HFK scRNA-Seq annotation
Sean Wilson
2020-11-09
Last updated: 2020-12-28
Checks: 7 0
Knit directory: HowdenWilson2020/
This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20201109) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 0a845cc. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: .Rhistory
Ignored: .Rproj.user/
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/HFK_Annotation.Rmd) and HTML (docs/HFK_Annotation.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 0a845cc | Sean Wilson | 2020-12-28 | update to flatly theme |
| html | 07adb2a | Sean Wilson | 2020-12-28 | Build site. |
| Rmd | 11477de | Sean Wilson | 2020-12-28 | Updating additional information around analysis |
| html | 1297fee | Sean Wilson | 2020-12-28 | Build site. |
| Rmd | d1d8b3f | Sean Wilson | 2020-12-28 | Initialise website |
| html | 84370d5 | Sean Wilson | 2020-11-13 | Build site. |
| Rmd | 711a2c5 | Sean Wilson | 2020-11-13 | Set up initial repo on github |
Introduction
One of the key analyses in this manuscript was the thorough analysis of the week 16 human fetal kidney datasets first published in our collaborators’s paper Holloway et al. 2020. Figure 1 and Supplementary Figure 1 show the analysis in the manuscript, the code used to generate that analysis is more fully described in this document.
The analysis is performed initially following the Satija Lab protocol
library(tidyverse)── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──✓ ggplot2 3.3.0 ✓ purrr 0.3.4
✓ tibble 3.0.4 ✓ dplyr 1.0.2
✓ tidyr 1.0.2 ✓ stringr 1.4.0
✓ readr 1.4.0 ✓ forcats 0.5.0── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()library(Seurat)Registered S3 method overwritten by 'spatstat':
method from
print.boxx cli library(patchwork)
library(here)here() starts at /group/kidn1/Group-Little_MCRI/People/Sean/PhD/R-projects/HowdenWilson2020library(plotly)
Attaching package: 'plotly'The following object is masked from 'package:ggplot2':
last_plotThe following object is masked from 'package:stats':
filterThe following object is masked from 'package:graphics':
layoutsource(here::here("code/functions.R"))
options(future.globals.maxSize = Inf)
knitr::opts_chunk$set(echo = FALSE,
fig.width = 12, fig.height = 9)This loads the Seurat object saved in my working folder, to get this same object you can download the raw data and metadata from GSE161255 and recreate the object.
We can visualise the object in either 2D or 3D. The labels are reorganised into a random order to make the colours on the graph more discernable.
Warning: Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
Please use `as_label()` or `as_name()` instead.
This warning is displayed once per session.
| Version | Author | Date |
|---|---|---|
| 84370d5 | Sean Wilson | 2020-11-13 |
Clustering
The kidney is a complex organ with many cell types generated within a number of distinct cell lineages. Due to this, the clearest way to use clustering on this dataset is to use a low resolution to get the lineage information, then go into each of these populations to identify more distinct cell identities.
The first clustering resolution allowed for us to classify the cell lineages within the kidney
Warning: Removed 153 rows containing missing values (geom_point).
| Version | Author | Date |
|---|---|---|
| 84370d5 | Sean Wilson | 2020-11-13 |
The large clusters can be easily classified based on their gene expression profiles. While we can distinguish between Nephron Progenitors, Distal Nephron, Proximal Nephron and Podocytes with this resolution of clustering, for the paper we called these all as Nephron and denoted all subclusters with the “N.” prefix. Ureteric Epithelium had a “U.” prefix, the Stroma with “S.”
These clusters could all be broken down into their subcluster identities in the same way.
In each of these populations is a further capacity to subset and identify more specific cell identities.
Ureteric Epithelium
The Ureteric Epithelium is the epithelial structure that the nephron plumbs into for the filtrate to be removed to the bladder. The ureteric buc cell is a progenitor cell type that gives rise to the entirety of this structure. The bud, or tip, cells are highly proliferative and move with the expanding kidney during development, as these cells differentiate and take their place in the stalk region their function changes, reflected in different gene expression profiles.
To identify these populations we can isolate the Ureteric cluster and reanalyse these in the same way we did the whole dataset.
| Version | Author | Date |
|---|---|---|
| 1297fee | Sean Wilson | 2020-12-28 |
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: /hpc/software/installed/R/3.6.1/lib64/R/lib/libRblas.so
LAPACK: /hpc/software/installed/R/3.6.1/lib64/R/lib/libRlapack.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] plotly_4.9.2.1 here_0.1 patchwork_1.0.0 Seurat_3.2.2
[5] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4
[9] readr_1.4.0 tidyr_1.0.2 tibble_3.0.4 ggplot2_3.3.0
[13] tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rtsne_0.15 colorspace_1.4-1 deldir_0.1-29
[4] ellipsis_0.3.1 ggridges_0.5.2 rprojroot_1.3-2
[7] fs_1.5.0 spatstat.data_1.4-3 rstudioapi_0.11
[10] farver_2.0.3 leiden_0.3.3 listenv_0.8.0
[13] ggrepel_0.8.2 fansi_0.4.1 lubridate_1.7.9
[16] xml2_1.2.5 codetools_0.2-16 splines_3.6.1
[19] knitr_1.28 polyclip_1.10-0 jsonlite_1.7.1
[22] broom_0.7.2 ica_1.0-2 cluster_2.1.0
[25] dbplyr_1.4.4 png_0.1-7 uwot_0.1.8
[28] sctransform_0.3.1 shiny_1.4.0.2 compiler_3.6.1
[31] httr_1.4.1 backports_1.1.10 assertthat_0.2.1
[34] Matrix_1.2-18 fastmap_1.0.1 lazyeval_0.2.2
[37] cli_2.1.0 later_1.0.0 htmltools_0.5.0
[40] tools_3.6.1 rsvd_1.0.3 igraph_1.2.5
[43] gtable_0.3.0 glue_1.4.2 reshape2_1.4.4
[46] RANN_2.6.1 rappdirs_0.3.1 spatstat_1.63-3
[49] Rcpp_1.0.5 cellranger_1.1.0 vctrs_0.3.4
[52] nlme_3.1-150 crosstalk_1.1.0.1 lmtest_0.9-38
[55] xfun_0.12 globals_0.13.0 rvest_0.3.5
[58] mime_0.9 miniUI_0.1.1.1 lifecycle_0.2.0
[61] irlba_2.3.3 goftest_1.2-2 future_1.16.0
[64] MASS_7.3-53 zoo_1.8-8 scales_1.1.1
[67] spatstat.utils_1.17-0 hms_0.5.3 promises_1.1.0
[70] parallel_3.6.1 RColorBrewer_1.1-2 yaml_2.2.1
[73] gridExtra_2.3 reticulate_1.16 pbapply_1.4-3
[76] rpart_4.1-15 stringi_1.5.3 rlang_0.4.7
[79] pkgconfig_2.0.3 matrixStats_0.57.0 evaluate_0.14
[82] lattice_0.20-41 tensor_1.5 ROCR_1.0-11
[85] labeling_0.4.2 htmlwidgets_1.5.1 cowplot_1.1.0
[88] tidyselect_1.1.0 RcppAnnoy_0.0.16 plyr_1.8.6
[91] magrittr_1.5 R6_2.5.0 generics_0.1.0
[94] DBI_1.1.0 mgcv_1.8-33 pillar_1.4.3
[97] haven_2.2.0 whisker_0.4 withr_2.2.0
[100] fitdistrplus_1.1-1 abind_1.4-5 survival_3.2-7
[103] future.apply_1.6.0 modelr_0.1.6 crayon_1.3.4
[106] KernSmooth_2.23-17 rmarkdown_2.1 grid_3.6.1
[109] readxl_1.3.1 data.table_1.13.0 blob_1.2.1
[112] git2r_0.27.1 reprex_0.3.0 digest_0.6.25
[115] xtable_1.8-4 httpuv_1.5.2 munsell_0.5.0
[118] viridisLite_0.3.0
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: /hpc/software/installed/R/3.6.1/lib64/R/lib/libRblas.so
LAPACK: /hpc/software/installed/R/3.6.1/lib64/R/lib/libRlapack.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] plotly_4.9.2.1 here_0.1 patchwork_1.0.0 Seurat_3.2.2
[5] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4
[9] readr_1.4.0 tidyr_1.0.2 tibble_3.0.4 ggplot2_3.3.0
[13] tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rtsne_0.15 colorspace_1.4-1 deldir_0.1-29
[4] ellipsis_0.3.1 ggridges_0.5.2 rprojroot_1.3-2
[7] fs_1.5.0 spatstat.data_1.4-3 rstudioapi_0.11
[10] farver_2.0.3 leiden_0.3.3 listenv_0.8.0
[13] ggrepel_0.8.2 fansi_0.4.1 lubridate_1.7.9
[16] xml2_1.2.5 codetools_0.2-16 splines_3.6.1
[19] knitr_1.28 polyclip_1.10-0 jsonlite_1.7.1
[22] broom_0.7.2 ica_1.0-2 cluster_2.1.0
[25] dbplyr_1.4.4 png_0.1-7 uwot_0.1.8
[28] sctransform_0.3.1 shiny_1.4.0.2 compiler_3.6.1
[31] httr_1.4.1 backports_1.1.10 assertthat_0.2.1
[34] Matrix_1.2-18 fastmap_1.0.1 lazyeval_0.2.2
[37] cli_2.1.0 later_1.0.0 htmltools_0.5.0
[40] tools_3.6.1 rsvd_1.0.3 igraph_1.2.5
[43] gtable_0.3.0 glue_1.4.2 reshape2_1.4.4
[46] RANN_2.6.1 rappdirs_0.3.1 spatstat_1.63-3
[49] Rcpp_1.0.5 cellranger_1.1.0 vctrs_0.3.4
[52] nlme_3.1-150 crosstalk_1.1.0.1 lmtest_0.9-38
[55] xfun_0.12 globals_0.13.0 rvest_0.3.5
[58] mime_0.9 miniUI_0.1.1.1 lifecycle_0.2.0
[61] irlba_2.3.3 goftest_1.2-2 future_1.16.0
[64] MASS_7.3-53 zoo_1.8-8 scales_1.1.1
[67] spatstat.utils_1.17-0 hms_0.5.3 promises_1.1.0
[70] parallel_3.6.1 RColorBrewer_1.1-2 yaml_2.2.1
[73] gridExtra_2.3 reticulate_1.16 pbapply_1.4-3
[76] rpart_4.1-15 stringi_1.5.3 rlang_0.4.7
[79] pkgconfig_2.0.3 matrixStats_0.57.0 evaluate_0.14
[82] lattice_0.20-41 tensor_1.5 ROCR_1.0-11
[85] labeling_0.4.2 htmlwidgets_1.5.1 cowplot_1.1.0
[88] tidyselect_1.1.0 RcppAnnoy_0.0.16 plyr_1.8.6
[91] magrittr_1.5 R6_2.5.0 generics_0.1.0
[94] DBI_1.1.0 mgcv_1.8-33 pillar_1.4.3
[97] haven_2.2.0 whisker_0.4 withr_2.2.0
[100] fitdistrplus_1.1-1 abind_1.4-5 survival_3.2-7
[103] future.apply_1.6.0 modelr_0.1.6 crayon_1.3.4
[106] KernSmooth_2.23-17 rmarkdown_2.1 grid_3.6.1
[109] readxl_1.3.1 data.table_1.13.0 blob_1.2.1
[112] git2r_0.27.1 reprex_0.3.0 digest_0.6.25
[115] xtable_1.8-4 httpuv_1.5.2 munsell_0.5.0
[118] viridisLite_0.3.0