NGS data analysis
Givanna Putri
2023-10-11
Last updated: 2023-10-20
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Knit directory: NextClone-analysis/
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Introduction
Analysis for the 8k and 10k library of MCF7 cell line tagged with ClonMapper protocol.
Working directory for NextClone run:
/vast/projects/Goel_senescence/nextclone_dev/07_analysis/ngs_v1/run_nextclone/output_20231016
library(CloneDetective)
library(data.table)
library(ggplot2)
library(scales)Read in data
NextClone and pycashier pipeline:
clones_nxclone <- fread("data/nextclone_out/ngs_clone_barcode_counts.csv")
# The samples in the sample_name column is way too complicated.
# Let's create a new column.
clones_nxclone[, sample_name_simple := gsub("vexGFP-", "", gsub("_.*", "", sample_name))]
clones_nxclone[, sample_name_simple := factor(sample_name_simple, levels = c("8k", "10k"))]
clones_pycashier <- lapply(c("8k", "10k"), function(samp) {
# read_count as the count column so we can use count_retained_clones
dt <- fread(
file = paste0("data/pycashier_out/", samp, ".tsv"),
header = FALSE,
col.names = c("clone_barcode", "read_count")
)
dt[, sample := samp]
return(dt)
})
clones_pycashier <- rbindlist(clones_pycashier)
clones_pycashier[, sample := factor(sample, levels = c("8k", "10k"))]Number of unique barcodes
Count the number of unique barcodes with at least x number of cells.
thresholds <- c(1, 20, 200, 1000)
n_barcodes_nxclone <- count_retained_clones(
count_data = clones_nxclone,
thresholds = thresholds,
grouping_col = "sample_name_simple",
count_column = "read_count"
)
n_barcodes_nxclone[, tool := 'NextClone']
setnames(n_barcodes_nxclone, "sample_name_simple", "sample")
n_barcodes_pycashier <- count_retained_clones(
count_data = clones_pycashier,
thresholds = thresholds,
grouping_col = "sample",
count_column = "read_count"
)
n_barcodes_pycashier[, tool := 'PyCashier']
n_barcodes <- rbind(n_barcodes_nxclone, n_barcodes_pycashier)n_barcodes_long <- melt(n_barcodes, id.vars = c("sample", "tool"),
variable.name = "filtering_threshold",
value.name = "n_barcode")
filtering_threshold_levels <- paste(">=", thresholds, "cells")
n_barcodes_long[, filtering_threshold := factor(
gsub("_"," ",gsub("at_least_", ">= ", filtering_threshold)),
levels = filtering_threshold_levels
)]
ggplot(n_barcodes_long, aes(x=factor(filtering_threshold), y=n_barcode, fill=tool)) +
geom_bar(stat="identity", position=position_dodge()) +
theme_bw(base_size = 18) +
theme(
axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
legend.position="bottom"
) +
facet_wrap(~ sample) +
scale_y_continuous(breaks = pretty_breaks(n=10), label = label_comma(accuracy = 1)) +
labs(
y = "Number of barcodes",
x = "Filtering thresholds",
fill = "Pipeline",
title = "Number of barcodes retrieved for 8k and 10k NGS data"
)
Elbow plot
To show the proportion of barcode’s frequency.
clones_nxclone_filtered <- remove_clones_below_threshold(
count_data = clones_nxclone,
threshold = 20,
count_column = "read_count"
)
clones_nxclone_filtered <- convert_count_to_proportion(
count_data = clones_nxclone_filtered,
grouping_col = "sample_name_simple",
count_column = "read_count"
)plt <- draw_elbow_plot(
count_data = clones_nxclone_filtered,
facet_column = "sample_name_simple",
y_axis_column = "read_proportion"
)
plt <- plt +
geom_point(size=0.5, colour='red') +
labs(
title = "Proportion of reads assigned to each barcode",
subtitle = "Barcode IDs are numerically assigned in order of read proportion",
x = "Numerical barcode ID",
y = "Proportion of reads per library"
)
plt
Using NGS data to plan single cell experiment
Let’s say we want to sequence 10,000 cells. Based on our NGS data, can we predict what will happen to our clone barcodes? Will we get enough representations?
n_cells_sequenced <- 10000Do projection by calculating proportion and multiply by amount of cells to be projected to.
clones_nxclone_proportion <- projecting_clones(
count_data = clones_nxclone,
grouping_col = "sample_name_simple",
count_column = "read_count",
project_amnt = 10000
)How many cells we will get per clone?
plt <- draw_elbow_plot(
count_data = clones_nxclone_proportion,
y_axis_column = 'projected_to_10000',
facet_column = 'sample_name_simple'
) +
geom_point(size = 0.5, colour='blue') +
labs(
y = 'Number of cells',
title = 'Number of cells assigned to each barcode',
subtitle = 'Cell counts computed after projection to 10,000 cells'
)
plt
How many clones that contain at least 10, 20, 50, 100 cells?
thresholds <- c(10, 20, 50, 100)
proj_n_clones_retained <- count_retained_clones(
count_data = clones_nxclone_proportion,
thresholds = thresholds,
grouping_col = "sample_name_simple",
count_column = "projected_to_10000"
)
names(proj_n_clones_retained) <- c("sample", paste(">=", thresholds, "cells"))
proj_n_clones_retained sample >= 10 cells >= 20 cells >= 50 cells >= 100 cells
1: 8k 6 1 1 0
2: 10k 6 2 1 0What are the frequency of top 200 clone barcodes? We can present this by computing the number of cells tagged by top 200 clone barcodes.
top_threshold <- 200top_barcodes <- get_top_barcodes(
count_data = clones_nxclone_proportion,
count_column = "projected_to_10000",
grouping_col = "sample_name_simple",
top_threshold = top_threshold
)Create a line chart that show cumulative number of cells.
# TODO convert me to function
top_barcodes <- top_barcodes[order(sample_name_simple, -projected_to_10000)]
top_barcodes[, barcode_id := seq(1, top_threshold), by=sample_name_simple]
top_barcodes[, cum_sum_projected_to_10000 := cumsum(projected_to_10000), by=sample_name_simple]
ggplot(top_barcodes, aes(x=barcode_id, y=cum_sum_projected_to_10000,
group=sample_name_simple, colour = sample_name_simple)) +
geom_line(linewidth=1) +
theme_bw(base_size = 16) +
scale_y_continuous(breaks = pretty_breaks(n=10), labels = label_comma(accuracy = 1)) +
scale_x_continuous(breaks = pretty_breaks(n=10)) +
theme(
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
legend.position = "bottom"
) +
labs(
y = "Number of cells",
x = "Barcode ID",
title = paste("Cumulative Number of cells for top", top_threshold, "clone barcodes"),
subtitle = "Number of cells computed after projection to 10,000 cells",
colour = "Library"
)
sessionInfo()R version 4.2.3 (2023-03-15)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 14.0
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] scales_1.2.1 ggplot2_3.4.1 data.table_1.14.8
[4] CloneDetective_0.1.0 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.10 highr_0.10 compiler_4.2.3 pillar_1.8.1
[5] bslib_0.4.2 later_1.3.0 git2r_0.31.0 jquerylib_0.1.4
[9] tools_4.2.3 getPass_0.2-2 digest_0.6.31 gtable_0.3.1
[13] jsonlite_1.8.4 evaluate_0.20 lifecycle_1.0.3 tibble_3.1.8
[17] pkgconfig_2.0.3 rlang_1.0.6 cli_3.6.1 rstudioapi_0.14
[21] yaml_2.3.7 xfun_0.39 fastmap_1.1.0 withr_2.5.0
[25] dplyr_1.1.0 httr_1.4.4 stringr_1.5.0 knitr_1.42
[29] generics_0.1.3 fs_1.6.1 vctrs_0.5.2 sass_0.4.5
[33] tidyselect_1.2.0 grid_4.2.3 rprojroot_2.0.3 glue_1.6.2
[37] R6_2.5.1 processx_3.8.0 fansi_1.0.4 rmarkdown_2.20
[41] farver_2.1.1 callr_3.7.3 magrittr_2.0.3 whisker_0.4.1
[45] ps_1.7.2 promises_1.2.0.1 htmltools_0.5.4 colorspace_2.1-0
[49] httpuv_1.6.9 utf8_1.2.3 stringi_1.7.12 munsell_0.5.0
[53] cachem_1.0.6