Last updated: 2019-12-03

Checks: 6 1

Knit directory: bentsen-rausch-2019/

This reproducible R Markdown analysis was created with workflowr (version 1.4.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: objects present

The global environment had objects present when the code in the R Markdown file was run. These objects can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment. Use wflow_publish or wflow_build to ensure that the code is always run in an empty environment.

The following objects were defined in the global environment when these results were created:

Name Class Size
data environment 56 bytes
env environment 56 bytes

Seed: set.seed(20191021)

The command set.seed(20191021) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: f007730

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    .Rproj.user/
    Ignored:    test_files/

Untracked files:
    Untracked:  analysis/figure_6.Rmd
    Untracked:  analysis/figure_7.Rmd
    Untracked:  analysis/supp1.Rmd
    Untracked:  code/sc_functions.R
    Untracked:  data/bulk/
    Untracked:  data/fgf_filtered_nuclei.RDS
    Untracked:  data/figures/
    Untracked:  data/filtglia.RDS
    Untracked:  data/glia/
    Untracked:  data/lps1.txt
    Untracked:  data/mcao1.txt
    Untracked:  data/mcao_d3.txt
    Untracked:  data/mcaod7.txt
    Untracked:  data/mouse_data/
    Untracked:  data/neur_astro_induce.xlsx
    Untracked:  data/neuron/
    Untracked:  data/synaptic_activity_induced.xlsx
    Untracked:  output/agrp_pcgenes.csv
    Untracked:  output/all_wc_markers.csv
    Untracked:  output/allglia_wgcna_genemodules.csv
    Untracked:  output/bulk/
    Untracked:  output/fig.RData
    Untracked:  output/fig4_part2.RData
    Untracked:  output/glia/
    Untracked:  output/glial_markergenes.csv
    Untracked:  output/integrated_all_markergenes.csv
    Untracked:  output/integrated_neuronmarkers.csv
    Untracked:  output/neuron/
    Untracked:  wc_de.pdf

Unstaged changes:
    Modified:   analysis/13_olig_pseudotime.Rmd
    Modified:   analysis/15_tany_wgcna_pseudo.Rmd
    Modified:   analysis/9_wc_processing.Rmd
    Modified:   analysis/figure_1.Rmd
    Modified:   analysis/index.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File Version Author Date Message
html f4dd96b Full Name 2019-10-29 Build site.
html 3b5cbe7 Full Name 2019-10-28 Build site.
Rmd 650ab6b Full Name 2019-10-28 wflow_git_commit(all = T)

Load libraries

library(GEOquery)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':

    anyDuplicated, append, as.data.frame, basename, cbind,
    colMeans, colnames, colSums, dirname, do.call, duplicated,
    eval, evalq, Filter, Find, get, grep, grepl, intersect,
    is.unsorted, lapply, lengths, Map, mapply, match, mget, order,
    paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind,
    Reduce, rowMeans, rownames, rowSums, sapply, setdiff, sort,
    table, tapply, union, unique, unsplit, which, which.max,
    which.min
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.
Setting options('download.file.method.GEOquery'='auto')
Setting options('GEOquery.inmemory.gpl'=FALSE)
library(here)
here() starts at /nfsdata/projects/dylan/bentsen-rausch-2019
library(Seurat)
library(tidyverse)
── Attaching packages ────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.2.1          ✔ purrr   0.3.2     
✔ tibble  2.1.3          ✔ dplyr   0.8.3     
✔ tidyr   0.8.3          ✔ stringr 1.4.0     
✔ readr   1.3.1.9000     ✔ forcats 0.4.0     
── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::combine()    masks Biobase::combine(), BiocGenerics::combine()
✖ dplyr::filter()     masks stats::filter()
✖ dplyr::lag()        masks stats::lag()
✖ ggplot2::Position() masks BiocGenerics::Position(), base::Position()
library(future)
library(data.table)

Attaching package: 'data.table'
The following objects are masked from 'package:dplyr':

    between, first, last
The following object is masked from 'package:purrr':

    transpose
library(rstatix)

Attaching package: 'rstatix'
The following object is masked from 'package:stats':

    filter
plan(multiprocess, workers=30)
options(future.globals.maxSize = 8000 * 1024^2)

Load Data

Read in Marques et al data

gse<-getGEO("GSE75330", GSEMatrix=T)
counts<-fread("/projects/dylan/fgf1/oligodendrocyte_figure/GSE75330_Marques_et_al_mol_counts2.tab")
counts<-as.data.frame(counts)
rownames(counts)<-counts[,1]
counts<-counts[,-1]
cellid<-pData(phenoData(gse[[1]]))[c(1,44)]
names(cellid)<-c("cell","label")
cellid<-data.frame(sapply(cellid, function(x) as.character(x)), stringsAsFactors = F)
counts<-counts[,colnames(counts)%in%cellid$cell]
cellid<-cellid[cellid$cell%in%colnames(counts),]
cellid<-cellid[match(colnames(counts),cellid$cell),]

Prep Marques et al data

CreateSeuratObject(counts=counts) %>% 
  SCTransform(verbose = F, return.only.var.genes = F) %>% 
  RunPCA(verbose=T) %>% RunUMAP(dims=1:10) -> branco
Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')
Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached
Warning in sqrt(1/i): NaNs produced
Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
branco$label<-cellid$label
Idents(branco)<-"label"
branco_sub<-subset(branco, cells=WhichCells(branco, idents=c("PPR"), invert=T))
branco_sub<-RenameIdents(branco_sub, "MOL2"="MOL1")
branco_sub<-RenameIdents(branco_sub, "MOL3"="MOL1")
branco_sub<-RenameIdents(branco_sub, "MOL4"="MOL1")
branco_sub<-RenameIdents(branco_sub, "MOL5"="MOL1")
branco_sub<-RenameIdents(branco_sub, "MOL6"="MOL1")
branco_sub<-RenameIdents(branco_sub, "NFOL1"="NFOL")
branco_sub<-RenameIdents(branco_sub, "NFOL2"="NFOL")
branco_sub<-RenameIdents(branco_sub, "MFOL1"="MFOL")
branco_sub<-RenameIdents(branco_sub, "MFOL2"="MFOL")

Propagate labels

oliglist <- list(branco=branco_sub, olig=olig)
olig.features <- SelectIntegrationFeatures(object.list = oliglist, nfeatures = 3000)
oliglist <- PrepSCTIntegration(object.list = oliglist, anchor.features = olig.features, 
    verbose = FALSE)
olig.anchors <- FindTransferAnchors(reference = oliglist[["branco"]], 
                                    query = oliglist[["olig"]], normalization.method = "SCT", 
                                    features = olig.features)
predictions <- TransferData(anchorset = olig.anchors, refdata = branco_sub@active.ident, 
    dims = 1:30)
hist(predictions$prediction.score.max)

Version Author Date
3b5cbe7 Full Name 2019-10-28

Filter data

olig <- AddMetaData(object = olig, metadata = predictions)
table(olig$predicted.id)

 COP MFOL MOL1 NFOL  OPC 
1004 2172 8049  760 3761 
DimPlot(olig, group.by = "predicted.id")

Version Author Date
3b5cbe7 Full Name 2019-10-28 
olig_sub<-subset(olig, subset=prediction.score.max>0.5)

Recluster data

olig_sub %>% 
  ScaleData(verbose = F, vars.to.regress = c("percent.mito","percent.ribo")) %>% 
  RunPCA(verbose=T) %>% FindNeighbors(dims = 1:20) %>% RunUMAP(dims = 1:20) %>% 
  FindClusters(resolution = 0.2) -> olig_sub
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 15274
Number of edges: 509268

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9281
Number of communities: 8
Elapsed time: 3 seconds
DimPlot(olig_sub, label = T)
Warning: Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
Please use `as_label()` or `as_name()` instead.
This warning is displayed once per session.

Version Author Date
3b5cbe7 Full Name 2019-10-28

Filter data

olig_sub <- subset(olig_sub, ident=c(3,4,6), invert=T)
olig_sub %>% 
  ScaleData(verbose = F, vars.to.regress = c("percent.mito","percent.ribo")) %>% 
  RunPCA(verbose=T) %>% FindNeighbors(dims = 1:20) %>% RunUMAP(dims = 1:20) %>% 
  FindClusters(resolution = 0.2) -> olig_sub
Warning in PrepDR(object = object, features = features, verbose = verbose):
The following 1 features requested have zero variance (running reduction
without them): 9130410C08Rik
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 13543
Number of edges: 432866

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9161
Number of communities: 5
Elapsed time: 2 seconds
Idents(olig_sub)<-"predicted.id"
DimPlot(olig_sub, label = T)

Version Author Date
3b5cbe7 Full Name 2019-10-28 

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

Matrix products: default
BLAS/LAPACK: /usr/lib64/libopenblas-r0.3.3.so

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

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

other attached packages:
 [1] rstatix_0.1.1       data.table_1.12.2   future_1.14.0      
 [4] forcats_0.4.0       stringr_1.4.0       dplyr_0.8.3        
 [7] purrr_0.3.2         readr_1.3.1.9000    tidyr_0.8.3        
[10] tibble_2.1.3        ggplot2_3.2.1       tidyverse_1.2.1    
[13] Seurat_3.0.3.9036   here_0.1            GEOquery_2.50.5    
[16] Biobase_2.42.0      BiocGenerics_0.28.0

loaded via a namespace (and not attached):
  [1] Rtsne_0.15          colorspace_1.4-1    rio_0.5.16         
  [4] ggridges_0.5.1      rprojroot_1.3-2     fs_1.3.1           
  [7] rstudioapi_0.10     leiden_0.3.1        listenv_0.7.0      
 [10] npsurv_0.4-0        ggrepel_0.8.0.9000  RSpectra_0.15-0    
 [13] lubridate_1.7.4     xml2_1.2.0          codetools_0.2-16   
 [16] splines_3.5.3       R.methodsS3_1.7.1   lsei_1.2-0         
 [19] knitr_1.23          zeallot_0.1.0       jsonlite_1.6       
 [22] workflowr_1.4.0     broom_0.5.2         ica_1.0-2          
 [25] cluster_2.1.0       png_0.1-7           R.oo_1.22.0        
 [28] uwot_0.1.3          sctransform_0.2.0   compiler_3.5.3     
 [31] httr_1.4.1          backports_1.1.4     assertthat_0.2.1   
 [34] Matrix_1.2-17       lazyeval_0.2.2      cli_1.1.0          
 [37] limma_3.38.3        htmltools_0.3.6     tools_3.5.3        
 [40] rsvd_1.0.2          igraph_1.2.4.1      gtable_0.3.0       
 [43] glue_1.3.1          RANN_2.6.1          reshape2_1.4.3     
 [46] Rcpp_1.0.2          carData_3.0-2       cellranger_1.1.0   
 [49] vctrs_0.2.0         gdata_2.18.0        ape_5.3            
 [52] nlme_3.1-140        gbRd_0.4-11         lmtest_0.9-37      
 [55] xfun_0.8            globals_0.12.4      openxlsx_4.1.0.1   
 [58] rvest_0.3.4         irlba_2.3.3         gtools_3.8.1       
 [61] MASS_7.3-51.4       zoo_1.8-6           scales_1.0.0       
 [64] hms_0.5.0           RColorBrewer_1.1-2  curl_4.0           
 [67] yaml_2.2.0          reticulate_1.13     pbapply_1.4-1      
 [70] gridExtra_2.3       stringi_1.4.3       highr_0.8          
 [73] caTools_1.17.1.2    zip_2.0.3           bibtex_0.4.2       
 [76] Rdpack_0.11-0       SDMTools_1.1-221.1  rlang_0.4.0        
 [79] pkgconfig_2.0.2     bitops_1.0-6        evaluate_0.14      
 [82] lattice_0.20-38     ROCR_1.0-7          labeling_0.3       
 [85] htmlwidgets_1.3     cowplot_1.0.0       tidyselect_0.2.5   
 [88] RcppAnnoy_0.0.12    plyr_1.8.4          magrittr_1.5       
 [91] R6_2.4.0            gplots_3.0.1.1      generics_0.0.2     
 [94] foreign_0.8-71      withr_2.1.2         haven_2.1.0        
 [97] pillar_1.4.2        whisker_0.3-2       fitdistrplus_1.0-14
[100] abind_1.4-5         survival_2.44-1.1   future.apply_1.3.0 
[103] tsne_0.1-3          car_3.0-3           modelr_0.1.4       
[106] crayon_1.3.4        KernSmooth_2.23-15  plotly_4.9.0       
[109] rmarkdown_1.13      readxl_1.3.1        grid_3.5.3         
[112] git2r_0.25.2        metap_1.1           digest_0.6.20      
[115] R.utils_2.9.0       RcppParallel_4.4.3  munsell_0.5.0      
[118] viridisLite_0.3.0