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Rmd 5c8e634 viktor_petukhov 2020-07-08 Fixed dates in fig_ notebooks
Rmd 10f9bdc viktor_petukhov 2020-07-07 Smart-seq notebook 
library(pagoda2)
library(conos)
library(magrittr)
library(ggplot2)
library(pbapply)
library(tidyverse)
library(cowplot)
library(readr)
library(scrattch.io)
library(ggalluvial)

devtools::load_all()
theme_set(theme_bw())

outPath <- function(...) OutputPath("fig_smart_seq", ...)
allenDataPath <- function(...) file.path("~/mh/Data/allen_human_cortex/", ...)

Load data

con <- CachePath("con_filt_cells.rds") %>% read_rds()

sample_per_cell <- con$getDatasetPerCell()

annotation_by_level <- read_csv(MetadataPath("annotation.csv"))
annotation <- annotation_by_level %$% setNames(l4, cell) %>% .[names(sample_per_cell)] %>% 
  .[. != "Excluded"]

neuron_type_per_type <- ifelse(grepl("L[2-6].+", unique(annotation)), "Excitatory", "Inhibitory") %>% 
  setNames(unique(annotation))
type_order <- names(neuron_type_per_type)[order(neuron_type_per_type, names(neuron_type_per_type))]

Alignment to our Smart-Seq dataset

so <- readRDS("/d0-mendel/home/demharters/R/projects/UPF9_14_17_19_22_23_24_32_33/seurat_upf.rds")
annot_old <- so@meta.data %$% setNames(subtypesKU, rownames(.))
cm_ss <- so@raw.data
dim(cm_ss)
[1] 30632   955
median(Matrix::colSums(cm_ss))
[1] 1796751
median(Matrix::colSums(cm_ss > 0))
[1] 10302
dim(cm_ss)
[1] 30632   955
Matrix::colSums(cm_ss > 0) %>% qplot(xlab="#Genes", ylab="#Cells")
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Matrix::colSums(cm_ss) %>% log10() %>% qplot()
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

p2_ss <- GetPagoda(cm_ss)
955 cells, 30632 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 5012 overdispersed genes ... 5012persisting ... done.
running PCA using 1000 OD genes .... done
calculating distance ... pearson ...running tSNE using 30 cores:
con_ss <- Conos$new(c(con$samples, list(SS2=p2_ss)), n.cores=40)
con_ss$buildGraph(verbose=T, var.scale=T, k=15, k.self=5, k.self.weight=0.1)
found 0 out of 190 cached PCA  space pairs ... running 190 additional PCA  space pairs  done
inter-sample links using  mNN   done
local pairs local pairs  done
building graph ..done
con_ss$findCommunities(method=conos::leiden.community, resolution=10)
con_ss$embedGraph(method="UMAP", spread=1.5, min.dist=1, verbose=T, n.cores=30, min.prob.lower=1e-5)
Convert graph to adjacency list...
Done
Estimate nearest neighbors and commute times...
Estimating hitting distances: 05:50:41.
Done.
Estimating commute distances: 05:50:52.
Hashing adjacency list: 05:50:52.
Done.
Estimating distances: 05:51:23.
Done
Done.
All done!: 05:51:47.
Done
Estimate UMAP embedding...
05:51:48 UMAP embedding parameters a = 0.1034 b = 1.524
05:51:48 Read 102937 rows and found 1 numeric columns
05:51:50 Commencing smooth kNN distance calibration using 30 threads
05:51:56 Initializing from normalized Laplacian + noise
05:52:22 Commencing optimization for 1000 epochs, with 3928774 positive edges using 30 threads
05:52:58 Optimization finished
Done
write_rds(con_ss, CachePath("con_ss.rds"))

con_ss <- CachePath("con_ss.rds") %>% read_rds()
annot_ss_new <- con_ss$propagateLabels(annotation, max.iters=50, verbose=T) %$%
  labels[rownames(con_ss$samples$SS2$counts)]

tibble(Cell=names(annot_ss_new), Type=annot_ss_new) %>% 
  write_csv(MetadataPath("annotation_smart_seq.csv"))
p_all <- con_ss$plotGraph(alpha=0.2, size=0.05, mark.groups=T, show.legend=F, groups=c(annotation, annot_ss_new), 
                          raster=T, raster.dpi=150, font.size=4, shuffle.colors=T, show.labels=T, plot.na=F) + 
  theme(panel.grid=element_blank()) + labs(x="UMAP 1", y="UMAP 2")

p_emb <- con_ss$plotGraph(alpha=0.2, size=0.05, mark.groups=T, show.legend=F, groups=annot_ss_new, 
                          raster=T, raster.dpi=150, font.size=4, show.labels=T) + 
  theme(panel.grid=element_blank()) + labs(x="UMAP 1", y="UMAP 2")

p_emb$layers[[3]]$aes_params$alpha <- 0.01
p_emb$layers[[1]]$aes_params$alpha <- 1
p_emb$layers[[2]] <- p_all$layers[[2]]
p_emb$layers <- p_emb$layers[c(3, 1, 2)]

p_emb$scales$scales[[2]] <- p_all$scales$scales[[2]]

# ggsave(outPath("ss_all.pdf"), p_all)
# ggsave(outPath("ss_subset.pdf"), p_emb)

p_all

p_emb

num_df <- table(Type=annot_ss_new) %>% as_tibble() %>% 
  mutate(Frac = n / sum(n), NeuronType=neuron_type_per_type[Type]) %>% 
  mutate(Type = factor(Type, levels=type_order))
ggplot(num_df) +
    geom_bar(aes(x=Type, y=Frac * 100, fill=NeuronType), stat="identity") +
    scale_y_continuous(expand=expansion(c(0, 0.05))) +
    scale_fill_brewer("", palette="Set2") +
    labs(x="", y="% of cells") +
    theme(legend.position=c(1, 1.1), legend.justification=c(1, 1), panel.grid.major.x=element_blank(),
          axis.text.x=element_text(angle=90, hjust=1, vjust=0.5), legend.background=element_blank())

ggsave(outPath("ss_type_frac.pdf"))

Annotation to Allen data

sample_info <- allenDataPath("sample_annotations.csv") %>% read_csv()

annot_allen <- sample_info %$% list(
  l0=setNames(as.character(class_label), sample_name),
  l1=setNames(as.character(subclass_label), sample_name),
  l3=setNames(as.character(cluster_label), sample_name)
)
annot_allen$l2 <- strsplit(annot_allen$l3, " ") %>% sapply(function(x) paste(x[1:(length(x)-1)], collapse=" "))
tome <- allenDataPath("transcrip.tome")
cm_allen <- read_tome_dgCMatrix(tome, "data/t_exon") %>%
  set_colnames(read_tome_sample_names(tome)) %>% set_rownames(read_tome_gene_names(tome)) %>%
  .[, !(annot_allen$l0[colnames(.)] %in% c("Exclude", "Non-neuronal"))]

dataset_id <- sample_info %$% setNames(external_donor_name_label, sample_name)
cms_per_dataset <- colnames(cm_allen) %>% split(dataset_id[.]) %>% lapply(function(ns) cm_allen[,ns])

p2s_allen <- lapply(cms_per_dataset, basicP2proc, n.cores=30, k=15,
                    get.largevis=F, make.geneknn=F, get.tsne=F)
16248 cells, 50281 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 13623 overdispersed genes ... 13623persisting ... done.
running PCA using 3000 OD genes .... done
9491 cells, 50281 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 9971 overdispersed genes ... 9971persisting ... done.
running PCA using 3000 OD genes .... done
17013 cells, 50281 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 14063 overdispersed genes ... 14063persisting ... done.
running PCA using 3000 OD genes .... done
con_allen <- con$samples[c("C6", "C7", "C8")] %>% c(p2s_allen) %>% Conos$new(n.cores=30)
source_fac <- names(con_allen$samples) %>% setNames(., .) %>% substr(1, 1)

con_allen$buildGraph(verbose=T, var.scale=T, k=20, k.self=5, k.self.weight=0.1, space="CCA",
                     same.factor.downweight=0.1, balancing.factor.per.sample=source_fac)
found 0 out of 15 cached CCA  space pairs ... running 15 additional CCA  space pairs  done
inter-sample links using  mNN   done
local pairs local pairs  done
building graph ..done
balancing edge weights done
con_allen$embedGraph(method="UMAP", spread=1, min.dist=0.5, verbose=T, n.cores=30, min.prob.lower=1e-7)
Convert graph to adjacency list...
Done
Estimate nearest neighbors and commute times...
Estimating hitting distances: 06:11:43.
Done.
Estimating commute distances: 06:11:50.
Hashing adjacency list: 06:11:50.
Done.
Estimating distances: 06:11:51.
Done
Done.
All done!: 06:12:02.
Done
Estimate UMAP embedding...
Done
write_rds(con_allen, CachePath("con_allen.rds"))

con_allen <- CachePath("con_allen.rds") %>% read_rds()
con_allen$plotGraph(color.by='sample', size=0.1, alpha=0.1, mark.groups=F, show.legend=T, 
                    legend.pos=c(1, 1), raster=T, raster.dpi=150) + 
  theme(legend.title=element_blank())

ggsave(outPath("allen_alignment_sample.pdf"))

con_allen$plotGraph(groups=annot_allen$l2, size=0.1, font.size=c(2, 3), alpha=0.1,
                    raster=T, raster.dpi=150)

ggsave(outPath("allen_alignment_allen.pdf"))

con_allen$plotGraph(groups=annotation, size=0.1, font.size=c(2, 3), alpha=0.1,
                    raster=T, raster.dpi=150)

ggsave(outPath("allen_alignment_ours.pdf"))
labels_prop <- con_allen$propagateLabels(annot_allen$l3, max.iters=50, verbose=T)
con_allen$plotGraph(groups=labels_prop$labels, size=0.1, font.size=c(2, 3), alpha=0.1)

type_ranks <- factor(type_order, levels=type_order) %>% as.integer() %>% setNames(type_order)
t_ann <- annotation %>% .[intersect(names(.), names(con_allen$getDatasetPerCell()))]

freq_df <- table(KU=t_ann, Allen=labels_prop$labels[names(t_ann)]) %>% as_tibble() %>% 
  group_by(KU) %>% mutate(total_n=sum(n), freq=n / total_n) %>% ungroup() %>% filter(n > 1, freq > 0.05) %>% 
  mutate(NeuronType=neuron_type_per_type[KU])

allen_type_order <- freq_df %>% split(.$Allen) %>% lapply(function(x) x %$% setNames(n, KU) %>% `/`(sum(.))) %>% 
  sapply(function(ws) as.numeric(type_ranks[names(ws)[which.max(ws)]]) + sum(type_ranks[names(ws)] * ws) / 10) %>% sort() %>% names()

freq_df %<>% mutate(KU=factor(KU, levels=type_order), Allen=factor(Allen, levels=allen_type_order))
plotAlluvium <- function(p.df, min.box.h, width.left, width.right, alpha=1.0, ...) {
  s.ku <- p.df %$% split(n, droplevels(KU)) %>% sapply(sum)
  s.allen <- p.df %$% split(n, droplevels(Allen)) %>% sapply(sum)
  s.total <- c(rev(s.ku), rev(s.allen)) %>% sqrt() %>% sqrt() %>% `/`(max(.))

  palette <- sccore::fac2col(p.df$KU, return.details=T, ...) %$%
    setNames(sample(palette), names(palette)) %>% alpha(alpha=alpha)
  
  ggplot(p.df, aes(axis1=KU, axis2=Allen, y=pmax(sqrt(n), min.box.h))) +
    geom_alluvium(aes(fill=KU)) +
    geom_stratum(width=c(rep(width.left, length(s.ku)), rep(width.right, length(s.allen)))) + 
    geom_text(stat="stratum", infer.label=TRUE, size=s.total * 2 + 1) +
    scale_x_continuous(expand=c(0, 0)) +
    scale_y_continuous(expand=c(0, 0)) +
    theme_void() + theme(legend.position="none") +
    scale_fill_manual(values=palette)
}
filter(freq_df, NeuronType == "Excitatory") %>% plotAlluvium(10, 0.3, 0.5, v=0.7, s=1.0)

ggsave(outPath("allen_mapping_ex.pdf"))
filter(freq_df, NeuronType == "Inhibitory") %>% 
  plotAlluvium(2, 0.25, 0.4, v=0.8, s=1.0)

ggsave(outPath("allen_mapping_inh.pdf"))

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language (EN)
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tz America/New_York
date 2020-07-08 
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stringi stringi 1.4.6 2020-02-17 CRAN (R 3.5.1)
stringr stringr 1.4.0 2019-02-10 CRAN (R 3.5.1)
testthat testthat 2.3.2 2020-03-02 CRAN (R 3.5.1)
tibble tibble 2.1.3 2019-06-06 CRAN (R 3.5.1)
tidyr tidyr 1.0.2 2020-01-24 CRAN (R 3.5.1)
tidyselect tidyselect 1.0.0 2020-01-27 CRAN (R 3.5.1)
tidyverse tidyverse 1.3.0 2019-11-21 CRAN (R 3.5.1)
triebeard triebeard 0.3.0 2016-08-04 CRAN (R 3.5.1)
urltools urltools 1.7.3 2019-04-14 CRAN (R 3.5.1)
usethis usethis 1.5.1 2019-07-04 CRAN (R 3.5.1)
uwot uwot 0.1.8 2020-03-16 CRAN (R 3.5.1)
vctrs vctrs 0.2.4 2020-03-10 CRAN (R 3.5.1)
vipor vipor 0.4.5 2017-03-22 CRAN (R 3.5.1)
viridis viridis 0.5.1 2018-03-29 CRAN (R 3.5.1)
viridisLite viridisLite 0.3.0 2018-02-01 CRAN (R 3.5.1)
whisker whisker 0.4 2019-08-28 CRAN (R 3.5.1)
withr withr 2.1.2 2018-03-15 CRAN (R 3.5.1)
workflowr workflowr 1.6.1 2020-03-11 CRAN (R 3.5.1)
xfun xfun 0.12 2020-01-13 CRAN (R 3.5.1)
xml2 xml2 1.2.5 2020-03-11 CRAN (R 3.5.1)
xtable xtable 1.8-4 2019-04-21 CRAN (R 3.5.1)
yaml yaml 2.2.1 2020-02-01 CRAN (R 3.5.1)