Last updated: 2019-06-20

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html	a3ebd26	Luke Zappia	2019-06-20	Add pre-processing

#### LIBRARIES ####
# Package conflicts
library("conflicted")

# Single-cell
library("SingleCellExperiment")

# File paths
library("fs")
library("here")

# Presentation
library("knitr")
library("jsonlite")

# Pipes
library("magrittr")

# Tidyverse
library("tidyverse")

### CONFLICT PREFERENCES ####
conflict_prefer("path", "fs")

### SOURCE FUNCTIONS ####
source(here("R/annotate.R"))
source(here("R/output.R"))

### OUTPUT DIRECTORY ####
OUT_DIR <- here("output", DOCNAME)
dir_create(OUT_DIR)

#### SET PARALLEL ####
bpparam <- BiocParallel::MulticoreParam(workers = 10)

#### SET GGPLOT THEME ####
theme_set(theme_minimal())

#### SET PATHS ####
source(here("R/set_paths.R"))

Introduction

In this document we are going to load the Swarbrick dataset and do some basic exploration to find out what it contains.

sce_raw <- readRDS(PATHS$sce_raw)
cite_raw <- read_csv(PATHS$cite_raw,
                     col_types = cols(
                         .default = col_double(),
                         Antibody = col_character()
                     ))

The raw dataset is provided as a SingleCellExperiment object with 29621 rows and 3412 columns.

print(sce_raw)

class: SingleCellExperiment 
dim: 29621 3412 
metadata(0):
assays(1): counts
rownames(29621): GRCh38-RP11-34P13.7 GRCh38-FO538757.2 ...
  mm10---PISD mm10---DHRSX
rowData names(0):
colnames(3412): AAACCTGAGGTGCTTT AAACCTGAGTTGCAGG ...
  TTTGTCAGTAGCGCAA TTTGTCAGTGAGGGAG
colData names(0):
reducedDimNames(0):
spikeNames(0):

The column names appear to be cell barcodes but the row names are more complicated.

The CITE-seq antibody data is provided as a CSV file with 96 row and 3413 columns.

print(cite_raw)

# A tibble: 96 x 3,413
   Antibody GCCAAATTCCGAGCCA CGCCAAGAGGGAAACA AAACCTGTCTCCAGGG
   <chr>               <dbl>            <dbl>            <dbl>
 1 CD3                     7               16                9
 2 CD4                     2                5               78
 3 CD8a                    3                5                1
 4 CD14                    1                1                1
 5 CD15                    7                7                8
 6 CD16                    0                1                1
 7 CD19                    4              142                2
 8 CD25                    2                0                0
 9 CD28                    0                0                0
10 CD40                    2              113               10
# … with 86 more rows, and 3,409 more variables: CCGTGGAAGCTGCAAG <dbl>,
#   TTTGCGCAGCGATGAC <dbl>, GCTGCAGCATCACCCT <dbl>,
#   GGAACTTGTCTGCCAG <dbl>, TTGGCAACAAGCGAGT <dbl>,
#   CCCAGTTGTCCGAATT <dbl>, GGATTACCATGTCTCC <dbl>,
#   GTTACAGTCGGTGTTA <dbl>, ACGCAGCGTTTGGGCC <dbl>,
#   TGTATTCGTCTCATCC <dbl>, ACTTACTAGCTGAAAT <dbl>,
#   GCGCAACGTAGCTAAA <dbl>, TTGACTTGTACTTCTT <dbl>,
#   ATTACTCAGGCATGGT <dbl>, AGAATAGAGCCGATTT <dbl>,
#   GAACGGAAGAAGGTTT <dbl>, TCTCTAAAGTGAATTG <dbl>,
#   AGGCCACAGCAATCTC <dbl>, CTAGAGTAGTGTTAGA <dbl>,
#   TCAGCAAGTAATCGTC <dbl>, AGCGGTCTCTGCCAGG <dbl>,
#   CTACCCAAGTACATGA <dbl>, GTACGTAAGTCAAGCG <dbl>,
#   CGGTTAACAGAAGCAC <dbl>, TCGCGAGTCGTCCGTT <dbl>,
#   CGTCTACGTCATCGGC <dbl>, TTGCGTCAGTTACGGG <dbl>,
#   TCGGTAACATCGTCGG <dbl>, CGGAGTCAGACAAGCC <dbl>,
#   GTTTCTAGTCATCGGC <dbl>, CATTATCTCGACCAGC <dbl>,
#   GGGCACTAGATCCCAT <dbl>, CCTAGCTGTTTGTTGG <dbl>,
#   TCGGTAAAGCTCCCAG <dbl>, CCGTGGACACACTGCG <dbl>,
#   CGGAGCTTCTTTCCTC <dbl>, CGATTGAGTCATCGGC <dbl>,
#   GACAGAGAGACCCACC <dbl>, TATCTCACAATGACCT <dbl>,
#   GGACAAGAGAGCAATT <dbl>, GTTCATTCACTTAACG <dbl>,
#   TTGCCGTGTTGGAGGT <dbl>, ACAGCCGCAAGAGTCG <dbl>,
#   GATGCTACACTCAGGC <dbl>, CACACAACAGCATACT <dbl>,
#   CAGAATCTCGAATGCT <dbl>, TTCCCAGAGAGGTTAT <dbl>,
#   TAAGCGTAGATGCCAG <dbl>, TGGCGCAAGACAAGCC <dbl>,
#   GTCCTCATCAACACTG <dbl>, CGATCGGGTTGTACAC <dbl>,
#   CGAGAAGGTCTTCTCG <dbl>, ATCCGAACACGACTCG <dbl>,
#   CCTAGCTAGCAGGTCA <dbl>, AAGGAGCTCACAATGC <dbl>,
#   GAAACTCCACATGGGA <dbl>, CTAAGACCATGAAGTA <dbl>,
#   CAGATCATCGTAGGAG <dbl>, ACGCCAGCATTCTCAT <dbl>,
#   GGTGCGTGTATCGCAT <dbl>, CACCAGGCATGCTGGC <dbl>,
#   CTGTTTAGTCGGGTCT <dbl>, GCTGGGTTCACCACCT <dbl>,
#   GGACAGATCCCATTTA <dbl>, TACTTACAGAGCTTCT <dbl>,
#   AGCTCCTGTCTAGCGC <dbl>, TTAGGACCATTGGCGC <dbl>,
#   TGTGTTTTCAAGCCTA <dbl>, ACATACGAGCCACGTC <dbl>,
#   AGCTCTCGTTCCTCCA <dbl>, ACAGCTACACGCGAAA <dbl>,
#   CCGTACTAGACGACGT <dbl>, CGCTATCTCAGTGTTG <dbl>,
#   CGATTGACAGCTGTTA <dbl>, GTGCTTCAGTGGTAGC <dbl>,
#   CCTTCCCTCTACTATC <dbl>, CTGCGGACACTTCTGC <dbl>,
#   GCATGATCAGCAGTTT <dbl>, CGGTTAAGTCCCGACA <dbl>,
#   ACATCAGGTCTTCTCG <dbl>, CACAGTAAGCCAGTAG <dbl>,
#   TAAACCGAGCACACAG <dbl>, CGTCACTTCCGAACGC <dbl>,
#   GCTGCAGCAGGCTCAC <dbl>, TCAGATGTCAGCATGT <dbl>,
#   GCGACCAGTAATCGTC <dbl>, ACGTCAATCTGGTGTA <dbl>,
#   TGGACGCCAATAGAGT <dbl>, ATAACGCCAGCTCCGA <dbl>,
#   GCTGCAGCAAGAGGCT <dbl>, CCTCTGAAGGAGTTTA <dbl>,
#   GGGAATGGTTACCAGT <dbl>, TACGGATCACCGTTGG <dbl>,
#   AACCGCGTCAAGAAGT <dbl>, CACAGTATCGTCCAGG <dbl>,
#   GCATACAAGTGCAAGC <dbl>, AGTGAGGGTTTAGGAA <dbl>,
#   CTCGGAGAGGCTAGCA <dbl>, TTGCGTCTCTCCGGTT <dbl>,
#   CTTTGCGCATATACCG <dbl>, …

The number of columns matches up with the SingleCellExperiment object and the column names are similar so we should be able to match up the cells.

Features

Let’s have a look at the feature names of the SingleCellExperiment in more detail. The first 10 rownames are:

head(rownames(sce_raw), n = 10)

 [1] "GRCh38-RP11-34P13.7"  "GRCh38-FO538757.2"    "GRCh38-AP006222.2"   
 [4] "GRCh38-RP4-669L17.2"  "GRCh38-RP4-669L17.10" "GRCh38-RP11-206L10.9"
 [7] "GRCh38-LINC00115"     "GRCh38-FAM41C"        "GRCh38-RP11-54O7.1"  
[10] "GRCh38-NOC2L"

And the last 10 are:

tail(rownames(sce_raw), n = 10)

 [1] "mm10---mt-Nd5"     "mm10---mt-Nd6"     "mm10---mt-Cytb"   
 [4] "mm10---Vamp7"      "mm10---Spry3"      "mm10---Tmlhe"     
 [7] "mm10---Csprs"      "mm10---AC168977.1" "mm10---PISD"      
[10] "mm10---DHRSX"

Some mouse cells were spiked into the dataset so the prefixes seem to indicate the genome for each feature, either human (GRCh38) or mouse (mm10). There are 17222 features starting with “GRCh38” and 12399 starting with “mm10”. Together they add up to the total number of features in the dataset. Let’s add this information to the object:

row_data <- tibble(FeatureID = rownames(sce_raw)) %>%
    mutate(
        FeatureName = str_remove(FeatureID, "GRCh38-"),
        FeatureName = str_remove(FeatureName,"mm10---")
    ) %>%
    mutate(
        Genome = str_extract(FeatureID, "[A-Za-z0-9]+-"),
        Genome = str_remove(Genome, "-")
    )

Selection

For this analysis we are only interested in the human cells so let’s try and select just those. To do that we will calculate the total counts assigned to human or mouse genes and see if we can use that to separate the different types of cells.

col_data <- tibble(Barcode = colnames(sce_raw)) %>%
    mutate(
        GRCh38Counts = counts(sce_raw) %>%
            magrittr::extract(row_data$Genome == "GRCh38", ) %>%
            colSums(),
        mm10Counts = counts(sce_raw) %>%
            magrittr::extract(row_data$Genome == "mm10", ) %>%
            colSums()
    ) %>%
    mutate(PropGRCh38 = GRCh38Counts / (GRCh38Counts + mm10Counts))

gg <- ggplot(col_data,
             aes(x = GRCh38Counts, y = mm10Counts, colour = PropGRCh38)) +
    geom_point() +
    scale_colour_distiller(palette = "RdYlBu") +
    labs(
        title = "Counts assigned to different genomes",
        x = "GRCh38",
        y = "mm10",
        colour = "Proportion\nGRCh38"
    )
gg

Version	Author	Date
a3ebd26	Luke Zappia	2019-06-20

It looks like it should be easy to separation most of the cells but because the range is so big it’s hard to see what is happening for low count cells. Let’s zoom in a bit to look at those.

gg + coord_cartesian(xlim = c(0, 1000), ylim = c(0, 1000)) +
    labs(title = "Counts assigned to different genomes (zoomed)")

Version	Author	Date
a3ebd26	Luke Zappia	2019-06-20

thresh_prop <- 0.95

Now we can see that there is a set of cells that have counts from both genomes. These are potentially doublets where two cells have been captured in the same droplet. There are 302 cells that have at least 5 percent of counts from each genome.

To select human cells I am going to set a threshold of having at least 95 percent of counts assigned to GRCh38.

col_data$Selected <- col_data$PropGRCh38 >= thresh_prop

ggplot(col_data, aes(x = GRCh38Counts, y = mm10Counts, colour = Selected)) +
    geom_point() +
    coord_cartesian(xlim = c(0, 1000), ylim = c(0, 1000)) +
    labs(
        title = "Selected human cells (zoomed)",
        x = "GRCh38",
        y = "mm10"
    )

Version	Author	Date
a3ebd26	Luke Zappia	2019-06-20

Doing this will remove 1013 cells, giving a dataset with 2399 cells remaining.

rowData(sce_raw) <- DataFrame(row_data)
colData(sce_raw) <- DataFrame(col_data)
colnames(sce_raw) <- col_data$Barcode

sce <- sce_raw[, colData(sce_raw)$Selected]

Now that we have removed the mouse cells we can also get rid of the mm10 genes.

sce <- sce[rowData(sce)$Genome == "GRCh38", ]
rownames(sce) <- rowData(sce)$FeatureName

The human only RNA-seq dataset now has 17222 features and 2399 cells.

Annotation

Now that we have a dataset that contains information from a single genome we can add some annotation from BioMart using the scater package. We also assign cell cycle stages using the cyclone method in scran.

sce <- annotate_sce(
    sce,
    org        = "human",
    id_type    = "symbol",
    add_anno   = TRUE,
    calc_qc    = TRUE,
    cell_cycle = TRUE,
    BPPARAM    = bpparam,
    verbose    = TRUE
)

Cell annotations

Barcode, GRCh38Counts, mm10Counts, PropGRCh38, Selected, G1Score, SScore, G2MScore, CellCycle, is_cell_control, total_features_by_counts, log10_total_features_by_counts, total_counts, log10_total_counts, pct_counts_in_top_50_features, pct_counts_in_top_100_features, pct_counts_in_top_200_features, pct_counts_in_top_500_features, total_features_by_counts_endogenous, log10_total_features_by_counts_endogenous, total_counts_endogenous, log10_total_counts_endogenous, pct_counts_endogenous, pct_counts_in_top_50_features_endogenous, pct_counts_in_top_100_features_endogenous, pct_counts_in_top_200_features_endogenous, pct_counts_in_top_500_features_endogenous, total_features_by_counts_feature_control, log10_total_features_by_counts_feature_control, total_counts_feature_control, log10_total_counts_feature_control, pct_counts_feature_control, pct_counts_in_top_50_features_feature_control, pct_counts_in_top_100_features_feature_control, pct_counts_in_top_200_features_feature_control, pct_counts_in_top_500_features_feature_control, total_features_by_counts_MT, log10_total_features_by_counts_MT, total_counts_MT, log10_total_counts_MT, pct_counts_MT, pct_counts_in_top_50_features_MT, pct_counts_in_top_100_features_MT, pct_counts_in_top_200_features_MT, pct_counts_in_top_500_features_MT

Feature annotations

FeatureID, FeatureName, Genome, ensembl_gene_id, entrezgene, external_gene_name, hgnc_symbol, chromosome_name, description, gene_biotype, percentage_gene_gc_content, is_feature_control, is_feature_control_MT, mean_counts, log10_mean_counts, n_cells_by_counts, pct_dropout_by_counts, total_counts, log10_total_counts

CITE data

Since we have selected cells in the RNA-seq dataset we want to extract those same cells from the CITE data. We are also going to store this data in another SingleCellExperiment.

cite_mat <- as.matrix(cite_raw[, colnames(sce)]) %>%
    set_rownames(paste0("Anti-", cite_raw$Antibody))

cite <- SingleCellExperiment(assays = list(counts = cite_mat)) %>%
    annotate_sce(calc_qc = TRUE)

cite

class: SingleCellExperiment 
dim: 96 2399 
metadata(0):
assays(1): counts
rownames(96): Anti-CD3 Anti-CD4 ... Anti-IgG2a Anti-IgG2b
rowData names(7): is_feature_control mean_counts ... total_counts
  log10_total_counts
colnames(2399): AAACCTGAGTTGCAGG AAACCTGGTCGTTGTA ...
  TTTGTCACAGGTCGTC TTTGTCAGTAGCGCAA
colData names(9): is_cell_control total_features_by_counts ...
  pct_counts_in_top_200_features pct_counts_in_top_500_features
reducedDimNames(0):
spikeNames(0):

Summary

Parameters

This table describes parameters used and set in this document.

params <- list(
    list(
        Parameter = "n_cells_raw",
        Value = ncol(sce_raw),
        Description = "Number of cells in the raw dataset"
    ),
    list(
        Parameter = "n_features_raw",
        Value = nrow(sce_raw),
        Description = "Number of cells in the raw dataset"
    ),
        list(
        Parameter = "n_cells_cite_raw",
        Value = ncol(cite_raw),
        Description = "Number of cells in the raw CITE dataset"
    ),
    list(
        Parameter = "n_features_cite_raw",
        Value = nrow(cite_raw),
        Description = "Number of cells in the raw CITE dataset"
    ),
    list(
        Parameter = "thresh_prop",
        Value = thresh_prop,
        Description = "GRCh38 proportion for selecting human cells"
    ),
    list(
        Parameter = "n_cells_sel",
        Value = ncol(sce),
        Description = "Number of cells in the selected dataset"
    ),
    list(
        Parameter = "n_features_sel",
        Value = nrow(sce),
        Description = "Number of cells in the selected dataset"
    ),
        list(
        Parameter = "n_cells_cite_sel",
        Value = ncol(cite),
        Description = "Number of cells in the selected CITE dataset"
    ),
    list(
        Parameter = "n_features_cite_sel",
        Value = nrow(cite),
        Description = "Number of cells in the selected CITE dataset"
    )
)
params <- toJSON(params, pretty = TRUE)
kable(fromJSON(params))

Parameter	Value	Description
n_cells_raw	3412	Number of cells in the raw dataset
n_features_raw	29621	Number of cells in the raw dataset
n_cells_cite_raw	3413	Number of cells in the raw CITE dataset
n_features_cite_raw	96	Number of cells in the raw CITE dataset
thresh_prop	0.95	GRCh38 proportion for selecting human cells
n_cells_sel	2399	Number of cells in the selected dataset
n_features_sel	17222	Number of cells in the selected dataset
n_cells_cite_sel	2399	Number of cells in the selected CITE dataset
n_features_cite_sel	96	Number of cells in the selected CITE dataset

Output files

This table describes the output files produced by this document. Right click and Save Link As… to download the results.

write_rds(sce, PATHS$sce_sel, compress = "bz", compression = 9)
write_rds(cite, PATHS$cite_sel, compress = "bz", compression = 9)
write_lines(params, path(OUT_DIR, "parameters.json"))

kable(data.frame(
    File = c(
        download_link("parameters.json", OUT_DIR)
    ),
    Description = c(
        "Parameters set and used in this analysis"
    )
))

File	Description
parameters.json	Parameters set and used in this analysis

Session information

sessioninfo::session_info()

─ Session info ──────────────────────────────────────────────────────────
 setting  value                       
 version  R version 3.6.0 (2019-04-26)
 os       CentOS release 6.7 (Final)  
 system   x86_64, linux-gnu           
 ui       X11                         
 language (EN)                        
 collate  en_US.UTF-8                 
 ctype    en_US.UTF-8                 
 tz       Australia/Melbourne         
 date     2019-06-20                  

─ Packages ──────────────────────────────────────────────────────────────
 ! package              * version   date       lib source        
   assertthat             0.2.1     2019-03-21 [1] CRAN (R 3.6.0)
   backports              1.1.4     2019-04-10 [1] CRAN (R 3.6.0)
   beeswarm               0.2.3     2016-04-25 [1] CRAN (R 3.6.0)
   Biobase              * 2.44.0    2019-05-02 [1] Bioconductor  
   BiocGenerics         * 0.30.0    2019-05-02 [1] Bioconductor  
   BiocNeighbors          1.2.0     2019-05-02 [1] Bioconductor  
   BiocParallel         * 1.18.0    2019-05-03 [1] Bioconductor  
   BiocSingular           1.0.0     2019-05-02 [1] Bioconductor  
   bitops                 1.0-6     2013-08-17 [1] CRAN (R 3.6.0)
   broom                  0.5.2     2019-04-07 [1] CRAN (R 3.6.0)
   cellranger             1.1.0     2016-07-27 [1] CRAN (R 3.6.0)
   cli                    1.1.0     2019-03-19 [1] CRAN (R 3.6.0)
   colorspace             1.4-1     2019-03-18 [1] CRAN (R 3.6.0)
   conflicted           * 1.0.3     2019-05-01 [1] CRAN (R 3.6.0)
   crayon                 1.3.4     2017-09-16 [1] CRAN (R 3.6.0)
   DelayedArray         * 0.10.0    2019-05-02 [1] Bioconductor  
   DelayedMatrixStats     1.6.0     2019-05-02 [1] Bioconductor  
   digest                 0.6.19    2019-05-20 [1] CRAN (R 3.6.0)
   dplyr                * 0.8.1     2019-05-14 [1] CRAN (R 3.6.0)
   evaluate               0.14      2019-05-28 [1] CRAN (R 3.6.0)
   fansi                  0.4.0     2018-10-05 [1] CRAN (R 3.6.0)
   forcats              * 0.4.0     2019-02-17 [1] CRAN (R 3.6.0)
   fs                   * 1.3.1     2019-05-06 [1] CRAN (R 3.6.0)
   generics               0.0.2     2018-11-29 [1] CRAN (R 3.6.0)
   GenomeInfoDb         * 1.20.0    2019-05-02 [1] Bioconductor  
   GenomeInfoDbData       1.2.1     2019-06-19 [1] Bioconductor  
   GenomicRanges        * 1.36.0    2019-05-02 [1] Bioconductor  
   ggbeeswarm             0.6.0     2017-08-07 [1] CRAN (R 3.6.0)
   ggplot2              * 3.2.0     2019-06-16 [1] CRAN (R 3.6.0)
   git2r                  0.25.2    2019-03-19 [1] CRAN (R 3.6.0)
   glue                   1.3.1     2019-03-12 [1] CRAN (R 3.6.0)
   gridExtra              2.3       2017-09-09 [1] CRAN (R 3.6.0)
   gtable                 0.3.0     2019-03-25 [1] CRAN (R 3.6.0)
   haven                  2.1.0     2019-02-19 [1] CRAN (R 3.6.0)
   here                 * 0.1       2017-05-28 [1] CRAN (R 3.6.0)
   highr                  0.8       2019-03-20 [1] CRAN (R 3.6.0)
   hms                    0.4.2     2018-03-10 [1] CRAN (R 3.6.0)
   htmltools              0.3.6     2017-04-28 [1] CRAN (R 3.6.0)
   httr                   1.4.0     2018-12-11 [1] CRAN (R 3.6.0)
   IRanges              * 2.18.1    2019-05-31 [1] Bioconductor  
   irlba                  2.3.3     2019-02-05 [1] CRAN (R 3.6.0)
   jsonlite             * 1.6       2018-12-07 [1] CRAN (R 3.6.0)
   knitr                * 1.23      2019-05-18 [1] CRAN (R 3.6.0)
   labeling               0.3       2014-08-23 [1] CRAN (R 3.6.0)
 P lattice                0.20-38   2018-11-04 [5] CRAN (R 3.6.0)
   lazyeval               0.2.2     2019-03-15 [1] CRAN (R 3.6.0)
   lubridate              1.7.4     2018-04-11 [1] CRAN (R 3.6.0)
   magrittr             * 1.5       2014-11-22 [1] CRAN (R 3.6.0)
 P Matrix                 1.2-17    2019-03-22 [5] CRAN (R 3.6.0)
   matrixStats          * 0.54.0    2018-07-23 [1] CRAN (R 3.6.0)
   memoise                1.1.0     2017-04-21 [1] CRAN (R 3.6.0)
   modelr                 0.1.4     2019-02-18 [1] CRAN (R 3.6.0)
   munsell                0.5.0     2018-06-12 [1] CRAN (R 3.6.0)
 P nlme                   3.1-139   2019-04-09 [5] CRAN (R 3.6.0)
   pillar                 1.4.1     2019-05-28 [1] CRAN (R 3.6.0)
   pkgconfig              2.0.2     2018-08-16 [1] CRAN (R 3.6.0)
   purrr                * 0.3.2     2019-03-15 [1] CRAN (R 3.6.0)
   R6                     2.4.0     2019-02-14 [1] CRAN (R 3.6.0)
   RColorBrewer           1.1-2     2014-12-07 [1] CRAN (R 3.6.0)
   Rcpp                   1.0.1     2019-03-17 [1] CRAN (R 3.6.0)
   RCurl                  1.95-4.12 2019-03-04 [1] CRAN (R 3.6.0)
   readr                * 1.3.1     2018-12-21 [1] CRAN (R 3.6.0)
   readxl                 1.3.1     2019-03-13 [1] CRAN (R 3.6.0)
   rlang                  0.3.4     2019-04-07 [1] CRAN (R 3.6.0)
   rmarkdown              1.13      2019-05-22 [1] CRAN (R 3.6.0)
   rprojroot              1.3-2     2018-01-03 [1] CRAN (R 3.6.0)
   rstudioapi             0.10      2019-03-19 [1] CRAN (R 3.6.0)
   rsvd                   1.0.1     2019-06-02 [1] CRAN (R 3.6.0)
   rvest                  0.3.4     2019-05-15 [1] CRAN (R 3.6.0)
   S4Vectors            * 0.22.0    2019-05-02 [1] Bioconductor  
   scales                 1.0.0     2018-08-09 [1] CRAN (R 3.6.0)
   scater                 1.12.2    2019-05-24 [1] Bioconductor  
   sessioninfo            1.1.1     2018-11-05 [1] CRAN (R 3.6.0)
   SingleCellExperiment * 1.6.0     2019-05-02 [1] Bioconductor  
   stringi                1.4.3     2019-03-12 [1] CRAN (R 3.6.0)
   stringr              * 1.4.0     2019-02-10 [1] CRAN (R 3.6.0)
   SummarizedExperiment * 1.14.0    2019-05-02 [1] Bioconductor  
   tibble               * 2.1.3     2019-06-06 [1] CRAN (R 3.6.0)
   tidyr                * 0.8.3     2019-03-01 [1] CRAN (R 3.6.0)
   tidyselect             0.2.5     2018-10-11 [1] CRAN (R 3.6.0)
   tidyverse            * 1.2.1     2017-11-14 [1] CRAN (R 3.6.0)
   utf8                   1.1.4     2018-05-24 [1] CRAN (R 3.6.0)
   vctrs                  0.1.0     2018-11-29 [1] CRAN (R 3.6.0)
   vipor                  0.4.5     2017-03-22 [1] CRAN (R 3.6.0)
   viridis                0.5.1     2018-03-29 [1] CRAN (R 3.6.0)
   viridisLite            0.3.0     2018-02-01 [1] CRAN (R 3.6.0)
   whisker                0.3-2     2013-04-28 [1] CRAN (R 3.6.0)
   withr                  2.1.2     2018-03-15 [1] CRAN (R 3.6.0)
   workflowr              1.4.0     2019-06-08 [1] CRAN (R 3.6.0)
   xfun                   0.7       2019-05-14 [1] CRAN (R 3.6.0)
   xml2                   1.2.0     2018-01-24 [1] CRAN (R 3.6.0)
   XVector                0.24.0    2019-05-02 [1] Bioconductor  
   yaml                   2.2.0     2018-07-25 [1] CRAN (R 3.6.0)
   zeallot                0.1.0     2018-01-28 [1] CRAN (R 3.6.0)
   zlibbioc               1.30.0    2019-05-02 [1] Bioconductor  

[1] /group/bioi1/luke/analysis/OzSingleCells2019/packrat/lib/x86_64-pc-linux-gnu/3.6.0
[2] /group/bioi1/luke/analysis/OzSingleCells2019/packrat/lib-ext/x86_64-pc-linux-gnu/3.6.0
[3] /group/bioi1/luke/analysis/OzSingleCells2019/packrat/lib-R/x86_64-pc-linux-gnu/3.6.0
[4] /home/luke.zappia/R/x86_64-pc-linux-gnu-library/3.6
[5] /usr/local/installed/R/3.6.0/lib64/R/library

 P ── Loaded and on-disk path mismatch.

Pre-processing

2019-06-20