Last updated: 2020-07-09
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Knit directory: Epilepsy19/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 83671cf | viktor_petukhov | 2020-07-09 | Pre-processing notebooks |
# library(Epilepsy19)
library(dataorganizer)
library(magrittr)
library(Matrix)
library(pbapply)
library(conos)
library(readr)
library(tidyverse)
theme_set(theme_bw())
devtools::load_all()annotation <- read_csv(MetadataPath("annotation.csv"))
cms_all <- CachePath("count_matrices.rds") %>% read_rds()
cms_all <- names(cms_all) %>% setNames(., .) %>% lapply(function(n)
cms_all[[n]] %>% set_colnames(paste0(n, "_", colnames(.))) %>%
set_rownames(make.unique(rownames(.))))
cms_all$NeuN <- NULLcms_filt <- cms_all %>% lapply(function(cm) cm[, (colnames(cm) %in% annotation$cell)])
p2s_filt <- lapply(cms_filt, GetPagoda, graph.k=10, embeding.type="UMAP", n.cores=30,
spread=5, min.dist=1.0, build.graph=F)9394 cells, 33681 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 881 overdispersed genes ... 881persisting ... done.
running PCA using 1000 OD genes .... done
6618 cells, 33681 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 1454 overdispersed genes ... 1454persisting ... done.
running PCA using 1000 OD genes .... done
3944 cells, 33681 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 463 overdispersed genes ... 463persisting ... done.
running PCA using 1000 OD genes .... done
7295 cells, 33681 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 819 overdispersed genes ... 819persisting ... done.
running PCA using 1000 OD genes .... done
1389 cells, 33681 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 1561 overdispersed genes ... 1561persisting ... done.
running PCA using 1000 OD genes .... done
217 cells, 33681 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 66 overdispersed genes ... 66persisting ... done.
running PCA using 1000 OD genes .... done
2045 cells, 33681 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 468 overdispersed genes ... 468persisting ... done.
running PCA using 1000 OD genes .... done
6472 cells, 33681 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 566 overdispersed genes ... 566persisting ... done.
running PCA using 1000 OD genes .... done
6034 cells, 33681 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 475 overdispersed genes ... 475persisting ... done.
running PCA using 1000 OD genes .... done
6074 cells, 33681 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 577 overdispersed genes ... 577persisting ... done.
running PCA using 1000 OD genes .... done
11023 cells, 33681 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 430 overdispersed genes ... 430persisting ... done.
running PCA using 1000 OD genes .... done
8431 cells, 33681 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 712 overdispersed genes ... 712persisting ... done.
running PCA using 1000 OD genes .... done
4095 cells, 33681 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 503 overdispersed genes ... 503persisting ... done.
running PCA using 1000 OD genes .... done
542 cells, 33681 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 10 overdispersed genes ... 10persisting ... done.
running PCA using 1000 OD genes .... done
3253 cells, 33681 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 265 overdispersed genes ... 265persisting ... done.
running PCA using 1000 OD genes .... done
2654 cells, 33681 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 86 overdispersed genes ... 86persisting ... done.
running PCA using 1000 OD genes .... done
9780 cells, 33681 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 471 overdispersed genes ... 471persisting ... done.
running PCA using 1000 OD genes .... done
5461 cells, 33681 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 246 overdispersed genes ... 246persisting ... done.
running PCA using 1000 OD genes .... done
5758 cells, 33681 genes; normalizing ... using plain model winsorizing ... log scale ... done.
calculating variance fit ... using gam 360 overdispersed genes ... 360persisting ... done.
running PCA using 1000 OD genes .... donecon_filt <- conos::Conos$new(p2s_filt, n.cores=30)
sample_per_cell <- con_filt$getDatasetPerCell()
condition_per_sample <- ifelse(grepl("ep|HB", levels(sample_per_cell)), "epilepsy", "control") %>%
setNames(levels(sample_per_cell))
con_filt$buildGraph(k=50, k.self=5, k.self.weight=0.1, same.factor.downweight=0.25,
k.same.factor=5, balancing.factor.per.sample=condition_per_sample)found 0 out of 171 cached PCA space pairs ... running 171 additional PCA space pairs done
inter-sample links using mNN done
local pairs local pairs done
building graph ..done
balancing edge weights donecon_filt$embedGraph(method="UMAP", n.cores=45, min.prob.lower=1e-5, n.neighbors=30,
n.epochs=1000, spread=5, min.dist=1.0, verbose=T)Convert graph to adjacency list...
Done
Estimate nearest neighbors and commute times...
Estimating hitting distances: 03:59:13.
Done.
Estimating commute distances: 03:59:31.
Hashing adjacency list: 03:59:31.
Done.
Estimating distances: 04:00:42.
Done
Done.
All done!: 04:01:30.
Done
Estimate UMAP embedding...04:01:30 UMAP embedding parameters a = 0.05 b = 1.00304:01:30 Read 100479 rows and found 1 numeric columns04:01:31 Commencing smooth kNN distance calibration using 45 threads04:01:36 Initializing from normalized Laplacian + noise04:01:55 Commencing optimization for 1000 epochs, with 4150582 positive edges using 45 threads04:02:24 Optimization finishedDonewrite_rds(con_filt, CachePath("con_filt_cells.rds"))bad_samples <- c("C3", "C5", "E5")
con_sf <- Conos$new(con_filt$samples[!(names(con_filt$samples) %in% bad_samples)], n.cores=30)
con_sf$buildGraph(verbose=T, var.scale=T, k=40, k.self=5, k.self.weight=0.1,
balancing.factor.per.sample=condition_per_sample, k.same.factor=5,
same.factor.downweight=0.25)
con_sf$findCommunities(method=conos::leiden.community, resolution=10)
con_sf$embedGraph(method="UMAP", spread=5, min.dist=1.0, n.epochs=2000, verbose=T,
n.cores=30, min.prob.lower=1e-5)write_rds(con_sf, CachePath("con_filt_samples.rds"))# con_sf <- read_rds(CachePath("con_filt_samples.rds"))
# con_filt <- read_rds(CachePath("con_filt_cells.rds"))# p2_con <- Pagoda2FromConos(con_sf, n.pcs=0)
# go_env <- pagoda2::p2.generate.human.go(p2_con)
#
# go_sets <- ExtractGoSets(go_env)Filtered samples:
# t_anns <- lapply(annotation[2:4], setNames, annotation$cell) %>%
# lapply(`[`, names(p2_con$clusters$dataset))
# metadata <- ConvertMetadataToPagoda2Format(
# AnnotationL1=t_anns$l1, AnnotationL2=t_anns$l2,
# AnnotationL3=t_anns$l3, Dataset=p2_con$clusters$dataset
# )
#
# p2_web <- GetPagodaWebApp(p2_con, con_sf$clusters$leiden$groups,
# additional.metadata=metadata, go.sets=go_sets, go.env=go_env)
#
# show.app(p2_web, "Samples Filtered")
# p2_web$serializeToStaticFast(CachePath("p2s", "con_filt_samples.bin"));Filtered cells:
# con_filt$findCommunities(method=leiden.community, resolution=10, n.iterations=10)
# p2_con_filt <- Pagoda2FromConos(con_filt, n.pcs=0)
# t_anns <- lapply(annotation[2:4], setNames, annotation$cell) %>% lapply(`[`, names(p2_con_filt$clusters$dataset))
# metadata <- ConvertMetadataToPagoda2Format(AnnotationL1=t_anns$l1, AnnotationL2=t_anns$l2,
# AnnotationL3=t_anns$l3, Dataset=p2_con_filt$clusters$dataset)
#
# p2_web_filt <- GetPagodaWebApp(p2_con_filt, con_filt$clusters$leiden$groups, additional.metadata=metadata,
# go.sets=go_sets, go.env=go_env)
#
# # show.app(p2_web_filt, "Cells Filtered")
# p2_web_filt$serializeToStaticFast(CachePath("p2s", "con_filt_cells.bin"));# p2 <- con_filt$samples[[1]]
# p2s_web <- lapply(con_filt$samples, function(p2) {
# p2$makeKnnGraph(k=30, type="PCA", center=T, distance="cosine", weight.type="none", verbose=F)
# p2$getKnnClusters(type="PCA", method=leiden.community, resolution=7, n.iterations=10, name="leiden")
#
# t_anns <- lapply(annotation[2:4], setNames, annotation$cell) %>% lapply(`[`, names(p2$clusters$PCA$leiden))
# metadata <- ConvertMetadataToPagoda2Format(AnnotationL1=t_anns$l1, AnnotationL2=t_anns$l2, AnnotationL3=t_anns$l3)
# GetPagodaWebApp(p2, p2$clusters$PCA$leiden, additional.metadata=metadata, go.sets=go_sets, go.env=go_env)
# })# for (n in names(p2s_web)) {
# p2s_web[[n]]$serializeToStaticFast(CachePath("p2s", paste0(n, ".bin")));
# }
data.frame(value=unlist(sessioninfo::platform_info()))| value | |
|---|---|
| version | R version 3.5.1 (2018-07-02) |
| os | Ubuntu 18.04.2 LTS |
| system | x86_64, linux-gnu |
| ui | X11 |
| language | (EN) |
| collate | en_US.UTF-8 |
| ctype | en_US.UTF-8 |
| tz | America/New_York |
| date | 2020-07-09 |
as.data.frame(sessioninfo::package_info())[c('package', 'loadedversion', 'date', 'source')]| package | loadedversion | date | source | |
|---|---|---|---|---|
| AnnotationDbi | AnnotationDbi | 1.44.0 | 2018-10-30 | Bioconductor |
| assertthat | assertthat | 0.2.1 | 2019-03-21 | CRAN (R 3.5.1) |
| backports | backports | 1.1.5 | 2019-10-02 | CRAN (R 3.5.1) |
| base64enc | base64enc | 0.1-3 | 2015-07-28 | CRAN (R 3.5.1) |
| beeswarm | beeswarm | 0.2.3 | 2016-04-25 | CRAN (R 3.5.1) |
| Biobase | Biobase | 2.42.0 | 2018-10-30 | Bioconductor |
| BiocGenerics | BiocGenerics | 0.28.0 | 2018-10-30 | Bioconductor |
| bit | bit | 1.1-15.2 | 2020-02-10 | CRAN (R 3.5.1) |
| bit64 | bit64 | 0.9-7 | 2017-05-08 | CRAN (R 3.5.1) |
| blob | blob | 1.2.1 | 2020-01-20 | CRAN (R 3.5.1) |
| brew | brew | 1.0-6 | 2011-04-13 | CRAN (R 3.5.1) |
| broom | broom | 0.5.5 | 2020-02-29 | CRAN (R 3.5.1) |
| callr | callr | 3.4.2 | 2020-02-12 | CRAN (R 3.5.1) |
| cellranger | cellranger | 1.1.0 | 2016-07-27 | CRAN (R 3.5.1) |
| cli | cli | 2.0.2 | 2020-02-28 | CRAN (R 3.5.1) |
| colorspace | colorspace | 1.4-1 | 2019-03-18 | CRAN (R 3.5.1) |
| conos | conos | 1.3.0 | 2020-05-12 | local |
| crayon | crayon | 1.3.4 | 2017-09-16 | CRAN (R 3.5.1) |
| data.table | data.table | 1.12.8 | 2019-12-09 | CRAN (R 3.5.1) |
| dataorganizer | dataorganizer | 0.1.0 | 2019-11-08 | local |
| DBI | DBI | 1.1.0 | 2019-12-15 | CRAN (R 3.5.1) |
| dbplyr | dbplyr | 1.4.2 | 2019-06-17 | CRAN (R 3.5.1) |
| dendextend | dendextend | 1.13.4 | 2020-02-28 | CRAN (R 3.5.1) |
| desc | desc | 1.2.0 | 2018-05-01 | CRAN (R 3.5.1) |
| devtools | devtools | 2.2.2 | 2020-02-17 | CRAN (R 3.5.1) |
| digest | digest | 0.6.25 | 2020-02-23 | CRAN (R 3.5.1) |
| dplyr | dplyr | 0.8.5 | 2020-03-07 | CRAN (R 3.5.1) |
| ellipsis | ellipsis | 0.3.0 | 2019-09-20 | CRAN (R 3.5.1) |
| Epilepsy19 | Epilepsy19 | 0.0.0.9000 | 2019-10-15 | local |
| evaluate | evaluate | 0.14 | 2019-05-28 | CRAN (R 3.5.1) |
| fansi | fansi | 0.4.1 | 2020-01-08 | CRAN (R 3.5.1) |
| fastmap | fastmap | 1.0.1 | 2019-10-08 | CRAN (R 3.5.1) |
| forcats | forcats | 0.5.0 | 2020-03-01 | CRAN (R 3.5.1) |
| fs | fs | 1.3.2 | 2020-03-05 | CRAN (R 3.5.1) |
| generics | generics | 0.0.2 | 2018-11-29 | CRAN (R 3.5.1) |
| ggbeeswarm | ggbeeswarm | 0.6.0 | 2018-10-16 | Github (eclarke/ggbeeswarm@fb85521) |
| ggplot2 | ggplot2 | 3.3.0 | 2020-03-05 | CRAN (R 3.5.1) |
| ggrastr | ggrastr | 0.1.7 | 2018-12-04 | Github (VPetukhov/ggrastr@203d5cc) |
| git2r | git2r | 0.26.1 | 2019-06-29 | CRAN (R 3.5.1) |
| glue | glue | 1.3.2 | 2020-03-12 | CRAN (R 3.5.1) |
| gridExtra | gridExtra | 2.3 | 2017-09-09 | CRAN (R 3.5.1) |
| gtable | gtable | 0.3.0 | 2019-03-25 | CRAN (R 3.5.1) |
| haven | haven | 2.2.0 | 2019-11-08 | CRAN (R 3.5.1) |
| highr | highr | 0.8 | 2019-03-20 | CRAN (R 3.5.1) |
| hms | hms | 0.5.3 | 2020-01-08 | CRAN (R 3.5.1) |
| htmltools | htmltools | 0.4.0 | 2019-10-04 | CRAN (R 3.5.1) |
| httpuv | httpuv | 1.5.2 | 2019-09-11 | CRAN (R 3.5.1) |
| httr | httr | 1.4.1 | 2019-08-05 | CRAN (R 3.5.1) |
| igraph | igraph | 1.2.4.2 | 2019-11-27 | CRAN (R 3.5.1) |
| IRanges | IRanges | 2.16.0 | 2018-10-30 | Bioconductor |
| irlba | irlba | 2.3.3 | 2019-02-05 | CRAN (R 3.5.1) |
| jsonlite | jsonlite | 1.6.1 | 2020-02-02 | CRAN (R 3.5.1) |
| knitr | knitr | 1.28 | 2020-02-06 | CRAN (R 3.5.1) |
| later | later | 1.0.0 | 2019-10-04 | CRAN (R 3.5.1) |
| lattice | lattice | 0.20-40 | 2020-02-19 | CRAN (R 3.5.1) |
| lifecycle | lifecycle | 0.2.0 | 2020-03-06 | CRAN (R 3.5.1) |
| lubridate | lubridate | 1.7.4 | 2018-04-11 | CRAN (R 3.5.1) |
| magrittr | magrittr | 1.5 | 2014-11-22 | CRAN (R 3.5.1) |
| MASS | MASS | 7.3-51.5 | 2019-12-20 | CRAN (R 3.5.1) |
| Matrix | Matrix | 1.2-18 | 2019-11-27 | CRAN (R 3.5.1) |
| memoise | memoise | 1.1.0 | 2017-04-21 | CRAN (R 3.5.1) |
| mgcv | mgcv | 1.8-31 | 2019-11-09 | CRAN (R 3.5.1) |
| mime | mime | 0.9 | 2020-02-04 | CRAN (R 3.5.1) |
| modelr | modelr | 0.1.6 | 2020-02-22 | CRAN (R 3.5.1) |
| munsell | munsell | 0.5.0 | 2018-06-12 | CRAN (R 3.5.1) |
| nlme | nlme | 3.1-145 | 2020-03-04 | CRAN (R 3.5.1) |
| org.Hs.eg.db | org.Hs.eg.db | 3.7.0 | 2019-10-08 | Bioconductor |
| pagoda2 | pagoda2 | 0.1.1 | 2019-12-10 | local |
| pbapply | pbapply | 1.4-2 | 2019-08-31 | CRAN (R 3.5.1) |
| pillar | pillar | 1.4.3 | 2019-12-20 | CRAN (R 3.5.1) |
| pkgbuild | pkgbuild | 1.0.6 | 2019-10-09 | CRAN (R 3.5.1) |
| pkgconfig | pkgconfig | 2.0.3 | 2019-09-22 | CRAN (R 3.5.1) |
| pkgload | pkgload | 1.0.2 | 2018-10-29 | CRAN (R 3.5.1) |
| prettyunits | prettyunits | 1.1.1 | 2020-01-24 | CRAN (R 3.5.1) |
| processx | processx | 3.4.2 | 2020-02-09 | CRAN (R 3.5.1) |
| promises | promises | 1.1.0 | 2019-10-04 | CRAN (R 3.5.1) |
| ps | ps | 1.3.2 | 2020-02-13 | CRAN (R 3.5.1) |
| purrr | purrr | 0.3.3 | 2019-10-18 | CRAN (R 3.5.1) |
| R6 | R6 | 2.4.1 | 2019-11-12 | CRAN (R 3.5.1) |
| Rcpp | Rcpp | 1.0.4 | 2020-03-17 | CRAN (R 3.5.1) |
| readr | readr | 1.3.1 | 2018-12-21 | CRAN (R 3.5.1) |
| readxl | readxl | 1.3.1 | 2019-03-13 | CRAN (R 3.5.1) |
| remotes | remotes | 2.1.1 | 2020-02-15 | CRAN (R 3.5.1) |
| reprex | reprex | 0.3.0 | 2019-05-16 | CRAN (R 3.5.1) |
| rjson | rjson | 0.2.20 | 2018-06-08 | CRAN (R 3.5.1) |
| rlang | rlang | 0.4.5 | 2020-03-01 | CRAN (R 3.5.1) |
| rmarkdown | rmarkdown | 2.1 | 2020-01-20 | CRAN (R 3.5.1) |
| Rook | Rook | 1.1-1 | 2014-10-20 | CRAN (R 3.5.1) |
| rprojroot | rprojroot | 1.3-2 | 2018-01-03 | CRAN (R 3.5.1) |
| RSpectra | RSpectra | 0.16-0 | 2019-12-01 | CRAN (R 3.5.1) |
| RSQLite | RSQLite | 2.2.0 | 2020-01-07 | CRAN (R 3.5.1) |
| rstudioapi | rstudioapi | 0.11 | 2020-02-07 | CRAN (R 3.5.1) |
| rvest | rvest | 0.3.5 | 2019-11-08 | CRAN (R 3.5.1) |
| S4Vectors | S4Vectors | 0.20.1 | 2018-11-09 | Bioconductor |
| scales | scales | 1.1.0 | 2019-11-18 | CRAN (R 3.5.1) |
| sccore | sccore | 0.1 | 2020-04-24 | Github (hms-dbmi/sccore@2b34b61) |
| sessioninfo | sessioninfo | 1.1.1 | 2018-11-05 | CRAN (R 3.5.1) |
| shiny | shiny | 1.4.0.2 | 2020-03-13 | CRAN (R 3.5.1) |
| 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) |