neuronal

Depending on the format of the data, you may be set to go straightaway, need to do some light processing, or reanalyze the whole thing.

Annotation with SingleR

SingleR is Bioconductor package which can be used to predict annotations of a single-cell dataset. It is maybe the most flexible way of annotating your data, as it will accept a variety of kinds of reference data including bulk and scRNAseq experiments. SingleR works with a very simple method: calculate a spearman rank correlation between reference and test dataset for each label.

Despite this simplicity, there is also scope to do more complex annotation with some advanced features i.e. to improve resolution of related labels or using multiple references. You can dig into this further in the singleR book.

Annotation with SingleR

Lets start out by using the Human Primary Cell Atlas. This is a collection of microarray datasets from human primary cells that have been aggregated together. To access this data we will use the celldex package.

library(celldex)
hpcad <- HumanPrimaryCellAtlasData()

Annotation with SingleR

In this case the data is stored as a SummarizedExpriment; a specialized Bioconductor format designed for holding data matrices and their metadata. The reference data for SingleR can either be in this format or as a SingleCellExpriment (another Bioconductor format specific to single cell analysis).

hpcad

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

Annotation with SingleR

At the moment our test data is a Seurat object which is not Bioconductor friendly.

my_seu_merge_rpca

## An object of class Seurat 
## 28110 features across 10018 samples within 2 assays 
## Active assay: integrated (2000 features, 2000 variable features)
##  2 layers present: data, scale.data
##  1 other assay present: RNA
##  2 dimensional reductions calculated: pca, umap

We can simply extract out a data matrix containing count information. This is good for SingleR.

my_seu_merge_rpca_mat <- GetAssayData(my_seu_merge_rpca)

my_seu_merge_rpca_mat[1:5, 1:5]

## 5 x 5 sparse Matrix of class "dgCMatrix"
##            AAACCCACAGCTGAAG-1_C1 AAACGAAAGGTCGTAG-1_C1 AAACGAAGTCTTGAGT-1_C1
## ADAMTSL1              0.39228540            0.37254012            1.11566489
## THSD7B                0.02286489            0.05190584           -0.01465315
## IL1RAPL2             -0.01904459           -0.08152581            0.12641628
## RELN                 -0.45800974           -0.30815332           -0.64158447
## AC093607.1            0.06373820            0.06256471            0.01170564
##            AAACGAATCACCTCGT-1_C1 AAACGAATCTCCGAAA-1_C1
## ADAMTSL1            -0.063393072           -0.05680296
## THSD7B               0.092400788           -0.02638186
## IL1RAPL2            -0.172235822           -0.00585319
## RELN                 3.338650923            0.15192283
## AC093607.1           0.007405319           -0.03035472

Annotation with SingleR

We are almost ready to run SingleR. We have our test and reference data. The last part we need is labels. This can just be a vector containing all the labels. We can grab this easily from our reference dataset. We can see there are several options here. Let’s just stick to label.main.

colData(hpcad)

## DataFrame with 713 rows and 3 columns
##            label.main             label.fine   label.ont
##           <character>            <character> <character>
## GSM112490          DC DC:monocyte-derived:..  CL:0000840
## GSM112491          DC DC:monocyte-derived:..  CL:0000840
## GSM112540          DC DC:monocyte-derived:..  CL:0000840
## GSM112541          DC DC:monocyte-derived:..  CL:0000451
## GSM112661          DC DC:monocyte-derived:..  CL:0000451
## ...               ...                    ...         ...
## GSM556665    Monocyte Monocyte:S._typhimur..  CL:0000576
## GSM92231      Neurons   Neurons:Schwann_cell  CL:0002573
## GSM92232      Neurons   Neurons:Schwann_cell  CL:0002573
## GSM92233      Neurons   Neurons:Schwann_cell  CL:0002573
## GSM92234      Neurons   Neurons:Schwann_cell  CL:0002573

library(SingleR)

pred_res <- SingleR(ref = hpcad, test = my_seu_merge_rpca_mat, labels = hpcad$label.main)

The score is generated comparing the expression levels of a cell in query dataset and the expression pattern of certain group (eg. cell types) in reference dataset. A cell would be assigned as the cell type which has highest score

head(pred_res, 2)

## DataFrame with 2 rows and 4 columns
##                                                    scores      labels
##                                                  <matrix> <character>
## AAACCCACAGCTGAAG-1_C1 0.0699139:0.0443145: 0.00782103:...     Neurons
## AAACGAAAGGTCGTAG-1_C1 0.0605863:0.0201250:-0.00486433:...         MSC
##                       delta.next pruned.labels
##                        <numeric>   <character>
## AAACCCACAGCTGAAG-1_C1 0.01087697       Neurons
## AAACGAAAGGTCGTAG-1_C1 0.00253192           MSC

Annotation with SingleR

By converting to a matrix, we can check the cell type scoring using a heatmap.

mat <- as.matrix(pred_res$scores)
rownames(mat) <- rownames(pred_res)
pheatmap::pheatmap(mat, scale = "row", show_rownames = FALSE, fontsize_col = 5)

Annotation with SingleR

We can now add our labels back to our original Seurat object by a quick assignment. This then allows us to start reviewing the annotation in the context of UMAPs and also versus our other annotation.

my_seu_merge_rpca$hpcad_singleR_labels <- pred_res$labels

summ_table <- table(my_seu_merge_rpca$hpcad_singleR_labels, my_seu_merge_rpca$paper_annot)

pheatmap(summ_table, scale = "column", fontsize_row = 5)

Annotation with SingleR

DimPlot(my_seu_merge_rpca, group.by = "hpcad_singleR_labels")

Annotation with SingleR

Lets try an alternative dataset. Often we will have data from other Seurat objects that we want to use as a reference. Here we have a processed version of human data from the Allen Brain Map. This is 10X data from 2020.

abm <- readRDS("data/abm.rds")

Annotation with SingleR

There are several interesting labels associated with this data. Lets focus on the class_label.

head(abm)

##                                        orig.ident nCount_RNA nFeature_RNA
## AAACCCAAGGATTTCC-LKTX_190129_01_A01 SeuratProject      14396         4815
## AAACCCAAGTATGGCG-LKTX_190129_01_A01 SeuratProject      12027         4372
## AAACCCACAAAGTGTA-LKTX_190129_01_A01 SeuratProject      16112         5280
## AAACCCACACTACTTT-LKTX_190129_01_A01 SeuratProject       2994         1649
## AAACCCACAGTGAGCA-LKTX_190129_01_A01 SeuratProject       5202         2499
## AAACCCAGTCACCCTT-LKTX_190129_01_A01 SeuratProject      26504         6381
## AAACCCAGTGTCCACG-LKTX_190129_01_A01 SeuratProject      21668         5470
## AAACCCATCATAAGGA-LKTX_190129_01_A01 SeuratProject      30141         7131
## AAACCCATCTGTCCCA-LKTX_190129_01_A01 SeuratProject      31944         6864
## AAACGAAGTATGAAGT-LKTX_190129_01_A01 SeuratProject      32476         7328
##                                     percent.mt   class_label
## AAACCCAAGGATTTCC-LKTX_190129_01_A01          0     GABAergic
## AAACCCAAGTATGGCG-LKTX_190129_01_A01          0 Glutamatergic
## AAACCCACAAAGTGTA-LKTX_190129_01_A01          0 Glutamatergic
## AAACCCACACTACTTT-LKTX_190129_01_A01          0 Glutamatergic
## AAACCCACAGTGAGCA-LKTX_190129_01_A01          0  Non-Neuronal
## AAACCCAGTCACCCTT-LKTX_190129_01_A01          0 Glutamatergic
## AAACCCAGTGTCCACG-LKTX_190129_01_A01          0 Glutamatergic
## AAACCCATCATAAGGA-LKTX_190129_01_A01          0 Glutamatergic
## AAACCCATCTGTCCCA-LKTX_190129_01_A01          0 Glutamatergic
## AAACGAAGTATGAAGT-LKTX_190129_01_A01          0 Glutamatergic
##                                                   cluster_label subclass_label
## AAACCCAAGGATTTCC-LKTX_190129_01_A01          Inh L1-2 SST CCNJL            Sst
## AAACCCAAGTATGGCG-LKTX_190129_01_A01     Exc L5-6 FEZF2 IFNG-AS1        L5/6 NP
## AAACCCACAAAGTGTA-LKTX_190129_01_A01     Exc L3-5 RORB LINC01202          L5 IT
## AAACCCACACTACTTT-LKTX_190129_01_A01      Exc L2 LINC00507 GLRA3        L2/3 IT
## AAACCCACAGTGAGCA-LKTX_190129_01_A01    Oligo L2-6 OPALIN FTH1P3          Oligo
## AAACCCAGTCACCCTT-LKTX_190129_01_A01 Exc L5-6 FEZF2 C9orf135-AS1          L6 CT
## AAACCCAGTGTCCACG-LKTX_190129_01_A01        Exc L3-5 FEZF2 ASGR2          L5 ET
## AAACCCATCATAAGGA-LKTX_190129_01_A01          Exc L3-5 RORB LNX2          L5 IT
## AAACCCATCTGTCCCA-LKTX_190129_01_A01          Exc L3-5 RORB LNX2          L5 IT
## AAACGAAGTATGAAGT-LKTX_190129_01_A01         Exc L5 THEMIS RGPD6          L6 CT
##                                           cell_type_alias_label
## AAACCCAAGGATTTCC-LKTX_190129_01_A01          Inh L1-2 SST CCNJL
## AAACCCAAGTATGGCG-LKTX_190129_01_A01     Exc L5-6 FEZF2 IFNG-AS1
## AAACCCACAAAGTGTA-LKTX_190129_01_A01     Exc L3-5 RORB LINC01202
## AAACCCACACTACTTT-LKTX_190129_01_A01      Exc L2 LINC00507 GLRA3
## AAACCCACAGTGAGCA-LKTX_190129_01_A01    Oligo L2-6 OPALIN FTH1P3
## AAACCCAGTCACCCTT-LKTX_190129_01_A01 Exc L5-6 FEZF2 C9orf135-AS1
## AAACCCAGTGTCCACG-LKTX_190129_01_A01        Exc L3-5 FEZF2 ASGR2
## AAACCCATCATAAGGA-LKTX_190129_01_A01          Exc L3-5 RORB LNX2
## AAACCCATCTGTCCCA-LKTX_190129_01_A01          Exc L3-5 RORB LNX2
## AAACGAAGTATGAAGT-LKTX_190129_01_A01         Exc L5 THEMIS RGPD6

Annotation with SingleR

As our reference data is in a Seurat format we can just extract out the data matrix of counts. We also already have our matrix from our test data.

pred_res2 <- SingleR(ref = GetAssayData(abm), test = my_seu_merge_rpca_mat, labels = abm$class_label)

head(pred_res2, 2)

## DataFrame with 2 rows and 4 columns
##                                            scores        labels delta.next
##                                          <matrix>   <character>  <numeric>
## AAACCCACAGCTGAAG-1_C1 0.138210:0.142565:0.0505849 Glutamatergic  0.0050974
## AAACGAAAGGTCGTAG-1_C1 0.218222:0.253712:0.0876373 Glutamatergic  0.0347486
##                       pruned.labels
##                         <character>
## AAACCCACAGCTGAAG-1_C1 Glutamatergic
## AAACGAAAGGTCGTAG-1_C1 Glutamatergic

Annotation with SingleR

By converting to a matrix, we can check the cell type scoring using a heatmap.

mat <- as.matrix(pred_res2$scores)
rownames(mat) <- rownames(pred_res2)
pheatmap::pheatmap(mat, scale = "row", show_rownames = FALSE, fontsize_col = 5)

Annotation with SingleR

We can now add our labels back to our original Seurat object by a quick assignment. This then allows us to start reviewing the annotation in the context of UMAPs and also versus our other annotation.

my_seu_merge_rpca$abm_singleR_labels <- pred_res2$labels

summ_table <- table(my_seu_merge_rpca$abm_singleR_labels, my_seu_merge_rpca$paper_annot)

pheatmap(summ_table, scale = "column", fontsize_row = 5)

Annotation with SingleR

DimPlot(my_seu_merge_rpca, group.by = "abm_singleR_labels")

Annotation with Seurat

Anotation with Seurat

Seurat uses a similar approach to integration to be able to find anchors between datasets. It can then use this to transfer label information from a reference to a test dataset.

A restriction of this approach is both datasets have to be Seurat objects and therefore single cell datasets. We will use the Allen Brain Map again for this analysis.

Reference and query datasets

As with integration, we find integration features in common, then scale and run PCA in the context of these specific features.

transfer_list <- list(ref = abm, query = my_seu_merge_rpca)
feats <- SelectIntegrationFeatures(transfer_list)

transfer_list <- lapply(transfer_list, function(seu, feats) {
    seu <- ScaleData(seu, features = feats, verbose = FALSE)
    seu <- RunPCA(seu, features = feats, verbose = FALSE)
    return(seu)
}, feats)

Predict cell types for query

Identify the anchors between reference and query data sets, using FindTransferAnchors(). These are essential to transfer information from our reference to our query. We can then transfer the cell type information. For each cell in query dataset, the score for each given cell type was estimated by the gene expression pattern of anchor genes using the TransferData() function.

anchors <- FindTransferAnchors(reference = transfer_list$ref, query = transfer_list$query,
    dims = 1:30, reference.reduction = "pca")
pred_res3 <- TransferData(anchorset = anchors, refdata = transfer_list$ref$class_label)

head(pred_res3, 2)

##                       predicted.id prediction.score.GABAergic
## AAACCCACAGCTGAAG-1_C1 Non-Neuronal                          0
## AAACGAAAGGTCGTAG-1_C1 Non-Neuronal                          0
##                       prediction.score.Glutamatergic
## AAACCCACAGCTGAAG-1_C1                      0.3253133
## AAACGAAAGGTCGTAG-1_C1                      0.1247228
##                       prediction.score.Non.Neuronal prediction.score.max
## AAACCCACAGCTGAAG-1_C1                     0.6746867            0.6746867
## AAACGAAAGGTCGTAG-1_C1                     0.8752772            0.8752772

Predict cell types for query

The cell type with highest score was assigned to the given cell. We can visualize this score with a heatmap.

mat <- as.matrix(pred_res3[, -c(1, 5)])
colnames(mat) <- gsub("prediction.score.", "", colnames(mat))
pheatmap(mat, scale = "row", show_rownames = FALSE)

Annotation with SingleR

We can now add our labels back to our original Seurat object by a quick assignment. This then allows us to start reviewing the annotation in the context of UMAPs and also versus our other annotation.

my_seu_merge_rpca$abm_seurat_labels <- pred_res3$predicted.id

summ_table <- table(my_seu_merge_rpca$abm_seurat_labels, my_seu_merge_rpca$paper_annot)

pheatmap(summ_table, scale = "column", fontsize_row = 5)

Annotation with SingleR

DimPlot(my_seu_merge_rpca, group.by = "abm_seurat_labels")

Seurat vs SingleR annotation

Next we can compare our two annotations:

table(my_seu_merge_rpca$abm_seurat_labels == my_seu_merge_rpca$abm_singleR_labels)

## 
## FALSE  TRUE 
##  3437  6581

tbl <- table(my_seu_merge_rpca$abm_seurat_labels, my_seu_merge_rpca$abm_singleR_labels)
pheatmap::pheatmap(tbl, scale = "row")

Seurat vs SingleR annotation

The results are similar but there is a clear difference in the identification of the OPC group. These are Oligodendrocyte precursor cells, and are considered non-neural.

In this case it seems that Seurat has done a better job. But maybe with a different reference, or with the more specific group labels SingleR may perform better.

## Seurat vs SingleR annotation

This isn’t always the case. As a general rule we find:

Seurat: Wins on speed
SingleR: More reliable and consistent

LLM annotation

There have also been some interesting efforts to use LLMs or Deep Learning to annotate cells, either with specialized models or generic models like ChatGPT.

We have not exhaustively tested these, but the consensus is that they provide a good quick estimate, especially when you do not have a reliable reference dataset.

Often domain specific knowledge and good reference datasets will outperform the more general LLM approaches.

##             used   (Mb) gc trigger    (Mb)   max used    (Mb)
## Ncells  14133235  754.8   24674155  1317.8   24674155  1317.8
## Vcells 626978247 4783.5 1739313404 13270.0 1739313404 13270.0

Differential expression analysis

We will use the rPCA integrated object from earlier lecture, and you can read it in from dropbox. It is the object called integrated.rds.

# downloaded from dropbox
seu_obj <- readRDS("~/Downloads/integrated.rds")
seu_obj

## An object of class Seurat 
## 28110 features across 10018 samples within 2 assays 
## Active assay: integrated (2000 features, 2000 variable features)
##  2 layers present: data, scale.data
##  1 other assay present: RNA
##  2 dimensional reductions calculated: pca, umap

Differential expression analysis

Compared to bulk analysis, single cell RNAseq data presents several opportunities and challenges when doing differential gene expression.

As we’ve explored so far, we can identify different cell types and then find markers for these cell types within a population of cells. We did this with the Seurat FindAllMarkers function.

p_all <- DimPlot(seu_obj, reduction = "umap", group.by = "paper_annot", label = T)
p_all

Differentials within cell types

In this section we will compare conditions within important subsets of cells, such as interesting cell types. We will look at gene expression differences between AD and control cells.

First, we add metadata columns that will eventually allow us to set up these comparisons.

# metadata column for condition
seu_obj$condition <- ifelse(grepl("AD", seu_obj$sample_id), "AD", "CON")

# metadata column for condition + cell type
seu_obj$celltype_condition <- paste(seu_obj$paper_annot, seu_obj$condition, sep = "_")

table(seu_obj$celltype_condition)

## 
##          Astrocytes_AD         Astrocytes_CON         Endothelial_AD 
##                    674                    353                     70 
##        Endothelial_CON  Excitatory Neurons_AD Excitatory Neurons_CON 
##                     51                   2338                   1295 
##  Inhibitory Neurons_AD Inhibitory Neurons_CON           Microglia_AD 
##                    423                    461                    391 
##          Microglia_CON                OPCs_AD               OPCs_CON 
##                    180                    257                    206 
##    Oligodendrocytes_AD   Oligodendrocytes_CON           Pericytes_AD 
##                   1974                   1107                    137 
##          Pericytes_CON 
##                    101

Excitatory Neurons

We will use these annotations to compare AD vs control within the cluster of excitatory neurons.

DimPlot(subset(seu_obj, paper_annot == "Excitatory Neurons"), reduction = "umap",
    group.by = "condition", label = F) + ggtitle("Excitatory Neurons only - AD vs Control")

Prepare object

Prep object - set assay to RNA

While the integrated data is critical for visualization and clustering samples together, for differential analyses we will use the counts from each individual object.

Before we start our analysis, we then need to change the active assay to ‘RNA’ (instead of ‘integrated’)

DefaultAssay(seu_obj)

[1] “integrated”

DefaultAssay(seu_obj) <- "RNA"
DefaultAssay(seu_obj)

[1] “RNA”

Prep object - join layers

As we learned, a Seurat object (version 5 or later) contains separate layers for each sample within an integrated object.

Layers(seu_obj)

[1] “counts.1” “counts.2” “counts.3” “counts.4” “data.1”
[6] “scale.data.1” “data.2” “scale.data.2” “data.3” “scale.data.3” [11] “data.4” “scale.data.4”

Prep object - join layers

Before doing differential gene expression, we first must join the layers of the Seurat object for get one counts matrix for all samples.

seu_obj <- JoinLayers(seu_obj)
Layers(seu_obj)

[1] “scale.data” “data” “counts”

Single cell DE considerations

Expression data at the single cell level adds another layer of complexity and how we handle this can have huge impacts on identifying differentially expressed genes.

Single cell DE considerations

Expression data at the single cell level adds another layer of complexity and how we handle this can have huge impacts on identifying differentially expressed genes. * How to handle a gene by cell matrix as opposed to gene by replicate?

Single cell DE considerations

Expression data at the single cell level adds another layer of complexity and how we handle this can have huge impacts on identifying differentially expressed genes. * How to handle a gene by cell matrix as opposed to gene by replicate? * How to handle the high dropout rate in single cell RNAseq?

counts <- GetAssayData(seu_obj, assay = "RNA", layer = "counts")
counts_mat <- as.matrix(counts)
actin_counts <- counts_mat[rownames(counts_mat) == "ACTB"]
hist(actin_counts, breaks = 50)

Wilcoxon Rank Sum Test

This is a non-parametric test that is used by default in the Seurat FindMarkers function. It is very fast and generally works well when finding markers of different and distinct cell types that have been identified through clustering.

It can also be used to find differences between conditions, though this is likely not the best choice for this comparison.

library(tibble)
library(dplyr)
Idents(seu_obj) <- "celltype_condition"
de_wilcox <- FindMarkers(seu_obj, ident.1 = "Excitatory Neurons_AD", ident.2 = "Excitatory Neurons_CON",
    logfc.threshold = 0) %>%
    tibble::rownames_to_column("geneID")

head(de_wilcox, 3)

##   geneID         p_val avg_log2FC pct.1 pct.2     p_val_adj
## 1   XIST 2.951723e-276  -3.027978 0.132 0.676 7.706949e-272
## 2 ADARB2 1.653428e-202  -1.727079 0.239 0.720 4.317100e-198
## 3  CDH13 6.851910e-138  -1.357173 0.201 0.605 1.789034e-133

Wilcoxon Rank Sum Test

de_wilcox$sig <- de_wilcox$p_val_adj < 0.05

ggplot(de_wilcox, aes(x = avg_log2FC, y = -log10(p_val), color = sig)) + geom_point() +
    scale_color_manual(values = c("grey", "red")) + theme_bw()

DE using parametric methods

MAST

Parametric methods have been developed that attempt to address the added complexities of single cell RNAseq data.

For more subtle changes within your data, such as comparing conditions within a cluster or cell type, these more advanced methods often work better.

One commonly used example is MAST (Model-based Analysis of Single-cell Transcriptomics), which uses a two part generalized linear model to account for the zero-inflated, bi-modal nature of single cell RNA seq.

MAST - hurdle model

MAST models both the rate of a gene being detected (i.e. expression not zero) and the continuous level of expression (conditional on expression > 0)

counts_seu <- GetAssayData(seu_obj, assay = "RNA", layer = "data")
# SNX31 was identified as differentially expressed in excitatory neurons
seu_obj$SNX31 <- counts_seu[rownames(counts_seu) == "SNX31", ]
exn_toPlot <- seu_obj@meta.data[seu_obj@meta.data$paper_annot == "Excitatory Neurons",
    ]
ggplot(exn_toPlot, aes(x = SNX31, fill = condition)) + geom_density() + ggtitle("SNX31 expression in AD vs CON in Excitatory Neruons") +
    theme_classic() + xlim(0, 2)

MAST - including covariates

Covariate or batch terms can be accounted for in the model formula * For example, the fraction of genes expressed in a cell, or the cellular detection rate (CDR), is a suggested covariate + CDR is thought to measure unwanted technical and biological factors (e.g. batch, cell size)

Finak et. al. Genome Biology, 2015

Using MAST

MAST requires a SingleCellAssay object with counts on a log scale. Importantly, zero counts should remain zero in any normalized data.

Here we are going to compare AD to control in excitatory neurons. These neurons are extracted from the Seurat object, and then we build the SingleCellAssay object.

library(MAST)
seu_exn <- subset(seu_obj, paper_annot == "Excitatory Neurons")
seu_exn_data <- GetAssayData(seu_exn, assay = "RNA", layer = "data")
# create SingleCellAssay for MAST
sca_exn <- MAST::FromMatrix(exprsArray = as.matrix(seu_exn_data), cData = seu_exn@meta.data,
    fData = data.frame(gene_id = rownames(seu_exn)))
sca_exn

## class: SingleCellAssay 
## dim: 26110 3633 
## metadata(0):
## assays(1): et
## rownames(26110): AL627309.1 AL627309.5 ... AC022486.1 AC004556.3
## rowData names(2): gene_id primerid
## colnames(3633): AAACCCACAGCTGAAG-1_C1 AAACGAAAGGTCGTAG-1_C1 ...
##   TTTGTTGGTGCGGATA-1_AD4 TTTGTTGGTTCGGCGT-1_AD4
## colData names(15): orig.ident nCount_RNA ... SNX31 wellKey
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):

Using MAST - calculate CDR

The CDR (the fraction of genes that are detected in each cell) is calculated for each cell, then scaled (z-score).

cdr <- colSums(assay(sca_exn) > 0)
cdr_scale <- scale(cdr)
head(cdr_scale)

##                             [,1]
## AAACCCACAGCTGAAG-1_C1 -0.9377860
## AAACGAAAGGTCGTAG-1_C1 -0.9048629
## AAACGCTGTGTCCACG-1_C1 -0.8526606
## AAAGAACAGGTCACCC-1_C1 -0.7360953
## AAAGAACCAGAAGTTA-1_C1 -0.9271082
## AAAGGATGTATTGGCT-1_C1  2.0736346

Using MAST - calculate CDR

Add the CDR to the metadata and set up the grouping factor we will use for the differential analysis.

colData(sca_exn)$ngeneson <- cdr_scale
cond <- factor(colData(sca_exn)$condition, levels = c("AD", "CON"))
cond <- relevel(cond, "CON")
colData(sca_exn)$Group <- cond

colData(sca_exn)[1:3, c("paper_annot", "ngeneson", "Group")]

## DataFrame with 3 rows and 3 columns
##                              paper_annot  ngeneson    Group
##                              <character>  <matrix> <factor>
## AAACCCACAGCTGAAG-1_C1 Excitatory Neurons -0.937786      CON
## AAACGAAAGGTCGTAG-1_C1 Excitatory Neurons -0.904863      CON
## AAACGCTGTGTCCACG-1_C1 Excitatory Neurons -0.852661      CON

Using MAST - plot CDR vs PCA

To understand how CDR is contributing to the variance of our data, we can look at correlations between CDR and principal components.

We obtain the embeddings from the PCA calculation previously done on the Seurat object.

pca_embed <- data.frame(Embeddings(seu_exn, reduction = "pca"))
pca_vars <- Stdev(seu_exn, reduction = "pca")^2
total_var <- sum(pca_vars)  # total variance
pct_var_explained <- pca_vars/total_var * 100
pct_var_explained <- round(pct_var_explained, 1)

for_plot <- data.frame(colData(sca_exn))
for_plot <- merge(for_plot, pca_embed, by = 0)

Using MAST - plot CDR vs PCA

CDR seems to correlate with PC1 from the data.

library(ggpubr)
p1 <- ggplot(for_plot, aes(x = ngeneson, y = PC_1, color = condition)) + geom_point() +
    ylab(paste0("PC_1 (", pct_var_explained[1], "%)"))
p2 <- ggplot(for_plot, aes(x = ngeneson, y = PC_2, color = condition)) + geom_point() +
    ylab(paste0("PC_2 (", pct_var_explained[2], "%)"))
ggarrange(p1, p2, nrow = 1, common.legend = TRUE, legend = "bottom")

Using MAST - fit hurdle model

The CDR is included in the model and will be accounted for in determining differentially expressed genes.

The zlm function can take a long time when you have a high number of cells. It returns a data table with information about the test.

Note: This can tke a long time to run, the result can be loaded on next slide

zlmCond_exn <- zlm(~ngeneson + Group, sca_exn)
sumZLM_exn <- summary(zlmCond_exn, doLRT = paste0("Group", "AD"))
sumDT_exn <- sumZLM_exn$datatable

MAST results

MAST can take some time to run, so to move forward download the following file from dropbox: sumDT_exn.rds

sumDT_exn <- readRDS("~/Downloads/sumDT_exn.rds")

MAST results

The table from the zlm function contains p-values for both the continuous gene expression (component = ‘C’), gene detection rate (component = ‘D’), and the combined p-value for the hurdle method (component = ‘H’).

The log2FC is also in this table in the ‘coef’ column.

sumDT_exn[1:12, c("primerid", "component", "contrast", "Pr(>Chisq)", "coef")]

##     primerid component    contrast Pr(>Chisq)         coef
##       <char>    <char>      <fctr>      <num>        <num>
##  1:     A1BG         C     GroupAD  0.1082826 -0.063046860
##  2:     A1BG         C (Intercept)         NA  1.124246862
##  3:     A1BG         C    ngeneson         NA -0.572281229
##  4:     A1BG         D     GroupAD  0.5100539  0.100513994
##  5:     A1BG         D (Intercept)         NA -3.662571467
##  6:     A1BG         D    ngeneson         NA  1.342774638
##  7:     A1BG         H     GroupAD  0.2216773           NA
##  8:     A1BG         S     GroupAD         NA           NA
##  9:     A1BG         S (Intercept)         NA           NA
## 10:     A1BG         S    ngeneson         NA           NA
## 11:     A1BG     logFC     GroupAD         NA  0.001152782
## 12:     A1BG     logFC    ngeneson         NA  0.021265736

MAST results

A data frame with the fold changes and combined hurdle p-values is constructed from this MAST output.

de_mast_exn <- merge(sumDT_exn[sumDT_exn$component == "H" & sumDT_exn$contrast ==
    paste0("Group", "AD"), ], sumDT_exn[sumDT_exn$component == "logFC" & sumDT_exn$contrast ==
    paste0("Group", "AD"), ], by = "primerid")
de_mast_exn <- dplyr::select(de_mast_exn, primerid, coef.y, z.y, `Pr(>Chisq).x`)
names(de_mast_exn) <- c("geneID", "log2Fc", "z", "pvalue")
de_mast_exn$FDR <- p.adjust(de_mast_exn$pvalue, "fdr")
de_mast_exn <- de_mast_exn[order(de_mast_exn$FDR), ]

head(de_mast_exn, 3)

##    geneID    log2Fc         z        pvalue           FDR
##    <char>     <num>     <num>         <num>         <num>
## 1:   XIST -1.415323 -35.84507 4.653001e-254 1.214899e-249
## 2:  PLCG2  1.114275  33.01194 4.636187e-233 6.052542e-229
## 3: ADARB2 -1.142667 -30.08747 6.272292e-183 5.458985e-179

MAST results

de_mast_exn$sig <- de_mast_exn$FDR < 0.05

ggplot(de_mast_exn, aes(x = log2Fc, y = -log10(pvalue), color = sig)) + geom_point() +
    scale_color_manual(values = c("grey", "red")) + theme_bw()

Visualize top DE genes

Look at the genes that go down in AD (up in control) in a violin plot

de_mast_exn_df <- data.frame(de_mast_exn)
de_mast_exn_df <- na.omit(de_mast_exn_df)
top_down_AD <- head(de_mast_exn_df[de_mast_exn_df$log2Fc < 0, ], 5)

VlnPlot(seu_exn, features = top_down_AD$geneID, stack = T, flip = T, pt.size = 1) +
    scale_x_discrete(labels = c("CON", "AD")) + NoLegend()

Visualize (sorta) DE genes

What about genes that barely pass the differential cutoff?

de_mast_exn_sig <- de_mast_exn_df[de_mast_exn_df$FDR < 0.05, ]
sorta_down_AD <- tail(de_mast_exn_sig[de_mast_exn_sig$log2Fc < 0, ], 5)
sorta_down_AD

##           geneID       log2Fc         z     pvalue        FDR  sig
## 15212 AC105230.1 -0.012419823 -2.377461 0.02896481 0.04971543 TRUE
## 15213      NRIP3 -0.012876226 -1.394409 0.02898560 0.04974785 TRUE
## 15214      CALCR -0.001228994 -1.138183 0.02899530 0.04976123 TRUE
## 15217  LINC00629 -0.003408750 -1.572098 0.02905931 0.04986125 TRUE
## 15223 AL096803.4 -0.007086156 -1.987236 0.02913228 0.04996675 TRUE

Visualize (sorta) DE genes

What about genes that barely pass the differential cutoff?

VlnPlot(seu_exn, features = sorta_down_AD$geneID, stack = T, flip = T, pt.size = 1) +
    scale_x_discrete(labels = c("CON", "AD")) + NoLegend()

XIST is the top gene!

We notice that the top DE gene from our analysis is XIST, which has much higher expression in females compared to males.

This suggests that sex could be a confounding factor and is potentially a red flag that we should explore. XIST does seem to have localized expression to the group of cells that are only in one of the controls.

FeaturePlot(seu_exn, features = "XIST", split.by = "sample_id")

Sample information

We can see from the sample info taken from the publication that C3 is indeed female. So we appear to have pulled out a sample dependent effect, not a group dependent effect like we are aiming for.

MAST - sex as covariate

We will re-run MAST with sex as a covariate.

Add sex to metadata of SingleCellAssay object

colData(sca_exn)$sex <- ifelse(colData(sca_exn)$sample_id == "AD2b", "female", ifelse(colData(sca_exn)$sample_id ==
    "AD4", "male", ifelse(colData(sca_exn)$sample_id == "C1", "male", ifelse(colData(sca_exn)$sample_id ==
    "C3", "female", "none!"))))

MAST - sex as covariate

Run MAST with sex as a covariate

Note: This can take a long time to run, the result can be loaded on next slide

zlmCond_exn_sex <- zlm(~ngeneson + sex + Group, sca_exn)
sumZLM_exn_sex <- summary(zlmCond_exn_sex, doLRT = paste0("Group", "AD"))
sumDT_exn_sex <- sumZLM_exn_sex$datatable

Load MAST results

To see results, load the following file from dropbox: sumDT_exn_sex.rds

sumDT_exn_sex <- readRDS("~/Downloads/sumDT_exn_sex.rds")

MAST results

A data frame with the fold changes and combined hurdle p-values is constructed from this MAST output.

de_mast_exn_sex <- merge(sumDT_exn_sex[sumDT_exn_sex$component == "H" & sumDT_exn_sex$contrast ==
    paste0("Group", "AD"), ], sumDT_exn_sex[sumDT_exn_sex$component == "logFC" &
    sumDT_exn_sex$contrast == paste0("Group", "AD"), ], by = "primerid")
de_mast_exn_sex <- dplyr::select(de_mast_exn_sex, primerid, coef.y, z.y, `Pr(>Chisq).x`)
names(de_mast_exn_sex) <- c("geneID", "log2Fc", "z", "pvalue")
de_mast_exn_sex$FDR <- p.adjust(de_mast_exn_sex$pvalue, "fdr")
de_mast_exn_sex <- de_mast_exn_sex[order(de_mast_exn_sex$FDR), ]

head(de_mast_exn_sex, 3)

##        geneID      log2Fc         z        pvalue           FDR
##        <char>       <num>     <num>         <num>         <num>
## 1:      PLCG2  1.17957273 22.907530 9.150363e-191 2.389160e-186
## 2: FP236383.3 -0.01912773 -4.251368 3.264977e-177 4.262428e-173
## 3:       PEG3  1.00613240 24.410163 8.852879e-168 7.704956e-164

MAST results for XIST

MAST output with Sex as covarivate:

de_mast_exn_sex[de_mast_exn_sex$geneID == "XIST", ]

##    geneID     log2Fc         z       pvalue          FDR
##    <char>      <num>     <num>        <num>        <num>
## 1:   XIST -0.3376534 -4.619174 3.986526e-12 5.897348e-11

Previous MAST output:

de_mast_exn[de_mast_exn$geneID == "XIST", ]

##    geneID    log2Fc         z        pvalue           FDR    sig
##    <char>     <num>     <num>         <num>         <num> <lgcl>
## 1:   XIST -1.415323 -35.84507 4.653001e-254 1.214899e-249   TRUE

MAST vs Wilcoxon

MAST models zeroes within the data, Wilcoxon does not
This can lead to biases where the differences in number of zero values can heavily influence DE calculation for Wilcoxon.
MAST will give more reliable log fold change values as its based on the continuous data
Wilcoxon doesn’t use fold change in it’s calculation, so often mean expression values are used and dropouts can skew these values
MAST can account for covariates
Wilcoxon results can be biased towards samples that have more cells
However, Wilcoxon is fast and generally pretty robust, especially for differences between cell types where large differences are expected, and you are only taking the top markers.

Pseudoreplication bias

Intra-sample correlation

Methods that don’t account for variation that exists between biological replicates can lead to inflated p-values and many false positives, known as the pseudoreplication bias (see here and here).

Treating each cell as an independent replicate is likely an incorrect assumption as cells from the same sample often correlate better than cells from different samples. ]

]

Zimmerman et al 2015, Nat Communications

Accounting for pseudoreplication bias

To help with pseudoreplication bias we can employ either a pseudobulk strategy or a mixed model with a random effect for each individual/sample

Various papers (for example, here and here) have compared these methods and it’s debatable which performs better and likely depends on the context.

Currently, the much lighter weight pseudobulk method is more commonly used and we will focus on this method.

Pseudobulk

Pseudobulk analysis

counts within a data set or subset of cells (e.g. cluster) are aggregated to get a gene by replicate table (like bulk RNAseq)
requires replicates
quick and lightweight

Pseudobulk using DESeq2

Pseudobulk analysis can leverage the well-established tools for bulk RNAseq, such as DESeq2 and edgeR. We will show you how to use DESeq2 here.

First, the AggregateExpression function from Seurat is used to get the sum of counts for each gene across all cells grouped by metadata columns.

Importantly, DESeq2 expects raw counts that have not been normalized in anyway.

seu_exn_aggr <- Seurat::AggregateExpression(seu_exn, return.seurat = T, group.by = c("sample_id",
    "condition"))
# get the raw un-normalized counts
counts_aggr <- as.matrix(GetAssayData(seu_exn_aggr, assay = "RNA", layer = "counts"))
head(counts_aggr, 3)

##            AD2b_AD AD4_AD C1_CON C3_CON
## AL627309.1      25     10     77     23
## AL627309.5      55     89     48     59
## AP006222.2       0      0      7      0

Pseudobulk - make DESeq2 object

Set up DESeq2 object with the aggregated counts and a data frame with column metadata. We now have reduced the analysis to two groups with only two samples each.

library(DESeq2)
column_meta <- data.frame(row.names = colnames(counts_aggr), condition = ifelse(grepl("AD",
    colnames(counts_aggr)), "AD", "CON"))
column_meta$condition <- factor(column_meta$condition, levels = c("CON", "AD"))


dds_pseudo_exn <- DESeqDataSetFromMatrix(countData = counts_aggr, colData = column_meta,
    design = ~condition)
colData(dds_pseudo_exn)

## DataFrame with 4 rows and 1 column
##         condition
##          <factor>
## AD2b_AD       AD 
## AD4_AD        AD 
## C1_CON        CON
## C3_CON        CON

Filter lowly expressed genes

While its not completely necessary, it is common to remove genes that are lowly expressed or are not expressed in enough samples.

Here, we keep those genes that 10 reads in at least two samples (the size of the smallest group)

smallestGroupSize <- 2
keep <- rowSums(counts(dds_pseudo_exn) >= 10) >= smallestGroupSize
table(keep)

## keep
## FALSE  TRUE 
##  5477 20633

dds_pseudo_exn <- dds_pseudo_exn[keep, ]

Run DESeq2

The DESeq function estimates size factors and dispersions before fitting the data to a negative binomial generalized linear model to get differential statistics.

Look at the DESeq2 manual and our course on bulk RNAseq to get more information about how DESeq2 normalizes and determines differentially expressed genes.

dds_pseudo_exn <- DESeq(dds_pseudo_exn)
resultsNames(dds_pseudo_exn)

## [1] "Intercept"           "condition_AD_vs_CON"

res_pseudo_exn <- results(dds_pseudo_exn, name = "condition_AD_vs_CON")

Run DESeq2

de_pseudo_exn <- res_pseudo_exn %>%
    data.frame %>%
    arrange(pvalue) %>%
    rownames_to_column(var = "geneID")

head(de_pseudo_exn, 3)

##      geneID baseMean log2FoldChange     lfcSE      stat       pvalue
## 1 SPRY4-AS1 803.0868      -1.959599 0.2493818 -7.857826 3.908571e-15
## 2     SNX31 459.1236       3.251960 0.4357934  7.462160 8.511567e-14
## 3     MOXD1 417.5197      -2.581761 0.3714799 -6.949936 3.654531e-12
##           padj
## 1 5.875363e-11
## 2 6.397294e-10
## 3 1.460451e-08

DESeq2 results - MA and PCA

ma <- ggplot(de_pseudo_exn, aes(x = baseMean, y = log2FoldChange)) + geom_point() +
    theme_bw() + scale_x_log10() + ggtitle("MA plot")
pca <- plotPCA(rlog(dds_pseudo_exn)) + coord_cartesian() + theme_bw() + ggtitle("PCA")

ggarrange(ma, pca, ncol = 2)

DESeq2 results - Volcano

It’s clear that we have many fewer differentially expressed genes using a pseudobulk strategy. This is not surprising with only two replicates per condition, but also highlights the potential for a very high false positive rate when each cell assumed to be independent.

de_pseudo_exn$sig = de_pseudo_exn$padj < 0.05
ggplot(de_pseudo_exn %>%
    na.omit, aes(x = log2FoldChange, y = -log10(pvalue), color = sig)) + geom_point() +
    scale_color_manual(values = c("grey", "red")) + theme_bw()

Visualize top DE genes

Look at the genes that go down in AD (up in control) with a violin plot

de_pseudo_exn_df <- na.omit(de_pseudo_exn)
top_down_AD_ps <- head(de_pseudo_exn_df[de_pseudo_exn_df$log2FoldChange < 0, ], 5)
VlnPlot(seu_exn, features = top_down_AD_ps$geneID, stack = T, flip = T, pt.size = 1) +
    scale_x_discrete(labels = c("CON", "AD"))

DESeq2 - sex as covariate

Add sex to metadata of DESeq2 dds object and make PCA plot

sample_ids <- rownames(colData(dds_pseudo_exn))
colData(dds_pseudo_exn)$sex <- ifelse(sample_ids == "AD2b_AD", "female", ifelse(sample_ids ==
    "AD4_AD", "male", ifelse(sample_ids == "C1_CON", "male", ifelse(sample_ids ==
    "C3_CON", "female", "none!"))))
colData(dds_pseudo_exn)$sex <- as.factor(colData(dds_pseudo_exn)$sex)
plotPCA(rlog(dds_pseudo_exn), intgroup = "sex") + coord_cartesian() + theme_bw() +
    ggtitle("PCA")

Re-run DESeq2 with sex as covariate

We notice that we now have no DE genes. This isn’t terribly surprising given that we only have two replicates per condition and the sex is driving a lot of our variance.

This doesn’t mean the genes that were identified before adding sex as a covariate are invalid, you would just approach them with caution. This likely would be an indication that more replicates were necessary to more confidently find DE genes.

dds_pseudo_exn_sex <- dds_pseudo_exn
design(dds_pseudo_exn_sex) <- ~sex + condition
dds_pseudo_exn_sex <- DESeq(dds_pseudo_exn_sex)
res_pseudo_exn_sex <- results(dds_pseudo_exn_sex, name = "condition_AD_vs_CON")

head(res_pseudo_exn_sex, 3)

## log2 fold change (MLE): condition AD vs CON 
## Wald test p-value: condition AD vs CON 
## DataFrame with 3 rows and 6 columns
##             baseMean log2FoldChange     lfcSE      stat    pvalue      padj
##            <numeric>      <numeric> <numeric> <numeric> <numeric> <numeric>
## AL627309.1   44.3124      -1.913196  1.333886 -1.434303  0.151486  0.692659
## AL627309.5   61.6567      -0.121863  0.849788 -0.143404  0.885971  0.992942
## LINC01409   589.3636      -0.408151  0.513194 -0.795315  0.426430  0.909770

Mixed model with random effect

A less commonly used approach for dealing with the pseudoreplication bias is the use of a mixed model with random effect for each sample.

random effects are used to measure and account for the variability from a population
- different from treating as a fixed effect where the effect of that variable is removed
- this benchmarking study suggests that this leads to higher precision than simply including subject as a fixed effect
computationally expensive compared to pseudobulk

In our case, including the sample as the random effect will model the variance that would exist in an entire population of samples (or patients) and then we include this in the model to understand if our fixed effect (AD vs control) changes while accounting for this expected population variance.

Filter genes and compute CDR for MAST

In fitting a mixed model with a random effect, MAST will often error when there are genes that have many zeroes. MAST includes the filterLowExpressedGenes function which will filter out genes that are expressed below a threshold percentage of cells.

# recommended here for RE: https://github.com/RGLab/MAST/issues/133
sca_exn_filt <- filterLowExpressedGenes(sca_exn, threshold = 0.1)

cdr_filt <- colSums(assay(sca_exn_filt) > 0)
colData(sca_exn_filt)$ngeneson <- scale(cdr_filt)

Run MAST with random effect

There is a special notation within the formula to add a random effect. Below we include sample_id as a random effect

zlm_re <- zlm(~ngeneson + sex + Group + (1 | sample_id), sca_exn_filt, ebayes = FALSE,
    method = "glmer")

sumZLM_re <- summary(zlm_re, doLRT = paste0("Group", "AD"))
sumDT_re <- sumZLM_re$datatable

MAST results

MAST can take some time to run, especially with random effects, so to move forward download the following file from dropbox: sumDT_exn_re.rds

sumDT_re <- readRDS("~/Downloads/sumDT_exn_re.rds")

Run MAST with random effect

de_mast_re <- merge(sumDT_re[sumDT_re$component == "H" & sumDT_re$contrast == paste0("Group",
    "AD"), ], sumDT_re[sumDT_re$component == "logFC" & sumDT_re$contrast == paste0("Group",
    "AD"), ], by = "primerid")
de_mast_re <- dplyr::select(de_mast_re, primerid, coef.y, z.y, `Pr(>Chisq).x`)
names(de_mast_re) <- c("geneID", "log2Fc", "z", "pvalue")
de_mast_re$FDR <- p.adjust(de_mast_re$pvalue, "fdr")
de_mast_re <- de_mast_re[order(de_mast_re$FDR), ]

head(de_mast_re, 3)

##        geneID    log2Fc        z       pvalue          FDR
##        <char>     <num>    <num>        <num>        <num>
## 1:   HSP90AB1 0.7011930 14.65392 5.486664e-09 6.003508e-05
## 2: AL133166.1 0.6755124 10.12379 1.346825e-08 7.368480e-05
## 3: AC120193.1 0.6986804 10.06780 2.992759e-08 1.091559e-04

Run MAST with random effect

de_mast_re$sig <- de_mast_re$FDR < 0.05

ggplot(de_mast_re, aes(x = log2Fc, y = -log10(pvalue), color = sig)) + geom_point() +
    scale_color_manual(values = c("grey", "red")) + theme_bw()

DESeq2 and MAST using Seurat FindMarkers?

Seurat FindMarkers for DESeq2

The Seurat FindMarkers function has an option to use MAST or DESeq2 with the ‘test.use’ argument, and the Seurat differential expression vignette describes this in their workflows. Shouldn’t we just use this?

# test deseq output to deseq using findmarkers
Idents(seu_exn_aggr) <- "condition"
seu_deseq <- FindMarkers(object = seu_exn_aggr, ident.1 = "AD", ident.2 = "CON",
    test.use = "DESeq2", slot = "counts", min.cells.group = 2)

seu_deseq$sig = seu_deseq$p_val_adj < 0.05

Compare DESeq2 results to Seurat

p_seuD <- ggplot(seu_deseq, aes(x = avg_log2FC, y = -log10(p_val_adj), color = sig)) +
    geom_point() + scale_color_manual(values = c("grey", "red")) + theme_bw() + ggtitle("FindMarkers")
p_pseudo <- ggplot(de_pseudo_exn %>%
    na.omit, aes(x = log2FoldChange, y = -log10(pvalue), color = sig)) + geom_point() +
    scale_color_manual(values = c("grey", "red")) + theme_bw() + ggtitle("Using DESeq2 directly")

ggarrange(p_seuD, p_pseudo, common.legend = TRUE, legend = "bottom")

Seurat FindMarkers for MAST

Note: Running this will take a while, so load the object on the next slide to see the plot

cdr_exn <- colSums(seu_exn[["RNA"]]$data > 0)
seu_exn$ngeneson <- scale(cdr_exn)

Idents(seu_exn_aggr) <- "condition"
seu_mast <- FindMarkers(object = seu_exn, group.by = "condition", ident.1 = "AD",
    ident.2 = "CON", test.use = "MAST", slot = "data", latent.vars = "ngeneson")

seu_mast$sig = seu_mast$p_val_adj < 0.05

Seurat FindMarkers for MAST

To make the following plot, read in the the MAST output from Seurat on dropbox: seu_mast_exn.rds

seu_mast <- readRDS("~/Downloads/seu_mast_exn.rds")

Compare results

p_seuM <- ggplot(seu_mast, aes(x = avg_log2FC, y = -log10(p_val_adj), color = sig)) +
    geom_point() + scale_color_manual(values = c("grey", "red")) + theme_bw() + ggtitle("FindMarkers")
p_mast <- ggplot(de_mast_exn, aes(x = log2Fc, y = -log10(pvalue), color = sig)) +
    geom_point() + scale_color_manual(values = c("grey", "red")) + theme_bw() + ggtitle("Using MAST directly")

ggarrange(p_seuM, p_mast, common.legend = TRUE, legend = "bottom")

Seurat FindMarkers take home

Currently (Seurat v5.0) the fold change information from DESeq2 or MAST is not carried over by FindMarkers. When defining gene sets with fold change cutoffs or visualizing gene expression changes, by not using the fold changes that account for normalization done by that algorithm, you can get funky results.

Also, using the packages directly allow for a higher level of fine tuning for more complex parameters (e.g random effect in MAST).

##             used   (Mb) gc trigger    (Mb)   max used    (Mb)
## Ncells  14134527  754.9   24674155  1317.8   24674155  1317.8
## Vcells 571212872 4358.1 1725144128 13161.9 1796958467 13709.8

CITE-seq

CITE-Seq

Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq).

overview

CITE-Seq

The CITE-Seq method labels different types of cells or different samples with hashtag-labeled antibodies After sequencing, the cells can be separated by the different antibody hashtags.

This means you can differentiate cell populations based on surface markers, or you can differentiate pooled samples.

Our data

Here, we will use a dataset from Seurat vignette, which is based on the a pooling approach: link

This data was processed by an alternative workflow called Drop-Seq for the GEX and CITE-seq-Count for the HTOs.

You can also use Cell Ranger count to do this, by providing an additional -feature-ref reference file containing the names and indexes of all of the hashtags. It will then generate counts over GEX and HTOs for you.

Load data

We have the data for you as processed matrices, with counts per gene and counts per tag for each cell.

rna.mat <- readRDS("data/pbmc_umi_mtx.rds")
dim(rna.mat)

## [1] 27117 16916

hto.mat <- readRDS("data/pbmc_hto_mtx.rds")
dim(hto.mat)

## [1]     8 16916

hto.mat[1:5, 1:5]

##       AGGCCACAGCGTCTAT ATTGGTGAGTTCGCAT GTGCAGCTCAGTCCCT TAGTTGGGTCATACTG
## HTO_A                3               19                3                6
## HTO_B                0               17               13               17
## HTO_C                1              110               48                9
## HTO_D               10               50                8               33
## HTO_E               10               53                0               22
##       TTCTTAGAGAAGGCCT
## HTO_A                7
## HTO_B               31
## HTO_C               26
## HTO_D             1769
## HTO_E               11

Prepare data

We first will create our Seurat data with the GEX data. We can then add the HTO count matrix as metadata information.

seu_obj <- CreateSeuratObject(counts = rna.mat, project = "citeSeq_demo")
seu_obj[["HTO"]] <- CreateAssayObject(counts = hto.mat)
seu_obj

## An object of class Seurat 
## 27125 features across 16916 samples within 2 assays 
## Active assay: RNA (27117 features, 0 variable features)
##  1 layer present: counts
##  1 other assay present: HTO

Cluster with regular workflow

Most of the workflow is very consistent between with our regular approach. We will normalize, select features and scale the GEX data.

DefaultAssay(seu_obj) <- "RNA"
seu_obj <- data_proc(seu_obj)
seu_obj <- ScaleData(seu_obj)

Cluster with regular workflow

Finally we can cluster our data. All looks good at this point, but remember this data is pooled.

seu_obj <- quick_clust(seu_obj)
DimPlot(seu_obj, group.by = "seurat_clusters", pt.size = 0.2, label = TRUE) + NoLegend()

Demultiplexing our data

To be able to seperate our samples we need to demulitplex the hastag information.

This will assign each cell to a specific hash, which will correspond to a specific sample.

Compositional data

Hashtag counts are a kind of compositional data; the counts represent a proportion of the whole.
The counts define the proportion of hashtags in each cell barcode.

hto.mat[, 1]

## HTO_A HTO_B HTO_C HTO_D HTO_E HTO_F HTO_G HTO_H 
##     3     0     1    10    10    12     9  4155

Centered log-ratio transformation

We cannot handle compositional data of this type in the same way as our GEX data.

CLR is a method to process compositional data. It is a log-ratio transformation that centers the data around the geometric mean of each cell barcode.

DefaultAssay(seu_obj) <- "HTO"
seu_obj <- NormalizeData(seu_obj, assay = "HTO", normalization.method = "CLR")

Differentiate Hashtags

We can use HTODemux() to classify our cells into groups. This uses clustering of the cells to help determine which sample each celll is from:

Cluster cells based on HTO signal into K+1 groups.
Create a “negative” cluster for each sample based on which cluster has lowest average value.
Compare each cell across the dataset to the negative group, using a negative binomial model.
Each cell is classified as positive/negative for every HTO.

Differentiate Hashtags

Demultiplex HTOs with HTODemux()
Threshold for positive call: 0.99 quantile

seu_obj <- HTODemux(seu_obj, assay = "HTO", positive.quantile = 0.99)

head(seu_obj, 2)

##                    orig.ident nCount_RNA nFeature_RNA nCount_HTO nFeature_HTO
## AGGCCACAGCGTCTAT citeSeq_demo        273          210       4200            7
## ATTGGTGAGTTCGCAT citeSeq_demo        305          174       3475            8
##                  RNA_snn_res.0.5 seurat_clusters HTO_maxID HTO_secondID
## AGGCCACAGCGTCTAT               0               0     HTO-H        HTO-E
## ATTGGTGAGTTCGCAT               2               2     HTO-H        HTO-G
##                  HTO_margin HTO_classification HTO_classification.global
## AGGCCACAGCGTCTAT 4.71133972              HTO-H                   Singlet
## ATTGGTGAGTTCGCAT 0.03995001        HTO-G_HTO-H                   Doublet
##                  hash.ID
## AGGCCACAGCGTCTAT   HTO-H
## ATTGGTGAGTTCGCAT Doublet

Decide postive hashtags

Use the “negative” distribution to calculate cut-off value (99% quantile).
Every cell barcode with HTO-A >= cut-off was assigned as HTO-A positive.

# Distribution of HTO-A level
RidgePlot(seu_obj, features = "HTO-A", group.by = "orig.ident") + NoLegend()

Demutiplex result

The demultiplex results are in the column HTO_classification.global and hash.ID
In the HTO_classification.global:
- The Singlet means the cell barcode with only one particular hashtag. It can be applied to further analysis.
- The Negative means no hashtags passed cut-off in the cell barcodes.
- The Doublet means multiple hashtags passed cut-off in the cell barcodes.
  - The Doublets here didn’t mean multiple cells in a single droplet.
In the column hash.ID, particular hashtag for each cell barcode assigned in the column hash.ID. The Negative or Doublet were the same as in the column HTO_classification.global.

Demutiplex result

RidgePlot(seu_obj, features = c("HTO-A", "HTO-B"), group.by = "hash.ID") + NoLegend()

Demutiplex result

We can review and check these values in our metadata.

table(seu_obj$HTO_classification.global)

## 
##  Doublet Negative  Singlet 
##     2598      346    13972

#
table(seu_obj$hash.ID)

## 
##  Doublet    HTO-H    HTO-D    HTO-E    HTO-G    HTO-F    HTO-B    HTO-C 
##     2598     1808     1716     1487     1660     1520     1993     1873 
##    HTO-A Negative 
##     1915      346

#
table(seu_obj$HTO_classification.global, seu_obj$hash.ID)

##           
##            Doublet HTO-H HTO-D HTO-E HTO-G HTO-F HTO-B HTO-C HTO-A Negative
##   Doublet     2598     0     0     0     0     0     0     0     0        0
##   Negative       0     0     0     0     0     0     0     0     0      346
##   Singlet        0  1808  1716  1487  1660  1520  1993  1873  1915        0

UMAP ~ split by Hashtags

DimPlot(seu_obj, group.by = "seurat_clusters", label = TRUE, pt.size = 0.2, split.by = "hash.ID",
    ncol = 5) + NoLegend()

Unassigned cells

The cell barcodes with Negative or Doublet would not be used for further analysis directly.
- Generally, the mis-labeled rate in CITE-Seq would be ranged from 10% ~ 80% (avg 30%).
- If you use hashtag to label cell types, we could try to rescue the mis-labeled cells by using the clustering in UMAP. [assume a given cell type shall group together]
- If you use hashtags to label samples, it would be safer to exclude these cells for following analysis.

Subset the data

We can quickly and easily subset the data base on our HTO demultiplex results.

seu_obj_onlysinglet <- subset(seu_obj, HTO_classification.global == "Singlet")


seu_obj_onlyHTOA <- subset(seu_obj, HTO_classification == "HTO-A")

Cell Ranger 9

New Cell Ranger came out last week. We are yet to test it but new features include:

Automated annotation for human data sets [in beta]
UMAPs are made instead of tSNE
New Web Summary layout
EmptyDrops false discovery rate (FDR) threshold has been lowered
cellranger mkfastq is being deprecated
New Workflow for hashtagged data

Review

overview

Resources

10X Single cell software
Seurat
Single Cell Tools
Prior versions of the course
- MNN
- SoupX
- Pseudotime analysis

Single-cell RNA sequencing ~ Session 3

Rockefeller University, Bioinformatics Resource Center

https://rockefelleruniversity.github.io/SingleCell_Bootcamp/

Outlines

A more advanced scRNAseq workflow

A more advanced scRNAseq workflow

Create some functions

Create some functions

Merging Datasets

An advanced scRNAseq workflow

Merge multiple datasets

Example dataset

Example dataset

Simple Merge

Concatenate the datasets

Process and make clusters

UMAP

Our samples don’t group

Our samples don’t group

Merge with reciprocal PCA

Merge with reciprocal PCA

Merge with reciprocal PCA

Merge with reciprocal PCA

RPCA workflow

Prepare for RPCA merge

Integrating data in RPCA merge

Evaluating RPCA using clusters

Assesing Integration

Evaluating RPCA using clusters

UMAP - Clusters

Heatmap - clusters

UMAP - Markers

UMAP - Annotation

Evaluating RPCA using cell types

Merge data with Harmony

Harmony

Harmony

Harmony

Prepare data for Harmony

Prepare data for Harmony

Merge data with Harmony

Evaluating Harmony

UMAP - Clusters

Heatmap - clusters

UMAP - Markers

UMAP - Annotation

Evaluating RPCA using cell types

Comparing our results

Compare

Compare

Compare

Data Integration Compared

Data Integration Compared

An advanced scRNAseq workflow

Cell type annotation

Manual Annotation

Manual Annotation

Automated annotation

Cell type annotation

Annotation with SingleR

Annotation with SingleR

Annotation with SingleR

Annotation with SingleR

Annotation with SingleR

Annotation with SingleR

Annotation with SingleR

Annotation with SingleR

Annotation with SingleR

Annotation with SingleR

Annotation with SingleR

Annotation with SingleR

Annotation with SingleR

Annotation with SingleR

Annotation with SingleR

Annotation with Seurat

Anotation with Seurat

Reference and query datasets

Predict cell types for query

Predict cell types for query

Annotation with SingleR