Last updated: 2023-03-14
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 2359a33 | Evgenii O. Tretiakov | 2023-03-14 | workflowr::wflow_publish("analysis/methods.Rmd", verbose = TRUE) |
| html | 2359a33 | Evgenii O. Tretiakov | 2023-03-14 | workflowr::wflow_publish("analysis/methods.Rmd", verbose = TRUE) |
| html | 98a2fc9 | Evgenii O. Tretiakov | 2023-03-14 | Build site. |
| Rmd | a7f002d | EugOT | 2023-03-14 | update bib |
| Rmd | edcb936 | Evgenii O. Tretiakov | 2023-03-14 | update methods |
| Rmd | 3b155ad | Evgenii O. Tretiakov | 2023-03-05 | add figures code |
# Presentation
library("glue")
library("knitr")
# JSON
library("jsonlite")
# Tidyverse
library("tidyverse")dir.create(here::here("output", DOCNAME), showWarnings = FALSE)
write_bib(c("base", "Seurat", "SeuratWrappers", "SeuratDisk", "sctransform",
"glmGamPoi", "patchwork", "scCustomize", "Nebulosa", "clustree",
"mrtree", "gprofiler2", "cowplot", "UpSetR", "ggstatsplot",
"gridExtra", "tidyverse", "dplyr", "tidyr", "magrittr", "stringr",
"skimr", "future", "purrr", "here", "workflowr", "zeallot", "knitr",
"kableExtra", "rmarkdown", "reticulate"),
file = here::here("output", DOCNAME, "packages.bib"))versions <- list(
biomaRt = packageVersion("biomaRt"),
cellbender = "docker://etretiakov/cellbender:v0.0.1",
cellranger = "7.1.0",
cellranger_ref = "mm10_optimized_v.1.0",
clustree = packageVersion("clustree"),
cowplot = packageVersion("cowplot"),
dplyr = packageVersion("dplyr"),
future = packageVersion("future"),
ggplot2 = packageVersion("ggplot2"),
ggstatsplot = packageVersion("ggstatsplot"),
glmGamPoi = packageVersion("glmGamPoi"),
gprofiler2 = packageVersion("gprofiler2"),
gridExtra = packageVersion("gridExtra"),
here = packageVersion("here"),
kableExtra = packageVersion("kableExtra"),
knitr = packageVersion("knitr"),
magrittr = packageVersion("magrittr"),
mrtree = packageVersion("mrtree"),
Nebulosa = packageVersion("Nebulosa"),
pandoc = rmarkdown::pandoc_version(),
patchwork = packageVersion("patchwork"),
purrr = packageVersion("purrr"),
python = "3.8.8",
R = str_extract(R.version.string, "[0-9\\.]+"),
reticulate = packageVersion("reticulate"),
rmarkdown = packageVersion("rmarkdown"),
scCustomize = packageVersion("scCustomize"),
sctransform = packageVersion("sctransform"),
Seurat = packageVersion("Seurat"),
skimr = packageVersion("skimr"),
stringr = packageVersion("stringr"),
Snakemake = "7.21.0",
souporcell = "shub://wheaton5/souporcell",
tidyr = packageVersion("tidyr"),
tidyverse = packageVersion("tidyverse"),
UpSetR = packageVersion("UpSetR"),
viridis = packageVersion("viridis"),
workflowr = packageVersion("workflowr"),
zeallot = packageVersion("zeallot")
)The Cell Ranger pipeline (v7.1.0) (Zheng et al. 2017) was used to perform sample demultiplexing, barcode processing and single-nuclei gene counting. Briefly, samples were demultiplexed to produce a pair of FASTQ files for each sample. Reads containing sequence information were aligned using the optimised mouse genome reference (vmm10_optimized_v.1.0) provided by Pool’s lab based on the default Cell Ranger mm10 genome version 2020-A that was cleared from gene overlaps, poorly annotated exons and 3’-UTRs and intergenic fragments (Pool et al. 2022). PCR duplicates were removed by selecting unique combinations of cell barcodes, unique molecular identifiers (UMIs) and gene ids with the final results being a gene expression matrix that was used for further analysis.
The droplet selection method of Cell Ranger identified 923 nuclei in ventrobasal thalamus and in principal sensory trigeminal nucleus - 731 nuclei based on EmptyDrops method (Lun et al. 2019) incorporated into cellranger count pipeline.
Using those values as the expected number of cells, we applied a neural network-based approach called CellBender (docker://etretiakov/cellbender:v0.0.1) (Fleming et al. 2022). We set a false positive rate threshold at the level of 0.01 and set the neural network to learn over 150 epochs with a total of 5000 droplets included based on knee plots (please see online supplementary Cell Ranger reports).
We quantified log probability to be a doublet for every cell based on apriori knowledge of genotypes from each input sample and called variant occurrence frequencies that allowed to cluster nuclei to source organism or classify as a doublet (Heaton et al. 2020) (see shub://wheaton5/souporcell).
Gene annotation information was added using the gprofiler2 package
(v0.2.1) (Reimand et al. 2016); thus we
filter cells based on high content of mitochondrial, ribosomal or
hemoglobin proteins genes, specifically thresholds set as 1%, 1% and
0.5%; additionally pseudogenes and poorly annotated genes were also
deleted from count matrix. Moreover, cells of low complexity were
filtered out as (\(\log_{10}Genes/\log_{10}UMI
< 0.8\)). Therefore, cells were assigned cell cycle scores
using the CellCycleScoring function in the Seurat package
(v4.3.0).
We used the selection method in the Seurat package (v4.3.0) (Satija et al. 2015; Stuart and Satija 2019), which uses a modern variance stabilising transformation statistical technic that utilises scaling to person residuals (Hafemeister and Satija 2019). That way, we selected 3000 highly variable genes per dataset and regressed out complexity and cell-cycle variability prior to the final scaling of filtered matrixes.
We performed Leiden algorithm graph-based clustering. PCA was
performed using the selected genes and the jackknife tested (Chung and Storey 2015) principal components (we
tested the significance of feature for randomly picked 1% of data over
1000 iterations; see PCScore function in
functions.R script of code directory) were used to
construct a shared nearest neighbour graph using the overlap between the
15 nearest neighbours of each cell. Leiden modularity
optimisation (Traag, Waltman, and Eck
2019) was used to partition this graph with an array of
resolution parameters where 30 modularity events were sampled between
0.2 and 2.5. Clustering tree visualisations
(Zappia and Oshlack 2018) were produced
using the clustree package (v0.5.0) showing the resolution of previously
identified clusters. By inspecting these resolutions reconcile tree
produced by mrtree package (v0.0.0.9000) (Peng et
al. 2021) and calculating adjusted multi-resolution Rand index
chosen as maximum value if there is no higher modularity within
0.05 AMRI difference (see SelectResolution in
function.R file of code directory).
Marker genes for each cluster were identified using logreg test (Ntranos et al. 2019) implemented in Seurat
framework (v) (Stuart and Satija 2019).
Genes were considered significant markers for a cluster if they had an
FDR less than 0.001. Identities were assigned to each
cluster by comparing the detected genes to previously published markers
and our own validation experiments.
Visualisations and figures were primarily created using the ggplot2 (v3.4.1), cowplot (v1.1.1) (Wilke 2020) and patchwork (v1.1.2.9000) packages using the viridis colour palette (v0.6.2) for continuous data. UpSet plots (Conway, Lex, and Gehlenborg 2017) were produced using the UpSetR package (v1.4.0) (Gehlenborg 2019) with help from the gridExtra package (v2.3) (Auguie 2017). Data manipulation was performed using other packages in the tidyverse (v1.3.2.9000) (Wickham 2023) particularly dplyr (v1.1.0) (Wickham et al. 2023), tidyr (v1.1.0) (Wickham, Vaughan, and Girlich 2023) and purrr (v1.0.1) (Wickham and Henry 2023). The analysis project was managed using the Snakemake system (v ) (Mölder et al. 2021) and the workflowr (v1.7.0) (Blischak, Carbonetto, and Stephens 2021) package which was also used to produce the publicly available website displaying the analysis code, results and output. Reproducible reports were produced using knitr (v1.42) (Xie 2023) and R Markdown (v2.20) (Allaire et al. 2023) and converted to HTML using Pandoc (v2.19.2).
versions <- purrr::map(versions, as.character)
versions <- jsonlite::toJSON(versions, pretty = TRUE)
readr::write_lines(versions,
here::here("output", DOCNAME, "package-versions.json"))
sessionInfo()R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.1 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] lubridate_1.9.0 timechange_0.1.1 forcats_0.5.2
[4] stringr_1.5.0 dplyr_1.1.0 purrr_1.0.1
[7] readr_2.1.3 tidyr_1.3.0 tibble_3.1.8
[10] ggplot2_3.4.1 tidyverse_1.3.2.9000 jsonlite_1.8.4
[13] knitr_1.42 glue_1.6.2 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] tidyselect_1.2.0 xfun_0.37 bslib_0.4.2 colorspace_2.1-0
[5] vctrs_0.5.2 generics_0.1.3 htmltools_0.5.4 yaml_2.3.7
[9] utf8_1.2.3 rlang_1.0.6 jquerylib_0.1.4 later_1.3.0
[13] pillar_1.8.1 withr_2.5.0 bit64_4.0.5 lifecycle_1.0.3
[17] munsell_0.5.0 gtable_0.3.1 evaluate_0.20 tzdb_0.3.0
[21] callr_3.7.3 fastmap_1.1.0 httpuv_1.6.9 ps_1.7.2
[25] parallel_4.2.2 fansi_1.0.4 Rcpp_1.0.10 promises_1.2.0.1
[29] scales_1.2.1 cachem_1.0.6 vroom_1.6.0 bit_4.0.5
[33] fs_1.6.1 hms_1.1.2 digest_0.6.31 stringi_1.7.12
[37] processx_3.8.0 getPass_0.2-2 rprojroot_2.0.3 grid_4.2.2
[41] here_1.0.1 cli_3.6.0 tools_4.2.2 magrittr_2.0.3
[45] sass_0.4.5 crayon_1.5.2 whisker_0.4.1 pkgconfig_2.0.3
[49] ellipsis_0.3.2 rmarkdown_2.20 httr_1.4.4 rstudioapi_0.14
[53] R6_2.5.1 git2r_0.30.1 compiler_4.2.2