Clustering of spatial transcriptomes (Slide-seqV2)

Jialei Duan

2022-01-27

---

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. 39, 313–319.

---

Rendered at 2022-01-27 12:39:56 America/Chicago.

Load required packages.

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

Sys.time()

[1] "2022-01-27 12:39:57 CST"

Data preparation

Functions loading

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

Data loading

Matrix

PROJECT_DIR <- file.path(
    "/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
)

Rows: 53208 Columns: 3
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): barcode
dbl (2): x_coord, y_coord

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

barcode_location |>
    head()

# A tibble: 6 × 3
  barcode        x_coord y_coord
  <chr>            <dbl>   <dbl>
1 AACGTCATAATCGT    889.   3220.
2 TACTTTAGCGCAGT   4762.   5020.
3 CATGCCTGGGTTCG    886.   3200.
4 TCGATATGGCACAA   2237.   5145.
5 TTATCTGACGAAGC   1032.   2425.
6 GATGCGACTCCTCG   5387    2292.

Create AnnData object.

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

[1] "0.7.6"

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,
    "matrix",
    "puck_200115_08.h5ad"
)
cat(file_name, "\n")

/Users/jialei/Dropbox/Data/Projects/UTSW/Spatial_sequencing/datasets/Slide-seqV2/matrix/puck_200115_08.h5ad 

if (!file.exists(file_name)) {
    adata$write_h5ad(
        filename = file_name,
        compression = "gzip",
        compression_opts = 9
    )
}

adata

AnnData object with n_obs × n_vars = 53208 × 23264
    obs: 'cell', 'num_umis', 'mt_percentage'
    var: 'feature'

adata$obs |> head()

                         cell num_umis mt_percentage
AACGTCATAATCGT AACGTCATAATCGT    23772    0.04029951
TACTTTAGCGCAGT TACTTTAGCGCAGT    19661    0.08031128
CATGCCTGGGTTCG CATGCCTGGGTTCG    15999    0.04419026
TCGATATGGCACAA TCGATATGGCACAA    15069    0.03610060
TTATCTGACGAAGC TTATCTGACGAAGC    14013    0.04859773
GATGCGACTCCTCG GATGCGACTCCTCG    12722    0.03049835

barcode_location <- 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[
                stringr::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()

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
RASTERISED <- TRUE

p_spatial_UMI <- plot_embedding(
    embedding = barcode_location |>
        dplyr::select(x_coord, y_coord),
    color_values = barcode_location$num_umis |> log10(),
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = TRUE,
    rasterise = RASTERISED
) +
    ggplot2::coord_fixed(
        xlim = quantile(barcode_location$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], " (UMI)"))
        )
    ) +
    ggplot2::scale_color_gradientn(
        colors = colorRampPalette(
            colors = rev(
                x = RColorBrewer::brewer.pal(n = 11, name = "Spectral")
            )
        )(n = 100)
    )

p_spatial_features <- plot_embedding(
    embedding = barcode_location |>
        dplyr::select(x_coord, y_coord),
    color_values = barcode_location$num_features |> log10(),
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = TRUE,
    rasterise = RASTERISED
) +
    ggplot2::coord_fixed(
        xlim = quantile(barcode_location$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)
    )

Fig. 1a

purrr::map2(
    c("num_umis", "num_features"),
    c(
        expression(paste("log"[10], " (UMI)")),
        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 = RASTERISED
        ) +
            ggplot2::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
                )
            ) +
            ggplot2::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 = ggplot2::theme(plot.margin = ggplot2::margin())
    )

plot_embedding(
    embedding = barcode_location |>
        dplyr::select(x_coord, y_coord),
    color_values = barcode_location$mt_ratio,
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE,
    sort_values = TRUE,
    rasterise = RASTERISED
) +
    ggplot2::coord_fixed(
        xlim = quantile(barcode_location$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)
    )

Visualize cells with different numbers of genes detected.

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$num_features >= x) |>
            as.factor(),
        show_color_legend = TRUE,
        geom_point_size = GEOM_POINT_SIZE / 1.5,
        sort_values = TRUE,
        rasterise = RASTERISED,
        dpi = 300
    ) +
        ggplot2::coord_fixed(
            xlim = quantile(barcode_location$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 = ggplot2::theme(plot.margin = ggplot2::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$barcode] + 1
        ),
        show_color_legend = TRUE,
        geom_point_size = GEOM_POINT_SIZE,
        sort_values = TRUE,
        rasterise = RASTERISED,
        dpi = 600
    ) +
        ggplot2::coord_fixed(
            xlim = quantile(barcode_location$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
            )
        ) +
        ggplot2::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 = ggplot2::theme(plot.margin = ggplot2::margin())
    )

Clustering

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

Rows: 27680 Columns: 16
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (1): cell
dbl (14): louvain, leiden, x_tsne, y_tsne, x_umap, y_umap, x_fitsne, y_fitsn...
lgl  (1): batch

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

dim(embedding)

[1] 27680    16

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

embedding |>
    head() |>
    knitr::kable()

cell	batch	louvain	leiden	x_tsne	y_tsne	x_umap	y_umap	x_fitsne	y_fitsne	x_phate	y_phate	x_umap_min_dist=0.1	y_umap_min_dist=0.1	x_multicoretsne	y_multicoretsne	num_umis	num_features	mt_ratio
AAAAAATTTACAAC	NA	3	2	-15.08988	80.86089	8.180862	12.681457	-26.663474	-38.30694	-0.0072554	-0.0072473	8.337963	12.029632	-0.7960197	-1.6586841	283	244	0.0777385
AAAAACACGGTGGT	NA	5	3	12.26837	29.86188	2.751835	6.606259	6.250895	11.37395	0.0059024	-0.0029451	2.909990	7.214894	-0.2892767	-0.1141422	234	205	0.0470085
AAAAACCCCGTTAG	NA	3	12	-22.61128	24.27204	6.830804	9.945548	-38.301001	9.58504	-0.0091227	-0.0084254	7.313702	8.334121	-0.7425821	-1.0912394	677	503	0.0487445
AAAAACGCTCCACA	NA	15	16	-22.22501	-11.74047	2.259175	10.879986	-16.115118	-84.07885	-0.0053393	-0.0013187	3.089149	9.882877	-1.5145455	0.6562469	355	290	0.0704225
AAAAACTCTCTATT	NA	0	0	15.82639	-91.21259	-2.902669	9.756977	38.563696	-55.52631	0.0141227	-0.0123033	-1.651397	8.959843	0.2110805	2.0115610	1592	1132	0.0188442
AAAAAGACATCACA	NA	10	9	56.72704	-28.31702	-1.619180	6.661331	-1.730514	-53.68672	0.0032096	-0.0050529	-1.551058	6.617124	0.2128970	1.0833045	432	372	0.0486111

library(formattable)

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)
    ) |>
    formattable::formattable(
        list(
            num_cells = formattable::color_bar("Lightpink"),
            median_umis = formattable::color_bar("lightgreen"),
            median_features = formattable::color_bar("lightblue"),
            median_mt_ratio = formattable::color_tile("transparent", "lightgrey")
        ),
        full_width = FALSE,
        caption = "Clustering"
    )

Clustering

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"

p_embedding_leiden <- plot_embedding(
    embedding = embedding[, c(x_column, y_column)],
    color_values = embedding$leiden |> as.factor(),
    label = paste(EMBEDDING_TITLE_PREFIX, "Leiden", sep = "; "),
    label_position = NULL,
    show_color_value_labels = TRUE,
    show_color_legend = FALSE,
    geom_point_size = GEOM_POINT_SIZE / 1.5,
    geom_point_alpha = 1,
    sort_values = FALSE,
    shuffle_values = TRUE,
    label_size = 6,
    label_hjust = 0,
    label_vjust = 0,
    rasterise = RASTERISED,
    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 = paste(EMBEDDING_TITLE_PREFIX, "UMI", sep = "; "),
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE / 1.5,
    sort_values = FALSE,
    shuffle_values = TRUE,
    rasterise = RASTERISED,
    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 = RASTERISED,
    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 <- barcode_location |>
    left_join(
        embedding |> select(cell, leiden),
        by = c("barcode" = "cell")
    )

p_spatial_na <- plot_embedding(
    embedding = barcode_location |>
        dplyr::select(x_coord, y_coord),
    color_values = as.numeric(is.na(barcode_location$leiden)) |> as.factor(),
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE / 1.5,
    sort_values = TRUE,
    rasterise = RASTERISED,
    dpi = 600
) +
    ggplot2::coord_fixed(
        xlim = quantile(barcode_location$x_coord, probs = c(0.01, 0.99)),
        ylim = quantile(barcode_location$y_coord, probs = c(0.01, 0.99))
    ) +
    ggplot2::guides(
        colour = ggplot2::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%
    ggplot2::theme(
        legend.justification = 0.5,
        legend.position = "bottom"
    ) +
    ggplot2::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$leiden |> as.factor(),
    show_color_legend = TRUE,
    geom_point_size = GEOM_POINT_SIZE / 1.5,
    sort_values = FALSE,
    rasterise = RASTERISED,
    dpi = 600
) +
    coord_fixed(
        xlim = quantile(barcode_location$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 fewer UMI 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())
    )

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 / 1.5,
        sort_values = TRUE,
        rasterise = RASTERISED,
        dpi = 300
    ) +
        ggplot2::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::scale_color_manual(
            values = c("grey70", "salmon"),
            labels = c("Other", x)
        )
}) |>
    purrr::reduce(`+`) +
    patchwork::plot_layout(ncol = 3) +
    patchwork::plot_annotation(
        theme = ggplot2::theme(plot.margin = ggplot2::margin())
    )

R session info

devtools::session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.1.2 (2021-11-01)
 os       macOS Monterey 12.2
 system   aarch64, darwin20.6.0
 ui       unknown
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       America/Chicago
 date     2022-01-27
 pandoc   2.17.0.1 @ /opt/homebrew/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package       * version     date (UTC) lib source
 assertthat      0.2.1       2019-03-21 [1] CRAN (R 4.1.1)
 backports       1.4.1       2021-12-13 [1] CRAN (R 4.1.2)
 beeswarm        0.4.0       2021-06-01 [1] CRAN (R 4.1.2)
 bit             4.0.4       2020-08-04 [1] CRAN (R 4.1.1)
 bit64           4.0.5       2020-08-30 [1] CRAN (R 4.1.1)
 brio            1.1.3       2021-11-30 [1] CRAN (R 4.1.2)
 broom           0.7.11      2022-01-03 [1] CRAN (R 4.1.2)
 cachem          1.0.6       2021-08-19 [1] CRAN (R 4.1.1)
 callr           3.7.0       2021-04-20 [1] CRAN (R 4.1.1)
 cellranger      1.1.0       2016-07-27 [1] CRAN (R 4.1.1)
 checkmate       2.0.0       2020-02-06 [1] CRAN (R 4.1.1)
 cli             3.1.1       2022-01-20 [1] CRAN (R 4.1.2)
 colorspace      2.0-2       2021-06-24 [1] CRAN (R 4.1.1)
 crayon          1.4.2       2021-10-29 [1] CRAN (R 4.1.1)
 data.table    * 1.14.2      2021-09-27 [1] CRAN (R 4.1.1)
 DBI             1.1.2       2021-12-20 [1] CRAN (R 4.1.2)
 dbplyr          2.1.1       2021-04-06 [1] CRAN (R 4.1.1)
 desc            1.4.0       2021-09-28 [1] CRAN (R 4.1.1)
 devtools        2.4.3.9000  2022-01-27 [1] Github (r-lib/devtools@41280ac)
 digest          0.6.29      2021-12-01 [1] CRAN (R 4.1.2)
 dplyr         * 1.0.7.9000  2022-01-27 [1] Github (tidyverse/dplyr@0501335)
 dtplyr          1.2.1.9000  2022-01-27 [1] Github (tidyverse/dtplyr@8542cf1)
 ellipsis        0.3.2       2021-04-29 [1] CRAN (R 4.1.1)
 evaluate        0.14        2019-05-28 [1] CRAN (R 4.1.1)
 extrafont     * 0.17        2014-12-08 [1] CRAN (R 4.1.1)
 extrafontdb     1.0         2012-06-11 [1] CRAN (R 4.1.1)
 fansi           1.0.2       2022-01-14 [1] CRAN (R 4.1.2)
 farver          2.1.0       2021-02-28 [1] CRAN (R 4.1.1)
 fastmap         1.1.0       2021-01-25 [1] CRAN (R 4.1.1)
 forcats       * 0.5.1.9000  2022-01-27 [1] Github (tidyverse/forcats@b4dade0)
 formattable   * 0.2.1       2021-01-07 [1] CRAN (R 4.1.2)
 fs              1.5.2.9000  2022-01-27 [1] Github (r-lib/fs@6d1182f)
 gargle          1.2.0       2021-07-02 [1] CRAN (R 4.1.1)
 generics        0.1.1       2021-10-25 [1] CRAN (R 4.1.1)
 ggbeeswarm      0.6.0       2017-08-07 [1] CRAN (R 4.1.2)
 ggplot2       * 3.3.5.9000  2022-01-27 [1] Github (tidyverse/ggplot2@c89c265)
 ggrastr         1.0.1       2021-12-08 [1] Github (VPetukhov/ggrastr@7aed9af)
 glue            1.6.1.9000  2022-01-27 [1] Github (tidyverse/glue@3da70df)
 googledrive     2.0.0       2021-07-08 [1] CRAN (R 4.1.1)
 googlesheets4   1.0.0       2021-07-21 [1] CRAN (R 4.1.1)
 gt              0.3.1       2021-08-07 [1] CRAN (R 4.1.2)
 gtable          0.3.0.9000  2022-01-27 [1] Github (r-lib/gtable@a0bd272)
 haven           2.4.3       2021-08-04 [1] CRAN (R 4.1.1)
 highr           0.9         2021-04-16 [1] CRAN (R 4.1.1)
 hms             1.1.1       2021-09-26 [1] CRAN (R 4.1.1)
 htmltools       0.5.2       2021-08-25 [1] CRAN (R 4.1.1)
 htmlwidgets     1.5.4       2021-09-08 [1] CRAN (R 4.1.1)
 httr            1.4.2       2020-07-20 [1] CRAN (R 4.1.1)
 jsonlite        1.7.3       2022-01-17 [1] CRAN (R 4.1.2)
 knitr           1.37        2021-12-16 [1] CRAN (R 4.1.2)
 labeling        0.4.2       2020-10-20 [1] CRAN (R 4.1.1)
 lattice         0.20-45     2021-09-22 [2] CRAN (R 4.1.2)
 lifecycle       1.0.1       2021-09-24 [1] CRAN (R 4.1.1)
 lubridate       1.8.0       2022-01-27 [1] Github (tidyverse/lubridate@566590f)
 magrittr        2.0.2       2022-01-26 [1] CRAN (R 4.1.2)
 Matrix        * 1.4-0       2021-12-08 [2] CRAN (R 4.1.2)
 memoise         2.0.1       2021-11-26 [1] CRAN (R 4.1.2)
 modelr          0.1.8.9000  2022-01-27 [1] Github (tidyverse/modelr@16168e0)
 munsell         0.5.0       2018-06-12 [1] CRAN (R 4.1.1)
 patchwork     * 1.1.0.9000  2022-01-27 [1] Github (thomasp85/patchwork@79223d3)
 pillar          1.6.5       2022-01-25 [1] CRAN (R 4.1.2)
 pkgbuild        1.3.1       2021-12-20 [1] CRAN (R 4.1.2)
 pkgconfig       2.0.3       2019-09-22 [1] CRAN (R 4.1.1)
 pkgload         1.2.4       2021-11-30 [1] CRAN (R 4.1.2)
 png             0.1-7       2013-12-03 [1] CRAN (R 4.1.1)
 prettyunits     1.1.1.9000  2022-01-27 [1] Github (r-lib/prettyunits@8706d89)
 processx        3.5.2       2021-04-30 [1] CRAN (R 4.1.1)
 ps              1.6.0       2021-02-28 [1] CRAN (R 4.1.1)
 purrr         * 0.3.4.9000  2022-01-27 [1] Github (tidyverse/purrr@5aca9df)
 R.cache         0.15.0      2021-04-30 [1] CRAN (R 4.1.1)
 R.methodsS3     1.8.1       2020-08-26 [1] CRAN (R 4.1.1)
 R.oo            1.24.0      2020-08-26 [1] CRAN (R 4.1.1)
 R.utils         2.11.0      2021-09-26 [1] CRAN (R 4.1.1)
 R6              2.5.1.9000  2022-01-27 [1] Github (r-lib/R6@1b05b89)
 ragg            1.2.1.9000  2022-01-27 [1] Github (r-lib/ragg@c68c666)
 RColorBrewer    1.1-2       2014-12-07 [1] CRAN (R 4.1.1)
 Rcpp            1.0.8       2022-01-13 [1] CRAN (R 4.1.2)
 readr         * 2.1.1.9000  2022-01-27 [1] Github (tidyverse/readr@718c42c)
 readxl          1.3.1.9000  2022-01-27 [1] Github (tidyverse/readxl@2ccb82c)
 remotes         2.4.2       2022-01-27 [1] Github (r-lib/remotes@9355549)
 reprex          2.0.1       2021-08-05 [1] CRAN (R 4.1.1)
 reticulate      1.24        2022-01-26 [1] CRAN (R 4.1.2)
 rlang           1.0.0.9000  2022-01-27 [1] Github (r-lib/rlang@27bcb65)
 rmarkdown       2.11.12     2022-01-27 [1] Github (rstudio/rmarkdown@b53a7ce)
 rprojroot       2.0.2       2020-11-15 [1] CRAN (R 4.1.1)
 rstudioapi      0.13.0-9000 2022-01-27 [1] Github (rstudio/rstudioapi@5d0f087)
 Rttf2pt1        1.3.9       2021-07-22 [1] CRAN (R 4.1.1)
 rvest           1.0.2       2021-10-16 [1] CRAN (R 4.1.1)
 sass            0.4.0       2021-05-12 [1] CRAN (R 4.1.1)
 scales          1.1.1       2020-05-11 [1] CRAN (R 4.1.1)
 sessioninfo     1.2.2       2021-12-06 [1] CRAN (R 4.1.2)
 stringi         1.7.6       2021-11-29 [1] CRAN (R 4.1.2)
 stringr       * 1.4.0.9000  2022-01-27 [1] Github (tidyverse/stringr@85f6140)
 styler        * 1.6.2.9000  2022-01-27 [1] Github (r-lib/styler@9274aed)
 systemfonts     1.0.3       2021-10-13 [1] CRAN (R 4.1.2)
 testthat        3.1.2.9000  2022-01-27 [1] Github (r-lib/testthat@54b9db2)
 textshaping     0.3.6       2021-10-13 [1] CRAN (R 4.1.1)
 tibble        * 3.1.6.9001  2022-01-27 [1] Github (tidyverse/tibble@64fefd5)
 tidyr         * 1.1.4.9000  2022-01-27 [1] Github (tidyverse/tidyr@d46d22a)
 tidyselect      1.1.1       2021-04-30 [1] CRAN (R 4.1.1)
 tidyverse     * 1.3.1.9000  2022-01-27 [1] Github (tidyverse/tidyverse@6186fbf)
 tzdb            0.2.0       2021-10-27 [1] CRAN (R 4.1.1)
 usethis         2.1.5.9000  2022-01-27 [1] Github (r-lib/usethis@57b109a)
 utf8            1.2.2       2021-07-24 [1] CRAN (R 4.1.1)
 vctrs           0.3.8       2021-04-29 [1] CRAN (R 4.1.1)
 vipor           0.4.5       2017-03-22 [1] CRAN (R 4.1.2)
 viridisLite     0.4.0       2021-04-13 [1] CRAN (R 4.1.1)
 vroom           1.5.7.9000  2022-01-27 [1] Github (r-lib/vroom@59261e3)
 withr           2.4.3       2021-11-30 [1] CRAN (R 4.1.2)
 xfun            0.29        2021-12-14 [1] CRAN (R 4.1.2)
 xml2            1.3.3       2021-11-30 [1] CRAN (R 4.1.2)
 yaml            2.2.2       2022-01-25 [1] CRAN (R 4.1.2)

 [1] /opt/homebrew/lib/R/4.1/site-library
 [2] /opt/homebrew/Cellar/r/4.1.2/lib/R/library

─ Python configuration ───────────────────────────────────────────────────────
 python:         /Users/jialei/.pyenv/shims/python
 libpython:      /Users/jialei/.pyenv/versions/miniforge3-4.10.1-5/lib/libpython3.9.dylib
 pythonhome:     /Users/jialei/.pyenv/versions/miniforge3-4.10.1-5:/Users/jialei/.pyenv/versions/miniforge3-4.10.1-5
 version:        3.9.5 | packaged by conda-forge | (default, Jun 19 2021, 00:24:55)  [Clang 11.1.0 ]
 numpy:          /Users/jialei/.pyenv/versions/miniforge3-4.10.1-5/lib/python3.9/site-packages/numpy
 numpy_version:  1.20.3
 anndata:        /Users/jialei/.pyenv/versions/miniforge3-4.10.1-5/lib/python3.9/site-packages/anndata
 
 NOTE: Python version was forced by RETICULATE_PYTHON

──────────────────────────────────────────────────────────────────────────────