Github for Wamaitha, S. article: "Defining the cell and molecular origins of the ovarian reserve"
This project is focused on running a full mixed probe analysis, as detailed in the 20231202_MixedProbesFullRun.nb.html file.
20231202_MixedProbesFullRun : The HTML document contains the full experimental run and includes the following sections
2024_Wamaitha_Rhesus_OvarianReserve/
├── README.md
├── VisiumHD.sh
├── spaceranger_array_submission.sh
├── 20231202_MixedProbesFullRun.nb.html
├── UpdatedSpaceRanger/
│ ├── MakingMixedProbes.sh
│ ├── MakingSingleProbes.R
│ ├── singlehit_mixed_single.probes.csv
│ ├── single.probe.csv
│ └── RhesusMacaqueProbeLists/
│ ├── FRP_Human_probes_on_Macaque_1.csv
│ ├── FRP_Human_probes_on_Macaque_2.csv
│ ├── FRP_Human_probes_on_Macaque_3.csv
│ └── FRP_Human_probes_on_Macaque_4.csv
├── Codes_CosMX
│ ├── Analysis_Embryo_1.R
│ └── Analysis_Embryo_2.R
└── Codes_Visium
├── Comparison_EMB_19_EMB_22_EMB_29.R
├── Comparison_EMB_19_vs_EMB_20_21.R
├── Comparison_EMB_22_vs_EMB_32_31.R
├── Comparison_EMB_29_vs_EMB_20_21.R
├── Comparison_EMB_31_vs_EMB_20_21.R
├── Comparison_EMB_31_vs_EMB_29.R
├── Comparison_EMB_32_vs_EMB_20_21.R
├── Comparison_EMB_32_vs_EMB_31.R
├── EMB_20.R
├── EMB_21.R
├── Emb_31.R
├── Emb_32.R
├── Testis_19.R
├── Testis_22.R
└── Testis_29.R
- Folder
UpdatedSpaceRangercontains the files below which will make the probe set list to align the data.UpdatedSpaceRanger/MakingSingleProbes.R: This is the file that will be run first to make the single hits of the Rhesus Mulatta using human probesUpdatedSpaceRanger/MakingMixedProbes.sh: This is the file that will then take the single hits and add in the mixed probesVisiumHD.shis the script to make the VisiumHD alignments- The rest of the
.csvfiles are the probes that were added from running the above files. And the folderUpdatedSpaceRanger/RhesusMacaqueProbeListscontains the 10X genomics hits that they found.
spaceranger_array_submission.sh: This is the script to run spaceranger on a cluster but clearly shows how to integrate the new probe set.20231202_MixedProbesFullRun.nb.html: The main output of the analysis, generated using the notebook20231202_MixedProbesFullRun.Rmd. This file contains the detailed steps, code, and results from the experiment.
- Viewing Results: Open the
20231202_MixedProbesFullRun.nb.htmlfile in any web browser to view the interactive analysis, which includes plots and tables. - Preparing for Reproducing Analysis:
- Clone the repository.
- Start by running the file
UpdatedSpaceRanger/MakingSingleProbes.RandUpdatedSpaceRanger/MakingMixedProbes.sh. These will make the probe lists that will be used in spaceranger - Then follow the steps in our
spaceranger_array_submission.shto include the correct probe list to your spaceranger submission.- Note: If you are trying to run VisiumHD results, please use
VisiumHD.sh
- Note: If you are trying to run VisiumHD results, please use
- Take your output from
spacerangerin the/outs/folder to the next step
- Running Analysis for Marker Gene DotPlots in Visium
- Follow the Methods section of the paper to set up the experimental environment and run your own version of the analysis in conjunction with this repository
- Ensure all required libraries and dependencies are installed, these are at the end in the SessionInfo() of the HTML
20231202_MixedProbesFullRun.nb.htmlor see below.
- Ensure all required libraries and dependencies are installed, these are at the end in the SessionInfo() of the HTML
- Use the code from RMarkdown file
20231202_MixedProbesFullRun.Rmdto get the full sized images from the paper- Please note that to make the images black and white we used ImageJ to turn the image to non-RGB. This is stated in the Methods and the
.Rmdfile.
- Please note that to make the images black and white we used ImageJ to turn the image to non-RGB. This is stated in the Methods and the
- Follow the Methods section of the paper to set up the experimental environment and run your own version of the analysis in conjunction with this repository
- Running Analysis for Differential Gene Expression (DGE) in Visium
- Follow the Methods section to set up the experimental environment and run your own version of the analysis in conjunction with this repository.
- Use the code as set up in folder
/Codes_Visium/- here you will find code that starts with
Comparison_...these are code blocks for comparing the different samples for finding DGE across the samples. - The code that is describing just the different
EMB_...orTestis_...will be used for finding marker genes for each cell type population across the cells in one sample
- here you will find code that starts with
- Running Analysis for all CosMX Data
- Follow the Methods section to set up the experimental environment and run your own version of the analysis in conjunction with this repository.
- Use the code set up in folder
/Codes_CosMX/where each embryo is grouped by their identity as..._Embryo_1and..._Embryo_2. See the paper's methods for details about the embryos
To rerun the analysis, make sure the following dependencies are installed for the Visium Marker Gene Visualization:
sessionInfo()
R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.2 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C 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 LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats4 grid stats graphics grDevices utils datasets methods base
other attached packages:
[1] patchwork_1.2.0 harmony_1.2.0 Rcpp_1.0.13 DoubletFinder_2.0.4
[5] SoupX_1.6.2 BiocManager_1.30.24 readxl_1.4.3 clustree_0.5.1
[9] ggraph_2.2.1 RCurl_1.98-1.16 cowplot_1.1.3 Matrix_1.6-5
[13] ggsignif_0.6.4 data.table_1.15.4 reshape_0.8.9 biomaRt_2.58.2
[17] scales_1.3.0 org.Hs.eg.db_3.18.0 AnnotationDbi_1.64.1 clusterProfiler_4.10.1
[21] RColorBrewer_1.1-3 pheatmap_1.0.12 ggdendro_0.2.0 ggrepel_0.9.5
[25] DESeq2_1.42.1 SummarizedExperiment_1.32.0 Biobase_2.62.0 MatrixGenerics_1.14.0
[29] matrixStats_1.3.0 GenomicRanges_1.54.1 GenomeInfoDb_1.38.8 IRanges_2.36.0
[33] S4Vectors_0.40.2 BiocGenerics_0.48.1 ggpattern_1.1.1 ComplexHeatmap_2.18.0
[37] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 dplyr_1.1.4
[41] purrr_1.0.2 readr_2.1.5 tidyr_1.3.1 tibble_3.2.1
[45] ggplot2_3.5.1 tidyverse_2.0.0 colorRamp2_0.1.0 ggprism_1.0.5
[49] scCustomize_2.1.2 Seurat_5.1.0 SeuratObject_5.0.2 sp_2.1-4
loaded via a namespace (and not attached):
[1] progress_1.2.3 goftest_1.2-3 Biostrings_2.70.3 vctrs_0.6.5 spatstat.random_3.3-1
[6] digest_0.6.37 png_0.1-8 shape_1.4.6.1 deldir_2.0-4 parallelly_1.38.0
[11] MASS_7.3-59 reshape2_1.4.4 httpuv_1.6.15 foreach_1.5.2 qvalue_2.34.0
[16] withr_3.0.1 ggrastr_1.0.2 xfun_0.47 ggfun_0.1.5 survival_3.7-0
[21] memoise_2.0.1 ggbeeswarm_0.7.2 janitor_2.2.0 gson_0.1.0 tidytree_0.4.6
[26] zoo_1.8-12 GlobalOptions_0.1.2 pbapply_1.7-2 prettyunits_1.2.0 rematch2_2.1.2
[31] KEGGREST_1.42.0 promises_1.3.0 httr_1.4.7 globals_0.16.3 fitdistrplus_1.2-1
[36] rstudioapi_0.16.0 miniUI_0.1.1.1 generics_0.1.3 DOSE_3.28.2 curl_5.2.2
[41] zlibbioc_1.48.2 polyclip_1.10-7 GenomeInfoDbData_1.2.11 SparseArray_1.2.4 xtable_1.8-4
[46] doParallel_1.0.17 evaluate_0.24.0 S4Arrays_1.2.1 BiocFileCache_2.10.2 hms_1.1.3
[51] irlba_2.3.5.1 colorspace_2.1-1 filelock_1.0.3 hdf5r_1.3.11 ROCR_1.0-11
[56] reticulate_1.38.0 spatstat.data_3.1-2 magrittr_2.0.3 lmtest_0.9-40 snakecase_0.11.1
[61] later_1.3.2 viridis_0.6.5 ggtree_3.10.1 lattice_0.22-6 glmGamPoi_1.14.3
[66] spatstat.geom_3.3-2 future.apply_1.11.2 scattermore_1.2 XML_3.99-0.17 shadowtext_0.1.4
[71] RcppAnnoy_0.0.22 pillar_1.9.0 nlme_3.1-166 iterators_1.0.14 compiler_4.3.0
[76] RSpectra_0.16-2 stringi_1.8.4 tensor_1.5 plyr_1.8.9 crayon_1.5.3
[81] abind_1.4-5 gridGraphics_0.5-1 locfit_1.5-9.10 graphlayouts_1.1.1 bit_4.0.5
[86] fastmatch_1.1-4 codetools_0.2-20 bslib_0.8.0 paletteer_1.6.0 GetoptLong_1.0.5
[91] plotly_4.10.4 mime_0.12 splines_4.3.0 circlize_0.4.16 fastDummies_1.7.4
[96] dbplyr_2.5.0 sparseMatrixStats_1.14.0 prismatic_1.1.2 HDO.db_0.99.1 cellranger_1.1.0
[101] knitr_1.48 blob_1.2.4 utf8_1.2.4 clue_0.3-65 fs_1.6.4
[106] listenv_0.9.1 DelayedMatrixStats_1.24.0 ggplotify_0.1.2 tzdb_0.4.0 tweenr_2.0.3
[111] pkgconfig_2.0.3 tools_4.3.0 cachem_1.1.0 RSQLite_2.3.7 viridisLite_0.4.2
[116] DBI_1.2.3 fastmap_1.2.0 rmarkdown_2.28 ica_1.0-3 sass_0.4.9
[121] dotCall64_1.1-1 RANN_2.6.2 farver_2.1.2 tidygraph_1.3.1 scatterpie_0.2.3
[126] yaml_2.3.10 cli_3.6.3 leiden_0.4.3.1 lifecycle_1.0.4 uwot_0.2.2
[131] BiocParallel_1.36.0 timechange_0.3.0 gtable_0.3.5 rjson_0.2.21 ggridges_0.5.6
[136] progressr_0.14.0 parallel_4.3.0 ape_5.8 jsonlite_1.8.8 RcppHNSW_0.6.0
[141] bitops_1.0-8 bit64_4.0.5 Rtsne_0.17 yulab.utils_0.1.7 spatstat.utils_3.1-0
[146] jquerylib_0.1.4 GOSemSim_2.28.1 spatstat.univar_3.0-0 lazyeval_0.2.2 shiny_1.9.1
[151] htmltools_0.5.8.1 enrichplot_1.22.0 GO.db_3.18.0 sctransform_0.4.1 rappdirs_0.3.3
[156] glue_1.7.0 spam_2.10-0 XVector_0.42.0 treeio_1.26.0 gridExtra_2.3
[161] igraph_2.0.3 R6_2.5.1 labeling_0.4.3 cluster_2.1.6 aplot_0.2.3
[166] DelayedArray_0.28.0 tidyselect_1.2.1 vipor_0.4.7 ggforce_0.4.2 xml2_1.3.6
[171] future_1.34.0 munsell_0.5.1 KernSmooth_2.23-24 htmlwidgets_1.6.4 fgsea_1.28.0
[176] rlang_1.1.4 spatstat.sparse_3.1-0 spatstat.explore_3.3-2 fansi_1.0.6 Cairo_1.6-2
[181] beeswarm_0.4.0
To rerun the analysis, make sure the following dependencies are installed for the Visium DGE:
TBD
To rerun the analysis, make sure the following dependencies are installed for the CosMX analysis:
TBD
For any questions or further information, feel free to reach out to the project lead.
Updated on: December 11 2024