D2 // Breakout Session 2 // Dimension Reduction
Lindsay N. Hayes
12-10-2025
Perform dimension reduction on the KZ sample from Session 1 Data use saved data from prior session
Load Libraries and set a seed
library(Seurat)
library(ggplot2)
library(here)
set.seed(9)Load Data and check
## load the data (or skip if you pick up from Session 1)
load( file = here("data/KZ2_object.rds"))
## check the object, how many cells and features?
seurat.filtAn object of class Seurat
32285 features across 6175 samples within 1 assay
Active assay: RNA (32285 features, 3000 variable features)
3 layers present: counts, data, scale.datarun the PCA analysis
seurat.filt <- RunPCA(seurat.filt)PC_ 1
Positive: Esrrg, Ank3, Pkhd1, Kcnj16, Tinag, Ptprd, Glis3, Bicc1, Slc25a21, Ptprk
Magi2, Ghr, Atp6v0a4, Igfbp7, Ndrg1, P3h2, Bnc2, Osbpl6, Fhod3, Fut9
Pter, Egfr, Col4a3, Trabd2b, Cyp2j5, Nhs, Slc4a4, Slc47a1, Pde4d, Phyhipl
Negative: Ctss, Epsti1, Cd74, Tyrobp, Fcer1g, H2-Aa, Spi1, H2-Eb1, Ly86, Lsp1
Cybb, Rgs10, H2-Ab1, Ctsc, Crip1, Ms4a6c, Pid1, Lyz2, Bcl2a1b, Lst1
Hck, Plbd1, H2-DMa, Cd300c2, Alox5ap, Ifi27l2a, Apobec1, March1, Cd68, Plek
PC_ 2
Positive: Meis2, Ldb2, Ptprb, Emcn, Plpp1, Flt1, Ptprm, Adgrl4, Egfl7, Prex2
Cyyr1, Adgrf5, Eng, Sparc, Fbxl7, Samd12, Tek, Cdh5, Dysf, Plpp3
Ly6c1, Pbx1, Rapgef4, Ptprg, Cald1, Tm4sf1, Cd34, Fgd5, Exoc3l2, Podxl
Negative: Ctss, Maf, Tyrobp, Cd74, Aoah, Fcer1g, Ly86, Spi1, Fbp1, Cybb
H2-Aa, H2-Eb1, Gatm, Epsti1, Pdzk1, Ctsc, Ass1, Gsta2, March1, Rgs10
Keg1, Plbd1, Gm2a, Cd300c2, Cyp2j5, Aldob, Fcgr2b, H2-DMa, Gk, Csf1r
PC_ 3
Positive: Fmo1, Cyp4b1, Abcg2, Aldob, Cyp2j5, Fbp1, Miox, Meis2, Spink1, Slc34a1
Gpx3, Ptprb, Pdzk1, Flt1, Emcn, Keg1, Gsta2, Lrp2, Akr1c21, Pakap.1
Ldb2, Pter, Adgrl4, Cda, Ass1, Cyyr1, Pdzd2, Egfl7, Lims2, Acsm2
Negative: Naaladl2, Slit2, Mal2, Epcam, Sim1, Rgs6, Tacstd2, Efna5, Tmem45b, Krt7
Scnn1b, Tmem213, Sgpp2, Cdh3, Cldn7, Aif1l, Kcnj1, Rhcg, Defb1, Tspan1
Sh2d4a, Cldn8, Scnn1g, Hsd11b2, Cdh1, Tfap2b, Efemp1, Krt18, Prdm16, Fras1
PC_ 4
Positive: Fcer1g, Pid1, Csf1r, Tyrobp, Cst3, Lyz2, Cd300c2, Adgre1, C1qa, C1qb
C1qc, Mpeg1, Cd68, Fcgr3, Spi1, Aif1, H2-DMb1, Ctsc, Cx3cr1, Lst1
Mafb, Ly86, Apobec1, Slc8a1, Mrc1, Clec4a3, Ms4a7, Psap, H2-Ab1, Ifi207
Negative: Skap1, Cd3g, Tnik, Trbc2, Cd247, Itk, Ms4a4b, Tox, Cd3d, Themis
Gm2682, Ikzf3, Trac, Ltb, Camk4, Thy1, Il7r, Prkca, Cd5, Trbc1
Cd226, Cd6, Icos, Cxcr6, Ctla4, Ikzf2, Sh2d1a, Ctsw, Nkg7, AW112010
PC_ 5
Positive: Ror1, Lhfp, Gucy1a2, Gpc6, Gucy1a1, Prkca, Pdgfrb, Fhl2, Pde3a, Pawr
Cacna2d1, Unc5c, Fign, 4921539H07Rik, Bnc2, Nhs, Auts2, Arhgap10, Plcl1, Pdzrn3
Lama2, Col8a1, Serping1, Agtr1a, Magi2, B130024G19Rik, Gm37245, Glis1, Pdgfra, Skap1
Negative: Serpina1f, Napsa, Aadat, Acot1, Cbr1, Kcnk1, Slc7a12, Guca2b, Acy3, Ttc36
Mep1b, Hao2, Mep1a, Defb29, Slc5a10, Slc22a13, Mpv17l, Cyp2a4, Cth, Acadm
Tmigd1, Rida, Chchd10, Serpina1d, Khk, Eci3, Ggt1, Cyp4a10, Bphl, Mettl7a2 ## check the object can you see where the pca is stored?
seurat.filtAn object of class Seurat
32285 features across 6175 samples within 1 assay
Active assay: RNA (32285 features, 3000 variable features)
3 layers present: counts, data, scale.data
1 dimensional reduction calculated: pcaDetermine how many PCs to include in the dimension reduction
will take 5-10 mins
DimPlot(seurat.filt, reduction="pca")
ggsave(here("plots/2.DimRed/PCA.jpg"), width = 5, height = 5)
ElbowPlot(seurat.filt, ndims = 50, reduction = "pca")
ggsave(here("plots/2.DimRed/ElbowPlot.jpg"), width = 5, height = 5)
seurat.filt <- JackStraw(seurat.filt, dims = 50) # 8 min
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s seurat.filt <- ScoreJackStraw(seurat.filt, dims = 1:50)
JackStrawPlot(seurat.filt, dims = 1:50, reduction = "pca")Warning: Removed 105585 rows containing missing values or values outside the scale range (`geom_point()`).ggsave(here("plots/2.DimRed/JackStrawPlot.jpg"), width = 8, height = 5)Warning: Removed 105585 rows containing missing values or values outside the scale range (`geom_point()`).
DimHeatmap(seurat.filt, dims = 1:18, cells = 100, balanced = TRUE, fast = FALSE)
ggsave(here("plots/2.DimRed/PCAHeatmap.jpg"), width = 10, height = 25)
## How many PCs are at the Elbow? Check out the UMAP
## are the dims being used reasonable?
## try out different resolution in the FindClusters to see how the clusters change
seurat.filt <- FindNeighbors(seurat.filt, dims = 1:30, reduction = "pca")Computing nearest neighbor graph
Computing SNNseurat.filt <- FindClusters(seurat.filt, resolution = 0.4, cluster.name = "clusters")Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 6175
Number of edges: 202054
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9521
Number of communities: 19
Elapsed time: 0 secondsseurat.filt <- RunUMAP(seurat.filt, dims = 1:30, reduction = "pca", reduction.name = "umap")Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
13:14:47 UMAP embedding parameters a = 0.9922 b = 1.112
13:14:47 Read 6175 rows and found 30 numeric columns
13:14:47 Using Annoy for neighbor search, n_neighbors = 30
13:14:47 Building Annoy index with metric = cosine, n_trees = 50
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:14:48 Writing NN index file to temp file /var/folders/tp/z4kv_q0d5j98lblm0rx44pl40000gr/T//Rtmp3RyVZV/file80e456815c3
13:14:48 Searching Annoy index using 1 thread, search_k = 3000
13:14:48 Annoy recall = 100%
13:14:49 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
13:14:49 Initializing from normalized Laplacian + noise (using RSpectra)
13:14:50 Commencing optimization for 500 epochs, with 250554 positive edges
Using method 'umap'
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:14:54 Optimization finishedDimPlot(seurat.filt, reduction = "umap")
ggsave(here("plots/2.DimRed/UMAP.jpg"), width = 5, height = 5)
Check out the expression of one of the HVGs
## change the "Gene of interest" to one of your HVGs
GOI <- "Cx3cr1"
FeaturePlot(seurat.filt, reduction ="umap", features = GOI, order = TRUE)
ggsave(here("plots/2.DimRed/Cx3cr1_umap.jpg"), width = 5, height = 5)
save your object for the next steps!
save(seurat.filt, file = here("data/KZ2_umap_object.rds"))sessionInfo()R version 4.4.1 (2024-06-14)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 14.5
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] future_1.49.0 here_1.0.1 scDblFinder_1.18.0 SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[6] Biobase_2.64.0 GenomicRanges_1.56.2 GenomeInfoDb_1.40.1 IRanges_2.38.1 S4Vectors_0.42.1
[11] BiocGenerics_0.50.0 MatrixGenerics_1.16.0 matrixStats_1.5.0 ggplot2_3.5.2 Seurat_5.2.1
[16] SeuratObject_5.0.2 sp_2.1-4
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.22 splines_4.4.1 later_1.4.2 BiocIO_1.14.0 bitops_1.0-9
[6] R.oo_1.27.0 tibble_3.2.1 polyclip_1.10-7 XML_3.99-0.18 fastDummies_1.7.5
[11] lifecycle_1.0.4 rprojroot_2.0.4 edgeR_4.2.2 globals_0.18.0 lattice_0.22-6
[16] MASS_7.3-64 magrittr_2.0.3 sass_0.4.10 rmarkdown_2.29 limma_3.60.6
[21] plotly_4.10.4 jquerylib_0.1.4 yaml_2.3.10 metapod_1.12.0 httpuv_1.6.16
[26] sctransform_0.4.1 spam_2.11-1 spatstat.sparse_3.1-0 reticulate_1.42.0 cowplot_1.1.3
[31] pbapply_1.7-2 RColorBrewer_1.1-3 abind_1.4-8 zlibbioc_1.50.0 Rtsne_0.17
[36] R.utils_2.12.3 purrr_1.0.4 RCurl_1.98-1.16 GenomeInfoDbData_1.2.12 ggrepel_0.9.6
[41] irlba_2.3.5.1 listenv_0.9.1 spatstat.utils_3.1-2 goftest_1.2-3 RSpectra_0.16-2
[46] dqrng_0.4.1 spatstat.random_3.3-2 fitdistrplus_1.2-2 parallelly_1.45.0 DelayedMatrixStats_1.26.0
[51] codetools_0.2-20 DelayedArray_0.30.1 scuttle_1.14.0 tidyselect_1.2.1 UCSC.utils_1.0.0
[56] farver_2.1.2 viridis_0.6.5 ScaledMatrix_1.12.0 spatstat.explore_3.3-4 GenomicAlignments_1.40.0
[61] jsonlite_2.0.0 BiocNeighbors_1.22.0 progressr_0.15.1 ggridges_0.5.6 survival_3.8-3
[66] scater_1.32.1 systemfonts_1.2.1 tools_4.4.1 ragg_1.3.3 ica_1.0-3
[71] Rcpp_1.0.14 glue_1.8.0 gridExtra_2.3 SparseArray_1.4.8 xfun_0.52
[76] dplyr_1.1.4 withr_3.0.2 fastmap_1.2.0 bluster_1.14.0 digest_0.6.37
[81] rsvd_1.0.5 R6_2.6.1 mime_0.13 textshaping_1.0.0 colorspace_2.1-1
[86] scattermore_1.2 tensor_1.5 spatstat.data_3.1-4 R.methodsS3_1.8.2 tidyr_1.3.1
[91] generics_0.1.3 data.table_1.17.0 rtracklayer_1.64.0 httr_1.4.7 htmlwidgets_1.6.4
[96] S4Arrays_1.4.1 uwot_0.2.2 pkgconfig_2.0.3 gtable_0.3.6 lmtest_0.9-40
[101] XVector_0.44.0 htmltools_0.5.8.1 dotCall64_1.2 scales_1.3.0 png_0.1-8
[106] spatstat.univar_3.1-1 scran_1.32.0 knitr_1.50 rstudioapi_0.17.1 reshape2_1.4.4
[111] rjson_0.2.23 nlme_3.1-167 curl_6.2.2 cachem_1.1.0 zoo_1.8-12
[116] stringr_1.5.1 KernSmooth_2.23-26 parallel_4.4.1 miniUI_0.1.1.1 vipor_0.4.7
[121] ggrastr_1.0.2 restfulr_0.0.15 pillar_1.10.1 grid_4.4.1 vctrs_0.6.5
[126] RANN_2.6.2 promises_1.3.3 BiocSingular_1.20.0 beachmat_2.20.0 xtable_1.8-4
[131] cluster_2.1.8 beeswarm_0.4.0 evaluate_1.0.4 locfit_1.5-9.10 cli_3.6.5
[136] compiler_4.4.1 Rsamtools_2.20.0 rlang_1.1.6 crayon_1.5.3 future.apply_1.11.3
[141] labeling_0.4.3 plyr_1.8.9 ggbeeswarm_0.7.2 stringi_1.8.4 viridisLite_0.4.2
[146] deldir_2.0-4 BiocParallel_1.38.0 munsell_0.5.1 Biostrings_2.72.1 lazyeval_0.2.2
[151] spatstat.geom_3.3-5 Matrix_1.7-2 RcppHNSW_0.6.0 patchwork_1.3.0 sparseMatrixStats_1.16.0
[156] statmod_1.5.0 shiny_1.10.0 ROCR_1.0-11 igraph_2.1.4 bslib_0.9.0
[161] xgboost_1.7.8.1 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