Last updated: 2020-09-11

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File	Version	Author	Date	Message
Rmd	761346b	khembach	2020-09-11	redo plots
html	29d2b15	khembach	2020-09-11	Build site.
Rmd	dfbc9a8	khembach	2020-09-11	Conos analysis with different parameters and label propagation

## set seed for reprocibility
set.seed(1)

Load packages

library(dplyr)
library(Seurat)
library(SingleCellExperiment)
library(pagoda2)
library(conos)
library(data.table)
library(magrittr)
library(ggplot2)

Preprocessing

n_cores <- 20 
sce_file <- file.path("output", "sce_06-1-prepare-sce.rds")
sce <- readRDS(sce_file)
cols_dt <- as.data.table(colData(sce))
cols_dt$cell_id <- rownames(colData(sce))

sample_list <- as.character(unique(sce$sample_id))
## Pagoda2 requires dgCMatrix matrix as input
counts_list <- lapply(sample_list, function(s)
  # as(counts(sce[, colData(sce)$sample_id == s]), "dgCMatrix"))
  counts(sce[, colData(sce)$sample_id == s]))
names(counts_list) <- sample_list
# check if cell names will be unique
stopifnot(any(duplicated(unlist(lapply(counts_list,colnames)))) == FALSE) 
## we do not filter lowly expressed genes
counts_proc <- lapply(counts_list, basicP2proc, 
                     n.cores = n_cores, nPcs = 50, min.cells.per.gene = 0, 
                     n.odgenes = 2e3, get.largevis = FALSE, get.tsne = FALSE, 
                     make.geneknn = FALSE)

8331 cells, 17890 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 365 overdispersed genes ... 365persisting ... done.
running PCA using 2000 OD genes .... done
8408 cells, 17890 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 369 overdispersed genes ... 369persisting ... done.
running PCA using 2000 OD genes .... done
8687 cells, 17890 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 1126 overdispersed genes ... 1126persisting ... done.
running PCA using 2000 OD genes .... done
7438 cells, 17890 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 1230 overdispersed genes ... 1230persisting ... done.
running PCA using 2000 OD genes .... done
3538 cells, 17890 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 1444 overdispersed genes ... 1444persisting ... done.
running PCA using 2000 OD genes .... done
4595 cells, 17890 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 1884 overdispersed genes ... 1884persisting ... done.
running PCA using 2000 OD genes .... done
1943 cells, 17890 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 172 overdispersed genes ... 172persisting ... done.
running PCA using 2000 OD genes .... done
2357 cells, 17890 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 186 overdispersed genes ... 186persisting ... done.
running PCA using 2000 OD genes .... done
2445 cells, 17890 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 264 overdispersed genes ... 264persisting ... done.
running PCA using 2000 OD genes .... done
855 cells, 17890 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 135 overdispersed genes ... 135persisting ... done.
running PCA using 2000 OD genes .... done
1871 cells, 17890 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 619 overdispersed genes ... 619persisting ... done.
running PCA using 2000 OD genes .... done
886 cells, 17890 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 307 overdispersed genes ... 307persisting ... done.
running PCA using 2000 OD genes .... done
920 cells, 17890 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 272 overdispersed genes ... 272persisting ... done.
running PCA using 2000 OD genes .... done
443 cells, 17890 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 138 overdispersed genes ... 138persisting ... done.
running PCA using 2000 OD genes .... done
2416 cells, 17890 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 670 overdispersed genes ... 670persisting ... done.
running PCA using 2000 OD genes .... done
2654 cells, 17890 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 659 overdispersed genes ... 659persisting ... done.
running PCA using 2000 OD genes .... done
4592 cells, 17890 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 702 overdispersed genes ... 702persisting ... done.
running PCA using 2000 OD genes .... done
3464 cells, 17890 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 712 overdispersed genes ... 712persisting ... done.
running PCA using 2000 OD genes .... done
4857 cells, 17890 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 634 overdispersed genes ... 634persisting ... done.
running PCA using 2000 OD genes .... done
4307 cells, 17890 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 784 overdispersed genes ... 784persisting ... done.
running PCA using 2000 OD genes .... done
2553 cells, 17890 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 773 overdispersed genes ... 773persisting ... done.
running PCA using 2000 OD genes .... done
3120 cells, 17890 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 722 overdispersed genes ... 722persisting ... done.
running PCA using 2000 OD genes .... done
3815 cells, 17890 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 404 overdispersed genes ... 404persisting ... done.
running PCA using 2000 OD genes .... done

con <- Conos$new(counts_proc, n.cores = n_cores)

Conos Pipeline - default parameters

Build joint graph

# define output files
clusts_file <- file.path("output", "conos", "conos_clusts_default.txt")
viz_file <- file.path("output", "conos", "conos_viz_default.txt")
umap_file <- file.path("output", "conos","conos_umap_default.txt")
graph_file <- file.path("output", "conos","conos_graph_default.txt")

# build joint graph
con$buildGraph(space = "PCA")

found 0 out of 253 cached PCA  space pairs ... running 253 additional PCA  space pairs  done
inter-sample links using  mNN   done
local pairs local pairs  done
building graph ..done

# find communities using Leiden community detection
res_list <- list(1, 1.2, 1.4, 1.6)
clusts_ls <- lapply(res_list, function(res) {
        con$findCommunities(method = leiden.community, resolution = res)
        con$clusters$leiden$groups})
## table with cell ID and cluster ID per resolution
conos_clusters <- do.call(cbind, clusts_ls) %>% 
    set_colnames(paste0('conos', res_list)) %>%
    data.table %>%
    .[, cell_id := names(con$clusters$leiden$groups)] %>%
    setcolorder('cell_id')        
# fwrite(conos_clusters, clusts_file)

Graph embeddings

We embed the joint graph with two different methods: largeVis and UMAP.

## graph embedding: largeVis visualization
## using default parameters
con$embedGraph(method = 'largeVis')

Estimating embeddings.

viz_dt <-  data.table(cell_id = rownames(con$embedding), con$embedding)
setnames(viz_dt, names(viz_dt), c("cell_id", "viz1", "viz2"))
# fwrite(viz_dt, viz_file)

## UMAP visualization
con$embedGraph(method = "UMAP", n.cores = n_cores)

Convert graph to adjacency list...
Done
Estimate nearest neighbors and commute times...
Estimating hitting distances: 19:57:28.
Done.
Estimating commute distances: 19:58:02.
Hashing adjacency list: 19:58:02.
Done.
Estimating distances: 19:58:39.
Done
Done.
All done!: 19:59:57.
Done
Estimate UMAP embedding...
Done

umap_dt <- data.table(cell_id = rownames(con$embedding), con$embedding)
setnames(umap_dt, names(umap_dt), c("cell_id", "umap1", "umap2"))
# fwrite(umap_dt, umap_file)

We define a plotting function to visualize the embeddings.

## function to plot the graph embedding
plot_conos <- function(dat, title = "", x = "", y = "", color = "sample_id") {
  p <- ggplot(dat, aes(x = get(x), y = get(y), color = as.factor(get(color)))) + 
    geom_point(alpha = 0.5) +
    scale_color_discrete(name = color) + 
    ggtitle(title)  +
    labs(x = x, y = y) +
    theme_bw() + 
    theme(aspect.ratio = 1) +
    guides(col = guide_legend(nrow = 16, 
                              override.aes = list(size = 3, alpha = 1)))
  print(p)
}

And merge all results in a data.table.

## Function for merging the data.tables and organizing the factors for coloring
prepare_dt <- function(cols_dt, viz_dt, umap_dt, conos_clusters, size = 1e+04){
  dat <- viz_dt %>% full_join(umap_dt) %>% 
  full_join(conos_clusters) %>%
  full_join(cols_dt) 

  ## label our cells with the group_id in the organoid metadata columns
  dat$Stage <- ifelse(is.na(dat$Stage), dat$group_id, dat$Stage)
  ## reorder factor levels for plotting
  dat$group_id <- factor(dat$group_id, 
                         levels = c("P22", "D52", "D96", "H9", "409b2"))
  ## order levels according to experiment timeline (Fig. 1a)
  dat$Stage <- factor(dat$Stage, levels = c("P22", "D52", "D96", "iPSCs", "EB", 
                                            "Neuroectoderm", "Neuroepithelium",
                                            "Organoid-1M", "Organoid-2M", 
                                            "Organoid-4M"))
  ## merge the lineage labels of identical cell types
  dat$cl_FullLineage <- as.factor(dat$cl_FullLineage)
  levels(dat$cl_FullLineage) <- c("choroid plexus/mesenchymal-like cells",  
                   "cortical neurons",  "cortical neurons", 
                   "cycling dorsal progenitors", "cycling ventral progenitors", 
                   "ectodermal/neuroectodermal-like cells", 
                   "gliogenic/outer RGCs and astrocytes",
                   "IPs and early cortical neurons", "midbrain/hindbrain cells", 
                   "neuroepithelial-like cells", "retina progenitors", "RGCs", 
                   "RGCs early", "RGCs early", "stem cells", "stem cells", 
                   "stem cells", "ventral progenitors and neurons", 
                   "ventral progenitors and neurons", 
                   "ventral progenitors and neurons")
  ## convert columns to factor for plotting
  dat <- dat %>% mutate_if(is.character, as.factor)

  ## we only plot a random sub sample of cells
  selected <- sample(nrow(dat), size = size)
  dat <- dat[selected,]
}

dat <- prepare_dt(cols_dt, viz_dt, umap_dt, conos_clusters, size = 1e+04)

largeVis

## plot the embedding
for(res in names(dat)[startsWith(names(dat), "conos")]){
  cat("#### ", res, "\n")
  plot_conos(dat, title = "largeVis", x = "viz1", y = "viz2", color = res)
  cat("\n\n")
}

conos1

conos1.2

conos1.4

conos1.6

for(g in c("sample_id", "group_id", "Stage", "cl_FullLineage")){
  cat("#### ", g, "\n")
  plot_conos(dat, title = "largeVis", x = "viz1", y = "viz2", color = g)
  cat("\n\n")
}

sample_id

group_id

Stage

cl_FullLineage

UMAP

for(res in names(dat)[startsWith(names(dat), "conos")]){
  cat("#### ", res, "\n")
  plot_conos(dat, title = "UMAP", x = "umap1", y = "umap2", color = res)
  cat("\n\n")
}

conos1

conos1.2

conos1.4

conos1.6

for(g in c("sample_id", "group_id", "Stage", "cl_FullLineage")){
  cat("#### ", g, "\n")
  plot_conos(dat, title = "UMAP", x = "umap1", y = "umap2", color = g)
  cat("\n\n")
}

sample_id

group_id

Stage

cl_FullLineage

Conos with different parameters

All our samples were measured with 10X genomics and "genes" space is supposed to give better resolution for such (simpler) cases. The overdispersed gene space is used for graph construction instead of PCs. However, the resulting plots (not shown) are still clearly separated.

CPCA space

CPCA space should provide more accurate alignment under greater dataset-specific distortions.

Build joint graph

# build joint graph
con$buildGraph(space = "CPCA")

found 0 out of 253 cached CPCA  space pairs ... running 253 additional CPCA  space pairs  done
inter-sample links using  mNN   done
local pairs local pairs  done
building graph ..done

# find communities using Leiden community detection
clusts_ls <- lapply(res_list, function(res) {
        con$findCommunities(method = leiden.community, resolution = res)
        con$clusters$leiden$groups})
## table with cell ID and cluster ID per resolution
conos_clusters <- do.call(cbind, clusts_ls) %>% 
    set_colnames(paste0('conos', res_list)) %>%
    data.table %>%
    .[, cell_id := names(con$clusters$leiden$groups)] %>%
    setcolorder('cell_id')        
# fwrite(conos_clusters, clusts_file)

Graph embeddings

We embed the joint graph with two different methods: largeVis and UMAP.

## graph embedding: largeVis visualization
con$embedGraph(method = 'largeVis')

Estimating embeddings.

viz_dt <-  data.table(cell_id = rownames(con$embedding), con$embedding)
setnames(viz_dt, names(viz_dt), c("cell_id", "viz1", "viz2"))
# fwrite(viz_dt, viz_file)

## UMAP visualization
con$embedGraph(method = "UMAP", n.cores = n_cores)

Convert graph to adjacency list...
Done
Estimate nearest neighbors and commute times...
Estimating hitting distances: 20:39:22.
Done.
Estimating commute distances: 20:40:14.
Hashing adjacency list: 20:40:14.
Done.
Estimating distances: 20:41:19.
Done
Done.
All done!: 20:42:45.
Done
Estimate UMAP embedding...
Done

umap_dt <- data.table(cell_id = rownames(con$embedding), con$embedding)
setnames(umap_dt, names(umap_dt), c("cell_id", "umap1", "umap2"))
# fwrite(umap_dt, umap_file)

And merge all results in a data.table.

dat <- prepare_dt(cols_dt, viz_dt, umap_dt, conos_clusters, size = 1e+04)

largeVis

## plot the embedding
for(res in names(dat)[startsWith(names(dat), "conos")]){
  cat("#### ", res, "\n")
  plot_conos(dat, title = "largeVis", x = "viz1", y = "viz2", color = res)
  cat("\n\n")
}

conos1

conos1.2

conos1.4

conos1.6

for(g in c("sample_id", "group_id", "Stage", "cl_FullLineage")){
  cat("#### ", g, "\n")
  plot_conos(dat, title = "largeVis", x = "viz1", y = "viz2", color = g)
  cat("\n\n")
}

sample_id

group_id

Stage

cl_FullLineage

UMAP

for(res in names(dat)[startsWith(names(dat), "conos")]){
  cat("#### ", res, "\n")
  plot_conos(dat, title = "UMAP", x = "umap1", y = "umap2", color = res)
  cat("\n\n")
}

conos1

conos1.2

conos1.4

conos1.6

for(g in c("sample_id", "group_id", "Stage", "cl_FullLineage")){
  cat("#### ", g, "\n")
  plot_conos(dat, title = "UMAP", x = "umap1", y = "umap2", color = g)
  cat("\n\n")
}

sample_id

group_id

Stage

cl_FullLineage

CCA space

CCA space optimizes conservation of correlation between datasets and can give yield very good alignments in low-similarity cases (e.g. large evolutionary distances).

Build joint graph

# build joint graph
con$buildGraph(space = "CCA")

found 0 out of 253 cached CCA  space pairs ... running 253 additional CCA  space pairs  done
inter-sample links using  mNN   done
local pairs local pairs  done
building graph ..done

# find communities using Leiden community detection
clusts_ls <- lapply(res_list, function(res) {
        con$findCommunities(method = leiden.community, resolution = res)
        con$clusters$leiden$groups})
## table with cell ID and cluster ID per resolution
conos_clusters <- do.call(cbind, clusts_ls) %>% 
    set_colnames(paste0('conos', res_list)) %>%
    data.table %>%
    .[, cell_id := names(con$clusters$leiden$groups)] %>%
    setcolorder('cell_id')        
# fwrite(conos_clusters, clusts_file)

Graph embeddings

We embed the joint graph with two different methods: largeVis and UMAP.

## graph embedding: largeVis visualization
con$embedGraph(method = 'largeVis')

Estimating embeddings.

viz_dt <-  data.table(cell_id = rownames(con$embedding), con$embedding)
setnames(viz_dt, names(viz_dt), c("cell_id", "viz1", "viz2"))
# fwrite(viz_dt, viz_file)

## UMAP visualization
con$embedGraph(method = "UMAP", n.cores = n_cores)

Convert graph to adjacency list...
Done
Estimate nearest neighbors and commute times...
Estimating hitting distances: 21:03:25.
Done.
Estimating commute distances: 21:04:12.
Hashing adjacency list: 21:04:12.
Done.
Estimating distances: 21:04:49.
Done
Done.
All done!: 21:06:13.
Done
Estimate UMAP embedding...
Done

umap_dt <- data.table(cell_id = rownames(con$embedding), con$embedding)
setnames(umap_dt, names(umap_dt), c("cell_id", "umap1", "umap2"))
# fwrite(umap_dt, umap_file)

And merge all results in a data.table.

dat <- prepare_dt(cols_dt, viz_dt, umap_dt, conos_clusters, size = 1e+04)

largeVis

## plot the embedding
for(res in names(dat)[startsWith(names(dat), "conos")]){
  cat("#### ", res, "\n")
  plot_conos(dat, title = "largeVis", x = "viz1", y = "viz2", color = res)
  cat("\n\n")
}

conos1

conos1.2

conos1.4

conos1.6

for(g in c("sample_id", "group_id", "Stage", "cl_FullLineage")){
  cat("#### ", g, "\n")
  plot_conos(dat, title = "largeVis", x = "viz1", y = "viz2", color = g)
  cat("\n\n")
}

sample_id

group_id

Stage

cl_FullLineage

UMAP

for(res in names(dat)[startsWith(names(dat), "conos")]){
  cat("#### ", res, "\n")
  plot_conos(dat, title = "UMAP", x = "umap1", y = "umap2", color = res)
  cat("\n\n")
}

conos1

conos1.2

conos1.4

conos1.6

for(g in c("sample_id", "group_id", "Stage", "cl_FullLineage")){
  cat("#### ", g, "\n")
  plot_conos(dat, title = "UMAP", x = "umap1", y = "umap2", color = g)
  cat("\n\n")
}

sample_id

group_id

Stage

cl_FullLineage

Embedding parameters

We choose CPCA space for building the graph and try different parameters for the largeVis and UMAP embedding. For largeVis we test larger alpha for tighter clusters and increased scd_batches to avoid that clusters intersect. For UMAP, we test lower min.dist which should lead to a more even dispersal of points and less clumped clusters.

We save the results to files.

# define output files
clusts_file <- file.path("output", "conos", "conos_clusts_CPCA.txt")
viz_file <- file.path("output", "conos", "conos_viz_CPCA.txt")
umap_file <- file.path("output", "conos","conos_umap_CPCA.txt")
graph_file <- file.path("output", "conos","conos_graph_CPCA.txt")
label_file <- file.path("output", "conos","conos_labels_CPCA.txt")
label_distr_file <- file.path("output", "conos","conos_label_distr_CPCA.txt")

Build joint graph using CPCA space

# build joint graph
con$buildGraph(space = "CPCA")

found 253 out of 253 cached CPCA  space pairs ...  done
inter-sample links using  mNN   done
local pairs local pairs  done
building graph ..done

# find communities using Leiden community detection
res_list <- list(1, 1.2, 1.4, 1.6)
clusts_ls <- lapply(res_list, function(res) {
        con$findCommunities(method = leiden.community, resolution = res)
        con$clusters$leiden$groups})
## table with cell ID and cluster ID per resolution
conos_clusters <- do.call(cbind, clusts_ls) %>% 
    set_colnames(paste0('conos', res_list)) %>%
    data.table %>%
    .[, cell_id := names(con$clusters$leiden$groups)] %>%
    setcolorder('cell_id')        
fwrite(conos_clusters, clusts_file)

Graph embeddings

We embed the joint graph with two different methods: largeVis and UMAP. Testing parameters alpha = 0.5, sgd_batches = 5e+08 and min.dist = 0.01.

## graph embedding: largeVis visualization
## Decreasing alpha results in less compressed clusters, and increasing 
## sgd_batches often helps to avoid cluster intersections and spread out the 
## clusters
con$embedGraph(method = 'largeVis', alpha = 0.5, sgd_batches = 5e+08)

Estimating embeddings.

viz_dt <-  data.table(cell_id = rownames(con$embedding), con$embedding)
setnames(viz_dt, names(viz_dt), c("cell_id", "viz1", "viz2"))
fwrite(viz_dt, viz_file)
## UMAP visualization
## the most important parameters are spread and min.dist which together control 
## how tight the clusters are
con$embedGraph(method = "UMAP", n.cores = n_cores, min.dist = 0.01, spread = 15)

Convert graph to adjacency list...
Done
Estimate nearest neighbors and commute times...
Estimating hitting distances: 21:12:46.
Done.
Estimating commute distances: 21:13:28.
Hashing adjacency list: 21:13:28.
Done.
Estimating distances: 21:13:51.
Done
Done.
All done!: 21:14:29.
Done
Estimate UMAP embedding...
Done

umap_dt <- data.table(cell_id = rownames(con$embedding), con$embedding)
setnames(umap_dt, names(umap_dt), c("cell_id", "umap1", "umap2"))
fwrite(umap_dt, umap_file)

And merge all results in a data.table.

dat <- prepare_dt(cols_dt, viz_dt, umap_dt, conos_clusters, size = 1e+04)

largeVis

## plot the embedding
for(res in names(dat)[startsWith(names(dat), "conos")]){
  cat("#### ", res, "\n")
  plot_conos(dat, title = "largeVis", x = "viz1", y = "viz2", color = res)
  cat("\n\n")
}

conos1

conos1.2

conos1.4

conos1.6

for(g in c("sample_id", "group_id", "Stage", "cl_FullLineage")){
  cat("#### ", g, "\n")
  plot_conos(dat, title = "largeVis", x = "viz1", y = "viz2", color = g)
  cat("\n\n")
}

sample_id

group_id

Stage

cl_FullLineage

UMAP

for(res in names(dat)[startsWith(names(dat), "conos")]){
  cat("#### ", res, "\n")
  plot_conos(dat, title = "UMAP", x = "umap1", y = "umap2", color = res)
  cat("\n\n")
}

conos1

conos1.2

conos1.4

conos1.6

for(g in c("sample_id", "group_id", "Stage", "cl_FullLineage")){
  cat("#### ", g, "\n")
  plot_conos(dat, title = "UMAP", x = "umap1", y = "umap2", color = g)
  cat("\n\n")
}

sample_id

group_id

Stage

cl_FullLineage

Label propagation

We want to propagate the cell annotations cl_FullLineage from the organoid dataset onto our cells.

labels <- cols_dt$cl_FullLineage
label_idx <- !is.na(labels)
labels <- labels[label_idx]
labels <- as.factor(labels)
levels(labels) <- c("choroid plexus/mesenchymal-like cells",  
                   "cortical neurons",  "cortical neurons", 
                   "cycling dorsal progenitors", "cycling ventral progenitors", 
                   "ectodermal/neuroectodermal-like cells", 
                   "gliogenic/outer RGCs and astrocytes",
                   "IPs and early cortical neurons", "midbrain/hindbrain cells", 
                   "neuroepithelial-like cells", "retina progenitors", "RGCs", 
                   "RGCs early", "RGCs early", "stem cells", "stem cells", 
                   "stem cells", "ventral progenitors and neurons", 
                   "ventral progenitors and neurons", 
                   "ventral progenitors and neurons")
labels <- setNames(labels, cols_dt$cell_id[label_idx])
new_label <- con$propagateLabels(labels = labels, verbose = TRUE)

label_df <-  data.table(cell_id = names(new_label$labels), new_label$labels, 
                        new_label$uncertainty)
setnames(label_df, names(label_df), c("cell_id", "label", "uncertainty"))
fwrite(label_df, label_file)
## distribution of labels per cell
label_dist <- data.table(cell_id = rownames(new_label$label.distribution), 
                         new_label$label.distribution)
fwrite(label_dist, label_distr_file)

UMAP with propagated labels

We plot the propagated labels and the uncertainty.

dat <- dat %>% left_join(label_df)
for(g in c("sample_id", "group_id", "Stage", "cl_FullLineage", "label")){
  cat("### ", g, "\n")
  plot_conos(dat, title = "UMAP", x = "umap1", y = "umap2", color = g)
  cat("\n\n")
}

sample_id

Version	Author	Date
29d2b15	khembach	2020-09-11

group_id

Version	Author	Date
29d2b15	khembach	2020-09-11

Stage

Version	Author	Date
29d2b15	khembach	2020-09-11

cl_FullLineage

Version	Author	Date
29d2b15	khembach	2020-09-11

label

Version	Author	Date
29d2b15	khembach	2020-09-11

cat("### uncertainty\n")

uncertainty

p <- ggplot(dat, aes(x = umap1, y = umap2, color = uncertainty)) + 
  geom_point(alpha = 0.5) +
  scale_colour_gradient(name = "uncertainty", low = "grey", high = "red") + 
  ggtitle("UMAP")  +
  theme_bw() + 
  theme(aspect.ratio = 1) +
  guides(col = guide_legend(nrow = 16, 
                            override.aes = list(size = 3, alpha = 1)))
print(p)

Version	Author	Date
29d2b15	khembach	2020-09-11

cat("\n\n")

sessionInfo()

R version 4.0.0 (2020-04-24)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.6 LTS

Matrix products: default
BLAS:   /usr/local/R/R-4.0.0/lib/libRblas.so
LAPACK: /usr/local/R/R-4.0.0/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats4    parallel  stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] ggplot2_3.3.2               magrittr_1.5               
 [3] data.table_1.12.8           conos_1.3.0                
 [5] pagoda2_0.1.1               igraph_1.2.5               
 [7] Matrix_1.2-18               SingleCellExperiment_1.10.1
 [9] SummarizedExperiment_1.18.1 DelayedArray_0.14.0        
[11] matrixStats_0.56.0          Biobase_2.48.0             
[13] GenomicRanges_1.40.0        GenomeInfoDb_1.24.2        
[15] IRanges_2.22.2              S4Vectors_0.26.1           
[17] BiocGenerics_0.34.0         Seurat_3.1.5               
[19] dplyr_1.0.2                 workflowr_1.6.2            

loaded via a namespace (and not attached):
  [1] Rtsne_0.15             colorspace_1.4-1       rjson_0.2.20          
  [4] ellipsis_0.3.1         ggridges_0.5.2         rprojroot_1.3-2       
  [7] XVector_0.28.0         base64enc_0.1-3        fs_1.4.2              
 [10] farver_2.0.3           leiden_0.3.3           listenv_0.8.0         
 [13] urltools_1.7.3         ggrepel_0.8.2          codetools_0.2-16      
 [16] splines_4.0.0          knitr_1.29             jsonlite_1.7.0        
 [19] ica_1.0-2              cluster_2.1.0          png_0.1-7             
 [22] uwot_0.1.8             shiny_1.5.0            sctransform_0.2.1     
 [25] compiler_4.0.0         httr_1.4.1             backports_1.1.9       
 [28] fastmap_1.0.1          lazyeval_0.2.2         later_1.1.0.1         
 [31] htmltools_0.5.0        tools_4.0.0            rsvd_1.0.3            
 [34] gtable_0.3.0           glue_1.4.2             GenomeInfoDbData_1.2.3
 [37] RANN_2.6.1             reshape2_1.4.4         rappdirs_0.3.1        
 [40] Rcpp_1.0.5             vctrs_0.3.4            ape_5.4               
 [43] nlme_3.1-148           lmtest_0.9-37          sccore_0.1.0          
 [46] xfun_0.15              stringr_1.4.0          globals_0.12.5        
 [49] mime_0.9               lifecycle_0.2.0        irlba_2.3.3           
 [52] dendextend_1.14.0      future_1.17.0          zlibbioc_1.34.0       
 [55] MASS_7.3-51.6          zoo_1.8-8              scales_1.1.1          
 [58] promises_1.1.1         RColorBrewer_1.1-2     yaml_2.2.1            
 [61] reticulate_1.16        pbapply_1.4-2          gridExtra_2.3         
 [64] triebeard_0.3.0        stringi_1.4.6          Rook_1.1-1            
 [67] rlang_0.4.7            pkgconfig_2.0.3        bitops_1.0-6          
 [70] evaluate_0.14          lattice_0.20-41        ROCR_1.0-11           
 [73] purrr_0.3.4            labeling_0.3           patchwork_1.0.1       
 [76] htmlwidgets_1.5.1      cowplot_1.0.0          tidyselect_1.1.0      
 [79] RcppAnnoy_0.0.16       plyr_1.8.6             R6_2.4.1              
 [82] generics_0.0.2         withr_2.2.0            pillar_1.4.6          
 [85] whisker_0.4            fitdistrplus_1.1-1     survival_3.2-3        
 [88] RCurl_1.98-1.2         tibble_3.0.3           future.apply_1.6.0    
 [91] tsne_0.1-3             crayon_1.3.4           KernSmooth_2.23-17    
 [94] plotly_4.9.2.1         rmarkdown_2.3          viridis_0.5.1         
 [97] grid_4.0.0             git2r_0.27.1           digest_0.6.25         
[100] xtable_1.8-4           tidyr_1.1.0            httpuv_1.5.4          
[103] brew_1.0-6             munsell_0.5.0          viridisLite_0.3.0

Conos analysis

Katharina Hembach

9/10/2020

Load packages

Preprocessing

Conos Pipeline - default parameters

Build joint graph

Graph embeddings

largeVis

conos1

conos1.2

conos1.4

conos1.6

sample_id

group_id

Stage

cl_FullLineage

UMAP

conos1

conos1.2

conos1.4

conos1.6

sample_id

group_id

Stage

cl_FullLineage

Conos with different parameters

CPCA space

Build joint graph

Graph embeddings

largeVis

conos1

conos1.2

conos1.4

conos1.6

sample_id

group_id

Stage

cl_FullLineage

UMAP

conos1

conos1.2

conos1.4

conos1.6

sample_id

group_id

Stage

cl_FullLineage

CCA space

Build joint graph

Graph embeddings

largeVis

conos1

conos1.2

conos1.4

conos1.6

sample_id

group_id

Stage

cl_FullLineage

UMAP

conos1

conos1.2

conos1.4

conos1.6

sample_id

group_id

Stage

cl_FullLineage

Embedding parameters

Build joint graph using CPCA space

Graph embeddings

largeVis

conos1

conos1.2

conos1.4

conos1.6

sample_id

group_id

Stage