Analysing 20k cells

Last updated: 2025-02-22

Checks: 7 0

Knit directory: muse/

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    Ignored:    data/293t_filtered_gene_bc_matrices.tar.gz
    Ignored:    data/5k_Human_Donor1_PBMC_3p_gem-x_5k_Human_Donor1_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5
    Ignored:    data/5k_Human_Donor2_PBMC_3p_gem-x_5k_Human_Donor2_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5
    Ignored:    data/5k_Human_Donor3_PBMC_3p_gem-x_5k_Human_Donor3_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5
    Ignored:    data/5k_Human_Donor4_PBMC_3p_gem-x_5k_Human_Donor4_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5
    Ignored:    data/Parent_SC3v3_Human_Glioblastoma_filtered_feature_bc_matrix.tar.gz
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    Ignored:    data/jurkat_293t/
    Ignored:    data/jurkat_filtered_gene_bc_matrices.tar.gz
    Ignored:    data/pbmc20k/
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    Ignored:    data/pbmc4k_filtered_gene_bc_matrices.tar.gz
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    Ignored:    data/seurat_1m_neuron.rds
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File	Version	Author	Date	Message
Rmd	6c0d4f0	Dave Tang	2025-02-22	Convert sparse matrix to iterable matrix
html	f6301ec	Dave Tang	2025-02-22	Build site.
Rmd	22af89e	Dave Tang	2025-02-22	Analysing 20k cells

Read HDF5 files into a list.

hdf5_files <- list.files(path = "data", pattern = "5k_Human", full.names = TRUE)
hdf5_files

[1] "data/5k_Human_Donor1_PBMC_3p_gem-x_5k_Human_Donor1_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5"
[2] "data/5k_Human_Donor2_PBMC_3p_gem-x_5k_Human_Donor2_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5"
[3] "data/5k_Human_Donor3_PBMC_3p_gem-x_5k_Human_Donor3_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5"
[4] "data/5k_Human_Donor4_PBMC_3p_gem-x_5k_Human_Donor4_PBMC_3p_gem-x_count_sample_filtered_feature_bc_matrix.h5"

Read raw counts into a list of matrices.

mats <- purrr::map(seq_along(hdf5_files), function(x){
  my_mat <- Seurat::Read10X_h5(hdf5_files[x])
  colnames(my_mat) <- paste0('donor', x, '_', colnames(my_mat))
  my_mat
})
str(mats, max.level = 1)

List of 4
 $ :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
 $ :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
 $ :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
 $ :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots

Create Seurat object from the list of matrices.

pbmc20k <- CreateSeuratObject(
  counts = mats,
  min.cells = 3,
  min.features = 200
)
pbmc20k

An object of class Seurat 
27385 features across 22061 samples within 1 assay 
Active assay: RNA (27385 features, 0 variable features)
 4 layers present: counts.1, counts.2, counts.3, counts.4

Create one count layer.

pbmc20k <- JoinLayers(pbmc20k)
pbmc20k

An object of class Seurat 
27385 features across 22061 samples within 1 assay 
Active assay: RNA (27385 features, 0 variable features)
 1 layer present: counts

Donor information in orig.ident.

head(pbmc20k@meta.data)

                          orig.ident nCount_RNA nFeature_RNA
donor1_AAACCAAAGGTGACGA-1     donor1      42833         7079
donor1_AAACCCTGTGACGAGT-1     donor1       4890         2102
donor1_AAACGAATCAGGCTAC-1     donor1      12498         3564
donor1_AAACGACAGATTGACT-1     donor1      22193         4366
donor1_AAACGATGTCTTGAAC-1     donor1      10305         2945
donor1_AAACGATGTGCGCGAA-1     donor1      15947         4160

Use {BPCells} to convert the matrices in your already created Seurat objects to on-disk matrices. Note, that this is only possible for V5 assays. Convert the counts matrix of the RNA assay to a BPCells matrix.

BPCells::write_matrix_dir(
  mat = BPCells::convert_matrix_type(matrix = pbmc20k@assays$RNA$counts, type = "uint32_t"),
  dir = 'data/pbmc20k',
  overwrite = TRUE
)

27385 x 22061 IterableMatrix object with class MatrixDir

Row names: ENSG00000238009, ENSG00000239945 ... AMELY
Col names: donor1_AAACCAAAGGTGACGA-1, donor1_AAACCCTGTGACGAGT-1 ... donor4_TGTGTTGAGTTACGGC-1

Data type: uint32_t
Storage order: column major

Queued Operations:
1. Load compressed matrix from directory /home/rstudio/muse/data/pbmc20k

pbmc20k.mat <- open_matrix_dir(dir = "data/pbmc20k")

pbmc20k@assays$RNA$counts <- pbmc20k.mat
pbmc20k@assays$RNA$counts

27385 x 22061 IterableMatrix object with class RenameDims

Row names: ENSG00000238009, ENSG00000239945 ... AMELY
Col names: donor1_AAACCAAAGGTGACGA-1, donor1_AAACCCTGTGACGAGT-1 ... donor4_TGTGTTGAGTTACGGC-1

Data type: uint32_t
Storage order: column major

Queued Operations:
1. Load compressed matrix from directory /home/rstudio/muse/data/pbmc20k
2. Reset dimnames

Process.

options(future.globals.maxSize = 2 * 1024^3)

fixed_PrepDR5 <- function(object, features = NULL, layer = 'scale.data', verbose = TRUE) {
  layer <- layer[1L]
  olayer <- layer
  layer <- SeuratObject::Layers(object = object, search = layer)
  if (is.null(layer)) {
    abort(paste0("No layer matching pattern '", olayer, "' not found. Please run ScaleData and retry"))
  }
  data.use <- SeuratObject::LayerData(object = object, layer = layer)
  features <- features %||% VariableFeatures(object = object)
  if (!length(x = features)) {
    stop("No variable features, run FindVariableFeatures() or provide a vector of features", call. = FALSE)
  }
  if (is(data.use, "IterableMatrix")) {
    features.var <- BPCells::matrix_stats(matrix=data.use, row_stats="variance")$row_stats["variance",]
  } else {
    features.var <- apply(X = data.use, MARGIN = 1L, FUN = var)
  }
  features.keep <- features[features.var > 0]
  if (!length(x = features.keep)) {
    stop("None of the requested features have any variance", call. = FALSE)
  } else if (length(x = features.keep) < length(x = features)) {
    exclude <- setdiff(x = features, y = features.keep)
    if (isTRUE(x = verbose)) {
      warning(
        "The following ",
        length(x = exclude),
        " features requested have zero variance; running reduction without them: ",
        paste(exclude, collapse = ', '),
        call. = FALSE,
        immediate. = TRUE
      )
    }
  }
  features <- features.keep
  features <- features[!is.na(x = features)]
  features.use <- features[features %in% rownames(data.use)]
  if(!isTRUE(all.equal(features, features.use))) {
    missing_features <- setdiff(features, features.use)
    if(length(missing_features) > 0) {
    warning_message <- paste("The following features were not available: ",
                             paste(missing_features, collapse = ", "),
                             ".", sep = "")
    warning(warning_message, immediate. = TRUE)
    }
  }
  data.use <- data.use[features.use, ]
  return(data.use)
}

assignInNamespace('PrepDR5', fixed_PrepDR5, 'Seurat')

seurat_wf_v4 <- function(seurat_obj, scale_factor = 1e4, num_features = 2000, num_pcs = 30, cluster_res = 0.5, debug_flag = FALSE){
  
  seurat_obj <- NormalizeData(seurat_obj, normalization.method = "LogNormalize", scale.factor = scale_factor, verbose = debug_flag)
  seurat_obj <- FindVariableFeatures(seurat_obj, selection.method = 'vst', nfeatures = num_features, verbose = debug_flag)
  seurat_obj <- ScaleData(seurat_obj, verbose = debug_flag)
  seurat_obj <- RunPCA(seurat_obj, verbose = debug_flag)
  seurat_obj <- RunHarmony(seurat_obj, "orig.ident")
  seurat_obj <- RunUMAP(seurat_obj, reduction = "harmony",  dims = 1:num_pcs, verbose = debug_flag)
  
  seurat_obj
}

pbmc20k <- seurat_wf_v4(pbmc20k)

Transposing data matrix

Initializing state using k-means centroids initialization

Harmony 1/10

Harmony 2/10

Harmony 3/10

Harmony converged after 3 iterations

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

UMAP.

DimPlot(pbmc20k, reduction = "umap", group.by = "orig.ident", pt.size = .1)

Version	Author	Date
f6301ec	Dave Tang	2025-02-22

sessionInfo()

R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

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       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] BPCells_0.3.0      Seurat_5.1.0       SeuratObject_5.0.2 sp_2.1-4          
 [5] harmony_1.2.1      Rcpp_1.0.13        patchwork_1.3.0    lubridate_1.9.3   
 [9] forcats_1.0.0      stringr_1.5.1      dplyr_1.1.4        purrr_1.0.2       
[13] readr_2.1.5        tidyr_1.3.1        tibble_3.2.1       ggplot2_3.5.1     
[17] tidyverse_2.0.0    workflowr_1.7.1   

loaded via a namespace (and not attached):
  [1] RColorBrewer_1.1-3     rstudioapi_0.17.1      jsonlite_1.8.9        
  [4] magrittr_2.0.3         spatstat.utils_3.1-0   farver_2.1.2          
  [7] rmarkdown_2.28         fs_1.6.4               vctrs_0.6.5           
 [10] ROCR_1.0-11            spatstat.explore_3.3-3 htmltools_0.5.8.1     
 [13] sass_0.4.9             sctransform_0.4.1      parallelly_1.38.0     
 [16] KernSmooth_2.23-24     bslib_0.8.0            htmlwidgets_1.6.4     
 [19] ica_1.0-3              plyr_1.8.9             plotly_4.10.4         
 [22] zoo_1.8-12             cachem_1.1.0           whisker_0.4.1         
 [25] igraph_2.1.1           mime_0.12              lifecycle_1.0.4       
 [28] pkgconfig_2.0.3        Matrix_1.7-0           R6_2.5.1              
 [31] fastmap_1.2.0          MatrixGenerics_1.18.1  fitdistrplus_1.2-1    
 [34] future_1.34.0          shiny_1.9.1            digest_0.6.37         
 [37] colorspace_2.1-1       ps_1.8.1               rprojroot_2.0.4       
 [40] tensor_1.5             RSpectra_0.16-2        irlba_2.3.5.1         
 [43] labeling_0.4.3         progressr_0.15.0       fansi_1.0.6           
 [46] spatstat.sparse_3.1-0  timechange_0.3.0       httr_1.4.7            
 [49] polyclip_1.10-7        abind_1.4-8            compiler_4.4.1        
 [52] bit64_4.5.2            withr_3.0.2            fastDummies_1.7.4     
 [55] highr_0.11             MASS_7.3-60.2          tools_4.4.1           
 [58] lmtest_0.9-40          httpuv_1.6.15          future.apply_1.11.3   
 [61] goftest_1.2-3          glue_1.8.0             callr_3.7.6           
 [64] nlme_3.1-164           promises_1.3.0         grid_4.4.1            
 [67] Rtsne_0.17             getPass_0.2-4          cluster_2.1.6         
 [70] reshape2_1.4.4         generics_0.1.3         hdf5r_1.3.11          
 [73] gtable_0.3.6           spatstat.data_3.1-2    tzdb_0.4.0            
 [76] data.table_1.16.2      hms_1.1.3              utf8_1.2.4            
 [79] spatstat.geom_3.3-3    RcppAnnoy_0.0.22       ggrepel_0.9.6         
 [82] RANN_2.6.2             pillar_1.9.0           spam_2.11-0           
 [85] RcppHNSW_0.6.0         later_1.3.2            splines_4.4.1         
 [88] lattice_0.22-6         bit_4.5.0              deldir_2.0-4          
 [91] survival_3.6-4         tidyselect_1.2.1       miniUI_0.1.1.1        
 [94] pbapply_1.7-2          knitr_1.48             git2r_0.35.0          
 [97] gridExtra_2.3          scattermore_1.2        RhpcBLASctl_0.23-42   
[100] xfun_0.48              matrixStats_1.4.1      stringi_1.8.4         
[103] lazyeval_0.2.2         yaml_2.3.10            evaluate_1.0.1        
[106] codetools_0.2-20       cli_3.6.3              uwot_0.2.2            
[109] xtable_1.8-4           reticulate_1.39.0      munsell_0.5.1         
[112] processx_3.8.4         jquerylib_0.1.4        spatstat.random_3.3-2 
[115] globals_0.16.3         png_0.1-8              spatstat.univar_3.0-1 
[118] parallel_4.4.1         dotCall64_1.2          listenv_0.9.1         
[121] viridisLite_0.4.2      scales_1.3.0           ggridges_0.5.6        
[124] leiden_0.4.3.1         rlang_1.1.4            cowplot_1.1.3