---
description: Combining and harmonizing samples or datasets from
  different batches such as experiments or conditions to enable
  meaningful cross-sample comparisons.
subtitle:  Bioconductor Toolkit
title:  Data Integration
---

<div>

> **Note**
>
> Code chunks run R commands unless otherwise specified.

</div>

In this tutorial we will look at different ways of integrating multiple
single cell RNA-seq datasets. We will explore a few different methods to
correct for batch effects across datasets. Seurat uses the data
integration method presented in Comprehensive Integration of Single Cell
Data, while Scran and Scanpy use a mutual Nearest neighbour method
(MNN). Below you can find a list of some methods for single data
integration:

  -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
  Markdown          Language          Library           Ref
  ----------------- ----------------- ----------------- -----------------------------------------------------------------------------------------------------------------------------------
  CCA               R                 Seurat            [Cell](https://www.sciencedirect.com/science/article/pii/S0092867419305598?via%3Dihub)

  MNN               R/Python          Scater/Scanpy     [Nat. Biotech.](https://www.nature.com/articles/nbt.4091)

  Conos             R                 conos             [Nat.
                                                        Methods](https://www.nature.com/articles/s41592-019-0466-z?error=cookies_not_supported&code=5680289b-6edb-40ad-9934-415dac4fdb2f)

  Scanorama         Python            scanorama         [Nat. Biotech.](https://www.nature.com/articles/s41587-019-0113-3)
  -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

## Data preparation

Let's first load necessary libraries and the data saved in the previous
lab.

``` {r}
# Activate scanorama Python venv
reticulate::use_virtualenv("/opt/venv/scanorama")
reticulate::py_discover_config()

suppressPackageStartupMessages({
    library(scater)
    library(scran)
    library(patchwork)
    library(ggplot2)
    library(batchelor)
    library(harmony)
    library(reticulate)
})

```

``` {r}
# download pre-computed data if missing or long compute
fetch_data <- TRUE

# url for source and intermediate data
path_data <- "https://export.uppmax.uu.se/naiss2023-23-3/workshops/workshop-scrnaseq"
path_file <- "data/covid/results/bioc_covid_qc_dr.rds"
if (!dir.exists(dirname(path_file))) dir.create(dirname(path_file), recursive = TRUE)
if (fetch_data && !file.exists(path_file)) download.file(url = file.path(path_data, "covid/results/bioc_covid_qc_dr.rds"), destfile = path_file)
sce <- readRDS(path_file)
print(reducedDims(sce))
```

We split the combined object into a list, with each dataset as an
element. We perform standard preprocessing (log-normalization), and
identify variable features individually for each dataset based on a
variance stabilizing transformation (**vst**).

``` {r}
sce.list <- lapply(unique(sce$sample), function(x) {
    x <- sce[, sce$sample == x]
})

hvgs_per_dataset <- lapply(sce.list, function(x) {
    x <- computeSumFactors(x, sizes = c(20, 40, 60, 80))
    x <- logNormCounts(x)
    var.out <- modelGeneVar(x, method = "loess")
    hvg.out <- var.out[which(var.out$FDR <= 0.05 & var.out$bio >= 0.2), ]
    hvg.out <- hvg.out[order(hvg.out$bio, decreasing = TRUE), ]
    return(rownames(hvg.out))
})
names(hvgs_per_dataset) <- unique(sce$sample)

# venn::venn(hvgs_per_dataset,opacity = .4,zcolor = scales::hue_pal()(3),cexsn = 1,cexil = 1,lwd=1,col="white",borders = NA)

temp <- unique(unlist(hvgs_per_dataset))
overlap <- sapply(hvgs_per_dataset, function(x) {
    temp %in% x
})
```

``` {r}
#| fig-height: 4
#| fig-width: 8

pheatmap::pheatmap(t(overlap * 1), cluster_rows = F, color = c("grey90", "grey20")) ## MNN
```

The mutual nearest neighbors (MNN) approach within the scran package
utilizes a novel approach to adjust for batch effects. The `fastMNN()`
function returns a representation of the data with reduced
dimensionality, which can be used in a similar fashion to other
lower-dimensional representations such as PCA. In particular, this
representation can be used for downstream methods such as clustering.
The BNPARAM can be used to specify the specific nearest neighbors method
to use from the BiocNeighbors package. Here we make use of the [Annoy
library](https://github.com/spotify/annoy) via the
`BiocNeighbors::AnnoyParam()` argument. We save the reduced-dimension
MNN representation into the reducedDims slot of our sce object.

``` {r}
mnn_out <- batchelor::fastMNN(sce, subset.row = unique(unlist(hvgs_per_dataset)), batch = factor(sce$sample), k = 20, d = 50)
```

<div>

> **Caution**
>
> `fastMNN()` does not produce a batch-corrected expression matrix.

</div>

``` {r}
mnn_out <- t(reducedDim(mnn_out, "corrected"))
colnames(mnn_out) <- unlist(lapply(sce.list, function(x) {
    colnames(x)
}))
mnn_out <- mnn_out[, colnames(sce)]
rownames(mnn_out) <- paste0("dim", 1:50)
reducedDim(sce, "MNN") <- t(mnn_out)
```

We can observe that a new assay slot is now created under the name
`MNN`.

``` {r}
reducedDims(sce)
```

Thus, the result from `fastMNN()` should solely be treated as a reduced
dimensionality representation, suitable for direct plotting, TSNE/UMAP,
clustering, and trajectory analysis that relies on such results.

``` {r}
set.seed(42)
sce <- runTSNE(sce, dimred = "MNN", n_dimred = 50, perplexity = 30, name = "tSNE_on_MNN")
sce <- runUMAP(sce, dimred = "MNN", n_dimred = 50, ncomponents = 2, name = "UMAP_on_MNN")
```

We can now plot the unintegrated and the integrated space reduced
dimensions.

``` {r}
#| fig-height: 6
#| fig-width: 12

wrap_plots(
    plotReducedDim(sce, dimred = "PCA", colour_by = "sample", point_size = 0.6) + ggplot2::ggtitle(label = "PCA"),
    plotReducedDim(sce, dimred = "tSNE_on_PCA", colour_by = "sample", point_size = 0.6) + ggplot2::ggtitle(label = "tSNE_on_PCA"),
    plotReducedDim(sce, dimred = "UMAP_on_PCA", colour_by = "sample", point_size = 0.6) + ggplot2::ggtitle(label = "UMAP_on_PCA"),
    plotReducedDim(sce, dimred = "MNN", colour_by = "sample", point_size = 0.6) + ggplot2::ggtitle(label = "MNN"),
    plotReducedDim(sce, dimred = "tSNE_on_MNN", colour_by = "sample", point_size = 0.6) + ggplot2::ggtitle(label = "tSNE_on_MNN"),
    plotReducedDim(sce, dimred = "UMAP_on_MNN", colour_by = "sample", point_size = 0.6) + ggplot2::ggtitle(label = "UMAP_on_MNN"),
    ncol = 3
) + plot_layout(guides = "collect")
```

Let's plot some marker genes for different cell types onto the
embedding.

  Markers                    Cell Type
  -------------------------- -------------------
  CD3E                       T cells
  CD3E CD4                   CD4+ T cells
  CD3E CD8A                  CD8+ T cells
  GNLY, NKG7                 NK cells
  MS4A1                      B cells
  CD14, LYZ, CST3, MS4A7     CD14+ Monocytes
  FCGR3A, LYZ, CST3, MS4A7   FCGR3A+ Monocytes
  FCER1A, CST3               DCs

``` {r}
#| fig-height: 16
#| fig-width: 13

plotlist <- list()
for (i in c("CD3E", "CD4", "CD8A", "NKG7", "GNLY", "MS4A1", "CD14", "LYZ", "MS4A7", "FCGR3A", "CST3", "FCER1A")) {
    plotlist[[i]] <- plotReducedDim(sce, dimred = "UMAP_on_MNN", colour_by = i, by_exprs_values = "logcounts", point_size = 0.6) +
        scale_fill_gradientn(colours = colorRampPalette(c("grey90", "orange3", "firebrick", "firebrick", "red", "red"))(10)) +
        ggtitle(label = i) + theme(plot.title = element_text(size = 20))
}
wrap_plots(plotlist = plotlist, ncol = 3)
```

## Harmony

An alternative method for integration is Harmony, for more details on
the method, please se their paper [Nat.
Methods](https://www.nature.com/articles/s41592-019-0619-0). This method
runs the integration on a dimensionality reduction, in most applications
the PCA. So first, we will rerun scaling and PCA with the same set of
genes that were used for the CCA integration.

``` {r}
#| fig-height: 5
#| fig-width: 14

library(harmony)

reducedDimNames(sce)

sce <- RunHarmony(
    sce,
    group.by.vars = "sample",
    reduction.save = "harmony",
    reduction = "PCA",
    dims.use = 1:50
)

# Here we use all PCs computed from Harmony for UMAP calculation
sce <- runUMAP(sce, dimred = "harmony", n_dimred = 50, ncomponents = 2, name = "UMAP_on_Harmony")
```

## Scanorama

<div>

> **Important**
>
> If you are running locally using Docker and you have a Mac with ARM
> chip, the Scanorama reticulate module is known to crash. In this case,
> you might want to skip this section.

</div>

Another integration method is Scanorama (see [Nat.
Biotech.](https://www.nature.com/articles/s41587-019-0113-3)). This
method is implemented in python, but we can run it through the
Reticulate package.

We will run it with the same set of variable genes, but first we have to
create a list of all the objects per sample.

``` {r}
#| fig-height: 5
#| fig-width: 15

hvgs <- unique(unlist(hvgs_per_dataset))

scelist <- list()
genelist <- list()
for (i in 1:length(sce.list)) {
    scelist[[i]] <- t(as.matrix(logcounts(sce.list[[i]])[hvgs, ]))
    genelist[[i]] <- hvgs
}

lapply(scelist, dim)
```

Now we can run scanorama:

``` {r}
#| fig-height: 5
#| fig-width: 15

scanorama <- reticulate::import("scanorama")

integrated.data <- scanorama$integrate(datasets_full = scelist, genes_list = genelist)

intdimred <- do.call(rbind, integrated.data[[1]])
colnames(intdimred) <- paste0("PC_", 1:100)
rownames(intdimred) <- colnames(logcounts(sce))

# Add standard deviations in order to draw Elbow Plots 
stdevs <- apply(intdimred, MARGIN = 2, FUN = sd)
attr(intdimred, "varExplained") <- stdevs

reducedDim(sce, "Scanorama_PCA") <- intdimred

# Here we use all PCs computed from Scanorama for UMAP calculation
sce <- runUMAP(sce, dimred = "Scanorama_PCA", n_dimred = 50, ncomponents = 2, name = "UMAP_on_Scanorama")

plotReducedDim(sce, dimred = "UMAP_on_Scanorama", colour_by = "sample", point_size = 0.6) + ggplot2::ggtitle(label = "UMAP_on_Scanorama")
```

## Overview all methods

Now we will plot UMAPS with all three integration methods side by side.

``` {r}
#| fig-height: 8
#| fig-width: 10

p1 <- plotReducedDim(sce, dimred = "UMAP_on_PCA", colour_by = "sample", point_size = 0.6) + ggplot2::ggtitle(label = "UMAP_on_PCA")
p2 <- plotReducedDim(sce, dimred = "UMAP_on_MNN", colour_by = "sample", point_size = 0.6) + ggplot2::ggtitle(label = "UMAP_on_MNN")
p3 <- plotReducedDim(sce, dimred = "UMAP_on_Harmony", colour_by = "sample", point_size = 0.6) + ggplot2::ggtitle(label = "UMAP_on_Harmony")
p4 <- plotReducedDim(sce, dimred = "UMAP_on_Scanorama", colour_by = "sample", point_size = 0.6) + ggplot2::ggtitle(label = "UMAP_on_Scanorama")

wrap_plots(p1, p2, p3, p4, nrow = 2) +
    plot_layout(guides = "collect")
```

Let's save the integrated data for further analysis.

``` {r}
saveRDS(sce, "data/covid/results/bioc_covid_qc_dr_int.rds")
```

## Session info

```{=html}
<details>
```
```{=html}
<summary>
```
Click here
```{=html}
</summary>
```
``` {r}
sessionInfo()
```

```{=html}
</details>
```
