Last updated: 2019-10-04

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Rmd dd9b9c0 githubz0r 2019-10-04 tutorial for simulated data and cleaned peter code utils example

Load packages and soem data

library(conos)
library(tidyverse)
devtools::load_all('/home/larsc/SecretUtils')
library(cowplot)
library(splatter)
devtools::load_all('/home/viktor_petukhov/Copenhagen/NeuronalMaturation')
devtools::load_all('/home/viktor_petukhov/SmallProjects/scConditionDifference')

We need to generate parameters for gamma function (initial gene means) and log normal (for library sizes). This shit takes a while so we load premade parameters, but just follow the commented lines otherwise.

#epilepsy_con <- readRDS(file.path('/home/larsc/data/10x_preproced_graphed.rds'))
#epilepsy_annot <- readRDS(file.path('/home/demharters/R/projects/UPF9_14_17_19_22_23_24_32_33/metadata_10x_final.rds'))
#epilepsy_annot$cellid <- rownames(epilepsy_annot)
#raw_cm <- RbindRaw(epilepsy_con)
#sub_matrices <- GetSubMatrices(epilepsy_annot$subtype, epilepsy_annot$cellid, epilepsy_annot$condition, raw_cm, 
                               #colnames(raw_cm), avg=F)
#testmat <- sub_matrices$healthy$L2_Lamp5 %>% as.matrix %>% removezerocols %>% Matrix::t()
#params <- splatEstimate(testmat)

lamp5_params <- readRDS('/home/larsc/data/splatter_lamp5_params.rds')

Initiate variables for the simulations. The basic principle is that we simulate a bunch of groups with different DE levels and then we use Group1 as the reference and compare the other groups to this reference. Then we do this several times with different seeds. Default values are set to 500 cells, libloc of 8 and 10000 genes (in MakeSimPerFactor). Instead of using seeds, we can also just use batches, but it should give the same result and I don’t feel like changing it. I’ve made some functions to calculate the distances in the distance list. In order to add a new distance, just make a function that compares Group1 to the other groups for a given factor and then weave it into the 10 layers of wrapper functions, it’s very simple.

de_prob <- c(0.0, 0.0, 0.3, 0.5)
ncellvec <- c(30, 200, 500)
ngenevec <- c(100, 1000, 10000)
liblocvec <- c(6.5, 7.5, 8.5)
seeds <- c(9001)
leiden_resolutions <- c(1, 2)
distances <- list('log.fold.change','jensen_shannon', 'correlation.distance', 'paga', 'CMD', 'euclidean', 'knncor.z', 'knncor.z.med', 'entropy')

Now we create the actual data. We’ll just use two factors(ncell and ngenes) to speed up shit. Can add more factors by changing the functions MakeSimPerFactor and SimulateGroups.

cell_sim <- MakeSimsAllSeeds(lamp5_params, seeds, ncellvec, de_prob, 'ncell', make.p2 = T, n.cl.tsne=30, n.cl.sim=3)
120 cells, 10000 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 0 overdispersed genes ... 0 persisting ... done.
running PCA using 3000 OD genes ..
Warning in irlba(x, nv = nPcs, nu = 0, center = cm, right_only = FALSE, :
You're computing too large a percentage of total singular values, use a
standard svd instead.
.. done
calculating distance ... pearson ...running tSNE using 30 cores:
800 cells, 10000 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 9 overdispersed genes ... 9 persisting ... done.
running PCA using 3000 OD genes .... done
calculating distance ... pearson ...running tSNE using 30 cores:
2000 cells, 10000 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 26 overdispersed genes ... 26 persisting ... done.
running PCA using 3000 OD genes .... done
calculating distance ... pearson ...running tSNE using 30 cores:
gene_sim <- MakeSimsAllSeeds(lamp5_params, seeds, ngenevec, de_prob, 'ngenes', make.p2 = T, n.cl.tsne=30, n.cl.sim=3)
2000 cells, 100 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 24 overdispersed genes ... 24 persisting ... done.
running PCA using 100 OD genes ..
Warning in irlba(x, nv = nPcs, nu = 0, center = cm, right_only = FALSE, :
You're computing too large a percentage of total singular values, use a
standard svd instead.
Warning in irlba(x, nv = nPcs, nu = 0, center = cm, right_only = FALSE, :
did not converge--results might be invalid!; try increasing work or maxit
.. done
calculating distance ... pearson ...running tSNE using 30 cores:
2000 cells, 1000 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 60 overdispersed genes ... 60 persisting ... done.
running PCA using 1000 OD genes .... done
calculating distance ... pearson ...running tSNE using 30 cores:
2000 cells, 10000 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 26 overdispersed genes ... 26 persisting ... done.
running PCA using 3000 OD genes .... done
calculating distance ... pearson ...running tSNE using 30 cores:

Now we create distance data frames.

dfs_per_distance <- AllDistsDfs(list(cell_sim, gene_sim), list('ncell', 'ngenes'),
                                distances, avg.meds=T, leiden.resolutions=leiden_resolutions)
Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector
Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector
Warning in irlba(x, nv = nPcs, nu = 0, center = cm, right_only = FALSE, :
You're computing too large a percentage of total singular values, use a
standard svd instead.

Warning in irlba(x, nv = nPcs, nu = 0, center = cm, right_only = FALSE, :
You're computing too large a percentage of total singular values, use a
standard svd instead.

Warning in irlba(x, nv = nPcs, nu = 0, center = cm, right_only = FALSE, :
You're computing too large a percentage of total singular values, use a
standard svd instead.

Warning in irlba(x, nv = nPcs, nu = 0, center = cm, right_only = FALSE, :
You're computing too large a percentage of total singular values, use a
standard svd instead.
Warning in irlba(x, nv = nPcs, nu = 0, center = cm, right_only = FALSE, :
did not converge--results might be invalid!; try increasing work or maxit
Warning in irlba(x, nv = nPcs, nu = 0, center = cm, right_only = FALSE, :
You're computing too large a percentage of total singular values, use a
standard svd instead.
Warning in irlba(x, nv = nPcs, nu = 0, center = cm, right_only = FALSE, :
did not converge--results might be invalid!; try increasing work or maxit
Warning in irlba(x, nv = nPcs, nu = 0, center = cm, right_only = FALSE, :
You're computing too large a percentage of total singular values, use a
standard svd instead.
Warning in irlba(x, nv = nPcs, nu = 0, center = cm, right_only = FALSE, :
did not converge--results might be invalid!; try increasing work or maxit

Create plots and make a grid

all_plots <- doPlotsPerFactor(dfs_per_distance, jitter=T, geom.smooth=F)
grid_all <- CreateGrid(all_plots, leiden_resolutions)
ggsave(grid_all, file='/home/larsc/plots/testshit.pdf', width=10, height=45)

Bound Paga

boundcellpaga <- doBoundPaga(cell_sim$`9001`, 'ncell')
2920 cells, 10000 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 137 overdispersed genes ... 137 persisting ... done.
running PCA using 3000 OD genes .... done
calculating distance ... pearson ...running tSNE using 30 cores:
Warning in bind_rows_(x, .id): Unequal factor levels: coercing to character
Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector

Warning in bind_rows_(x, .id): binding character and factor vector,
coercing into character vector
unboundcellpaga <- SimPagaFactor(cell_sim$`9001`, 'ncell')

boundcellpaga %>% filter(de.level!='ref') %>% ggplot(aes(x=ncell, y=paga.connectivity.value, col=de.level))+
  geom_point(size=1, alpha=0.8)

unboundcellpaga %>% filter(de.level!='ref') %>% ggplot(aes(x=ncell, y=paga.connectivity.value, col=de.level))+
  geom_point(size=1, alpha=0.8)


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] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] scConditionDifference_0.1.0 NeuronalMaturation_0.1.0   
 [3] splatter_1.6.1              SingleCellExperiment_1.4.1 
 [5] SummarizedExperiment_1.12.0 DelayedArray_0.8.0         
 [7] BiocParallel_1.16.6         matrixStats_0.54.0         
 [9] Biobase_2.42.0              GenomicRanges_1.34.0       
[11] GenomeInfoDb_1.18.2         IRanges_2.16.0             
[13] S4Vectors_0.20.1            BiocGenerics_0.28.0        
[15] cowplot_0.9.4               SecretUtils_0.1.0          
[17] reshape2_1.4.3              magrittr_1.5               
[19] forcats_0.4.0               stringr_1.4.0              
[21] dplyr_0.8.3                 purrr_0.3.2                
[23] readr_1.3.1                 tidyr_0.8.3                
[25] tibble_2.1.3                ggplot2_3.2.0              
[27] tidyverse_1.2.1             conos_1.0.0                
[29] igraph_1.2.4.1              Matrix_1.2-17              

loaded via a namespace (and not attached):
 [1] Rtsne_0.15             colorspace_1.4-1       rjson_0.2.20          
 [4] rprojroot_1.3-2        XVector_0.22.0         base64enc_0.1-3       
 [7] fs_1.3.1               rstudioapi_0.10        ggpubr_0.2            
[10] urltools_1.7.3         remotes_2.0.4          lubridate_1.7.4       
[13] xml2_1.2.0             splines_3.5.3          knitr_1.22            
[16] pkgload_1.0.2          jsonlite_1.6           workflowr_1.3.0       
[19] broom_0.5.2            shiny_1.3.2            compiler_3.5.3        
[22] httr_1.4.0             backports_1.1.4        assertthat_0.2.1      
[25] lazyeval_0.2.2         cli_1.1.0              later_0.8.0           
[28] htmltools_0.3.6        prettyunits_1.0.2      tools_3.5.3           
[31] gtable_0.3.0           glue_1.3.1             GenomeInfoDbData_1.2.0
[34] Rcpp_1.0.1             cellranger_1.1.0       nlme_3.1-139          
[37] xfun_0.6               ps_1.3.0               testthat_2.1.1        
[40] rvest_0.3.4            irlba_2.3.3            mime_0.6              
[43] devtools_2.0.2         dendextend_1.12.0      MASS_7.3-51.3         
[46] zlibbioc_1.28.0        scales_1.0.0           hms_0.4.2             
[49] promises_1.0.1         yaml_2.2.0             memoise_1.1.0         
[52] pbapply_1.4-0          gridExtra_2.3          triebeard_0.3.0       
[55] stringi_1.4.3          Rook_1.1-1             desc_1.2.0            
[58] checkmate_1.9.1        pkgbuild_1.0.3         rlang_0.4.0           
[61] pkgconfig_2.0.2        bitops_1.0-6           evaluate_0.13         
[64] lattice_0.20-38        pagoda2_0.1.0          labeling_0.3          
[67] processx_3.3.1         tidyselect_0.2.5       plyr_1.8.4            
[70] R6_2.4.0               generics_0.0.2         mgcv_1.8-28           
[73] pillar_1.4.2           haven_2.1.0            whisker_0.3-2         
[76] withr_2.1.2            abind_1.4-5            RCurl_1.95-4.12       
[79] modelr_0.1.4           crayon_1.3.4           rmarkdown_1.12        
[82] viridis_0.5.1          usethis_1.5.0          locfit_1.5-9.1        
[85] grid_3.5.3             readxl_1.3.1           data.table_1.12.2     
[88] callr_3.2.0            git2r_0.25.2           digest_0.6.20         
[91] xtable_1.8-4           httpuv_1.5.1           brew_1.0-6            
[94] munsell_0.5.0          viridisLite_0.3.0      sessioninfo_1.1.1