Load libraries

Public release 0 includes 586 samples, 12 rounds, 385 10X pools, 299 donors and 1,406,788 cells passing QC.

Summarize data

UMAP

Summarize cell counts

## Loading required package: HDF5Array
## Loading required package: DelayedArray
## Loading required package: stats4
## Loading required package: Matrix
## Loading required package: BiocGenerics
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## Attaching package: 'BiocGenerics'
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##     get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
##     match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
##     Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,
##     table, tapply, union, unique, unsplit, which.max, which.min
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## Loading required package: MatrixGenerics
## Loading required package: matrixStats
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##     colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
##     colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
##     colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
##     colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
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##     colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
##     rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
##     rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
##     rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
##     rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
##     rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
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## Loading required package: S4Vectors
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## Attaching package: 'S4Vectors'
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## Loading required package: IRanges
## Loading required package: S4Arrays
## Loading required package: abind
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## Attaching package: 'S4Arrays'
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## Loading required package: SparseArray
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## Attaching package: 'DelayedArray'
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## Loading required package: rhdf5
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## Attaching package: 'HDF5Array'
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##     h5ls

Summarize demographics

##          Sex
## Dx        Female Male
##   Control     70   79
##   AD          71   79

Summarize read counts

Cell type specificity

Show cell markers

Process data: log2 CPM + voom precision weights

Variance Partitioning Analysis

Batch effects for each cell type

Batch effect for NXPE1

Correlation with and between donors

Differential expression: dreamlet analysis

Volcano

Highlight genes

Summarize differential expression

Interpreting variance partitioning analysis

Observe that fraction of variation across batches correlates with GC content of the corresponding gene. Also genes with higher counts per million show high variation across donor since more counts corresponds to a reduction in ‘shot noise’ due to low counts.

Gene set analysis

Combine plots

Session Info

## R version 4.3.3 (2024-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
## 
## Matrix products: default
## BLAS/LAPACK: /hpc/packages/minerva-centos7/oneAPI/p_2024.1.0.560/toolkits/mkl/2024.1/lib/libmkl_gf_lp64.so.2;  LAPACK version 3.11.0
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## 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       
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## time zone: America/New_York
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
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## other attached packages:
##  [1] broom_1.0.6                 gridExtra_2.3              
##  [3] ggrepel_0.9.5               EnrichmentBrowser_2.32.0   
##  [5] org.Hs.eg.db_3.18.0         viridis_0.6.5              
##  [7] viridisLite_0.4.2           corrplot_0.92              
##  [9] RColorBrewer_1.1-3          scattermore_1.2            
## [11] qvalue_2.34.0               kableExtra_1.4.0           
## [13] lubridate_1.9.3             forcats_1.0.0              
## [15] stringr_1.5.1               dplyr_1.1.4                
## [17] purrr_1.0.2                 readr_2.1.5                
## [19] tidyr_1.3.1                 tibble_3.2.1               
## [21] tidyverse_2.0.0             scater_1.30.1              
## [23] scuttle_1.12.0              cowplot_1.1.3              
## [25] muscat_1.16.0               GSEABase_1.64.0            
## [27] graph_1.80.0                annotate_1.80.0            
## [29] XML_3.99-0.16.1             AnnotationDbi_1.64.1       
## [31] DelayedArray_0.28.0         SparseArray_1.2.4          
## [33] S4Arrays_1.2.1              abind_1.4-5                
## [35] Matrix_1.6-5                zenith_1.4.2               
## [37] dreamlet_1.1.23             variancePartition_1.33.15  
## [39] BiocParallel_1.36.0         limma_3.58.1               
## [41] ggplot2_3.5.1               zellkonverter_1.13.3       
## [43] SingleCellExperiment_1.24.0 SummarizedExperiment_1.32.0
## [45] Biobase_2.62.0              GenomicRanges_1.54.1       
## [47] GenomeInfoDb_1.38.8         IRanges_2.36.0             
## [49] S4Vectors_0.40.2            BiocGenerics_0.48.1        
## [51] MatrixGenerics_1.14.0       matrixStats_1.3.0          
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## loaded via a namespace (and not attached):
##   [1] bitops_1.0-7              httr_1.4.7               
##   [3] doParallel_1.0.17         Rgraphviz_2.46.0         
##   [5] numDeriv_2016.8-1.1       tools_4.3.3              
##   [7] sctransform_0.4.1         backports_1.5.0          
##   [9] utf8_1.2.4                R6_2.5.1                 
##  [11] metafor_4.6-0             mgcv_1.9-1               
##  [13] GetoptLong_1.0.5          withr_3.0.0              
##  [15] prettyunits_1.2.0         cli_3.6.2                
##  [17] labeling_0.4.3            sass_0.4.9               
##  [19] KEGGgraph_1.62.0          SQUAREM_2021.1           
##  [21] mvtnorm_1.2-5             blme_1.0-5               
##  [23] mixsqp_0.3-54             systemfonts_1.1.0        
##  [25] svglite_2.1.3             parallelly_1.37.1        
##  [27] invgamma_1.1              rstudioapi_0.16.0        
##  [29] RSQLite_2.3.6             generics_0.1.3           
##  [31] shape_1.4.6.1             gtools_3.9.5             
##  [33] metadat_1.2-0             ggbeeswarm_0.7.2         
##  [35] fansi_1.0.6               lifecycle_1.0.4          
##  [37] yaml_2.3.8                edgeR_4.0.16             
##  [39] mathjaxr_1.6-0            gplots_3.1.3.1           
##  [41] grid_4.3.3                blob_1.2.4               
##  [43] crayon_1.5.2              dir.expiry_1.10.0        
##  [45] lattice_0.22-5            beachmat_2.18.1          
##  [47] msigdbr_7.5.1             KEGGREST_1.42.0          
##  [49] pillar_1.9.0              knitr_1.46               
##  [51] ComplexHeatmap_2.18.0     rjson_0.2.21             
##  [53] boot_1.3-29               corpcor_1.6.10           
##  [55] future.apply_1.11.2       codetools_0.2-19         
##  [57] glue_1.7.0                data.table_1.15.4        
##  [59] vctrs_0.6.5               png_0.1-8                
##  [61] Rdpack_2.6                gtable_0.3.5             
##  [63] assertthat_0.2.1          cachem_1.1.0             
##  [65] xfun_0.43                 rbibutils_2.2.16         
##  [67] Rfast_2.1.0               iterators_1.0.14         
##  [69] statmod_1.5.0             nlme_3.1-164             
##  [71] pbkrtest_0.5.2            bit64_4.0.5              
##  [73] progress_1.2.3            EnvStats_2.8.1           
##  [75] filelock_1.0.3            bslib_0.7.0              
##  [77] TMB_1.9.11                irlba_2.3.5.1            
##  [79] vipor_0.4.7               KernSmooth_2.23-22       
##  [81] colorspace_2.1-0          rmeta_3.0                
##  [83] DBI_1.2.2                 DESeq2_1.42.1            
##  [85] tidyselect_1.2.1          bit_4.0.5                
##  [87] compiler_4.3.3            BiocNeighbors_1.20.2     
##  [89] basilisk.utils_1.14.1     xml2_1.3.6               
##  [91] scales_1.3.0              caTools_1.18.2           
##  [93] remaCor_0.0.18            digest_0.6.35            
##  [95] minqa_1.2.7               rmarkdown_2.27           
##  [97] basilisk_1.14.3           aod_1.3.3                
##  [99] XVector_0.42.0            RhpcBLASctl_0.23-42      
## [101] htmltools_0.5.8.1         pkgconfig_2.0.3          
## [103] lme4_1.1-35.2             sparseMatrixStats_1.14.0 
## [105] highr_0.10                mashr_0.2.79             
## [107] fastmap_1.2.0             rlang_1.1.3              
## [109] GlobalOptions_0.1.2       DelayedMatrixStats_1.24.0
## [111] farver_2.1.2              jquerylib_0.1.4          
## [113] jsonlite_1.8.8            BiocSingular_1.18.0      
## [115] RCurl_1.98-1.14           magrittr_2.0.3           
## [117] GenomeInfoDbData_1.2.11   munsell_0.5.1            
## [119] Rcpp_1.0.12               babelgene_22.9           
## [121] reticulate_1.37.0         RcppZiggurat_0.1.6       
## [123] stringi_1.8.4             zlibbioc_1.48.2          
## [125] MASS_7.3-60.0.1           plyr_1.8.9               
## [127] parallel_4.3.3            listenv_0.9.1            
## [129] Biostrings_2.70.3         splines_4.3.3            
## [131] hms_1.1.3                 circlize_0.4.16          
## [133] locfit_1.5-9.9            reshape2_1.4.4           
## [135] ScaledMatrix_1.10.0       evaluate_0.23            
## [137] RcppParallel_5.1.7        tzdb_0.4.0               
## [139] nloptr_2.0.3              foreach_1.5.2            
## [141] future_1.33.2             clue_0.3-65              
## [143] ashr_2.2-63               rsvd_1.0.5               
## [145] xtable_1.8-4              fANCOVA_0.6-1            
## [147] truncnorm_1.0-9           lmerTest_3.1-3           
## [149] glmmTMB_1.1.9             memoise_2.0.1            
## [151] beeswarm_0.4.0            cluster_2.1.6            
## [153] timechange_0.3.0          globals_0.16.3