Last updated: 2019-06-06

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Knit directory: SecretUtils/

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Rmd 056d849 githubz0r 2019-06-06 some new plots

Load conos, pagoda2 and fuck.

library(conos)
Loading required package: Matrix
Loading required package: igraph

Attaching package: 'igraph'
The following objects are masked from 'package:stats':

    decompose, spectrum
The following object is masked from 'package:base':

    union
library(tidyverse)
── Attaching packages ────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.1     ✔ purrr   0.3.2
✔ tibble  2.1.2     ✔ dplyr   0.8.1
✔ tidyr   0.8.3     ✔ stringr 1.4.0
✔ readr   1.3.1     ✔ forcats 0.4.0
── Conflicts ───────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::as_data_frame() masks tibble::as_data_frame(), igraph::as_data_frame()
✖ purrr::compose()       masks igraph::compose()
✖ tidyr::crossing()      masks igraph::crossing()
✖ tidyr::expand()        masks Matrix::expand()
✖ dplyr::filter()        masks stats::filter()
✖ dplyr::groups()        masks igraph::groups()
✖ dplyr::lag()           masks stats::lag()
✖ purrr::simplify()      masks igraph::simplify()
devtools::load_all('/home/larsc/SecretUtils')
Loading SecretUtils
Loading required package: magrittr

Attaching package: 'magrittr'
The following object is masked from 'package:purrr':

    set_names
The following object is masked from 'package:tidyr':

    extract
Loading required package: reshape2

Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':

    smiths
require(pagoda2)
Loading required package: pagoda2
library(pheatmap)
library(irlba)
library(igraph)
mouse_annot <- read.csv(file.path('/home/larsc/data/mouse_alzheimer/mouse_alzheimers_annotation_filtered_subtypes.csv'))
mouse_annot$subtype_condition <- paste0(mouse_annot$celltype, '.', mouse_annot$condition)

load conos object

mouse_con <- readRDS('/home/larsc/data/mouse_alzheimer/mouse_alzheimers_conos_procced_graphed.rds')

Rbind panels from conos objects

rbound_panel <- RbindPanel(mouse_con)
# sorting it just in case
rbound_panel <- rbound_panel[order(rbound_panel %>% rownames),]

Make groups for plotting

nr_annot <- setNames(mouse_annot$mouse_nr, mouse_annot$Well_ID)
batch_annot <- setNames(mouse_annot$Amp_batch_ID, mouse_annot$Well_ID)
condition_annot <- setNames(mouse_annot$condition, mouse_annot$Well_ID)
celltype_annot <- setNames(mouse_annot$celltype, mouse_annot$Well_ID)
sub_cond_annot <- setNames(mouse_annot$subtype_condition, mouse_annot$Well_ID)
table(nr_annot)
nr_annot
AD6m_mouse1 AD6m_mouse2 AD6m_mouse3 WT6m_mouse1 WT6m_mouse2 WT6m_mouse3 
       1517        2264        2264        1514        2651        2273 
table(celltype_annot)
celltype_annot
         Bcells    granulocytes       microglia PvascMacro+Mono 
            723             446           10018             469 
      T+NKcells 
            827

Plot graph with different annotations

mouse_con$plotGraph(groups=condition_annot, font.size=3, size=0.3, alpha=0.3, show.legend=T)

mouse_con$plotGraph(groups=celltype_annot, font.size=3, size=0.3, alpha=0.3, show.legend=T)

mouse_con$plotGraph(groups=sub_cond_annot, font.size=3, size=0.3, alpha=0.3, show.legend=T)

mouse_con$plotGraph(groups=nr_annot, font.size=3, size=0.3, alpha=0.3, show.legend=T)

Initiate some variables

od_genes = conos:::getOdGenesUniformly(mouse_con$samples, 3000)
state_split <- split(mouse_annot, mouse_annot$condition, drop=TRUE)
subtype_split <- state_split %>% lapply(function(x){split(x, x$celltype, drop=TRUE)})

Jensen Shannon, overall (microglia has by far the most cells so this will heavily skew the result due to dropout)

wt_probs <- subtype_split$WT %>% GetSampProbs(rbound_panel, od_genes, cellid.col = 1, pseudo.count=10^(-8))
ad_probs <- subtype_split$AD %>% GetSampProbs(rbound_panel, od_genes, cellid.col = 1, pseudo.count=10^(-8))

all_dists <- Map(JensenShannon, wt_probs, ad_probs) %>% as_tibble
all_dists_gathered <- gather(all_dists, key=subtype, value=js_distance)
ggplot(all_dists_gathered, aes(y=js_distance, x=subtype)) +geom_bar(stat='identity') +
  theme(axis.text.x = element_text(angle = -90, hjust = 1))

PCA for correlation

pca_cm <- prcomp_irlba(rbound_panel[, od_genes],n=100)
pca_cmat <- pca_cm$x
rownames(pca_cmat) <- rownames(rbound_panel)
pca_genes <- colnames(pca_cmat)
wt_vecs <- subtype_split$WT %>% GetSubMatrices(pca_cmat, pca_genes, cellid.col = 1, avg=T)
ad_vecs <- subtype_split$AD %>% GetSubMatrices(pca_cmat, pca_genes, cellid.col = 1, avg=T)

#ad_vecs <- subtype_split$AD %>% GetSampProbs(pca_cmat, pca_genes, cellid.col = 1, pseudo.count=0) # remember sign
#wt_vecs <- subtype_split$WT %>% GetSampProbs(pca_cmat, pca_genes, cellid.col = 1, pseudo.count=0)

all_dists <- Map(function(x,y){1-cor(x,y)}, wt_vecs, ad_vecs) %>% as_tibble
all_dists_gathered <- gather(all_dists, key=subtype, value=corcomplement)
ggplot(all_dists_gathered, aes(y=corcomplement, x=subtype)) +geom_bar(stat='identity') +
  theme(axis.text.x = element_text(angle = -90, hjust = 1))

Fractional plot

FractionalPlot(mouse_annot$mouse_nr, mouse_annot$celltype, mouse_annot$condition)

PAGA

conos_distances <- Matrix::readMM('/home/larsc/data/mouse_alzheimer/for_paga/graph_distances.mtx')
mouse_annot$subtype_sample <- paste(mouse_annot$celltype, mouse_annot$mouse_nr, sep='-')

mem_levels <- factor(mouse_annot$subtype_sample) %>% levels
subtype_order <- (paste0(mouse_annot$celltype) %>% unique)[order(paste0(mouse_annot$celltype) %>% unique)]
membership_vec <- as.numeric(factor(mouse_annot$subtype_condition))
membership_levels <- factor(mouse_annot$subtype_sample) %>% levels
membership_vec_subsamp <- as.numeric(factor(mouse_annot$subtype_sample))
connectivities <- GetPagaMatrix(conos_distances, membership_vec, scale=F)
linearized_stats <- seq(1, dim(connectivities)[1], 2) %>% sapply(function(i){connectivities[i,i+1]})

paga_df <- bind_cols(value=linearized_stats, subtype=subtype_order)
ggplot(paga_df, aes(y=-linearized_stats, x=subtype)) +geom_point()+
  theme(axis.text.x = element_text(angle = -90, hjust = 1))


sessionInfo()
R version 3.5.3 (2019-03-11)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.2 LTS

Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/atlas/libblas.so.3.10.3
LAPACK: /usr/lib/x86_64-linux-gnu/atlas/liblapack.so.3.10.3

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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] irlba_2.3.3       pheatmap_1.0.12   pagoda2_0.1.0    
 [4] SecretUtils_0.1.0 reshape2_1.4.3    magrittr_1.5     
 [7] forcats_0.4.0     stringr_1.4.0     dplyr_0.8.1      
[10] purrr_0.3.2       readr_1.3.1       tidyr_0.8.3      
[13] tibble_2.1.2      ggplot2_3.1.1     tidyverse_1.2.1  
[16] conos_1.0.0       igraph_1.2.4      Matrix_1.2-17    

loaded via a namespace (and not attached):
 [1] nlme_3.1-139       fs_1.3.1           usethis_1.5.0     
 [4] lubridate_1.7.4    devtools_2.0.2     RColorBrewer_1.1-2
 [7] httr_1.4.0         rprojroot_1.3-2    tools_3.5.3       
[10] backports_1.1.4    R6_2.4.0           lazyeval_0.2.2    
[13] colorspace_1.4-1   withr_2.1.2        tidyselect_0.2.5  
[16] gridExtra_2.3      prettyunits_1.0.2  processx_3.3.1    
[19] compiler_3.5.3     git2r_0.25.2       cli_1.1.0         
[22] rvest_0.3.4        xml2_1.2.0         desc_1.2.0        
[25] labeling_0.3       triebeard_0.3.0    scales_1.0.0      
[28] callr_3.2.0        digest_0.6.18      rmarkdown_1.12    
[31] base64enc_0.1-3    pkgconfig_2.0.2    htmltools_0.3.6   
[34] sessioninfo_1.1.1  rlang_0.3.4        readxl_1.3.1      
[37] rstudioapi_0.10    shiny_1.3.2        generics_0.0.2    
[40] jsonlite_1.6       dendextend_1.12.0  Rcpp_1.0.1        
[43] munsell_0.5.0      abind_1.4-5        viridis_0.5.1     
[46] stringi_1.4.3      whisker_0.3-2      yaml_2.2.0        
[49] MASS_7.3-51.3      pkgbuild_1.0.3     Rtsne_0.15        
[52] plyr_1.8.4         grid_3.5.3         ggrepel_0.8.1     
[55] parallel_3.5.3     promises_1.0.1     crayon_1.3.4      
[58] lattice_0.20-38    haven_2.1.0        hms_0.4.2         
[61] knitr_1.22         ps_1.3.0           pillar_1.4.1      
[64] rjson_0.2.20       pkgload_1.0.2      glue_1.3.1        
[67] evaluate_0.13      data.table_1.12.2  remotes_2.0.4     
[70] modelr_0.1.4       urltools_1.7.3     httpuv_1.5.1      
[73] testthat_2.1.1     cellranger_1.1.0   gtable_0.3.0      
[76] assertthat_0.2.1   xfun_0.6           mime_0.6          
[79] xtable_1.8-4       broom_0.5.2        later_0.8.0       
[82] viridisLite_0.3.0  memoise_1.1.0      workflowr_1.3.0   
[85] Rook_1.1-1         brew_1.0-6