Last updated: 2023-03-14

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Knit directory: Hevesi_2023/

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    Ignored:    cellbender_latest.sif
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    Ignored:    data/neuro_fin.h5seurat
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    Ignored:    mm10_optimized/
    Ignored:    souporcell/
    Ignored:    souporcell_latest.sif

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    Modified:   output/figures/Pr5P7_sex_umap.pdf
    Modified:   output/figures/Pr5P7_stress_umap.pdf
    Modified:   output/figures/Pr5P7_top5_umap.pdf
    Modified:   output/figures/THP7_sex_umap.pdf
    Modified:   output/figures/THP7_stress_umap.pdf
    Modified:   output/figures/THP7_top5_umap.pdf
    Modified:   output/figures/combined_sex_umap.pdf
    Modified:   output/figures/combined_stress_umap.pdf
    Modified:   output/figures/combined_top5_umap.pdf
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File	Version	Author	Date	Message
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")
)

Pre-processing

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.

Quality control

Droplet selection

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.

Ambient RNA removal

Using those values as expected number of cells we applied neural network-based approach called CellBender (docker://etretiakov/cellbender:v0.0.1) (flemingUnsupervisedRemovalSystematic2022?). We set false positive rate threshold at the level of 0.01 and set neural network to learn over 150 epoch with total 5000 droplets included based on knee plots (please see online supplementary Cell Ranger reports).

Doublets detection

We quantified log probability to be a doublet for every cell based on apriori knowledge of genotypes from each input samples and called variant occurrence frequencies that allowed to cluster nuclei to source organism or classify as a doublet (heatonSouporcellRobustClustering2020?) (see shub://wheaton5/souporcell) .

Further filtering

Gene annotation information was added using the gprofiler2 package (v2.54.0, docker://etretiakov/cellbender:v0.0.1, 7.1.0, mm10_optimized_v.1.0, 0.5.0, 1.1.1, 1.1.0, 1.31.0, 3.4.1, 0.10.0, 1.10.2, 0.2.1, 2.3, 1.0.1, 1.3.4, 1.42, 2.0.3, 0.0.0.9000, 1.8.0, 2.19.2, 1.1.2.9000, 1.0.1, 3.8.8, 4.2.2, 1.28, 2.20, 1.1.1, 0.3.5, 4.3.0, 2.1.5, 1.5.0, 7.21.0, shub://wheaton5/souporcell, 1.3.0, 1.3.2.9000, 1.4.0, 0.6.2, 1.7.0, 0.1.0$gprofiler2) [@reimandProfileraWebServer2016]; thus we filter cells based on high content of mitochondrial, ribosomal or hemoglobin proteins genes, specifically 1%, 1% and 0.5%; additionally pseudogenes and poorly annotated genes were also deleted from count matrix. Moreover, cells of low complexity were filtered out as ($\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).

Clustering

Gene selection

We used the selection method in the Seurat package (v4.3.0) (Satija2015-or?; 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 final scaling of filtered matrixes.

Graph-based and multi-level reconcile tree clustering

We performed Leiden algorithm graph-based clustering. PCA was performed using the selected genes and the jacknife tested (chungStatisticalSignificanceVariables2015?) principal components (we tested 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 (pengCellTypeHierarchy2021?) 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

Marker genes for each cluster were identified using logreg test (ntranosDiscriminativeLearningApproach2019?) 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.

Other packages

Visualisations and figures were primarily created using the ggplot2 (v3.4.1) (Wickham2010-zq?) and cowplot (v1.1.1) (Wilke 2020) 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) (Xie2014-ha?; Xie2016-ct?; Xie 2023) and R Markdown (v2.20) (Xie2018-tw?; Allaire et al. 2023) and converted to HTML using Pandoc (v2.19.2).

References

Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, Winston Chang, and Richard Iannone. 2023. Rmarkdown: Dynamic Documents for r.

Auguie, Baptiste. 2017. gridExtra: Miscellaneous Functions for "Grid" Graphics.

Blischak, John, Peter Carbonetto, and Matthew Stephens. 2021. Workflowr: A Framework for Reproducible and Collaborative Data Science. https://github.com/workflowr/workflowr.

Conway, Jake R, Alexander Lex, and Nils Gehlenborg. 2017. “UpSetR: An R Package for the Visualization of Intersecting Sets and Their Properties.” Edited by John Hancock. Bioinformatics 33 (18): 2938–40. https://doi.org/10.1093/bioinformatics/btx364.

Gehlenborg, Nils. 2019. UpSetR: A More Scalable Alternative to Venn and Euler Diagrams for Visualizing Intersecting Sets. http://github.com/hms-dbmi/UpSetR.

Hafemeister, Christoph, and Rahul Satija. 2019. “Normalization and Variance Stabilization of Single-Cell RNA-Seq Data Using Regularized Negative Binomial Regression.” Genome Biology 20 (1): 296. https://doi.org/10.1186/s13059-019-1874-1.

Lun, Aaron T. L., Samantha Riesenfeld, Tallulah Andrews, The Phuong Dao, Tomas Gomes, and John C. Marioni. 2019. “EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data.” Genome biology 20 (1): 63. https://doi.org/10.1186/s13059-019-1662-y.

Mölder, Felix, Kim Philipp Jablonski, Brice Letcher, Michael B. Hall, Christopher H. Tomkins-Tinch, Vanessa Sochat, Jan Forster, et al. 2021. “Sustainable Data Analysis with Snakemake.” F1000Research 10 (January): 33. https://doi.org/gjjkwv.

Pool, Allan-Hermann, Helen Poldsam, Sisi Chen, Matt Thomson, and Yuki Oka. 2022. “Enhanced Recovery of Single-Cell RNA-Sequencing Reads for Missing Gene Expression Data,” April. https://doi.org/10.1101/2022.04.26.489449.

Stuart, Tim, and Rahul Satija. 2019. “Integrative single-cell analysis.” Nature reviews. Genetics 20 (5): 257–72. https://doi.org/10.1038/s41576-019-0093-7.

Traag, V. A., L. Waltman, and N. J. van Eck. 2019. “From Louvain to Leiden: Guaranteeing Well-Connected Communities.” Scientific Reports 9 (1): 5233. https://doi.org/10.1038/s41598-019-41695-z.

Wickham, Hadley. 2023. Tidyverse: Easily Install and Load the Tidyverse.

Wickham, Hadley, Romain François, Lionel Henry, Kirill Müller, and Davis Vaughan. 2023. Dplyr: A Grammar of Data Manipulation.

Wickham, Hadley, and Lionel Henry. 2023. Purrr: Functional Programming Tools.

Wickham, Hadley, Davis Vaughan, and Maximilian Girlich. 2023. Tidyr: Tidy Messy Data.

Wilke, Claus O. 2020. Cowplot: Streamlined Plot Theme and Plot Annotations for Ggplot2. https://wilkelab.org/cowplot/.

Xie, Yihui. 2023. Knitr: A General-Purpose Package for Dynamic Report Generation in r. https://yihui.org/knitr/.

Zappia, Luke, and Alicia Oshlack. 2018. “Clustering Trees: A Visualization for Evaluating Clusterings at Multiple Resolutions.” GigaScience 7 (7): giy083. https://doi.org/10.1093/gigascience/giy083.

Zheng, Grace X. Y., Jessica M. Terry, Phillip Belgrader, Paul Ryvkin, Zachary W. Bent, Ryan Wilson, Solongo B. Ziraldo, et al. 2017. “Massively parallel digital transcriptional profiling of single cells.” Nature communications 8 (January): 14049. https://doi.org/10.1038/ncomms14049.

Summary

Output files`

versions <- purrr::map(versions, as.character)
versions <- jsonlite::toJSON(versions, pretty = TRUE)
readr::write_lines(versions,
                   here::here("output", DOCNAME, "package-versions.json"))

Session information

devtools::session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.2.2 (2022-10-31)
 os       Ubuntu 22.04.1 LTS
 system   x86_64, linux-gnu
 ui       X11
 language en_US:en
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Etc/UTC
 date     2023-03-14
 pandoc   2.19.2 @ /usr/lib/rstudio-server/bin/quarto/bin/tools/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package     * version    date (UTC) lib source
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 bit64         4.0.5      2020-08-30 [2] RSPM (R 4.2.0)
 bslib         0.4.2      2022-12-16 [2] RSPM (R 4.2.0)
 cachem        1.0.6      2021-08-19 [2] RSPM (R 4.2.0)
 callr         3.7.3      2022-11-02 [2] RSPM (R 4.2.0)
 cli           3.6.0      2023-01-09 [2] RSPM (R 4.2.2)
 colorspace    2.1-0      2023-01-23 [2] RSPM (R 4.2.2)
 crayon        1.5.2      2022-09-29 [2] RSPM (R 4.2.0)
 devtools      2.4.5      2022-10-11 [2] RSPM (R 4.2.0)
 digest        0.6.31     2022-12-11 [2] RSPM (R 4.2.0)
 dplyr       * 1.1.0      2023-01-29 [2] RSPM (R 4.2.2)
 ellipsis      0.3.2      2021-04-29 [2] RSPM (R 4.2.0)
 evaluate      0.20       2023-01-17 [2] RSPM (R 4.2.2)
 fansi         1.0.4      2023-01-22 [2] RSPM (R 4.2.2)
 fastmap       1.1.0      2021-01-25 [2] RSPM (R 4.2.0)
 forcats     * 0.5.2      2022-08-19 [2] RSPM (R 4.2.0)
 fs            1.6.1      2023-02-06 [2] RSPM (R 4.2.2)
 generics      0.1.3      2022-07-05 [2] RSPM (R 4.2.0)
 getPass       0.2-2      2017-07-21 [2] RSPM (R 4.2.0)
 ggplot2     * 3.4.1      2023-02-10 [2] RSPM (R 4.2.2)
 git2r         0.30.1     2022-03-16 [2] RSPM (R 4.2.0)
 glue        * 1.6.2      2022-02-24 [2] RSPM (R 4.2.0)
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 here          1.0.1      2020-12-13 [2] RSPM (R 4.2.0)
 hms           1.1.2      2022-08-19 [2] RSPM (R 4.2.0)
 htmltools     0.5.4      2022-12-07 [2] RSPM (R 4.2.0)
 htmlwidgets   1.6.1      2023-01-07 [2] RSPM (R 4.2.0)
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 httr          1.4.4      2022-08-17 [2] RSPM (R 4.2.0)
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 knitr       * 1.42       2023-01-25 [2] RSPM (R 4.2.2)
 later         1.3.0      2021-08-18 [2] RSPM (R 4.2.0)
 lifecycle     1.0.3      2022-10-07 [2] RSPM (R 4.2.0)
 lubridate   * 1.9.0      2022-11-06 [2] RSPM (R 4.2.0)
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 xtable        1.8-4      2019-04-21 [2] RSPM (R 4.2.0)
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 [1] /home/etretiakov/R/x86_64-pc-linux-gnu-library/4.2
 [2] /opt/R/4.2.2/lib/R/library

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