07_Combined_Clustering.Rmd

---
title: "Combined Clustering"
output:
    workflowr::wflow_html:
        code_folding: hide
        df_print: paged
---

```{r knitr, include = FALSE}
DOCNAME = "07_Combined_Clustering"
knitr::opts_chunk$set(autodep        = TRUE,
                      cache          = TRUE,
                      cache.path     = paste0("cache/", DOCNAME, "/"),
                      cache.comments = FALSE,
                      echo           = TRUE,
                      error          = FALSE,
                      fig.align      = "center",
                      fig.width      = 10,
                      fig.height     = 8,
                      message        = FALSE,
                      warning        = FALSE)
```

```{r libaries, cache = FALSE}
# scRNA-seq
library("Seurat")
library("limma")

# Plotting
library("clustree")
library("viridis")

# Presentation
library("glue")
library("knitr")

# Parallel
library("BiocParallel")

# Paths
library("here")

# Output
library("writexl")
library("jsonlite")

# Tidyverse
library("tidyverse")
```

```{r source, cache = FALSE}
source(here("R/output.R"))
```

```{r paths}
combined.path <- here("data/processed/Combined_Seurat.Rds")
```

```{r bpparam, cache = FALSE}
bpparam <- MulticoreParam(workers = 10)
```

Introduction
============

In this document we are going to load the combined Organoids and Linstrom
dataset and complete the rest of the Seurat analysis.

```{r load, cache.extra = tools::md5sum(combined.path), cache.lazy = FALSE}
if (file.exists(combined.path)) {
    combined <- read_rds(combined.path)
} else {
    stop("Combined dataset is missing. ",
         "Please run '06_Combined_Integration.Rmd' first.",
         call. = FALSE)
}
```

Clustering
==========

Selecting resolution
--------------------

```{r cluster, cache.lazy = FALSE}
n.dims <- 20
resolutions <- seq(0, 1, 0.1)
combined <- FindClusters(combined, reduction.type = "cca.aligned",
                         dims.use = 1:n.dims, resolution = resolutions)
```

`Seurat` has a resolution parameter that indirectly controls the number of
clusters it produces. We tried clustering at a range of resolutions from
`r min(resolutions)` to `r max(resolutions)`.

### t-SNE plots {.tabset}

Here are t-SNE plots of the different clusterings.

```{r cluster-tSNE, results = "hide"}
src_list <- lapply(resolutions, function(res) {
    src <- c("#### Res {{res}} {.unnumbered}",
             "```{r cluster-tSNE-{{res}}}",
             "TSNEPlot(combined, group.by = 'res.{{res}}', do.return = TRUE)",  
             "```",
             "")
    knit_expand(text = src)
})

out <- knit_child(text = unlist(src_list), options = list(cache = FALSE))
```

`r out`

### Clustering tree

#### Standard

Coloured by clustering resolution.

```{r clustree}
clustree(combined)
```

#### Stability

Coloured by the SC3 stability metric.

```{r clustree-stability}
clustree(combined, node_colour = "sc3_stability")
```

#### Gene expression {.tabset}

Coloured by the expression of some well-known kidney marker genes.

```{r select-clustree-genes}
genes <- c("PECAM1", "CDH5", "MEIS1", "PDGFRA", "HMGB2", "CENPA", "SIX1",
           "DAPL1", "NPHS1", "PODXL", "S100A8", "TYROBP", "MAL", "EMX2",
           "LRP2", "GATA3", "SLC12A1", "SPINT2", "TUBB2B", "STMN2", "TTHY1",
           "HBA1", "HBG1")

is_present <- genes %in% rownames(combined@data)
```

> The following genes aren't present in this dataset and will be skipped:
> `r genes[!is_present]`

```{r clustree-genes, results = "hide"}
src_list <- lapply(genes[is_present], function(gene) {
    src <- c("##### {{gene}} {.unnumbered}",
             "```{r clustree-{{gene}}}",
             "clustree(combined, node_colour = '{{gene}}',",
                      "node_colour_aggr = 'mean',",
             "exprs = 'scale.data') +",
             "scale_colour_viridis_c(option = 'plasma', begin = 0.3)",
             "```",
             "")
    knit_expand(text = src)
})

out <- knit_child(text = unlist(src_list), options = list(cache = FALSE))
```

`r out`

### Selected resolution

```{r res, cache.lazy = FALSE}
res <- 0.5
combined <- SetIdent(combined,
                     ident.use = combined@meta.data[, paste0("res.", res)])
n.clusts <- length(unique(combined@ident))
```

Based on these plots we will use a resolution of `r res`.

Clusters
--------

Let's have a look at the clusters on a t-SNE plot.

```{r tSNE, fig.height = 6, cache.lazy = FALSE}
p1 <- TSNEPlot(combined, do.return = TRUE, pt.size = 0.5,
               group.by = "DatasetSample")
p2 <- TSNEPlot(combined, do.label = TRUE, do.return = TRUE, pt.size = 0.5)
plot_grid(p1, p2)
```

We can also look at the number of cells in each cluster.

```{r cluster-sizes}
plot.data <- combined@meta.data %>%
    select(Dataset, cluster = paste0("res.", res)) %>%
    mutate(cluster = factor(as.numeric(cluster))) %>%
    group_by(cluster, Dataset) %>%
    summarise(count = n()) %>%
    mutate(clust_total = sum(count)) %>%
    mutate(clust_prop = count / clust_total) %>%
    group_by(Dataset) %>%
    mutate(dataset_total = sum(count)) %>%
    ungroup() %>%
    mutate(dataset_prop = count / dataset_total)

ggplot(plot.data, aes(x = cluster, y = count, fill = Dataset)) +
    geom_col()
```

We are also interested in what proportions of the cells in each cluster come
from each datasets (i.e. are there dataset specific clusters?).

```{r cluster-props}
ggplot(plot.data, aes(x = cluster, y = clust_prop, fill = Dataset)) +
    geom_col()
```

Alternatively we can look at what proportion of the cells in each **dataset**
are in each cluster. If each dataset has the same distribution of cell types the
heights of the bars should be the same.

```{r dataset-props}
ggplot(plot.data, aes(x = cluster, y = dataset_prop, fill = Dataset)) +
    geom_col(position = position_dodge(0.9))
```

Marker genes
============

Clustering is not very useful if we don't know what cell types the clusters
represent. One way to work that out is to look at marker genes, genes that are
differentially expressed in one cluster compared to all other cells. Here we
use the Wilcoxon rank sum test genes that are present in at least 10 percent
of cells in at least one group (a cluster or all other cells).

```{r markers}
markers <- bplapply(seq_len(n.clusts) - 1, function(cl) {
    cl.markers <- FindMarkers(combined, cl, logfc.threshold = 0, min.pct = 0.1,
                              print.bar = FALSE)
    cl.markers$cluster <- cl
    cl.markers$gene <- rownames(cl.markers)
    return(cl.markers)
}, BPPARAM = bpparam)

markers <- bind_rows(markers) %>%
    select(gene, cluster, everything())
```

Here we print out the top two markers for each cluster.

```{r print-markers}
markers %>% group_by(cluster) %>% top_n(2, abs(avg_logFC)) %>% data.frame
```

A heatmap can give us a better view. We show the top five positive marker genes
for each cluster.

```{r markers-heatmap, fig.height = 10}
top <- markers %>% group_by(cluster) %>% top_n(5, avg_logFC)

cols <- viridis(100)[c(1, 50, 100)]

DoHeatmap(combined, genes.use = top$gene, slim.col.label = TRUE,
          remove.key = TRUE, col.low = cols[1], col.mid = cols[2],
          col.high = cols[3])
```

By cluster {.tabset}
----------

```{r markers-cluster}
markers.list <- lapply(0:(n.clusts - 1), function(x) {
    markers %>%
        filter(cluster == x, p_val < 0.05) %>%
        dplyr::arrange(-avg_logFC) %>%
        select(Gene = gene, LogFC = avg_logFC, pVal = p_val)
})

names(markers.list) <- paste("Cluster", 0:(n.clusts - 1))
```

```{r marker-cluster-counts}
marker.summary <- markers.list %>%
    map2_df(seq_along(markers.list), ~ mutate(.x, Cluster = .y)) %>%
    mutate(IsUp = LogFC > 0) %>%
    group_by(Cluster) %>%
    summarise(Up = sum(IsUp), Down = sum(!IsUp)) %>%
    mutate(Down = -Down) %>%
    gather(key = "Direction", value = "Count", -Cluster)

ggplot(marker.summary,
       aes(x = fct_rev(factor(Cluster)), y = Count, fill = Direction)) +
    geom_col() +
    geom_text(aes(y = Count + sign(Count) * max(abs(Count)) * 0.07,
                  label = abs(Count)),
              size = 6, colour = "grey25") +
    coord_flip() +
    scale_fill_manual(values = c("#377eb8", "#e41a1c")) +
    ggtitle("Number of identified genes") +
    xlab("Cluster") +
    theme(axis.title.x = element_blank(),
          axis.line = element_blank(),
          axis.ticks = element_blank(),
          axis.text.x = element_blank(),
          legend.position = "bottom")
```

We can also look at the full table of significant marker genes for each cluster.

```{r marker-cluster-table, results = "hide"}
src_list <- lapply(0:(n.clusts - 1), function(i) {
    src <- c("### {{i}} {.unnumbered}",
             "```{r marker-cluster-{{i}}}",
             "markers.list[[{{i}} + 1]]",
             "```",
             "")
    knit_expand(text = src)
})

out <- knit_child(text = unlist(src_list),
                  options = list(echo = FALSE, cache = FALSE))
```

`r out`

Conserved markers
=================

Here we are going to look for genes that are cluster markers in both the
Organoid and Lindstrom datasets. Each dataset will be tested individually and the 
results combined to see if they are present in both datasets.

```{r skip-clusters, cache.lazy = FALSE}
combined@meta.data$Group <- gsub("[0-9]", "", combined@meta.data$Dataset)

skip <- combined@meta.data %>%
    count(Group, Cluster = !! rlang::sym(paste0("res.", res))) %>%
    spread(Group, n) %>%
    replace_na(list(Organoid = 0L, Lindstrom = 0L)) %>%
    rowwise() %>%
    mutate(Skip = min(Organoid, Lindstrom) < 3) %>%
    arrange(as.numeric(Cluster)) %>%
    pull(Skip)
```

> **Skipped clusters**
>
> Testing conserved markers isn't possible for clusters that only contain cells
> from one dataset. In this case the following clusters are skipped:
> `r glue_collapse(seq(0, n.clusts - 1)[skip], sep = ", ", last = " and ")`

```{r con-markers-mc}
con.markers <- bplapply(seq_len(n.clusts) - 1, function(cl) {

    if (skip[cl + 1]) {
        message("Skipping cluster ", cl)
        cl.markers <- c()
    } else {
        cl.markers <- FindConservedMarkers(combined, cl, grouping.var = "Group",
                                           logfc.threshold = 0, min.pct = 0.1,
                                           print.bar = FALSE)
        cl.markers$cluster <- cl
        cl.markers$gene <- rownames(cl.markers)
    }

    return(cl.markers)
}, BPPARAM = bpparam)

con.markers <- bind_rows(con.markers) %>%
    mutate(mean_avg_logFC = rowMeans(select(., ends_with("avg_logFC")))) %>%
    select(gene, cluster, mean_avg_logFC, max_pval, minimump_p_val,
           everything())
```

Here we print out the top two conserved markers for each cluster.

```{r print-con-markers}
con.markers %>%
    group_by(cluster) %>%
    top_n(2, abs(mean_avg_logFC)) %>%
    data.frame
```

Again a heatmap can give us a better view. We show the top five positive
conserved marker genes for each cluster.

```{r con-markers-heatmap, fig.height = 10}
top <- con.markers %>% group_by(cluster) %>% top_n(5, mean_avg_logFC)

cols <- viridis(100)[c(1, 50, 100)]

DoHeatmap(combined, genes.use = top$gene, slim.col.label = TRUE,
          remove.key = TRUE, col.low = cols[1], col.mid = cols[2],
          col.high = cols[3])
```

By cluster {.tabset}
----------

```{r con-markers-cluster}
con.markers.list <- lapply(0:(n.clusts - 1), function(x) {
    con.markers %>%
        filter(cluster == x, max_pval < 0.05) %>%
        dplyr::arrange(-mean_avg_logFC) %>%
        select(Gene = gene,
               MeanLogFC= mean_avg_logFC,
               MaxPVal = max_pval,
               MinPVal = minimump_p_val,
               OrganoidLogFC = Organoid_avg_logFC,
               OrganoidPVal = Organoid_p_val,
               LindstromLogFC = Lindstrom_avg_logFC,
               LindstromPVal = Lindstrom_p_val)
})

names(con.markers.list) <- paste("Cluster", 0:(n.clusts - 1))
```

```{r con-marker-cluster-counts}
con.marker.summary <- con.markers.list %>%
    map2_df(seq_along(con.markers.list), ~ mutate(.x, Cluster = .y)) %>%
    mutate(IsUp = MeanLogFC > 0) %>%
    group_by(Cluster) %>%
    summarise(Up = sum(IsUp), Down = sum(!IsUp)) %>%
    mutate(Down = -Down) %>%
    gather(key = "Direction", value = "Count", -Cluster)

ggplot(con.marker.summary,
       aes(x = fct_rev(factor(Cluster)), y = Count, fill = Direction)) +
    geom_col() +
    geom_text(aes(y = Count + sign(Count) * max(abs(Count)) * 0.07,
                  label = abs(Count)),
              size = 6, colour = "grey25") +
    coord_flip() +
    scale_fill_manual(values = c("#377eb8", "#e41a1c")) +
    ggtitle("Number of identified genes") +
    xlab("Cluster") +
    theme(axis.title.x = element_blank(),
          axis.line = element_blank(),
          axis.ticks = element_blank(),
          axis.text.x = element_blank(),
          legend.position = "bottom")
```

We can also look at the full table of significant conserved marker genes for
each cluster.

```{r con-marker-cluster-table, results = "hide"}
src_list <- lapply(0:(length(con.markers.list)-1), function(i) {
    src <- c("### {{i}} {.unnumbered}",
             "```{r con-marker-cluster-{{i}}}",
             "con.markers.list[[{{i}} + 1]]",
             "```",
             "")
    knit_expand(text = src)
})

out <- knit_child(text = unlist(src_list),
                 options = list(echo = FALSE, cache = FALSE))
```

`r out`

Within cluster DE
=================

We can also look for genes that are differentially expressed between the two
datasets in the same cluster. This might help to identify differences in the
same cell type between the difference experiments.

```{r set-ident, cache.lazy = FALSE}
combined@meta.data$GroupCluster <- paste(combined@meta.data$Group,
                                         combined@ident, sep = "_")
combined <- StashIdent(combined, save.name = "Cluster")
combined <- SetAllIdent(combined, id = "GroupCluster")
```

```{r de-plots, fig.height = 8}
plot.data <- AverageExpression(combined, show.progress = FALSE) %>%
    rownames_to_column("Gene") %>%
    gather(key = "GroupCluster", value = "AvgExp", -Gene) %>%
    separate(GroupCluster, c("Group", "Cluster"), sep = "_") %>%
    mutate(Cluster = factor(as.numeric(Cluster))) %>%
    mutate(LogAvgExp = log1p(AvgExp)) %>%
    select(-AvgExp) %>%
    spread(Group, LogAvgExp) %>%
    replace_na(list(Organoid = 0, Lindstrom = 0)) %>%
    mutate(Avg = 0.5 * (Organoid + Lindstrom),
           Diff = Organoid - Lindstrom)

ggplot(plot.data, aes(x = Avg, y = Diff)) +
    geom_hline(yintercept = 0, colour = "red") +
    geom_point(size = 0.6, alpha = 0.2) +
    xlab("0.5 * (Organoid + Lindstrom)") +
    ylab("Organoid - Lindstrom") +
    facet_wrap(~ Cluster)
```

```{r cluster-de-mc}
cluster.de <- bplapply(seq_len(n.clusts) - 1, function(cl) {

    if (skip[cl + 1]) {
        message("Skipping cluster ", cl)
        cl.de <- c()
    } else {
        cl.de <- FindMarkers(combined, paste("Organoid", cl, sep = "_"),
                             paste("Lindstrom", cl, sep = "_"),
                             logfc.threshold = 0, min.pct = 0.1,
                             print.bar = FALSE)
        cl.de$cluster <- cl
        cl.de$gene <- rownames(cl.de)
    }

    return(cl.de)
}, BPPARAM = bpparam)

cluster.de <- bind_rows(cluster.de) %>%
    select(gene, cluster, everything())
```

Here we print out the top two DE genes for each cluster.

```{r print-de}
cluster.de %>% group_by(cluster) %>% top_n(2, abs(avg_logFC)) %>% data.frame
```

Again a heatmap can give us a better view. We show the top five positive DE
genes for each cluster.

```{r de-heatmap, fig.height = 10}
top <- cluster.de %>% group_by(cluster) %>% top_n(5, avg_logFC)

cols <- viridis(100)[c(1, 50, 100)]

DoHeatmap(combined, genes.use = top$gene, slim.col.label = TRUE,
          remove.key = TRUE, col.low = cols[1], col.mid = cols[2],
          col.high = cols[3])
```

By cluster {.tabset}
----------

```{r de-cluster}
cluster.de.list <- lapply(0:(n.clusts - 1), function(x) {
    cluster.de %>%
        filter(cluster == x, p_val < 0.05) %>%
        dplyr::arrange(p_val) %>%
        select(Gene = gene, LogFC = avg_logFC, pVal = p_val)
})

names(cluster.de.list) <- paste("Cluster", 0:(n.clusts - 1))
```

```{r de-cluster-counts}
cluster.de.summary <- cluster.de.list %>%
    map2_df(seq_along(cluster.de.list), ~ mutate(.x, Cluster = .y)) %>%
    mutate(IsUp = LogFC > 0) %>%
    group_by(Cluster) %>%
    summarise(Up = sum(IsUp), Down = sum(!IsUp)) %>%
    mutate(Down = -Down) %>%
    gather(key = "Direction", value = "Count", -Cluster)

ggplot(cluster.de.summary,
       aes(x = fct_rev(factor(Cluster)), y = Count, fill = Direction)) +
    geom_col() +
    geom_text(aes(y = Count + sign(Count) * max(abs(Count)) * 0.07,
                  label = abs(Count)),
              size = 6, colour = "grey25") +
    coord_flip() +
    scale_fill_manual(values = c("#377eb8", "#e41a1c")) +
    ggtitle("Number of identified genes") +
    xlab("Cluster") +
    theme(axis.title.x = element_blank(),
          axis.line = element_blank(),
          axis.ticks = element_blank(),
          axis.text.x = element_blank(),
          legend.position = "bottom")
```

We can also look at the full table of significant DE genes for each cluster.

```{r de-cluster-table, results = "hide"}
src_list <- lapply(0:(length(cluster.de.list) - 1), function(i) {
    src <- c("### {{i}} {.unnumbered}",
             "```{r de-cluster-{{i}}}",
             "cluster.de.list[[{{i}} + 1]]",
             "```",
             "")
    knit_expand(text = src)
})

out <- knit_child(text = unlist(src_list),
                  options = list(echo = FALSE, cache = FALSE))
```

`r out`

Traditional DE
==============

To see if there are any global differences between the datasets we are going to
perform traditional differential expression testing between the two groups.

```{r set-ident-DE, cache.lazy = FALSE}
combined <- SetAllIdent(combined, id = "Group")
```

```{r de}
de <- FindMarkers(combined, "Organoid", "Lindstrom", logfc.threshold = 0,
                  min.pct = 0.1, print.bar = FALSE)
```

```{r print-trad-de}
de <- de %>% rownames_to_column("gene")
de %>% top_n(10, avg_logFC) %>% data.frame
```

```{r reset-ident, cache.lazy = FALSE}
combined <- SetAllIdent(combined, id = "Cluster")
```

From these results we can identify an overall signature of the differences
between the datasets by selecting significant genes with an absolute log
foldchange greater than 0.5.

```{r de-sig}
de.sig <- de %>%
    filter(p_val_adj < 0.05, abs(avg_logFC) > 0.5) %>%
    pull(gene)
```

This identifies a signature with `r length(de.sig)` genes.

This signature can then be removed from the within cluster differential
expression results to better highlight biological differences.

```{r filter-cluster-de}
cluster.de.filt <- filter(cluster.de, !(gene %in% de.sig))

cluster.de.list.filt <- map(cluster.de.list,
                            function(x) {filter(x, !(Gene %in% de.sig))})
```

Gene plots
==========

Plots of known kidney genes we are specifically interested in.

```{r split-dotplot}
SplitDotPlotGG(combined, grouping.var = "Group",
               genes.plot = c("ZEB2", "PDGFRA", "PDGFRB", "MFAP4", "KCNE4",
                              "TCF12", "REN", "DLK1", "GATA3", "TCF21",
                              "PRRX1", "DCN", "PECAM1", "CDH5", "KDR", "PAX2",
                              "LYPD1", "DAPL1", "PODXL", "NPHS2", "MAFB",
                              "HMGB2", "CENPU", "SFRP2", "NFIA", "ASPN",
                              "CENPA", "IGFBP7", "EMX2", "MAL"),
               cols.use = c("#E41A1C", "#377EB8"),
               x.lab.rot = TRUE)
```

```{r split-dotplot2}
SplitDotPlotGG(combined, grouping.var = "Group",
               genes.plot = c("SULT1E1", "ACTA2", "TAGLN", "MAB21L2", "CXCL14",
                              "PRRX1", "REN", "DLK1", "GATA3", "CRABP1",
                              "FIBIN", "OGN", "PECAM1", "CDH5", "KDR", "LYPD1",
                              "DAPL1", "TMEM100", "PODXL", "NPHS2", "MAFB",
                              "HIST1H1A", "HMGB2", "CENPU", "DCN", "LUM",
                              "SFRP2", "IGFBP7", "EMX2", "MAL", "TTYH1",
                              "FABP7", "TUBB2B", "TYROBP", "LYZ", "S100A9",
                              "STMN2", "ATOH1", "HBG2", "HBG1",
                              "HBB"),
               cols.use = c("#E41A1C", "#377EB8"),
               x.lab.rot = TRUE)
```

```{r split-dotplot3}
SplitDotPlotGG(combined, grouping.var = "Group",
               genes.plot = c("HSPA1A", "DNAJB1", "FOS", "H3F3A", "EEF1A1",
                              "MARCKS"),
               cols.use = c("#E41A1C", "#377EB8"),
               x.lab.rot = TRUE)
```

```{r feature-plots}
FeaturePlot(combined, c("HSPA1A", "DNAJB1", "FOS", "H3F3A", "EEF1A1", "MARCKS"),
            cols.use = viridis(100), no.axes = TRUE)
```

Heat shock response
-------------------

```{r heatshock}
hs.genes <- read_tsv(here("data/response_to_heat_shock_BP.txt"),
                     col_names = c("gene", "description"),
                     col_types = cols(
                         gene = col_character(),
                         description = col_character()
                     ))

fc <- de$avg_logFC
idx <- rownames(de) %in% hs.genes$gene
barcodeplot(fc, idx)

idx <- rownames(combined@data) %in% hs.genes$gene
group.fac <- factor(combined@meta.data$Group,
                    levels = c("Organoid", "Lindstrom"))
des.mat <- cbind(Intercept = 1, Group = as.numeric(group.fac) - 1)
roast.res <- roast(combined@data, idx, des.mat)
```

A ROAST test for this gene set testing for up-regulation in the Lindstrom data
gives a p-value of `r roast.res$p.value["Up", "P.Value"]`.

Summary
=======

Parameters
----------

This table describes parameters used and set in this document.

```{r parameters}
params <- toJSON(list(
    list(
        Parameter = "resolutions",
        Value = resolutions,
        Description = "Range of possible clustering resolutions"
    ),
    list(
        Parameter = "res",
        Value = res,
        Description = "Selected resolution parameter for clustering"
    ),
    list(
        Parameter = "n.clusts",
        Value = n.clusts,
        Description = "Number of clusters produced by selected resolution"
    ),
    list(
        Parameter = "skipped",
        Value = paste(seq(0, n.clusts - 1))[skip],
        Description = "Clusters skipped for conserved marker and DE testing"
    ),
    list(
        Parameter = "n.sig",
        Value = length(de.sig),
        Description = "Number of genes in the group DE signature"
    )
), pretty = TRUE)

kable(fromJSON(params))
```

Output files
------------

This table describes the output files produced by this document. Right click
and _Save Link As..._ to download the results.

```{r write-combined}
write_rds(combined, here("data/processed/Combined_clustered.Rds"))
```

```{r cluster-details}
expr <- AverageExpression(combined, show.progress = FALSE) %>%
    rename_all(function(x) {paste0("Mean", x)}) %>%
    rownames_to_column("Gene")

prop <- AverageDetectionRate(combined) %>%
    rename_all(function(x) {paste0("Prop", x)}) %>%
    rownames_to_column("Gene")

alt.cols <- c(rbind(colnames(prop), colnames(expr)))[-1]

cluster.expr <- expr %>%
    left_join(prop, by = "Gene") %>%
    select(alt.cols)

cluster.assign <- combined@meta.data %>%
    select(Cell, Dataset, Sample, Barcode, Cluster)
```

```{r output}
dir.create(here("output", DOCNAME), showWarnings = FALSE)

write_lines(params, here("output", DOCNAME, "parameters.json"))
write_csv(cluster.assign, here("output", DOCNAME, "cluster_assignments.csv"))
write_csv(cluster.expr, here("output", DOCNAME, "cluster_expression.csv"))
write_csv(markers, here("output", DOCNAME, "markers.csv"))
write_xlsx(markers.list, here("output", DOCNAME, "markers.xlsx"))
write_csv(con.markers, here("output", DOCNAME, "conserved_markers.csv"))
write_xlsx(con.markers.list, here("output", DOCNAME, "conserved_markers.xlsx"))
write_csv(cluster.de, here("output", DOCNAME, "cluster_de.csv"))
write_xlsx(cluster.de.list, here("output", DOCNAME, "cluster_de.xlsx"))
write_csv(de, here("output", DOCNAME, "group_de.csv"))
write_csv(data.frame(Gene = de.sig),
          here("output", DOCNAME, "de_signature.csv"))
write_csv(cluster.de.filt, here("output", DOCNAME, "cluster_de_filtered.csv"))
write_xlsx(cluster.de.list.filt,
           here("output", DOCNAME, "cluster_de_filtered.xlsx"))

kable(data.frame(
    File = c(
        glue("[parameters.json]({getDownloadURL('parameters.json', DOCNAME)})"),
        glue("[cluster_assignments.csv]",
             "({getDownloadURL('cluster_assignments.csv', DOCNAME)})"),
        glue("[cluster_expression.csv]",
             "({getDownloadURL('cluster_expression.csv', DOCNAME)})"),
        glue("[markers.csv]({getDownloadURL('markers.csv', DOCNAME)})"),
        glue("[markers.xlsx]({getDownloadURL('markers.xlsx', DOCNAME)})"),
        glue("[conserved_markers.csv]",
             "({getDownloadURL('conserved_markers.csv', DOCNAME)})"),
        glue("[conserved_markers.xlsx]",
             "({getDownloadURL('conserved_markers.xlsx', DOCNAME)})"),
        glue("[cluster_de.csv]",
             "({getDownloadURL('cluster_de.csv', DOCNAME)})"),
        glue("[cluster_de.xlsx]",
             "({getDownloadURL('cluster_de.xlsx', DOCNAME)})"),
        glue("[group_de.csv]({getDownloadURL('group_de.csv', DOCNAME)})"),
        glue("[de_signature.csv]",
             "({getDownloadURL('de_signature.csv', DOCNAME)})"),
        glue("[cluster_de_filtered.csv]",
             "({getDownloadURL('cluster_de_filtered.csv', DOCNAME)})"),
        glue("[cluster_de_filtered.xlsx]",
             "({getDownloadURL('cluster_de_filtered.xlsx', DOCNAME)})")
    ),
    Description = c(
        "Parameters set and used in this analysis",
        "Cluster assignments for each cell",
        "Cluster expression for each gene",
        "Results of marker gene testing in CSV format",
        paste("Results of marker gene testing in XLSX format with one tab",
              "per cluster"),
        "Results of conserved marker gene testing in CSV format",
        paste("Results of conserved marker gene testing in XLSX format with",
              "one tab per cluster"),
        paste("Results of within cluster differential expression testing",
             "in CSV format"),
        paste("Results of within cluster differential expression testing",
             "in XLSX format with one cluster per tab"),
        paste("Results of between group differential expression testing",
             "in CSV format"),
        "Between group differential expression signature genes",
        paste("Results of within cluster differential expression testing",
             "after removing group DE signature in CSV format"),
        paste("Results of within cluster differential expression testing",
             "after removing group DE signature in XLSX format with one",
             "cluster per tab")
        )
    )
)
```