Using pochi with Seurat • pochi

Introduction

Installation

Currently, pochi is available at Github and can be downloaded using the devtools (or remotes) package.

devtools::install_github("diegoalexespi/pochi")

Loading data

Loading required packages

The pochi package operates on Seurat objects. We load the Seurat, SeuratData, and pochi packages here for our analyses, as well as the magrittr package in order to improve legibility of code through using the pipe %>% operator.

require("Seurat")
require("SeuratData")
require("pochi")
require("ggplot2")
require("tidyr")

We load the pbmc3k data from the SeuratData package.

InstallData("pbmc3k")

## Error in `InstallData()`:
## ! could not find function "InstallData"

pbmc3k_seurat <- LoadData("pbmc3k")

## Error in `LoadData()`:
## ! could not find function "LoadData"

set.seed(789)
pbmc3k_seurat <- pbmc3k_seurat[,!is.na(pbmc3k_seurat$seurat_annotations)]

## Error:
## ! object 'pbmc3k_seurat' not found

pbmc3k_seurat <- NormalizeData(pbmc3k_seurat)

## Error:
## ! object 'pbmc3k_seurat' not found

pbmc3k_seurat$condition <- sample(c("WT", "KO"), size = ncol(pbmc3k_seurat), replace = TRUE)

## Error:
## ! object 'pbmc3k_seurat' not found

pbmc3k_seurat$replicate <- sample(1:3, size = ncol(pbmc3k_seurat), replace = TRUE)

## Error:
## ! object 'pbmc3k_seurat' not found

pbmc3k_seurat@meta.data %>% head()

## Error:
## ! object 'pbmc3k_seurat' not found

Visualizations

`AbundancePlot`

AbundancePlot(pbmc3k_seurat, group.by = "seurat_annotations", split.by = "condition", replicate.by = "replicate")

## Error:
## ! object 'pbmc3k_seurat' not found

AbundancePlot(pbmc3k_seurat, group.by = "seurat_annotations", split.by = "condition", replicate.by = "replicate", paired = TRUE, draw_paths = TRUE, sina_shift = FALSE)

## Error in `AbundancePlot()`:
## ! unused argument (sina_shift = FALSE)

`AssignQuantiles`

pbmc3k_seurat <- AssignQuantiles(pbmc3k_seurat, feature = "B2M", assay = "RNA", slot = "data", split.by = "condition", quantile.probs = c(0,0.4,0.8,1))

## Error in `AssignQuantiles()`:
## ! unused argument (slot = "data")

RidgePlot(pbmc3k_seurat, group.by = "B2M.quantile", features = "B2M")

## Error:
## ! object 'pbmc3k_seurat' not found

`BackGatePlot`

BackgatePlot(pbmc3k_seurat, feature1 = "CD4", feature2 = "CD8A", metadata.col = "seurat_annotations", metadata.selection = "Memory CD4 T")

## Error:
## ! object 'pbmc3k_seurat' not found

BackgatePlot(pbmc3k_seurat, feature1 = "CD4", feature2 = "CD8A", metadata.col = "seurat_annotations", metadata.selection = "CD8 T")

## Error:
## ! object 'pbmc3k_seurat' not found

`Heatmaps`

rna_markers <- presto::wilcoxauc(pbmc3k_seurat, group_by = "seurat_annotations")

## Error in `loadNamespace()`:
## ! there is no package called 'presto'

top_rna_markers <- rna_markers %>% 
    dplyr::filter(padj < 0.01, logFC > 0) %>%
    dplyr::group_by(group) %>% 
    dplyr::slice_min(padj, with_ties = FALSE, n = 3)

## Error:
## ! object 'rna_markers' not found

DoStarHeatmap(pbmc3k_seurat, diff_exp_results = rna_markers %>% dplyr::filter(group != "Platelet"), assay = "RNA", slot = "data", group.by = "seurat_annotations", p_val_choice = 0.01, logFC_choice = 2)

## Error in `DoStarHeatmap()`:
## ! unused argument (slot = "data")

DoClusteredHeatmap(pbmc3k_seurat, features = top_rna_markers$feature, assay = "RNA", group.by = "seurat_annotations")

## Error:
## ! object 'pbmc3k_seurat' not found

`MetaDataPlot`

MetaDataPlot(pbmc3k_seurat, group.by = "seurat_annotations", split.by = "replicate")

## Error:
## ! object 'pbmc3k_seurat' not found

`ModulePlot`

pbmc3k_seurat <- CellCycleScoring(pbmc3k_seurat, s.features = cc.genes.updated.2019$s.genes, g2m.features = cc.genes.updated.2019$g2m.genes)

## Error:
## ! object 'pbmc3k_seurat' not found

ModulePlot(pbmc3k_seurat, features = "S.Score", assay = "RNA", split.by = "condition", replicate.by = "replicate")

## Error:
## ! object 'pbmc3k_seurat' not found

Session Info

sessionInfo()

## R version 4.6.0 (2026-04-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.4 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-r0.3.26.so;  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
##  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
##  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
## [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
## 
## time zone: UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] tidyr_1.3.2        ggplot2_4.0.3      pochi_0.1.0        Seurat_5.5.0      
## [5] SeuratObject_5.4.0 sp_2.2-1          
## 
## loaded via a namespace (and not attached):
##   [1] deldir_2.0-4           pbapply_1.7-4          gridExtra_2.3         
##   [4] rlang_1.2.0            magrittr_2.0.5         RcppAnnoy_0.0.23      
##   [7] otel_0.2.0             spatstat.geom_3.7-3    matrixStats_1.5.0     
##  [10] ggridges_0.5.7         compiler_4.6.0         png_0.1-9             
##  [13] systemfonts_1.3.2      vctrs_0.7.3            reshape2_1.4.5        
##  [16] stringr_1.6.0          pkgconfig_2.0.3        fastmap_1.2.0         
##  [19] promises_1.5.0         rmarkdown_2.31         ragg_1.5.2            
##  [22] purrr_1.2.2            xfun_0.57              cachem_1.1.0          
##  [25] jsonlite_2.0.0         goftest_1.2-3          later_1.4.8           
##  [28] spatstat.utils_3.2-2   irlba_2.3.7            parallel_4.6.0        
##  [31] cluster_2.1.8.2        R6_2.6.1               ica_1.0-3             
##  [34] spatstat.data_3.1-9    bslib_0.10.0           stringi_1.8.7         
##  [37] RColorBrewer_1.1-3     reticulate_1.46.0      spatstat.univar_3.1-7 
##  [40] parallelly_1.47.0      lmtest_0.9-40          jquerylib_0.1.4       
##  [43] scattermore_1.2        Rcpp_1.1.1-1.1         knitr_1.51            
##  [46] tensor_1.5.1           future.apply_1.20.2    zoo_1.8-15            
##  [49] sctransform_0.4.3      httpuv_1.6.17          Matrix_1.7-5          
##  [52] splines_4.6.0          igraph_2.3.1           tidyselect_1.2.1      
##  [55] abind_1.4-8            yaml_2.3.12            spatstat.random_3.4-5 
##  [58] spatstat.explore_3.8-0 codetools_0.2-20       miniUI_0.1.2          
##  [61] listenv_0.10.1         lattice_0.22-9         tibble_3.3.1          
##  [64] plyr_1.8.9             withr_3.0.2            shiny_1.13.0          
##  [67] S7_0.2.2               ROCR_1.0-12            evaluate_1.0.5        
##  [70] Rtsne_0.17             future_1.70.0          fastDummies_1.7.6     
##  [73] desc_1.4.3             survival_3.8-6         polyclip_1.10-7       
##  [76] fitdistrplus_1.2-6     pillar_1.11.1          KernSmooth_2.23-26    
##  [79] plotly_4.12.0          generics_0.1.4         RcppHNSW_0.6.0        
##  [82] scales_1.4.0           globals_0.19.1         xtable_1.8-8          
##  [85] glue_1.8.1             lazyeval_0.2.3         tools_4.6.0           
##  [88] data.table_1.18.4      RSpectra_0.16-2        RANN_2.6.2            
##  [91] fs_2.1.0               dotCall64_1.2          cowplot_1.2.0         
##  [94] grid_4.6.0             nlme_3.1-169           patchwork_1.3.2       
##  [97] cli_3.6.6              spatstat.sparse_3.1-0  textshaping_1.0.5     
## [100] spam_2.11-3            viridisLite_0.4.3      dplyr_1.2.1           
## [103] uwot_0.2.4             gtable_0.3.6           sass_0.4.10           
## [106] digest_0.6.39          progressr_0.19.0       ggrepel_0.9.8         
## [109] htmlwidgets_1.6.4      farver_2.1.2           htmltools_0.5.9       
## [112] pkgdown_2.2.0          lifecycle_1.0.5        httr_1.4.8            
## [115] mime_0.13              MASS_7.3-65