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Last updated: 2024-07-19
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Knit directory: KODAMA-Analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 5b97082 | tkcaccia | 2024-07-15 | updates |
| Rmd | 7be8f59 | tkcaccia | 2024-07-15 | updates |
| html | 7be8f59 | tkcaccia | 2024-07-15 | updates |
| Rmd | 79f73a2 | GitHub | 2024-07-14 | Update VisiumHD.Rmd |
| html | f8ca54a | tkcaccia | 2024-07-14 | update |
| html | d04c1e7 | GitHub | 2024-07-08 | Update VisiumHD.html |
| html | 754c8bf | GitHub | 2024-07-04 | Update VisiumHD.html |
| html | ee4ee17 | GitHub | 2024-06-19 | Add files via upload |
| Rmd | 615fc05 | GitHub | 2024-06-19 | Add files via upload |
Describe your project. The data can be downloaded using the following script: VisiumHD_CRC_download.sh. This script facilitates access to the raw data, which will then be preprocessed and analyzed in the subsequent steps of our pipeline.
The data can be downloaded using the following script: VisiumHD_CRC_download.sh. This script facilitates access to the raw data, which will then be preprocessed and analyzed in the subsequent steps of our pipeline.
library("ggplot2")
library("patchwork")
library("dplyr")
library("Seurat")
localdir="../Colorectal/outs/"
object <- Load10X_Spatial(data.dir = localdir, bin.size = c(8))
vln.plot <- VlnPlot(object, features = "nCount_Spatial.008um", pt.size = 0) + NoLegend()
count.plot <- SpatialFeaturePlot(object, features = "nCount_Spatial.008um", pt.size.factor = 1.2) +
theme(legend.position = "right")
nCount_Spatial=colSums(object@assays$Spatial.008um$counts)
#w= which(nCount_Spatial >10)
#object@assays$Spatial.008um$counts= object@assays$Spatial.008um$counts[,w]
#object@meta.data=object@meta.data[w,]
sp_obj <- subset(
object,
subset = nCount_Spatial.008um > 100)
nCount_Spatial=colSums(sp_obj@assays$Spatial.008um$counts)
counts=sp_obj@assays$Spatial.008um$counts
is_mito <- grepl("(^MT-)|(^mt-)", rownames(counts))
counts <- counts[!is_mito,]
filter_genes_ncounts=1
filter_genes_pcspots=0.5
nspots <- ceiling(filter_genes_pcspots/100 * ncol(counts))
ix_remove <- rowSums(counts >= filter_genes_ncounts) < nspots
counts <- counts[!ix_remove,]
QCgenes <- rownames(counts)
VariableFeatures(sp_obj) = QCgenes
rm(counts)
DefaultAssay(sp_obj) <- "Spatial.008um"
sp_obj <- NormalizeData(sp_obj)
sp_obj <- FindVariableFeatures(sp_obj)
sp_obj <- ScaleData(sp_obj)
xy=as.matrix(GetTissueCoordinates(sp_obj))
sp_obj <- RunPCA(sp_obj, reduction.name = "pca.008um")
dim(sp_obj)[1] 18085 428381plot(Seurat::Embeddings(sp_obj, reduction = "pca.008um"))
| Version | Author | Date |
|---|---|---|
| 7be8f59 | tkcaccia | 2024-07-15 |
sessionInfo()R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 20.04.6 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
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] Seurat_5.1.0 SeuratObject_5.0.2 sp_2.1-4 dplyr_1.1.4
[5] patchwork_1.2.0 ggplot2_3.5.1 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 rstudioapi_0.16.0 jsonlite_1.8.8
[4] magrittr_2.0.3 ggbeeswarm_0.7.2 spatstat.utils_3.0-5
[7] farver_2.1.2 rmarkdown_2.27 fs_1.6.4
[10] vctrs_0.6.5 ROCR_1.0-11 spatstat.explore_3.3-1
[13] htmltools_0.5.8.1 sass_0.4.9 sctransform_0.4.1
[16] parallelly_1.37.1 KernSmooth_2.23-24 bslib_0.7.0
[19] htmlwidgets_1.6.4 ica_1.0-3 plyr_1.8.9
[22] plotly_4.10.4 zoo_1.8-12 cachem_1.1.0
[25] whisker_0.4.1 igraph_2.0.3 mime_0.12
[28] lifecycle_1.0.4 pkgconfig_2.0.3 Matrix_1.7-0
[31] R6_2.5.1 fastmap_1.2.0 fitdistrplus_1.2-1
[34] future_1.33.2 shiny_1.8.1.1 digest_0.6.36
[37] colorspace_2.1-0 ps_1.7.7 rprojroot_2.0.4
[40] tensor_1.5 RSpectra_0.16-1 irlba_2.3.5.1
[43] progressr_0.14.0 fansi_1.0.6 spatstat.sparse_3.1-0
[46] httr_1.4.7 polyclip_1.10-6 abind_1.4-5
[49] compiler_4.4.1 bit64_4.0.5 withr_3.0.0
[52] fastDummies_1.7.3 highr_0.11 MASS_7.3-61
[55] tools_4.4.1 vipor_0.4.7 lmtest_0.9-40
[58] beeswarm_0.4.0 httpuv_1.6.15 future.apply_1.11.2
[61] goftest_1.2-3 glue_1.7.0 callr_3.7.6
[64] nlme_3.1-165 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_0.3.5 spatstat.data_3.1-2 tidyr_1.3.1
[76] data.table_1.15.4 utf8_1.2.4 spatstat.geom_3.3-2
[79] RcppAnnoy_0.0.22 ggrepel_0.9.5 RANN_2.6.1
[82] pillar_1.9.0 stringr_1.5.1 spam_2.10-0
[85] RcppHNSW_0.6.0 later_1.3.2 splines_4.4.1
[88] lattice_0.22-6 bit_4.0.5 survival_3.7-0
[91] deldir_2.0-4 tidyselect_1.2.1 miniUI_0.1.1.1
[94] pbapply_1.7-2 knitr_1.48 git2r_0.33.0
[97] gridExtra_2.3 scattermore_1.2 xfun_0.45
[100] matrixStats_1.3.0 stringi_1.8.4 lazyeval_0.2.2
[103] yaml_2.3.9 evaluate_0.24.0 codetools_0.2-20
[106] tibble_3.2.1 cli_3.6.3 uwot_0.2.2
[109] arrow_16.1.0 xtable_1.8-4 reticulate_1.38.0
[112] munsell_0.5.1 processx_3.8.4 jquerylib_0.1.4
[115] Rcpp_1.0.12 globals_0.16.3 spatstat.random_3.3-1
[118] png_0.1-8 ggrastr_1.0.2 spatstat.univar_3.0-0
[121] parallel_4.4.1 assertthat_0.2.1 dotCall64_1.1-1
[124] listenv_0.9.1 viridisLite_0.4.2 scales_1.3.0
[127] ggridges_0.5.6 leiden_0.4.3.1 purrr_1.0.2
[130] rlang_1.1.4 cowplot_1.1.3