Visualization of Slide-seqV2

---

Stickels, R.R., Murray, E., Kumar, P., Li, J., Marshall, J.L., Di Bella, D.J., Arlotta, P., Macosko, E.Z., and Chen, F. (2020). Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol.

---

Load required packages.

library(data.table)
library(tidyverse)
library(magrittr)
library(patchwork)
library(extrafont)
library(Matrix)

Sys.time()

## [1] "2021-02-06 18:19:50 CST"

Data preparation

Functions

source(
    file = file.path(
        SCRIPT_DIR,
        "utilities.R"
    )
)

Matrix

PROJECT_DIR <- "/Users/jialei/Dropbox/Data/Projects/UTSW/Spatial_sequencing/datasets/Slide-seqV2"

list.files(path = file.path(PROJECT_DIR, "raw/SCP815/other"))

##  [1] "Puck_190921_19_bead_locations.csv.gz"    
##  [2] "Puck_190921_19.digital_expression.txt.gz"
##  [3] "Puck_190921_21_bead_locations.csv.gz"    
##  [4] "Puck_190921_21.digital_expression.txt.gz"
##  [5] "Puck_190926_01_bead_locations.csv.gz"    
##  [6] "Puck_190926_01.digital_expression.txt.gz"
##  [7] "Puck_190926_02_bead_locations.csv.gz"    
##  [8] "Puck_190926_02.digital_expression.txt.gz"
##  [9] "Puck_190926_03_bead_locations.csv.gz"    
## [10] "Puck_190926_03.digital_expression.txt.gz"
## [11] "Puck_190926_06_bead_locations.csv.gz"    
## [12] "Puck_190926_06.digital_expression.txt.gz"
## [13] "Puck_191007_07_bead_locations.csv.gz"    
## [14] "Puck_191007_07.digital_expression.txt.gz"
## [15] "Puck_191204_01_bead_locations.csv.gz"    
## [16] "Puck_191204_01.digital_expression.txt.gz"
## [17] "Puck_200115_08_bead_locations.csv.gz"    
## [18] "Puck_200115_08.digital_expression.txt.gz"

mRNA detection sensitivity

Load matrix and coordinates.

matrix_readcount_use <- load_matrix(
    x = file.path(
        PROJECT_DIR,
        "raw/SCP815/other",
        "Puck_200115_08.digital_expression.txt.gz"
    )
)

barcode_location <- read_csv(
    file = file.path(
        PROJECT_DIR,
        "raw/SCP815/other",
        "Puck_200115_08_bead_locations.csv.gz"
    ),
    col_names = c("barcode", "x_coord", "y_coord"),
    skip = 1
)

## 
## ── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
##   barcode = col_character(),
##   x_coord = col_double(),
##   y_coord = col_double()
## )

Create AnnData object.

ad <- reticulate::import(module = "anndata", convert = TRUE)
print(ad$`__version__`)

## [1] "0.7.5"

adata <- ad$AnnData(
    X = t(matrix_readcount_use),
    obs = data.frame(
        cell = colnames(matrix_readcount_use),
        num_umis = colSums(matrix_readcount_use),
        mt_percentage = colSums(
            matrix_readcount_use[
                str_detect(
                    string = rownames(matrix_readcount_use),
                    pattern = "mt-"
                ),
            ]
        ) / colSums(matrix_readcount_use),
        row.names = colnames(matrix_readcount_use)
    ),
    var = data.frame(
        feature = rownames(matrix_readcount_use),
        row.names = rownames(matrix_readcount_use)
    )
)

file_name <- file.path(
    PROJECT_DIR,
    "clustering",
    "matrices",
    "puck_200115_08.h5ad"
)
# file_name
if (!file.exists(file_name)) {
    adata$write(
        file_name
    )
}

barcode_location %<>%
    mutate(
        num_umis = colSums(matrix_readcount_use[, barcode]),
        num_features = colSums(matrix_readcount_use[, barcode] > 0),
        num_umis_mt = colSums(
            matrix_readcount_use[
                str_detect(string = rownames(matrix_readcount_use), pattern = "mt-"),
                barcode
            ]
        ),
        mt_ratio = num_umis_mt / num_umis
    )

barcode_location %>%
    summarise(
        num_cells = n(),
        median_umis = median(num_umis),
        median_features = median(num_features),
        median_mt_ratio = median(mt_ratio)
    ) %>%
    gt::gt() %>%
    # gt::cols_label(n = "num_cells") %>%
    gt::tab_options(table.font.size = "median")

num_cells	median_umis	median_features	median_mt_ratio
53208	302	235	0.08514392

purrr::map(seq(0, 1, 0.25), function(x) {
    cat(x, "\n")

    barcode_location %>%
        summarise(
            num_umis = quantile(num_umis, x),
            num_features = quantile(num_features, x),
            mt_ratio = quantile(mt_ratio, x)
        ) %>%
        mutate(
            quantile = x
        ) %>%
        select(quantile, everything())
}) %>%
    bind_rows() %>%
    gt::gt() %>%
    # gt::cols_label(n = "num_cells") %>%
    gt::tab_options(table.font.size = "median")

## 0 
## 0.25 
## 0.5 
## 0.75 
## 1

quantile	num_umis	num_features	mt_ratio
0.00	10.00	6	0.00000000
0.25	118.00	98	0.04605263
0.50	302.00	235	0.08514392
0.75	736.25	553	0.15544586
1.00	23772.00	8557	0.68421053

Spatial visualization

GEOM_POINT_SIZE <- 0.5

p_spatial_UMI <- plot_embedding(
    embedding = barcode_location %>%
        dplyr::select(x_coord, y_coord),
    color_values = barcode_location$num_umis %>% log10(),
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = TRUE,
    rasterise = TRUE,
    dpi = 600
) +
    coord_fixed(
        xlim = quantile(barcode_location$x_coord, probs = c(0.01, 0.99)),
        ylim = quantile(barcode_location$y_coord, probs = c(0.01, 0.99))
    ) +
    theme_customized(x = 0.02, legend_key_size = 2, legend_text_size = 5) +
    theme_customized_void() +
    ggplot2::guides(
        color = ggplot2::guide_colourbar(
            title = expression(paste("Log"[10], " (UMIs)"))
        )
    ) +
    scale_color_gradientn(
        colors = colorRampPalette(
            colors = rev(
                x = RColorBrewer::brewer.pal(n = 11, name = "Spectral")
            )
        )(n = 100)
    )

## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.

p_spatial_features <- plot_embedding(
    embedding = barcode_location %>%
        dplyr::select(x_coord, y_coord),
    color_values = barcode_location$num_features %>% log10(),
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = TRUE,
    rasterise = TRUE,
    dpi = 600
) +
    coord_fixed(
        xlim = quantile(barcode_location$x_coord, probs = c(0.01, 0.99)),
        ylim = quantile(barcode_location$y_coord, probs = c(0.01, 0.99))
    ) +
    theme_customized(x = 0.02, legend_key_size = 2, legend_text_size = 5) +
    theme_customized_void() +
    ggplot2::guides(
        color = ggplot2::guide_colourbar(
            title = expression(paste("Log"[10], " (Genes)"))
        )
    ) +
    scale_color_gradientn(
        colors = colorRampPalette(
            colors = rev(
                x = RColorBrewer::brewer.pal(n = 11, name = "Spectral")
            )
        )(n = 100)
    )

## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.

Fig. 1a

GEOM_POINT_SIZE <- 0.5

purrr::map2(
    c("num_umis", "num_features"),
    c(
        expression(paste("Log"[10], " (UMIs)")),
        expression(paste("Log"[10], " (Genes)"))
    ), function(x, y) {
        plot_embedding(
            embedding = barcode_location %>%
                dplyr::select(x_coord, y_coord),
            color_values = barcode_location[[x]] %>% log10(),
            show_color_legend = TRUE,
            geom_point_size = GEOM_POINT_SIZE,
            sort_values = TRUE,
            rasterise = TRUE,
            dpi = 600
        ) +
            coord_fixed(
                xlim = quantile(barcode_location$x_coord, probs = c(0.01, 0.99)),
                ylim = quantile(barcode_location$y_coord, probs = c(0.01, 0.99))
            ) +
            theme_customized(x = 0.02, legend_key_size = 2, legend_text_size = 5) +
            theme_customized_void() +
            ggplot2::guides(
                color = ggplot2::guide_colourbar(
                    title = y
                )
            ) +
            scale_color_gradientn(
                colors = colorRampPalette(
                    colors = rev(
                        x = RColorBrewer::brewer.pal(n = 11, name = "Spectral")
                    )
                )(n = 100)
            )
    }
) %>%
    purrr::reduce(`+`) +
    patchwork::plot_layout(
        ncol = 2
    ) +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )

## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.

plot_embedding(
    embedding = barcode_location %>%
        dplyr::select(x_coord, y_coord),
    color_values = barcode_location$mt_ratio,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = TRUE,
    rasterise = TRUE,
    dpi = 600
) +
    coord_fixed(
        xlim = quantile(barcode_location$x_coord, probs = c(0.01, 0.99)),
        ylim = quantile(barcode_location$y_coord, probs = c(0.01, 0.99))
    ) +
    theme_customized(x = 0.02, legend_key_size = 2, legend_text_size = 5) +
    theme_customized_void() +
    ggplot2::guides(
        color = ggplot2::guide_colourbar(
            title = "MT %"
        )
    ) +
    scale_color_gradientn(
        colors = colorRampPalette(
            colors = rev(
                x = RColorBrewer::brewer.pal(n = 11, name = "Spectral")
            )
        )(n = 100)
    )

## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.

Visualize cells with different numbers of genes detected.

GEOM_POINT_SIZE <- 0.3

purrr::map(c(200, 500, 1000, 2000), function(x) {
    plot_embedding(
        embedding = barcode_location %>%
            dplyr::select(x_coord, y_coord),
        color_values = as.numeric(barcode_location$num_features >= x) %>%
            as.factor(),
        show_color_legend = TRUE,
        geom_point_size = GEOM_POINT_SIZE,
        sort_values = TRUE,
        rasterise = TRUE,
        dpi = 600
    ) +
        coord_fixed(
            xlim = quantile(barcode_location$x_coord, probs = c(0.01, 0.99)),
            ylim = quantile(barcode_location$y_coord, probs = c(0.01, 0.99))
        ) +
        theme_customized(x = 0.02, legend_key_size = 2, legend_text_size = 5) +
        theme_customized_void() +
        scale_color_manual(
            values = c("grey70", "salmon"),
            labels = paste(c("<", ">="), x)
        )
}) %>%
    purrr::reduce(`+`) +
    patchwork::plot_layout(
        ncol = 2
    ) +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )

Fig. 1c

purrr::map(c("Gapdh", "Atp2b1", "Ociad2", "Slc17a7"), function(x) {
    plot_embedding(
        embedding = barcode_location %>%
            dplyr::select(x_coord, y_coord),
        color_values = log10(matrix_readcount_use[x, barcode_location$barcode] + 1),
        show_color_legend = TRUE,
        geom_point_size = GEOM_POINT_SIZE,
        sort_values = TRUE,
        rasterise = TRUE,
        dpi = 600
    ) +
        coord_fixed(
            xlim = quantile(barcode_location$x_coord, probs = c(0.01, 0.99)),
            ylim = quantile(barcode_location$y_coord, probs = c(0.01, 0.99))
        ) +
        theme_customized(x = 0.02, legend_key_size = 2, legend_text_size = 5) +
        theme_customized_void() +
        ggplot2::guides(
            color = ggplot2::guide_colourbar(
                title = x
            )
        ) +
        scale_color_gradientn(
            colors = colorRampPalette(
                colors = rev(
                    x = RColorBrewer::brewer.pal(n = 11, name = "Spectral")
                )
            )(n = 100)
        )
}) %>%
    purrr::reduce(`+`) +
    patchwork::plot_layout(
        ncol = 2
    ) +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )

## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.

Clustering

embedding <- read_csv(
    file = file.path(
        PROJECT_DIR,
        "clustering/puck_200115_08/exploring",
        "embedding_ncomponents32_ccc1_seed20200416.csv.gz"
    )
)

## 
## ── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## cols(
##   cell = col_character(),
##   batch = col_logical(),
##   louvain = col_double(),
##   leiden = col_double(),
##   x_tsne = col_double(),
##   y_tsne = col_double(),
##   x_umap = col_double(),
##   y_umap = col_double(),
##   x_fitsne = col_double(),
##   y_fitsne = col_double(),
##   x_phate = col_double(),
##   y_phate = col_double(),
##   `x_umap_min_dist=0.1` = col_double(),
##   `y_umap_min_dist=0.1` = col_double(),
##   x_multicoretsne = col_double(),
##   y_multicoretsne = col_double()
## )

embedding %>% dim()

## [1] 27680    16

embedding %<>%
    mutate(
        num_umis = colSums(matrix_readcount_use[, cell]),
        num_features = colSums(matrix_readcount_use[, cell] > 0),
        mt_ratio = colSums(
            matrix_readcount_use[
                str_detect(string = rownames(matrix_readcount_use), pattern = "mt-"),
                cell
            ]
        ) / num_umis
    )

embedding %>% head()

embedding %>%
    dplyr::group_by(leiden) %>%
    dplyr::summarise(
        num_cells = n(),
        median_umis = median(num_umis),
        median_features = median(num_features),
        median_mt_ratio = median(mt_ratio)
    ) %>%
    gt::gt() %>%
    gt::tab_options(table.font.size = "median")

leiden	num_cells	median_umis	median_features	median_mt_ratio
0	3791	979.0	737.0	0.04683728
1	3268	587.0	426.0	0.05598757
2	3205	451.0	351.0	0.09631148
3	2822	382.0	302.0	0.10734285
4	2074	1183.0	866.5	0.04478874
5	1729	968.0	724.0	0.03016241
6	1432	1070.0	781.0	0.03886781
7	1424	1236.0	924.0	0.02750982
8	1259	826.0	638.0	0.06024096
9	1247	796.0	612.0	0.05173913
10	1141	560.0	409.0	0.08976378
11	963	820.0	627.0	0.06049822
12	897	588.0	443.0	0.08113590
13	746	1159.5	892.0	0.03335862
14	551	1324.0	995.0	0.03610060
15	542	1282.0	785.5	0.05035454
16	362	479.5	359.5	0.09965678
17	227	567.0	445.0	0.08333333

EMBEDDING_TITLE_PREFIX <- "UMAP"
x_column <- "x_umap_min_dist=0.1"
y_column <- "y_umap_min_dist=0.1"

GEOM_POINT_SIZE <- 0.2

p_embedding_leiden <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = embedding$leiden %>% as.factor(),
    label = paste0(EMBEDDING_TITLE_PREFIX, "; Leiden"),
    label_position = NULL,
    show_color_value_labels = TRUE,
    show_color_legend = FALSE,
    geom_point_size = GEOM_POINT_SIZE,
    geom_point_alpha = 1,
    sort_values = FALSE,
    shuffle_values = FALSE,
    label_size = 6,
    label_hjust = 0,
    label_vjust = 0,
    rasterise = TRUE,
    dpi = 600,
    legend_size = 3,
    legend_ncol = 2
) +
    theme_customized()

p_embedding_UMI <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = embedding$num_umis %>% log10(),
    label = paste0(EMBEDDING_TITLE_PREFIX, "; UMIs"),
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = TRUE,
    rasterise = TRUE,
    dpi = 600
) +
    theme_customized(x = 0.02, legend_key_size = 2, legend_text_size = 5)

p_embedding_MT <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = embedding$mt_ratio,
    label = paste0(EMBEDDING_TITLE_PREFIX, "; MT %"),
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = TRUE,
    rasterise = TRUE,
    dpi = 600
) +
    theme_customized(x = 0.02, legend_key_size = 2, legend_text_size = 5)

list(
    p_embedding_leiden,
    p_embedding_UMI,
    p_embedding_MT
) %>%
    purrr::reduce(`+`) +
    patchwork::plot_layout(nrow = 1) +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )

barcode_location %<>%
    left_join(
        embedding %>% select(cell, leiden),
        by = c("barcode" = "cell")
    )

GEOM_POINT_SIZE <- 0.3

p_spatial_na <- plot_embedding(
    embedding = barcode_location %>%
        dplyr::select(x_coord, y_coord),
    color_values = as.numeric(is.na(barcode_location$leiden)) %>% as.factor(),
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = TRUE,
    rasterise = TRUE,
    dpi = 600
) +
    coord_fixed(
        xlim = quantile(barcode_location$x_coord, probs = c(0.01, 0.99)),
        ylim = quantile(barcode_location$y_coord, probs = c(0.01, 0.99))
    ) +
    guides(colour = guide_legend(override.aes = list(size = 2), nrow = 2, title = NULL)) +
    theme_customized(x = 0.02, legend_key_size = 1, legend_text_size = 5) +
    theme_customized_void() %+replace%
    theme(
        legend.justification = 0.5,
        legend.position = "bottom"
    ) +
    scale_color_manual(
        values = c("grey70", "salmon"),
        labels = c("incl.", "not incl.")
    )

p_spatial_cluster <- plot_embedding(
    embedding = barcode_location %>%
        dplyr::select(x_coord, y_coord),
    color_values = barcode_location$leiden %>% as.factor(),
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = FALSE,
    rasterise = TRUE,
    dpi = 600
) +
    coord_fixed(
        xlim = quantile(barcode_location$x_coord, probs = c(0.01, 0.99)),
        ylim = quantile(barcode_location$y_coord, probs = c(0.01, 0.99))
    ) +
    guides(colour = guide_legend(override.aes = list(size = 2), nrow = 2, title = NULL)) +
    theme_customized(legend_key_size = 1, legend_text_size = 5) %+replace%
    theme_customized_void() %+replace%
    theme(
        legend.justification = 0.5,
        legend.position = "bottom"
    )

Clustering; cells with lower UMI counts and higher MT percentages are discarded.

list(
    p_spatial_na,
    p_spatial_cluster
) %>%
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 2) +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )

GEOM_POINT_SIZE <- 0.3

purrr::map(sort(unique(embedding$leiden)), function(x) {
    values <- barcode_location$leiden == x
    # values <- values[! is.na(values)]
    values[is.na(values)] <- FALSE

    plot_embedding(
        embedding = barcode_location %>%
            # dplyr::filter(! is.na(leiden)) %>%
            dplyr::select(x_coord, y_coord),
        color_values = as.numeric(values) %>% as.factor(),
        show_color_legend = TRUE,
        geom_point_size = GEOM_POINT_SIZE,
        sort_values = TRUE,
        rasterise = TRUE,
        dpi = 600
    ) +
        coord_fixed(
            xlim = quantile(barcode_location$x_coord, probs = c(0.01, 0.99)),
            ylim = quantile(barcode_location$y_coord, probs = c(0.01, 0.99))
        ) +
        theme_customized(x = 0.02, legend_key_size = 2, legend_text_size = 5) +
        theme_customized_void() +
        scale_color_manual(
            values = c("grey70", "salmon"),
            labels = c("Other", x)
        )
}) %>%
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 3) +
    patchwork::plot_annotation(
        theme = theme(plot.margin = margin())
    )

R session info

devtools::session_info()$platform

##  setting  value                       
##  version  R version 4.0.3 (2020-10-10)
##  os       macOS  11.2                 
##  system   x86_64, darwin20.2.0        
##  ui       unknown                     
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  ctype    en_US.UTF-8                 
##  tz       America/Chicago             
##  date     2021-02-06

devtools::session_info()$pack %>%
    as_tibble() %>%
    dplyr::select(
        package,
        loadedversion,
        date,
        `source`
    ) %>%
    # print(n = Inf)
    gt::gt() %>%
    gt::tab_options(table.font.size = "median")

package	loadedversion	date	source
assertthat	0.2.1	2019-03-21	CRAN (R 4.0.0)
backports	1.2.1	2020-12-09	CRAN (R 4.0.3)
beeswarm	0.2.3	2016-04-25	CRAN (R 4.0.0)
broom	0.7.4.9000	2021-02-05	Github (tidymodels/broom@81e10a8)
bslib	0.2.4.9001	2021-02-05	Github (rstudio/bslib@3d98072)
cachem	1.0.3.9000	2021-02-06	Github (r-lib/cachem@805f469)
callr	3.5.1.9000	2021-01-06	Github (r-lib/callr@743069f)
cellranger	1.1.0	2016-07-27	CRAN (R 4.0.0)
checkmate	2.0.0	2020-02-06	CRAN (R 4.0.0)
cli	2.3.0	2021-01-31	CRAN (R 4.0.3)
colorspace	2.0-0	2020-11-10	R-Forge (R 4.0.3)
crayon	1.4.0	2021-01-30	CRAN (R 4.0.3)
data.table	1.13.7	2021-02-04	local
DBI	1.1.1	2021-01-15	CRAN (R 4.0.3)
dbplyr	2.1.0	2021-02-03	CRAN (R 4.0.3)
desc	1.2.0	2018-05-01	CRAN (R 4.0.0)
devtools	2.3.1.9000	2021-02-05	Github (r-lib/devtools@5a047ba)
digest	0.6.27	2020-10-24	CRAN (R 4.0.3)
dplyr	1.0.4.9000	2021-02-04	Github (tidyverse/dplyr@2455c77)
ellipsis	0.3.1	2020-05-15	CRAN (R 4.0.3)
evaluate	0.14	2019-05-28	CRAN (R 4.0.0)
extrafont	0.17	2014-12-08	CRAN (R 4.0.3)
extrafontdb	1.0	2012-06-11	CRAN (R 4.0.0)
fansi	0.4.2	2021-01-15	CRAN (R 4.0.3)
farver	2.0.3	2020-01-16	CRAN (R 4.0.0)
fastmap	1.1.0	2021-01-25	CRAN (R 4.0.3)
forcats	0.5.1.9000	2021-01-28	Github (tidyverse/forcats@b5fce89)
fs	1.5.0	2020-07-31	CRAN (R 4.0.3)
generics	0.1.0	2020-10-31	CRAN (R 4.0.3)
ggbeeswarm	0.6.0	2017-08-07	CRAN (R 4.0.3)
ggplot2	3.3.3.9000	2021-02-04	Github (tidyverse/ggplot2@dbd7d79)
ggrastr	0.2.1	2021-01-27	Github (VPetukhov/ggrastr@f1ce011)
glue	1.4.1.9000	2021-02-04	Github (tidyverse/glue@f0a7b2a)
gt	0.2.2	2020-11-25	Github (rstudio/gt@bae32f4)
gtable	0.3.0	2019-03-25	CRAN (R 4.0.0)
haven	2.3.1	2020-06-01	CRAN (R 4.0.0)
highr	0.8	2019-03-20	CRAN (R 4.0.0)
hms	1.0.0	2021-01-13	CRAN (R 4.0.3)
htmltools	0.5.1.9000	2021-02-05	Github (rstudio/htmltools@8d5a998)
httr	1.4.2	2020-07-20	CRAN (R 4.0.2)
jquerylib	0.1.3	2020-12-17	Github (rstudio/jquerylib@8f8e639)
jsonlite	1.7.2	2020-12-09	CRAN (R 4.0.3)
knitr	1.31.5	2021-02-05	Github (yihui/knitr@b2ce792)
labeling	0.4.2	2020-10-20	CRAN (R 4.0.3)
lattice	0.20-41	2020-04-02	CRAN (R 4.0.3)
lifecycle	0.2.0	2020-03-06	CRAN (R 4.0.0)
lubridate	1.7.9.2	2021-01-04	Github (tidyverse/lubridate@aab2e30)
magrittr	2.0.1.9000	2020-12-14	Github (tidyverse/magrittr@bb1c86a)
Matrix	1.3-2	2021-01-06	CRAN (R 4.0.3)
memoise	2.0.0	2021-01-26	CRAN (R 4.0.3)
modelr	0.1.8.9000	2021-01-23	Github (tidyverse/modelr@16168e0)
munsell	0.5.0	2018-06-12	CRAN (R 4.0.0)
patchwork	1.1.1	2020-12-17	CRAN (R 4.0.3)
pillar	1.4.99.9006	2021-02-04	Github (r-lib/pillar@bb460c1)
pkgbuild	1.2.0	2020-12-15	CRAN (R 4.0.3)
pkgconfig	2.0.3	2019-09-22	CRAN (R 4.0.0)
pkgload	1.1.0	2020-05-29	CRAN (R 4.0.0)
png	0.1-7	2013-12-03	CRAN (R 4.0.0)
prettyunits	1.1.1.9000	2020-11-23	Github (r-lib/prettyunits@b1cdad8)
processx	3.4.5	2020-11-30	CRAN (R 4.0.3)
ps	1.5.0	2020-12-05	CRAN (R 4.0.3)
purrr	0.3.4.9000	2021-02-04	Github (tidyverse/purrr@af06d45)
R.methodsS3	1.8.1	2020-08-26	CRAN (R 4.0.2)
R.oo	1.24.0	2020-08-26	CRAN (R 4.0.2)
R.utils	2.10.1	2020-08-26	CRAN (R 4.0.2)
R6	2.5.0	2021-02-04	Github (r-lib/R6@6cf7d4e)
ragg	0.4.1	2021-01-11	CRAN (R 4.0.3)
RColorBrewer	1.1-2	2014-12-07	CRAN (R 4.0.0)
Rcpp	1.0.6	2021-01-15	CRAN (R 4.0.3)
readr	1.4.0.9000	2021-01-23	Github (tidyverse/readr@483ca6c)
readxl	1.3.1.9000	2021-01-23	Github (tidyverse/readxl@9f85fa5)
remotes	2.2.0.9000	2021-02-05	Github (r-lib/remotes@62ff718)
reprex	1.0.0	2021-01-27	CRAN (R 4.0.3)
reticulate	1.18	2020-10-25	CRAN (R 4.0.3)
rlang	0.4.10.9000	2021-02-04	Github (r-lib/rlang@14b7cb4)
rmarkdown	2.6.6	2021-02-05	Github (rstudio/rmarkdown@44d7b10)
rprojroot	2.0.2	2020-11-15	CRAN (R 4.0.3)
rstudioapi	0.13.0-9000	2021-02-06	Github (rstudio/rstudioapi@9d21f50)
Rttf2pt1	1.3.8	2020-01-10	CRAN (R 4.0.0)
rvest	0.3.6	2020-07-25	CRAN (R 4.0.2)
sass	0.3.1	2021-01-24	CRAN (R 4.0.3)
scales	1.1.1	2020-05-11	CRAN (R 4.0.3)
sessioninfo	1.1.1	2018-11-05	CRAN (R 4.0.3)
stringi	1.5.3	2020-09-09	CRAN (R 4.0.3)
stringr	1.4.0.9000	2021-01-23	Github (tidyverse/stringr@1f03eb0)
styler	1.3.2.9000	2021-02-02	Github (r-lib/styler@3560b39)
systemfonts	1.0.0.9000	2021-02-02	Github (r-lib/systemfonts@d85abe2)
testthat	3.0.1	2021-02-04	Github (r-lib/testthat@2072757)
textshaping	0.2.1.9000	2021-02-06	Github (r-lib/textshaping@862fbf0)
tibble	3.0.6.9000	2021-02-04	Github (tidyverse/tibble@eb99cb6)
tidyr	1.1.2.9000	2021-01-23	Github (tidyverse/tidyr@c338aa9)
tidyselect	1.1.0	2020-05-11	CRAN (R 4.0.3)
tidyverse	1.3.0.9000	2021-02-04	Github (hadley/tidyverse@8a0bb99)
usethis	2.0.0.9000	2021-02-06	Github (r-lib/usethis@1ae30a1)
utf8	1.1.4	2018-05-24	CRAN (R 4.0.0)
vctrs	0.3.6	2020-12-17	CRAN (R 4.0.3)
vipor	0.4.5	2017-03-22	CRAN (R 4.0.0)
viridisLite	0.3.0	2018-02-01	CRAN (R 4.0.0)
withr	2.4.1	2021-01-26	CRAN (R 4.0.3)
xfun	0.20	2021-01-06	CRAN (R 4.0.3)
xml2	1.3.2	2020-04-23	CRAN (R 4.0.0)
yaml	2.2.1	2020-02-01	CRAN (R 4.0.0)