Last updated: 2021-07-04

Checks: 6 1

Knit directory: Embryoid_Body_Pilot_Workflowr/analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


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.

Great job! The global environment was empty. Objects defined in the global environment 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.

The command set.seed(20200804) 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.

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

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

Using absolute paths to the files within your workflowr project makes it difficult for you and others to run your code on a different machine. Change the absolute path(s) below to the suggested relative path(s) to make your code more reproducible.

absolute relative
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/mergedObjects/Harmony.Batchindividual.rds ../output/mergedObjects/Harmony.Batchindividual.rds
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/fig1.1.pdf ../output/figs/fig1.1.pdf
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/fig1.2.pdf ../output/figs/fig1.2.pdf
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/fig1.1.png ../output/figs/fig1.1.png
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/fig1.2.png ../output/figs/fig1.2.png
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/Supp_ClusterPropHeat.png ../output/figs/Supp_ClusterPropHeat.png
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/Supp_ClusterProp_JUSTLEGEND.png ../output/figs/Supp_ClusterProp_JUSTLEGEND.png
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/Supp_nCountnFeat_RNA.png ../output/figs/Supp_nCountnFeat_RNA.png
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/Supp_nCountnFeat_SCT.png ../output/figs/Supp_nCountnFeat_SCT.png
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/SUPP_DimPlot_res0.5and0.8.png ../output/figs/SUPP_DimPlot_res0.5and0.8.png
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/SUPP_clust22_primitiveStreakVlnPlot.png ../output/figs/SUPP_clust22_primitiveStreakVlnPlot.png

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 068f5cb. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

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:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    analysis/.Rhistory
    Ignored:    output/.Rhistory

Untracked files:
    Untracked:  GSE122380_raw_counts.txt.gz
    Untracked:  UTF1_plots.Rmd
    Untracked:  analysis/IntegrateReference_SCTregressCaoPlusScHCL.Rmd
    Untracked:  analysis/IntegrateReference_SCTregressCaoPlusScHCL_JustEarlyEcto.Rmd
    Untracked:  analysis/IntegrateReference_SCTregressCaoPlusScHCL_JustEndo.Rmd
    Untracked:  analysis/IntegrateReference_SCTregressCaoPlusScHCL_JustMeso.Rmd
    Untracked:  analysis/IntegrateReference_SCTregressCaoPlusScHCL_JustNeuralCrest.Rmd
    Untracked:  analysis/IntegrateReference_SCTregressCaoPlusScHCL_JustNeuron.Rmd
    Untracked:  analysis/IntegrateReference_SCTregressCaoPlusScHCL_JustPluri.Rmd
    Untracked:  analysis/OLD/
    Untracked:  analysis/Pseudobulk_Limma_Harmony.BatchIndividual_ClusterRes0.8_minPCT0.2.Rmd
    Untracked:  analysis/Pseudobulk_Limma_Harmony.BatchIndividual_ClusterRes1_minPCT0.2.Rmd
    Untracked:  analysis/Pseudobulk_VariancePartition_Harmony.Batchindividual_ClusterRes0.1_byCluster.Rmd
    Untracked:  analysis/RefInt_ComparingFulltoPartialIntegrationAnnotations.Rmd
    Untracked:  analysis/ReferenceAnn_DE.Rmd
    Untracked:  analysis/SingleCell_HierarchicalClustering_NoGeneFilter.Rmd
    Untracked:  analysis/SingleCell_VariancePartitionByCluster_Harmony.Batchindividual_ClusterRes0.1_minPCT0.2.Rmd
    Untracked:  analysis/VarPartPlots_res0.1_SCT.Rmd
    Untracked:  analysis/VarPart_SC_res0.1_SCT.Rmd
    Untracked:  analysis/child/
    Untracked:  analysis/k10topics_Explore.Rmd
    Untracked:  analysis/k6topics_Explore.Rmd
    Untracked:  build_refint_scale.R
    Untracked:  build_refint_sct.R
    Untracked:  build_stuff.R
    Untracked:  build_varpart_sc.R
    Untracked:  code/.ipynb_checkpoints/
    Untracked:  code/CellRangerPreprocess.Rmd
    Untracked:  code/ConvertToDGE.Rmd
    Untracked:  code/ConvertToDGE_PseudoBulk.Rmd
    Untracked:  code/ConvertToDGE_SingleCellRes_minPCT0.2.Rmd
    Untracked:  code/EB.getHumanMetadata.Rmd
    Untracked:  code/GEO_processed_data.Rmd
    Untracked:  code/PowerAnalysis_NoiseRatio.ipynb
    Untracked:  code/Untitled.ipynb
    Untracked:  code/Untitled1.ipynb
    Untracked:  code/compile_fits.Rmd
    Untracked:  code/fit_all_models.sh
    Untracked:  code/fit_poisson_nmf.R
    Untracked:  code/fit_poisson_nmf.sbatch
    Untracked:  code/functions_for_fit_comparison.Rmd
    Untracked:  code/get_genelist_byPCTthresh.Rmd
    Untracked:  code/prefit_poisson_nmf.R
    Untracked:  code/prefit_poisson_nmf.sbatch
    Untracked:  code/prepare_data_for_fastTopics.Rmd
    Untracked:  data/HCL_Fig1_adata.h5ad
    Untracked:  data/HCL_Fig1_adata.h5seurat
    Untracked:  data/dge/
    Untracked:  data/dge_raw_data.tar.gz
    Untracked:  data/ref.expr.rda
    Untracked:  figure/
    Untracked:  output/CR_sampleQCrds/
    Untracked:  output/CaoEtAl.Obj.CellsOfAllClusters.ProteinCodingGenes.rds
    Untracked:  output/CaoEtAl.Obj.rds
    Untracked:  output/ClusterInfo_res0.1.csv
    Untracked:  output/DGELists/
    Untracked:  output/DownSampleVarPart.rds
    Untracked:  output/Frequency.MostCommonAnnotation.FiveNearestRefCells.csv
    Untracked:  output/GEOsubmissionProcessedFiles/
    Untracked:  output/GeneLists_by_minPCT/
    Untracked:  output/MostCommonAnnotation.FiveNearestRefCells.csv
    Untracked:  output/NearestReferenceCell.Cao.hESC.EuclideanDistanceinHarmonySpace.csv
    Untracked:  output/NearestReferenceCell.Cao.hESC.FrequencyofEachAnnotation.csv
    Untracked:  output/NearestReferenceCell.SCTregressRNAassay.Cao.hESC.EuclideanDistanceinHarmonySpace.csv
    Untracked:  output/NearestReferenceCell.SCTregressRNAassay.Cao.hESC.FrequencyofEachAnnotation.csv
    Untracked:  output/Pseudobulk_Limma_res0.1_OnevAllTopTables.csv
    Untracked:  output/Pseudobulk_Limma_res0.1_OnevAll_top10Upregby_adjP.csv
    Untracked:  output/Pseudobulk_Limma_res0.1_OnevAll_top10Upregby_logFC.csv
    Untracked:  output/Pseudobulk_Limma_res0.5_OnevAllTopTables.csv
    Untracked:  output/Pseudobulk_Limma_res0.8_OnevAllTopTables.csv
    Untracked:  output/Pseudobulk_Limma_res1_OnevAllTopTables.csv
    Untracked:  output/Pseudobulk_VarPart.ByCluster.Res0.1.rds
    Untracked:  output/ResidualVariances_fromDownSampAnalysis.csv
    Untracked:  output/SingleCell_VariancePartition_RNA_Res0.1_minPCT0.2.rds
    Untracked:  output/SingleCell_VariancePartition_Res0.1_minPCT0.2.rds
    Untracked:  output/SingleCell_VariancePartition_SCT_Res0.1_minPCT0.2.rds
    Untracked:  output/TopicModelling_k10_top10drivergenes.byBeta.csv
    Untracked:  output/TopicModelling_k6_top10drivergenes.byBeta.csv
    Untracked:  output/TopicModelling_k6_top15drivergenes.byZ.csv
    Untracked:  output/TranferredAnnotations_ReferenceInt_JustEarlyEcto.csv
    Untracked:  output/TranferredAnnotations_ReferenceInt_JustEndoderm.csv
    Untracked:  output/TranferredAnnotations_ReferenceInt_JustMeso.csv
    Untracked:  output/TranferredAnnotations_ReferenceInt_JustNeuralCrest.csv
    Untracked:  output/TranferredAnnotations_ReferenceInt_JustNeuron.csv
    Untracked:  output/TranferredAnnotations_ReferenceInt_JustPluripotent.csv
    Untracked:  output/VarPart.ByCluster.Res0.1.rds
    Untracked:  output/azimuth/
    Untracked:  output/downsamp_10800cells_10subreps_medianexplainedbyresiduals_varpart_PsB.rds
    Untracked:  output/downsamp_16200cells_10subreps_medianexplainedbyresiduals_varpart_PsB.rds
    Untracked:  output/downsamp_21600cells_10subreps_medianexplainedbyresiduals_varpart_PsB.rds
    Untracked:  output/downsamp_2700cells_10subreps_medianexplainedbyresiduals_varpart_PsB.rds
    Untracked:  output/downsamp_2700cells_10subreps_medianexplainedbyresiduals_varpart_scres.rds
    Untracked:  output/downsamp_5400cells_10subreps_medianexplainedbyresiduals_varpart_PsB.rds
    Untracked:  output/downsamp_7200cells_10subreps_medianexplainedbyresiduals_varpart_PsB.rds
    Untracked:  output/fasttopics/
    Untracked:  output/figs/
    Untracked:  output/merge.Cao.SCTwRegressOrigIdent.rds
    Untracked:  output/merge.all.SCTwRegressOrigIdent.Harmony.rds
    Untracked:  output/merged.SCT.counts.matrix.rds
    Untracked:  output/merged.raw.counts.matrix.rds
    Untracked:  output/mergedObjects/
    Untracked:  output/pdfs/
    Untracked:  output/sampleQCrds/
    Untracked:  output/splitgpm_gsea_results/
    Untracked:  slurm-12005914.out
    Untracked:  slurm-12005923.out

Unstaged changes:
    Deleted:    analysis/IntegrateAnalysis.afterFilter.HarmonyBatch.Rmd
    Deleted:    analysis/IntegrateAnalysis.afterFilter.HarmonyBatchSampleIDindividual.Rmd
    Modified:   analysis/IntegrateAnalysis.afterFilter.HarmonyBatchindividual.Rmd
    Deleted:    analysis/IntegrateAnalysis.afterFilter.NOHARMONYjustmerge.Rmd
    Deleted:    analysis/IntegrateAnalysis.afterFilter.SCTregressBatchIndividual.Rmd
    Deleted:    analysis/IntegrateAnalysis.afterFilter.SCTregressBatchIndividualHarmonyBatchindividual.Rmd
    Modified:   analysis/Pseudobulk_HierarchicalClustering_Harmony.Batchindividual_ClusterRes0.1_minPCT0.2.Rmd
    Modified:   analysis/Pseudobulk_HierarchicalClustering_Harmony.Batchindividual_ClusterRes0.5_minPCT0.2.Rmd
    Modified:   analysis/Pseudobulk_HierarchicalClustering_Harmony.Batchindividual_ClusterRes0.8_minPCT0.2.Rmd
    Modified:   analysis/Pseudobulk_HierarchicalClustering_Harmony.Batchindividual_ClusterRes1_minPCT0.2.Rmd
    Modified:   analysis/Pseudobulk_Limma_Harmony.BatchIndividual_ClusterRes0.1_minPCT0.2.Rmd
    Modified:   analysis/Pseudobulk_Limma_Harmony.BatchIndividual_ClusterRes0.5_minPCT0.2.Rmd
    Modified:   analysis/Pseudobulk_VariancePartition_Harmony.Batchindividual_ClusterRes0.1_minPCT0.2.Rmd
    Modified:   analysis/Pseudobulk_VariancePartition_Harmony.Batchindividual_ClusterRes0.5_minPCT0.2.Rmd
    Modified:   analysis/Pseudobulk_VariancePartition_Harmony.Batchindividual_ClusterRes0.8_minPCT0.2.Rmd
    Modified:   analysis/Pseudobulk_VariancePartition_Harmony.Batchindividual_ClusterRes1_minPCT0.2.Rmd
    Deleted:    analysis/RunscHCL_HarmonyBatchInd.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 repository in which changes were made to the R Markdown (analysis/Fig1.Rmd) and HTML (docs/Fig1.html) files. If you've configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd 068f5cb KLRhodes 2021-07-04 wflow_publish(c("analysis/CompiledFits_BatchvInd.Rmd", "analysis/DownSamp_NoiseRatio.Rmd",

library(Seurat)
library(patchwork)
library(reshape2)
library(ggplot2)
library(tidyr)

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

    smiths
library(ComplexHeatmap)
Loading required package: grid
========================================
ComplexHeatmap version 2.7.4
Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
Github page: https://github.com/jokergoo/ComplexHeatmap
Documentation: http://jokergoo.github.io/ComplexHeatmap-reference

If you use it in published research, please cite:
Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional 
  genomic data. Bioinformatics 2016.

This message can be suppressed by:
  suppressPackageStartupMessages(library(ComplexHeatmap))
========================================
library(circlize)
========================================
circlize version 0.4.11
CRAN page: https://cran.r-project.org/package=circlize
Github page: https://github.com/jokergoo/circlize
Documentation: https://jokergoo.github.io/circlize_book/book/

If you use it in published research, please cite:
Gu, Z. circlize implements and enhances circular visualization
  in R. Bioinformatics 2014.

This message can be suppressed by:
  suppressPackageStartupMessages(library(circlize))
========================================
library(ggplotify)
library(dplyr)

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

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
merged<- readRDS("/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/mergedObjects/Harmony.Batchindividual.rds")
p0<- FeaturePlot(merged, features = c("POU5F1", "SOX17", "HAND1", "PAX6"), cols= c("grey","blue"), combine=T, slot= "data", ncol=4)
p1<- DimPlot(merged, group.by= "SCT_snn_res.1")
p2<- DimPlot(merged, group.by= "SCT_snn_res.0.5")
p4<- DimPlot(merged, group.by= "SCT_snn_res.0.8")
p3<- DimPlot(merged, group.by= "SCT_snn_res.0.1")

make at able of number of cells per group in each cluster at resolution 0.1

test<- merged@meta.data %>% unite(group, c("individual", "Batch"))
gr<- gsub("SNG-NA", "", test$group)
merged<- AddMetaData(merged, metadata = gr, col.name= "group")
tabi<- table(merged@meta.data$group, merged@meta.data$SCT_snn_res.0.1)

manipulate table to prepare for ggplot

tot<- rowSums(tabi)
tabprop<- tabi/tot
dat<- melt(tabprop)

dat$Var2<- factor(dat$Var2, levels = c("6", "5", "4", "3", "2", "1", "0"))
colnames(dat)<- c("Var1", "Cluster_Res0.1", "value")

make stacked barplot of cell type / cluster composition of each line

samps<- c("18511_Rep1","18511_Rep2","18511_Rep3","19160_Rep1","19160_Rep2","19160_Rep3","18858_Rep1","18858_Rep2","18858_Rep3")
clrs2<- c("#8dd3c7", "#ffffb3", "#bebada", "#fb8072", "#80b1d3", "#fdb462", "#b3de69", "#fccde5", "#bc80bd", "#ccebc5", "#ffed6f", "#a6cee3", "#1f78b4", "midnightblue", "#33a02c", "#fb9a99", "#e31a1c", "#fdbf6f", "#ff7f00", "#cab2d6", "#6a3d9a", "#ffff99", "#b15928", "darkseagreen4", "darkorange3", "darkorchid4", "palevioletred2", "khaki3", "cornsilk3")
V<- ggplot(dat, aes(x= factor(Var1, levels = c("18511_Batch1","18511_Batch2","18511_Batch3","19160_Batch1","19160_Batch2","19160_Batch3","18858_Batch1","18858_Batch2","18858_Batch3")), y=value))+
  geom_col(aes(fill=Cluster_Res0.1), width = 0.8)+
  xlab("sample")+
  ylab("cell type proportion")+
  scale_fill_manual(values = clrs2)+
  theme(axis.text.x = element_text(angle=45, hjust=1))+
  scale_x_discrete(labels=samps)+
  labs(fill = "Cluster (Res. 0.1)")


V

fig1<- p0+p3 +p1
  

fig1

pdf(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/fig1.1.pdf", width= 16, height=8)

fig1

dev.off()
png 
  2 
pdf(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/fig1.2.pdf", width= 7, height=4)

V

dev.off()
png 
  2 
png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/fig1.1.png", width= 16, height=8, units= "in", res= 1080)

fig1

dev.off()
png 
  2 
png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/fig1.2.png", width=7, height=4, units= "in", res=1080)

V

dev.off()
png 
  2 

Supp Figure showing hierarchical clustering of samples based on cluster proportions

New metadata w/ clean names

#add clean names to samples df
replicate<- merged@meta.data$Batch
replicate[replicate == "Batch1"]<- "Rep1"
replicate[replicate == "Batch2"]<- "Rep2"
replicate[replicate == "Batch3"]<- "Rep3"

ind<- merged@meta.data$individual
ind[ind == "SNG-NA18511"]<- "18511"
ind[ind == "SNG-NA19160"]<- "19160"
ind[ind == "SNG-NA18858"]<- "18858"

merged<- AddMetaData(merged, replicate, col.name = "replicate")
merged<- AddMetaData(merged, ind, col.name = "ind")
merged@meta.data<- merged@meta.data %>% unite(SampGroup, c("ind", "replicate"))
col_fun<- colorRamp2(c(0,0.3, 1), c("white", '#117733', "black"))
hp<- list()
res<- c("SCT_snn_res.0.1", "SCT_snn_res.0.5", "SCT_snn_res.0.8", "SCT_snn_res.1")
fortitle<- c("0.1", "0.5", "0.8", "1")

for (i in 1:length(res)){
v<- res[i]
tabi<- table(merged@meta.data$SampGroup, merged@meta.data[,v])
tot<- rowSums(tabi)
tabprop<- tabi/tot

m<- t(as.data.frame.matrix(tabprop))

hp[[i]]<-Heatmap(m, col= col_fun, show_heatmap_legend = FALSE, row_names_gp = gpar(fontsize = 8), column_title = paste0("Resolution ", fortitle[i]), cluster_rows= FALSE)
}

hp
[[1]]


[[2]]


[[3]]


[[4]]

all<-(as.ggplot(hp[[1]]) + as.ggplot(hp[[2]])+ as.ggplot(hp[[3]]) + as.ggplot(hp[[4]]))
all

png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/Supp_ClusterPropHeat.png", width=5, height=8.5, units= "in", res=1080)

all

dev.off()
png 
  2 
lgd<- Legend(col_fun = col_fun, title="Proportion \nof cells")

png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/Supp_ClusterProp_JUSTLEGEND.png", width=1, height=2, units= "in", res=1080)

draw(lgd)

dev.off()
png 
  2 
Idents(merged)<- 'SampGroup'
a<-VlnPlot(merged, features = 'nCount_RNA', pt.size=0)+NoLegend()
a

b<-VlnPlot(merged, features = 'nFeature_RNA', pt.size=0)+NoLegend()
b

c<-VlnPlot(merged, features = 'nCount_SCT', pt.size=0)+NoLegend()
c

d<-VlnPlot(merged, features = 'nFeature_SCT', pt.size=0)+NoLegend()
d

a|b

png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/Supp_nCountnFeat_RNA.png", width= 7.5, height=3, units= "in", res= 1080)

a|b

dev.off()
png 
  2 
png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/Supp_nCountnFeat_SCT.png", width= 7.5, height=3, units= "in", res= 1080)

c|d

dev.off()
png 
  2 
G<- DimPlot(merged, group.by = "SCT_snn_res.0.5", pt.size = 0.005)
H<- DimPlot(merged, group.by = "SCT_snn_res.0.8", pt.size = 0.005)

G|H

png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/SUPP_DimPlot_res0.5and0.8.png", width=9, height=5, units= "in", res=1080)

G|H

dev.off()
png 
  2 
w<- VlnPlot(merged, features= c("EOMES", "MIXL1"), group.by= "SCT_snn_res.1", pt.size=0)
w

png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/SUPP_clust22_primitiveStreakVlnPlot.png", width=11, height=4, units= "in", res=1080)

w

dev.off()
png 
  2 
sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
[1] C

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

other attached packages:
 [1] dplyr_1.0.2          ggplotify_0.0.5      circlize_0.4.11     
 [4] ComplexHeatmap_2.7.4 tidyr_1.1.0          ggplot2_3.3.3       
 [7] reshape2_1.4.4       patchwork_1.1.1      Seurat_3.2.0        
[10] workflowr_1.6.2     

loaded via a namespace (and not attached):
  [1] Rtsne_0.15            colorspace_2.0-0      rjson_0.2.20         
  [4] deldir_0.1-28         ellipsis_0.3.1        ggridges_0.5.2       
  [7] rprojroot_2.0.2       GlobalOptions_0.1.2   fs_1.4.2             
 [10] clue_0.3-58           spatstat.data_1.4-3   farver_2.0.3         
 [13] leiden_0.3.3          listenv_0.8.0         npsurv_0.4-0         
 [16] ggrepel_0.9.0         codetools_0.2-16      splines_3.6.1        
 [19] lsei_1.2-0            knitr_1.29            polyclip_1.10-0      
 [22] jsonlite_1.7.2        Cairo_1.5-12.2        ica_1.0-2            
 [25] cluster_2.1.0         png_0.1-7             uwot_0.1.10          
 [28] shiny_1.5.0           sctransform_0.2.1     BiocManager_1.30.10  
 [31] compiler_3.6.1        httr_1.4.2            rvcheck_0.1.8        
 [34] Matrix_1.2-18         fastmap_1.0.1         lazyeval_0.2.2       
 [37] later_1.1.0.1         htmltools_0.5.0       tools_3.6.1          
 [40] rsvd_1.0.3            igraph_1.2.6          gtable_0.3.0         
 [43] glue_1.4.2            RANN_2.6.1            rappdirs_0.3.3       
 [46] Rcpp_1.0.6            spatstat_1.64-1       vctrs_0.3.6          
 [49] gdata_2.18.0          ape_5.4-1             nlme_3.1-140         
 [52] lmtest_0.9-37         xfun_0.16             stringr_1.4.0        
 [55] globals_0.12.5        mime_0.9              miniUI_0.1.1.1       
 [58] lifecycle_0.2.0       irlba_2.3.3           gtools_3.8.2         
 [61] goftest_1.2-2         future_1.18.0         MASS_7.3-51.4        
 [64] zoo_1.8-8             scales_1.1.1          promises_1.1.1       
 [67] spatstat.utils_1.17-0 parallel_3.6.1        RColorBrewer_1.1-2   
 [70] yaml_2.2.1            reticulate_1.20       pbapply_1.4-2        
 [73] gridExtra_2.3         rpart_4.1-15          stringi_1.5.3        
 [76] highr_0.8             S4Vectors_0.24.4      BiocGenerics_0.32.0  
 [79] caTools_1.18.0        shape_1.4.5           matrixStats_0.57.0   
 [82] rlang_0.4.10          pkgconfig_2.0.3       bitops_1.0-6         
 [85] evaluate_0.14         lattice_0.20-38       ROCR_1.0-7           
 [88] purrr_0.3.4           tensor_1.5            labeling_0.4.2       
 [91] htmlwidgets_1.5.1     cowplot_1.1.1         tidyselect_1.1.0     
 [94] RcppAnnoy_0.0.18      plyr_1.8.6            magrittr_2.0.1       
 [97] R6_2.5.0              magick_2.4.0          IRanges_2.20.2       
[100] gplots_3.0.4          generics_0.1.0        withr_2.4.2          
[103] pillar_1.4.7          whisker_0.4           mgcv_1.8-28          
[106] fitdistrplus_1.0-14   survival_3.2-3        abind_1.4-5          
[109] tibble_3.0.4          future.apply_1.6.0    crayon_1.3.4         
[112] KernSmooth_2.23-15    plotly_4.9.2.1        rmarkdown_2.3        
[115] GetoptLong_1.0.5      data.table_1.13.4     git2r_0.26.1         
[118] digest_0.6.27         xtable_1.8-4          httpuv_1.5.4         
[121] gridGraphics_0.5-0    stats4_3.6.1          munsell_0.5.0        
[124] viridisLite_0.3.0    

sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
[1] C

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

other attached packages:
 [1] dplyr_1.0.2          ggplotify_0.0.5      circlize_0.4.11     
 [4] ComplexHeatmap_2.7.4 tidyr_1.1.0          ggplot2_3.3.3       
 [7] reshape2_1.4.4       patchwork_1.1.1      Seurat_3.2.0        
[10] workflowr_1.6.2     

loaded via a namespace (and not attached):
  [1] Rtsne_0.15            colorspace_2.0-0      rjson_0.2.20         
  [4] deldir_0.1-28         ellipsis_0.3.1        ggridges_0.5.2       
  [7] rprojroot_2.0.2       GlobalOptions_0.1.2   fs_1.4.2             
 [10] clue_0.3-58           spatstat.data_1.4-3   farver_2.0.3         
 [13] leiden_0.3.3          listenv_0.8.0         npsurv_0.4-0         
 [16] ggrepel_0.9.0         codetools_0.2-16      splines_3.6.1        
 [19] lsei_1.2-0            knitr_1.29            polyclip_1.10-0      
 [22] jsonlite_1.7.2        Cairo_1.5-12.2        ica_1.0-2            
 [25] cluster_2.1.0         png_0.1-7             uwot_0.1.10          
 [28] shiny_1.5.0           sctransform_0.2.1     BiocManager_1.30.10  
 [31] compiler_3.6.1        httr_1.4.2            rvcheck_0.1.8        
 [34] Matrix_1.2-18         fastmap_1.0.1         lazyeval_0.2.2       
 [37] later_1.1.0.1         htmltools_0.5.0       tools_3.6.1          
 [40] rsvd_1.0.3            igraph_1.2.6          gtable_0.3.0         
 [43] glue_1.4.2            RANN_2.6.1            rappdirs_0.3.3       
 [46] Rcpp_1.0.6            spatstat_1.64-1       vctrs_0.3.6          
 [49] gdata_2.18.0          ape_5.4-1             nlme_3.1-140         
 [52] lmtest_0.9-37         xfun_0.16             stringr_1.4.0        
 [55] globals_0.12.5        mime_0.9              miniUI_0.1.1.1       
 [58] lifecycle_0.2.0       irlba_2.3.3           gtools_3.8.2         
 [61] goftest_1.2-2         future_1.18.0         MASS_7.3-51.4        
 [64] zoo_1.8-8             scales_1.1.1          promises_1.1.1       
 [67] spatstat.utils_1.17-0 parallel_3.6.1        RColorBrewer_1.1-2   
 [70] yaml_2.2.1            reticulate_1.20       pbapply_1.4-2        
 [73] gridExtra_2.3         rpart_4.1-15          stringi_1.5.3        
 [76] highr_0.8             S4Vectors_0.24.4      BiocGenerics_0.32.0  
 [79] caTools_1.18.0        shape_1.4.5           matrixStats_0.57.0   
 [82] rlang_0.4.10          pkgconfig_2.0.3       bitops_1.0-6         
 [85] evaluate_0.14         lattice_0.20-38       ROCR_1.0-7           
 [88] purrr_0.3.4           tensor_1.5            labeling_0.4.2       
 [91] htmlwidgets_1.5.1     cowplot_1.1.1         tidyselect_1.1.0     
 [94] RcppAnnoy_0.0.18      plyr_1.8.6            magrittr_2.0.1       
 [97] R6_2.5.0              magick_2.4.0          IRanges_2.20.2       
[100] gplots_3.0.4          generics_0.1.0        withr_2.4.2          
[103] pillar_1.4.7          whisker_0.4           mgcv_1.8-28          
[106] fitdistrplus_1.0-14   survival_3.2-3        abind_1.4-5          
[109] tibble_3.0.4          future.apply_1.6.0    crayon_1.3.4         
[112] KernSmooth_2.23-15    plotly_4.9.2.1        rmarkdown_2.3        
[115] GetoptLong_1.0.5      data.table_1.13.4     git2r_0.26.1         
[118] digest_0.6.27         xtable_1.8-4          httpuv_1.5.4         
[121] gridGraphics_0.5-0    stats4_3.6.1          munsell_0.5.0        
[124] viridisLite_0.3.0