Last updated: 2018-11-23

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Expand here to see past versions:
    File Version Author Date Message
    Rmd 7d8a1dd Luke Zappia 2018-11-23 Minor fixes to output
    html a61f9c9 Luke Zappia 2018-09-13 Rebuild site
    html ad10b21 Luke Zappia 2018-09-13 Switch to GitHub
    Rmd 7755ac7 Luke Zappia 2018-08-15 Add methods document
    Rmd e590658 Luke Zappia 2018-07-31 Add hidden code
    Rmd db268a5 Luke Zappia 2018-07-31 Add Organoid 4 QC 

# scRNA-seq
library("scater")

# Matrices
library("Matrix")

# Plotting
library("cowplot")

# Presentation
library("glue")
library("knitr")

# Parallel
library("BiocParallel")

# Paths
library("here")

# Output
library("jsonlite")

# Tidyverse
library("tidyverse")
source(here("R/load.R"))
source(here("R/output.R"))

Introduction

In this document we are going to read in the first batch of our Organoid data, produce various quality control plots and remove any low-quality cells or uninformative genes.

sce <- load10xSCE(here("data/Organoid4"),
                  dataset    = "Organoid4",
                  org        = "human",
                  add.anno   = TRUE,
                  calc.qc    = TRUE,
                  calc.cpm   = TRUE,
                  pct.mt     = TRUE,
                  pct.ribo   = TRUE,
                  cell.cycle = TRUE,
                  sparse     = TRUE,
                  bpparam    = MulticoreParam(workers = 10),
                  verbose    = TRUE)

sce <- normalise(sce)
sce <- runPCA(sce)
sce <- runTSNE(sce)

write_rds(sce, here("data/processed/Organoid4_SCE_complete.Rds"))

QC plots

By cell

Total counts

Violin plots of the library size (total counts) for each of the samples.

plotColData(sce, x = "Sample", y = "total_counts", colour_by = "Sample")

Expand here to see past versions of total-counts-1.png:
Version Author Date
a1d89bd Luke Zappia 2018-07-31

Number of features by library size

Relationship between the total counts for each cell and the number of expressed genes. We expect the number of genes to increase with the number of counts, hopefully reaching saturation.

plotColData(sce, x = "total_counts", y = "total_features", colour_by = "Sample")

Expand here to see past versions of count-features-1.png:
Version Author Date
a1d89bd Luke Zappia 2018-07-31

PCA

PCA plots coloured by different variables.

p1 <- plotPCA(sce, colour_by = "Sample", shape_by = "Sample") +
    ggtitle("Sample")
p2 <- plotPCA(sce, colour_by = "log10_total_counts", shape_by = "Sample") +
    ggtitle("Total Counts")
p3 <- plotPCA(sce, colour_by = "CellCycle", shape_by = "Sample") +
    ggtitle("Cell Cycle")
p4 <- plotPCA(sce, colour_by = "pct_dropout", shape_by = "Sample") +
    ggtitle("Dropout")
p5 <- plotPCA(sce, colour_by = "PctCountsMT", shape_by = "Sample") +
    ggtitle("Mitochondrial Genes")
p6 <- plotPCA(sce, colour_by = "PctCountsRibo", shape_by = "Sample") +
    ggtitle("Ribosomal Genes")

plot_grid(p1, p2, p3, p4, p5, p6, ncol = 2)

Expand here to see past versions of pca-1.png:
Version Author Date
a1d89bd Luke Zappia 2018-07-31

t-SNE

t-SNE plots coloured by different variables.

p1 <- plotTSNE(sce, colour_by = "Sample", shape_by = "Sample") +
    ggtitle("Sample")
p2 <- plotTSNE(sce, colour_by = "log10_total_counts", shape_by = "Sample") +
    ggtitle("Total Counts")
p3 <- plotTSNE(sce, colour_by = "CellCycle", shape_by = "Sample") +
    ggtitle("Cell Cycle")
p4 <- plotTSNE(sce, colour_by = "pct_dropout", shape_by = "Sample") +
    ggtitle("Dropout")
p5 <- plotTSNE(sce, colour_by = "PctCountsMT", shape_by = "Sample") +
    ggtitle("Mitochondrial Genes")
p6 <- plotTSNE(sce, colour_by = "PctCountsRibo", shape_by = "Sample") +
    ggtitle("Ribosomal Genes")

plot_grid(p1, p2, p3, p4, p5, p6, ncol = 2)

Expand here to see past versions of t-SNE-1.png:
Version Author Date
a1d89bd Luke Zappia 2018-07-31

Explantory variables

Plots of the variance explained by various variables.

exp.vars <- c("Sample", "CellCycle", "log10_total_counts",
              "log10_total_features", "pct_dropout",
              "pct_counts_top_200_features", "PctCountsMT", "PctCountsRibo")
all.zero <- rowSums(as.matrix(counts(sce))) == 0
plotExplanatoryVariables(sce[!all.zero, ], variables = exp.vars)

Expand here to see past versions of exp-vars-1.png:
Version Author Date
a1d89bd Luke Zappia 2018-07-31

Correlation between explanatory variables.

plotExplanatoryVariables(sce[!all.zero, ], variables = exp.vars,
                         method = "pairs")

Expand here to see past versions of exp-vars-pairs-1.png:
Version Author Date
a1d89bd Luke Zappia 2018-07-31

RLE

Relative Log Expression (RLE) plots. Ideally all boxes should be aligned and have the size size.

plotRLE(sce[!all.zero], list(logcounts = "logcounts", counts = "counts"),
        c(TRUE, FALSE), colour_by = "Sample")

Expand here to see past versions of rle-1.png:
Version Author Date
a1d89bd Luke Zappia 2018-07-31

Mitochondrial genes

Looking at the effect of mitchondrial genes. We define mitochondrial genes as genes on the MT chromosome or with “mitochondrial” in the description.

plotColData(sce, x = "Sample", y = "PctCountsMT", colour_by = "Sample")

Expand here to see past versions of pct-mt-1.png:
Version Author Date
a1d89bd Luke Zappia 2018-07-31

Ribosomal genes

Looking at the effect of ribosomal genes. We define ribosomal genes as genes with “ribosom” in the description.

plotColData(sce, x = "Sample", y = "PctCountsRibo", colour = "Sample")

Expand here to see past versions of pct-ribo-1.png:
Version Author Date
a1d89bd Luke Zappia 2018-07-31

Housekeeping genes

Plots of housekeeping genes. We may want to use these for filtering as a proxy for the health of the cell.

actb.id <- filter(data.frame(rowData(sce)), feature_symbol == "ACTB")[1, 1]
gapdh.id <- filter(data.frame(rowData(sce)), feature_symbol == "GAPDH")[1, 1]

key <- c("ACTB", "GAPDH")
names(key) <- c(actb.id, gapdh.id)
plotExpression(sce, c(actb.id, gapdh.id), colour_by = "Sample") + 
    scale_x_discrete(labels = key)

Expand here to see past versions of hk-exprs-1.png:
Version Author Date
a1d89bd Luke Zappia 2018-07-31

By gene

High expression

plotHighestExprs(sce)

Expand here to see past versions of high-expression-1.png:
Version Author Date
a1d89bd Luke Zappia 2018-07-31

Expression frequency by mean

plotExprsFreqVsMean(sce)

Expand here to see past versions of mean-expression-freq-1.png:
Version Author Date
a1d89bd Luke Zappia 2018-07-31

Total counts by num cells expressed

plotRowData(sce, x = "n_cells_counts", y = "log10_total_counts")

Expand here to see past versions of gene-expression-1.png:
Version Author Date
a1d89bd Luke Zappia 2018-07-31

Filtering

colData(sce)$Filtered <- FALSE

To begin with we have 1421 cells with 33694 features from the ENSEMBL annotation.

Cells

Quantification

Let’s consider how many reads are assigned to features. We can plot the total number of counts in each cell against the number of genes that are expressed.

Cells that have been filtered are shown as triangles.

thresh.h <- 9000

plotColData(sce, x = "total_counts", y = "total_features",
            colour_by = "Sample", shape_by = "Filtered") +
    geom_hline(yintercept = thresh.h, colour = "red", size = 1.5,
               linetype = "dashed") +
    xlab("Total counts") +
    ylab("Total features") +
    ggtitle("Quantification metrics")

Expand here to see past versions of quantification-1.png:
Version Author Date
a1d89bd Luke Zappia 2018-07-31

Cells that express many genes are potential multiplets (multiple cells captured in a single droplet). We will remove 33 cells with more than 9000 genes expressed.

colData(sce)$Filtered <- colData(sce)$Filtered |
                         colData(sce)$total_features > thresh.h

We now have 1388 cells.

Mitochondrial genes

Over-expression of mitochondrial genes can be an indication that a cell is stressed or damaged in some way. Let’s have a look at the percentage of counts that are assigned to mitchondrial genes.

thresh.h <- 10

plotColData(sce, x = "Sample", y = "PctCountsMT", colour_by = "Sample",
            shape_by = "Filtered") +
    geom_hline(yintercept = thresh.h, colour = "red", size = 1.5,
               linetype = "dashed") +
    xlab("Sample") +
    ylab("% counts MT") +
    ggtitle("Mitochondrial genes")

Expand here to see past versions of mt-1.png:
Version Author Date
a1d89bd Luke Zappia 2018-07-31

Some of the cells show high proportions of MT counts. We will remove 39 cells with greater than 10% MT counts.

colData(sce)$Filtered <- colData(sce)$Filtered |
                         colData(sce)$PctCountsMT > thresh.h

That leaves 1354 cells.

Ribsomal genes

We can do a similar thing for ribosomal gene expression.

thresh.h <- 50

plotColData(sce, x = "Sample", y = "PctCountsRibo", colour_by = "Sample",
            shape_by = "Filtered") +
    geom_hline(yintercept = thresh.h, colour = "red", size = 1.5,
               linetype = "dashed") +
    xlab("Sample") +
    ylab("% counts ribosomal") +
    ggtitle("Ribosomal genes")

Expand here to see past versions of ribosomal-1.png:
Version Author Date
a1d89bd Luke Zappia 2018-07-31

Some of the cells show high proportions of ribosomal counts. We will remove 62 cells with greater than 50% ribosomal counts.

colData(sce)$Filtered <- colData(sce)$Filtered |
                         colData(sce)$PctCountsRibo > thresh.h

That leaves 1293 cells.

Housekeeping

Similarly we can look at the expression of the “housekeeping” genes GAPDH and ACTB.

thresh.h <- 3.5
thresh.v <- 2

plotExpression(sce, gapdh.id, x = actb.id, colour_by = "Sample",
               shape_by = "Filtered") +
    geom_hline(yintercept = thresh.h, colour = "red", size = 1.5,
               linetype = "dashed") +
    geom_vline(xintercept = thresh.v, colour = "red", size = 1.5,
               linetype = "dashed") +
    xlab("ACTB") +
    ylab("GAPDH") +
    ggtitle("Housekeepking genes") +
    theme(
        strip.background = element_blank(),
        strip.text.x = element_blank()
    )

Expand here to see past versions of housekeeping-1.png:
Version Author Date
a1d89bd Luke Zappia 2018-07-31

We will remove cells where ACTB is expressed below 2 or GAPDH is expressed below 3.5. This removes 5 cells.

colData(sce)$Filtered <- colData(sce)$Filtered |
                         exprs(sce)[actb.id, ] < thresh.v |
                         exprs(sce)[gapdh.id, ] < thresh.h
sce <- sce[, !colData(sce)$Filtered]

After filtering we are left with 1288 cells.

Dimensionality reduction (filtered cells)

Now that we are relatively confident we have a set of good quality cells, let’s see what they look like in reduced dimensions.

PCA

sce <- runPCA(sce)

p1 <- plotPCA(sce, colour_by = "Sample", shape_by = "Sample") +
    ggtitle("Sample")
p2 <- plotPCA(sce, colour_by = "log10_total_counts", shape_by = "Sample") +
    ggtitle("Total Counts")
p3 <- plotPCA(sce, colour_by = "CellCycle", shape_by = "Sample") +
    ggtitle("Cell Cycle")
p4 <- plotPCA(sce, colour_by = "pct_dropout", shape_by = "Sample") +
    ggtitle("Dropout")
p5 <- plotPCA(sce, colour_by = "PctCountsMT", shape_by = "Sample") +
    ggtitle("Mitochondrial Genes")
p6 <- plotPCA(sce, colour_by = "PctCountsRibo", shape_by = "Sample") +
    ggtitle("Ribosomal Genes")

plot_grid(p1, p2, p3, p4, p5, p6, ncol = 2)

Expand here to see past versions of pca-filtered-cells-1.png:
Version Author Date
a1d89bd Luke Zappia 2018-07-31

t-SNE

sce <- runTSNE(sce)

p1 <- plotTSNE(sce, colour_by = "Sample", shape_by = "Sample") +
    ggtitle("Sample")
p2 <- plotTSNE(sce, colour_by = "log10_total_counts", shape_by = "Sample") +
    ggtitle("Total Counts")
p3 <- plotTSNE(sce, colour_by = "CellCycle", shape_by = "Sample") +
    ggtitle("Cell Cycle")
p4 <- plotTSNE(sce, colour_by = "pct_dropout", shape_by = "Sample") +
    ggtitle("Dropout")
p5 <- plotTSNE(sce, colour_by = "PctCountsMT", shape_by = "Sample") +
    ggtitle("Mitochondrial Genes")
p6 <- plotTSNE(sce, colour_by = "PctCountsRibo", shape_by = "Sample") +
    ggtitle("Ribosomal Genes")

plot_grid(p1, p2, p3, p4, p5, p6, ncol = 2)

Expand here to see past versions of tSNE-filtered-cells-1.png:
Version Author Date
a1d89bd Luke Zappia 2018-07-31

Genes

Expression

Some of the features we would never expect to see expressed in an RNA-seq experiment. Before doing anything else I am going to remove the features that have less than two counts across all cells.

keep <- rowSums(counts(sce)) > 1
sce <- sce[keep]

This removes 13665 genes and leaves us with 20029.

We will also going remove genes that are expressed in less than two individual cells.

keep <- rowSums(counts(sce) != 0) > 1
sce <- sce[keep, ]

This removes 44 genes and leaves us with 19985.

HGNC genes

We are also going to filter out any genes that don’t have HGNC symbols. These are mostly pseudogenes and are unlikely to be informative.

keep <- !(rowData(sce)$hgnc_symbol == "") &
        !(is.na(rowData(sce)$hgnc_symbol))
sce <- sce[keep, ]

This removes 3100 genes and leaves us with 16885.

dups <- which(duplicated(rowData(sce)$feature_symbol))

There are 1 gene(s) with duplicate HGNC symbol names. For these genes we will use an alternative symbol name. Once we have done this we can rename the features using feature symbols instead of ENSEMBL IDs which will make interpreting results easier.

rowData(sce)[dups, "feature_symbol"] <- rowData(sce)[dups, "symbol"]
rownames(sce) <- rowData(sce)$feature_symbol

Dimensionality reduction (filtered genes)

Let’s see what our final dataset looks like in reduced dimensions.

PCA

sce <- runPCA(sce)

p1 <- plotPCA(sce, colour_by = "Sample", shape_by = "Sample") +
    ggtitle("Sample")
p2 <- plotPCA(sce, colour_by = "log10_total_counts", shape_by = "Sample") +
    ggtitle("Total Counts")
p3 <- plotPCA(sce, colour_by = "CellCycle", shape_by = "Sample") +
    ggtitle("Cell Cycle")
p4 <- plotPCA(sce, colour_by = "pct_dropout", shape_by = "Sample") +
    ggtitle("Dropout")
p5 <- plotPCA(sce, colour_by = "PctCountsMT", shape_by = "Sample") +
    ggtitle("Mitochondrial Genes")
p6 <- plotPCA(sce, colour_by = "PctCountsRibo", shape_by = "Sample") +
    ggtitle("Ribosomal Genes")

plot_grid(p1, p2, p3, p4, p5, p6, ncol = 2)

Expand here to see past versions of pca-filtered-genes-1.png:
Version Author Date
a1d89bd Luke Zappia 2018-07-31

t-SNE

sce <- runTSNE(sce)

p1 <- plotTSNE(sce, colour_by = "Sample", shape_by = "Sample") +
    ggtitle("Sample")
p2 <- plotTSNE(sce, colour_by = "log10_total_counts", shape_by = "Sample") +
    ggtitle("Total Counts")
p3 <- plotTSNE(sce, colour_by = "CellCycle", shape_by = "Sample") +
    ggtitle("Cell Cycle")
p4 <- plotTSNE(sce, colour_by = "pct_dropout", shape_by = "Sample") +
    ggtitle("Dropout")
p5 <- plotTSNE(sce, colour_by = "PctCountsMT", shape_by = "Sample") +
    ggtitle("Mitochondrial Genes")
p6 <- plotTSNE(sce, colour_by = "PctCountsRibo", shape_by = "Sample") +
    ggtitle("Ribosomal Genes")

plot_grid(p1, p2, p3, p4, p5, p6, ncol = 2)

Expand here to see past versions of tSNE-filtered-genes-1.png:
Version Author Date
a1d89bd Luke Zappia 2018-07-31

We now have a high-quality dataset for our analysis with 16885 genes and 1288 cells. A median of 3248 genes are expressed in each cell.

Summary

Parameters

This table describes parameters used and set in this document.

params <- toJSON(list(
    list(
        Parameter = "total_features",
        Value = 9000,
        Description = "Maximum threshold for total features expressed"
    ),
    list(
        Parameter = "mt_counts",
        Value = 10,
        Description = "Maximum threshold for percentage counts mitochondrial"
    ),
    list(
        Parameter = "ribo_counts",
        Value = 50,
        Description = "Maximum threshold for percentage counts ribosomal"
    ),
    list(
        Parameter = "ACTB_expr",
        Value = 2,
        Description = "Minimum threshold for ACTB expression"
    ),
    list(
        Parameter = "GAPDH_expr",
        Value = 3.5,
        Description = "Minimum threshold for GAPDH expression"
    ),
    list(
        Parameter = "n_cells",
        Value = ncol(sce),
        Description = "Number of cells in the filtered dataset"
    ),
    list(
        Parameter = "n_genes",
        Value = nrow(sce),
        Description = "Number of genes in the filtered dataset"
    ),
    list(
        Parameter = "median_genes",
        Value = median(colSums(counts(sce) != 0)),
        Description = paste("Median number of expressed genes per cell in the",
                            "filtered dataset")
    )
), pretty = TRUE)

kable(fromJSON(params))
Parameter Value Description
total_features 9000 Maximum threshold for total features expressed
mt_counts 10 Maximum threshold for percentage counts mitochondrial
ribo_counts 50 Maximum threshold for percentage counts ribosomal
ACTB_expr 2 Minimum threshold for ACTB expression
GAPDH_expr 3.5 Minimum threshold for GAPDH expression
n_cells 1288 Number of cells in the filtered dataset
n_genes 16885 Number of genes in the filtered dataset
median_genes 3248 Median number of expressed genes per cell in the filtered 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, here("data/processed/Organoid4_SCE_filtered.Rds"))
dir.create(here("output", DOCNAME), showWarnings = FALSE)

write_lines(params, here("output", DOCNAME, "parameters.json"))

kable(data.frame(
    File = c(
        glue("[parameters.json]({getDownloadURL('parameters.json', DOCNAME)})")
    ),
    Description = c(
        "Parameters set and used in this analysis"
    )
))
File Description
parameters.json Parameters set and used in this analysis

Session information

devtools::session_info()
 setting  value                       
 version  R version 3.5.0 (2018-04-23)
 system   x86_64, linux-gnu           
 ui       X11                         
 language (EN)                        
 collate  en_US.UTF-8                 
 tz       Australia/Melbourne         
 date     2018-11-23                  

 package              * version   date      
 assertthat             0.2.0     2017-04-11
 backports              1.1.2     2017-12-13
 base                 * 3.5.0     2018-06-18
 beeswarm               0.2.3     2016-04-25
 bindr                  0.1.1     2018-03-13
 bindrcpp             * 0.2.2     2018-03-29
 Biobase              * 2.40.0    2018-07-30
 BiocGenerics         * 0.26.0    2018-07-30
 BiocParallel         * 1.14.2    2018-07-08
 bitops                 1.0-6     2013-08-17
 broom                  0.5.0     2018-07-17
 cellranger             1.1.0     2016-07-27
 cli                    1.0.0     2017-11-05
 codetools              0.2-15    2016-10-05
 colorspace             1.3-2     2016-12-14
 compiler               3.5.0     2018-06-18
 cowplot              * 0.9.3     2018-07-15
 crayon                 1.3.4     2017-09-16
 data.table             1.11.4    2018-05-27
 datasets             * 3.5.0     2018-06-18
 DelayedArray         * 0.6.2     2018-07-23
 DelayedMatrixStats     1.2.0     2018-07-30
 devtools               1.13.6    2018-06-27
 digest                 0.6.15    2018-01-28
 dplyr                * 0.7.6     2018-06-29
 edgeR                  3.22.3    2018-06-21
 evaluate               0.10.1    2017-06-24
 forcats              * 0.3.0     2018-02-19
 GenomeInfoDb         * 1.16.0    2018-07-30
 GenomeInfoDbData       1.1.0     2018-07-30
 GenomicRanges        * 1.32.6    2018-07-20
 ggbeeswarm             0.6.0     2017-08-07
 ggplot2              * 3.0.0     2018-07-03
 git2r                  0.21.0    2018-01-04
 glue                 * 1.3.0     2018-07-17
 graphics             * 3.5.0     2018-06-18
 grDevices            * 3.5.0     2018-06-18
 grid                   3.5.0     2018-06-18
 gridExtra              2.3       2017-09-09
 gtable                 0.2.0     2016-02-26
 haven                  1.1.2     2018-06-27
 here                 * 0.1       2017-05-28
 highr                  0.7       2018-06-09
 hms                    0.4.2     2018-03-10
 htmltools              0.3.6     2017-04-28
 httpuv                 1.4.5     2018-07-19
 httr                   1.3.1     2017-08-20
 IRanges              * 2.14.10   2018-07-30
 jsonlite             * 1.5       2017-06-01
 knitr                * 1.20      2018-02-20
 later                  0.7.3     2018-06-08
 lattice                0.20-35   2017-03-25
 lazyeval               0.2.1     2017-10-29
 limma                  3.36.2    2018-06-21
 locfit                 1.5-9.1   2013-04-20
 lubridate              1.7.4     2018-04-11
 magrittr               1.5       2014-11-22
 Matrix               * 1.2-14    2018-04-09
 matrixStats          * 0.54.0    2018-07-23
 memoise                1.1.0     2017-04-21
 methods              * 3.5.0     2018-06-18
 mime                   0.5       2016-07-07
 modelr                 0.1.2     2018-05-11
 munsell                0.5.0     2018-06-12
 nlme                   3.1-137   2018-04-07
 parallel             * 3.5.0     2018-06-18
 pillar                 1.3.0     2018-07-14
 pkgconfig              2.0.1     2017-03-21
 plyr                   1.8.4     2016-06-08
 promises               1.0.1     2018-04-13
 purrr                * 0.2.5     2018-05-29
 R.methodsS3            1.7.1     2016-02-16
 R.oo                   1.22.0    2018-04-22
 R.utils                2.6.0     2017-11-05
 R6                     2.2.2     2017-06-17
 Rcpp                   0.12.18   2018-07-23
 RCurl                  1.95-4.11 2018-07-15
 readr                * 1.1.1     2017-05-16
 readxl                 1.1.0     2018-04-20
 reshape2               1.4.3     2017-12-11
 rhdf5                  2.24.0    2018-07-30
 Rhdf5lib               1.2.1     2018-07-30
 rjson                  0.2.20    2018-06-08
 rlang                  0.2.1     2018-05-30
 rmarkdown              1.10.2    2018-07-30
 rprojroot              1.3-2     2018-01-03
 rstudioapi             0.7       2017-09-07
 rvest                  0.3.2     2016-06-17
 S4Vectors            * 0.18.3    2018-07-30
 scales                 0.5.0     2017-08-24
 scater               * 1.8.2     2018-07-27
 shiny                  1.1.0     2018-05-17
 shinydashboard         0.7.0     2018-03-21
 SingleCellExperiment * 1.2.0     2018-07-30
 stats                * 3.5.0     2018-06-18
 stats4               * 3.5.0     2018-06-18
 stringi                1.2.4     2018-07-20
 stringr              * 1.3.1     2018-05-10
 SummarizedExperiment * 1.10.1    2018-07-30
 tibble               * 1.4.2     2018-01-22
 tidyr                * 0.8.1     2018-05-18
 tidyselect             0.2.4     2018-02-26
 tidyverse            * 1.2.1     2017-11-14
 tools                  3.5.0     2018-06-18
 tximport               1.8.0     2018-07-30
 utils                * 3.5.0     2018-06-18
 vipor                  0.4.5     2017-03-22
 viridis                0.5.1     2018-03-29
 viridisLite            0.3.0     2018-02-01
 whisker                0.3-2     2013-04-28
 withr                  2.1.2     2018-03-15
 workflowr              1.1.1     2018-07-06
 xml2                   1.2.0     2018-01-24
 xtable                 1.8-2     2016-02-05
 XVector                0.20.0    2018-07-30
 yaml                   2.2.0     2018-07-25
 zlibbioc               1.26.0    2018-07-30
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 local                             
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 cran (@0.1.1)                     
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 cran (@1.0-6)                     
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 cran (@0.7.6)                     
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 cran (@0.2.1)                     
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 cran (@1.7.4)                     
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 local                             
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 cran (@0.5.0)                     
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 local                             
 cran (@1.3.0)                     
 cran (@2.0.1)                     
 cran (@1.8.4)                     
 cran (@1.0.1)                     
 cran (@0.2.5)                     
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 cran (@0.12.18)                   
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 cran (@1.4.3)                     
 Bioconductor                      
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 Github (rstudio/rmarkdown@18207b9)
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 cran (@1.4.2)                     
 cran (@0.8.1)                     
 cran (@0.2.4)                     
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 local                             
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 local                             
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 cran (@0.5.1)                     
 cran (@0.3.0)                     
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 CRAN (R 3.5.0)                    
 cran (@1.8-2)                     
 Bioconductor                      
 cran (@2.2.0)                     
 Bioconductor

This reproducible R Markdown analysis was created with workflowr 1.1.1