2.4 Main Theme: Accessing KPMP Data
Single-Cell Omics Research and Education
SCORE #5: Accessing KPMP Datasets
Jonathan Nelson and Xiao-Tong Su
30 June 2025
library("dplyr")
library("Seurat")
library("knitr")
library("ggplot2")
library("BiocManager")
library("here")
#BiocManager::install("EnhancedVolcano")
library("EnhancedVolcano") #volcano plot
#install.packages('DESeq2') #for DEG
library("DESeq2")
library("tidyverse") #tidy up data
library("styler") #tidy up data
library("scCustomize") #for color scales)
if (!require("kableExtra")) {install.packages("kableExtra"); require("kableExtra")} # for color brewer
if (!require("RColorBrewer")) {install.packages("RColorBrewer"); require("RColorBrewer")} # for color brewer
if (!require("sctransform")) {install.packages("sctransform"); require("sctransform")} # for data normalization
if (!require("glmGamPoi")) {BiocManager::install('glmGamPoi'); require("glmGamPoi")} # for data normalization, sctransform
if (!require("cowplot")) {install.packages("cowplot"); require("cowplot")} # for figure layout
if (!require("patchwork")) {install.packages("patchwork"); require("patchwork")} # for figure patching
if (!require("openxlsx")) {install.packages("openxlsx"); require("openxlsx")} # to save .xlsx files
# install.packages("styler")
set.seed(12345)
# here()1 Welcome
Welcome to the Single-Cell Omics Research and Education Club!
If this is your time to the club, I want to extend and extra-special welcome to you!
I’m Jonathan Nelson, an Assistant Professor at the University of Southern California. I’m a wet scientist turned wet+dry scientist. I’ve been working with single-cell RNAseq data for the past 5 years and I’m excited to share what I’ve learned with you.
1.1 SCORE Values
1.1.1 Learning
We believe that bioinformatics is a constantly evolving field, and that ongoing learning and professional development is essential to staying up-to-date. We encourage members to share their knowledge and experiences with each other, and to seek out opportunities for continued learning.
1.1.2 Accessibility
We believe that access to bioinformatics support should be available to everyone. We strive to create a welcoming and inclusive environment where all members can feel comfortable asking for help and contributing to the group.
1.1.3 Collaboration
We believe that working together is key to achieving success in bioinformatics. We value the diversity of perspectives and backgrounds that each member brings, and we encourage open communication and the sharing of ideas.
1.1.4 Integrity
We believe in conducting ourselves with honesty and professionalism in all our interactions. We hold ourselves to high ethical standards and respect the privacy and confidentiality of all members.
1.1.5 Empathy
We believe in approaching each other with empathy and kindness. We understand that bioinformatics can be a challenging and sometimes frustrating field, and we strive to support each other through these difficulties.
1.2 Context and Expectations
I know a lot of this has been going on in the background for everyeone and I wanted to bring it to the forefront. My expectation is that we have about 6 of these meetings together and then we can re-evaluate if we want to continue as a group or not.
Email me you would like me to add anyone: j.nelson@med.usc.edu
Today’s code (this html file) will be posted to the SCORE website (https://usckrc.github.io/website/score.html)
2 The Agenda!
2.1 Music and Memes
2.2 Coding Crumbs: renv package
2.3 Recreating a Figure: Stacked Violin Plot
3 Music and Memes
3.1 This Months Coding Music
3.1.1 GIVĒON: TAKE TIME
https://open.spotify.com/album/1zHR48K6XtWYm6bhrw4J6C?si=y4Jy1TloTuuRLYQLkyDgRg
3.1.2 Heartbreak Anniversay Hits
3.2 The Memes
3.2.1 Not a strong month for bioinfromatic memes
3.2.2 But I hope you enjoy these!
3.2.3 Meme 1: Periodic table of “Can I lick it?”
3.2.4 Meme 2: What goes into Arxiv
4 Coding Crumbs: renv package
4.1 It finally happened!
4.1.1 I got too tired of switching between my Seurat v4 and Seurat v5 computers
4.1.1.1 This was my solution for the Seurat v5
4.1.1.2 Screenshot from Score #1
4.1.1.3 Someone (Jesse Goodrich or Matt Salomon?) recommended trying out the renv package
4.2 What is renv?
https://rstudio.github.io/renv/articles/renv.html
4.2.1 A package for managing R project environments
4.2.2 Let’s be honest…we often get stuck in between projects as packages change
4.2.3 It allows you to create a snapshot of your R packages and versions
4.2.4 It allows you to restore your R packages and versions
4.2.6 It took me 5 minutes to set this up on my system
4.2.7 I started by using the renv package in a new github repo that I was working on
4.2.8 This easy!
4.2.8.1 Open up project from the folder where you want to use renv
4.2.9 Now when I open the project from this location: Seurat v5
4.2.10 When I open project from any other folder: Seurat v4
4.3 Take Home
4.3.1 Implimenting renv is a great way to manage your R packages and versions
4.3.2 It’s been seamless and helped me to move between Seurat v4 and Seurat v5
4.3.3 Important for making your code reproducible!
4.3.3.1 Add a renv.lock file to your git repo so that others can use the same packages and versions
5 Recreating a Figure: Stacked Violin Plot
5.1 Final Product!
5.2 Publication Examples of Stacked Violin Plot
5.3 Start with a Seurat Object
5.3.1 Data from Figure 1 from Burfeind et al. Phys Genomics 2025
https://pubmed.ncbi.nlm.nih.gov/39982410/
5.3.2 Load Dataset
SO <- readRDS(here("Week 4 CellChat", "data", "CACPR_integrated.rds"))
DimPlot(SO, group.by = "subclass.CACPR") + ggtitle("")
DefaultAssay(SO) <- "RNA"
SO@meta.data
5.3.3 Step 1: Create a list of genes
markers.to.plot1 <- c("Lrp2", # PT
"Slc5a12", # PT-S1
"Slc13a3", # PT-S2
"Slc16a9", # PT-S3
"Havcr1", # Injured PT
"Epha7", # dTL
"Slc12a1", # TAL
"Cldn10", # TAL
"Cldn16", # TAL
"Nos1", # MD
"Slc12a3", # DCT
"Pvalb", # DCT1
"Slc8a1", # DCT2, CNT
"Aqp2", # PC
"Slc4a1", # IC-A
"Slc26a4", # IC-B
"Upk1b", # Uro
"Ncam1", # PEC
"Pdgfrb", # Perivascular
"Piezo2", # Mesangial
"Pdgfra", # Fib
"Acta2", # Mural
"Nphs1", # Podo
"Kdr", # Capillary Endo
"Lyve1", # Lymph
"Ptprc", # Immune
"Cd74", # Macrophage
"Skap1" # B/T Cells
)5.3.4 Step 1b: Multidimentional Dotplot code
DotPlot(SO,
features = markers.to.plot1,
dot.scale = 8,
dot.min = 0,
scale.max = 100,
scale.min = 0,
col.min = -2.5,
col.max = 2.5,
group.by = "class.CACPR") +
coord_flip() +
theme_classic() +
theme(axis.line = element_line(size = 1, colour = "black"),
axis.ticks.x = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
text = element_text(size=20),
axis.text.x = element_text(angle = 45, hjust = 1)) +
xlab(NULL) +
ylab(NULL)5.3.5 Step 2: Violin Plot of List in Features
5.3.5.1 Looks really bad!
5.4 Lessons for Me
5.4.1 Sometimes creating graphs is as sample as knowing the arguments in a function
5.4.2 Still need to work on coloring by subclass.CACPR rather than feature
5.4.2.0.1 Challenge for the next SCORE?
6 Main Theme: Accessing KPMP Data
6.2 Manuscript Describing the Goals of KPMP
https://pubmed.ncbi.nlm.nih.gov/33637194/
6.2.1 Overarching Goals
6.2.2 Patient Population
6.2.3 Tissue Handling
6.2.4 Outcomes
6.3 Atlas 1.0: Lake et al. Nature 2023
https://pubmed.ncbi.nlm.nih.gov/37468583/
6.3.1 Lessons I learned from Atlas 1.0
6.3.1.1 Multi-layered meta.data
6.3.1.2 Cell states in meta.data
6.4 There are a lot of great outcomes of the KPMP
6.5 A well-curated atlas of human kidney tissues is one of them!
6.6 Accessing KPMP Datasets
https://www.kpmp.org/available-data
6.6.1 Office hours have been very helpful
6.6.2 KPMP very responsive to data access problems
6.6.2.1 Overlaping datasets by Participant
6.6.2.2 Regret I didn’t set this up as an Upset Plot
6.6.3 Single-nucleus RNA-seq data
6.6.3.1 This has a lot of the individual sample files
6.6.4 Shortcut to get to annotated object
6.7 snRNAseq object: WashU-UCSD_HuBMAP_KPMP-Biopsy_10X-R_12032021.h5Seurat
6.7.1 Code to load
6.7.2 Notes
6.7.2.1 This is Atlas 1.0 data
6.7.2.2 Newer files have more samples…but are a smaller file size because it’s filtered more
6.7.2.3 When publishing…make sure to define the file you used
6.7.2.4 KPMP is consistently updating files with more samples
6.7.2.5 Goals is to have 1000 participants
6.7.3 Meta.data in the snRNASeq object
6.7.4 subclass.l2: Highest Resolution Cell Type/State
6.7.5 subclass.l1: Cell Type
6.7.6 class: Cell Group
6.8 There is a lot that you can do with just this information
6.8.1 But what about the clinical meta.data?
6.8.2 Separate cells according to disease type
6.8.2.2 Data portal
6.8.2.3 This is a .csv file
Participant ID
Tissue Source
Protocol
Sample Type
Enrollment Category
Primary Adjudicated Category
Sex
Age (Years) (Binned)
Race
KDIGO Stage
Baseline eGFR (ml/min/1.73m2) (Binned)
Proteinuria (mg) (Binned)
A1c (%) (Binned)
Albuminuria (mg) (Binned)
Diabetes
History
Diabetes Duration (Years)
Hypertension History
Hypertension Duration (Years)
On RAAS Blockade
6.8.2.4 Adding Meta.data to Seurat Object
6.8.2.4.1 Old way (Xiao-Tong Su)
6.8.2.4.2 New Way (dplr!)
6.8.2.4.3 Link the SpecimenID to the Participant.ID
6.8.2.5 Flexible enough to use with any object (as they are updated)
6.8.2.6 Meta.data in the object!
6.8.2.7 Application: Split data by disease type
6.9 Examples of Publishing with KPMP Data
6.9.1 So far I’ve published 4 articles using KPMP data
6.9.1.1 JASN 2024
6.9.1.2 JCI INsight 2025
6.9.1.3 Phys Genomics 2025
6.9.1.4 AJPR 2025
6.9.2 Publishing Tips
6.9.2.1 Must convert from mouse -> human (see SCORE #4)
6.9.2.2 Directions for citing KPMP Data in manuscripts
https://www.kpmp.org/help-docs/study-overview?tabname=citingkpmpdata
6.9.2.4 Add to Methods
6.9.2.5 Email Help
Nikki Bonevich is a great point of contact for KPMP data questions
email: bonevicn@umich.edu
7 Closing Remarks
I hope that you found this session helpful.
7.2 Questions?
7.3 Community Questions
Do you have a coding problem that you’d like some
support on?
Do you have a topic you’d like covered
at a future meeting?
Email me: j.nelson@med.usc.edu
7.4 Upcoming Schedule
7.5 Brainstorming Upcoming Topics
7.5.1 Potential Future Topics
7.5.1.1 Getting Started with Spatial Transcriptomics Data (Lu Zhang)
7.5.1.2 Marker selection for Spatial Transcriptomics (Jonathan Nelson)
7.5.1.3 Tissue Preparation for Spatial Transcriptomics (Jonathan Nelson)
7.5.1.4 Machine Learning in Single Cell Analysis (Michael Haidar)
7.5.1.5 Single Cell Analysis with Continuous Variables (Kelly Street)
7.5.1.6 Defining Cells with Gene sets (Jonathan Nelson)
7.5.1.7 Integrating Single-Cell and Single-Nucleus RNAseq Data (Katie Emberley)
8 Session Info
## R version 4.4.3 (2025-02-28 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 11 x64 (build 22631)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=English_United States.utf8
## [2] LC_CTYPE=English_United States.utf8
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.utf8
##
## time zone: America/Los_Angeles
## tzcode source: internal
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] openxlsx_4.2.8 patchwork_1.3.0
## [3] cowplot_1.1.3 glmGamPoi_1.18.0
## [5] sctransform_0.4.1 RColorBrewer_1.1-3
## [7] kableExtra_1.4.0 scCustomize_3.0.1
## [9] styler_1.10.3 lubridate_1.9.4
## [11] forcats_1.0.0 stringr_1.5.1
## [13] purrr_1.0.4 readr_2.1.5
## [15] tidyr_1.3.1 tibble_3.2.1
## [17] tidyverse_2.0.0 DESeq2_1.46.0
## [19] SummarizedExperiment_1.36.0 Biobase_2.66.0
## [21] MatrixGenerics_1.18.1 matrixStats_1.5.0
## [23] GenomicRanges_1.58.0 GenomeInfoDb_1.42.3
## [25] IRanges_2.40.1 S4Vectors_0.44.0
## [27] BiocGenerics_0.52.0 EnhancedVolcano_1.24.0
## [29] ggrepel_0.9.6 here_1.0.1
## [31] BiocManager_1.30.25 ggplot2_3.5.1
## [33] knitr_1.50 SeuratObject_5.0.2
## [35] Seurat_4.4.0 dplyr_1.1.4
##
## loaded via a namespace (and not attached):
## [1] RcppAnnoy_0.0.22 splines_4.4.3 later_1.4.1
## [4] R.oo_1.27.0 polyclip_1.10-7 janitor_2.2.1
## [7] lifecycle_1.0.4 rprojroot_2.0.4 globals_0.16.3
## [10] lattice_0.22-6 MASS_7.3-64 magrittr_2.0.3
## [13] plotly_4.10.4 sass_0.4.9 rmarkdown_2.29
## [16] jquerylib_0.1.4 yaml_2.3.10 httpuv_1.6.15
## [19] zip_2.3.2 spam_2.11-1 sp_2.2-0
## [22] spatstat.sparse_3.1-0 reticulate_1.41.0.1 pbapply_1.7-2
## [25] abind_1.4-8 zlibbioc_1.52.0 Rtsne_0.17
## [28] R.cache_0.16.0 R.utils_2.13.0 circlize_0.4.16
## [31] GenomeInfoDbData_1.2.13 irlba_2.3.5.1 listenv_0.9.1
## [34] spatstat.utils_3.1-3 goftest_1.2-3 spatstat.random_3.3-2
## [37] fitdistrplus_1.2-2 parallelly_1.42.0 svglite_2.1.3
## [40] leiden_0.4.3.1 codetools_0.2-20 DelayedArray_0.32.0
## [43] xml2_1.3.8 shape_1.4.6.1 tidyselect_1.2.1
## [46] UCSC.utils_1.2.0 farver_2.1.2 spatstat.explore_3.3-4
## [49] jsonlite_1.9.1 progressr_0.15.1 ggridges_0.5.6
## [52] survival_3.8-3 systemfonts_1.2.1 tools_4.4.3
## [55] ica_1.0-3 Rcpp_1.0.14 glue_1.8.0
## [58] gridExtra_2.3 SparseArray_1.6.2 xfun_0.51
## [61] withr_3.0.2 fastmap_1.2.0 digest_0.6.37
## [64] timechange_0.3.0 R6_2.6.1 mime_0.12
## [67] ggprism_1.0.6 colorspace_2.1-1 scattermore_1.2
## [70] tensor_1.5 spatstat.data_3.1-6 R.methodsS3_1.8.2
## [73] generics_0.1.3 data.table_1.17.0 httr_1.4.7
## [76] htmlwidgets_1.6.4 S4Arrays_1.6.0 uwot_0.2.3
## [79] pkgconfig_2.0.3 gtable_0.3.6 lmtest_0.9-40
## [82] XVector_0.46.0 htmltools_0.5.8.1 dotCall64_1.2
## [85] scales_1.3.0 png_0.1-8 snakecase_0.11.1
## [88] spatstat.univar_3.1-2 rstudioapi_0.17.1 tzdb_0.5.0
## [91] reshape2_1.4.4 nlme_3.1-167 GlobalOptions_0.1.2
## [94] cachem_1.1.0 zoo_1.8-14 KernSmooth_2.23-26
## [97] parallel_4.4.3 miniUI_0.1.1.1 vipor_0.4.7
## [100] ggrastr_1.0.2 pillar_1.10.1 grid_4.4.3
## [103] vctrs_0.6.5 RANN_2.6.2 promises_1.3.2
## [106] xtable_1.8-4 cluster_2.1.8 paletteer_1.6.0
## [109] beeswarm_0.4.0 evaluate_1.0.3 cli_3.6.4
## [112] locfit_1.5-9.12 compiler_4.4.3 rlang_1.1.5
## [115] crayon_1.5.3 future.apply_1.11.3 rematch2_2.1.2
## [118] plyr_1.8.9 ggbeeswarm_0.7.2 stringi_1.8.4
## [121] viridisLite_0.4.2 deldir_2.0-4 BiocParallel_1.40.0
## [124] munsell_0.5.1 lazyeval_0.2.2 spatstat.geom_3.3-5
## [127] Matrix_1.7-2 hms_1.1.3 future_1.34.0
## [130] shiny_1.10.0 ROCR_1.0-11 igraph_2.1.4
## [133] bslib_0.9.0