Last updated: 2021-10-29
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Knit directory: mistyMBC/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 6b773b6 | Jovan Tanevski | 2021-10-24 | update normalization and view parameteres |
| html | cb33a3e | Jovan Tanevski | 2021-09-23 | Build site. |
| Rmd | 41f5e45 | Jovan Tanevski | 2021-09-23 | add juxta and zoi for slideseq |
| html | 6a3c444 | Jovan Tanevski | 2021-09-15 | Build site. |
| Rmd | 2fc907d | Jovan Tanevski | 2021-09-15 | add celltype based analysis |
library(reticulate)
library(mistyR)
library(future)
library(dplyr)
library(purrr)
library(stringr)
use_python("/usr/local/bin/python3")
plan(multisession)Requires python3 and anndata installed (pip install anndata).
import anndata
import numpy
def read_and_extract(datapath):
adata = anndata.read(datapath)
ots = adata.obsm["OT"]
geom = adata.obs[["x_orig", "y_orig"]]
return ots, geomUse SlideSeq data. Bypass modeling of intrinsic view and focus on cell type composition of a neighborhood. Take 100 nearest neighbors with constant weights.
(list.files("data", ".h5ad", recursive = TRUE, full.names = TRUE) %>%
keep(~ str_detect(.x, "slide_seq"))) %>%
walk(function(datapath) {
data <- py$read_and_extract(datapath)
ctype <- data[[1]] %>% rename_with(~make.names(., allow_ = FALSE))
pos <- data[[2]]
unique(str_extract(rownames(pos), "-\\d$")) %>% walk(function(replicate) {
output.folder <- paste0(
str_replace(
str_remove(datapath, ".h5ad"),
"data", "output"
),
replicate,
"/ctype"
)
output.folder.failed <- paste0(output.folder, "_failed")
if (!(dir.exists(output.folder) | dir.exists(output.folder.failed))) {
ind <- str_which(rownames(pos), paste0(replicate, "$"))
misty.views.ctype <- create_initial_view(ctype[ind, ]) %>%
add_juxtaview(pos[ind, ], neighbor.thr = 25) %>%
add_paraview(pos[ind, ], l = 150, zoi = 25, family = "constant")
tryCatch(
run_misty(misty.views.ctype, results.folder = output.folder, bypass.intra = TRUE),
error = function(e) file.rename(output.folder, output.folder.failed)
)
}
})
})
Computing triangulation
Generating juxtaview
Generating paraview using 150 nearest neighbors per unit
Training modelsWarning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target BWarning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target EndothelialWarning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target FibroblastWarning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target HepatocyteWarning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target MBCWarning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target MacrophageWarning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target MonocyteWarning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target NKWarning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target T
Computing triangulation
Generating juxtaview
Generating paraview using 150 nearest neighbors per unit
Training modelsWarning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target BWarning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target EndothelialWarning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target FibroblastWarning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target HepatocyteWarning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target MBCWarning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target MacrophageWarning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target MonocyteWarning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target NKWarning in ...furrr_fn(...): Negative performance detected and replaced with 0
for target T
sessionInfo()R version 4.1.1 (2021-08-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] stringr_1.4.0 purrr_0.3.4 dplyr_1.0.7 future_1.22.1
[5] mistyR_1.1.14 reticulate_1.22 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] tidyselect_1.1.1 xfun_0.27 bslib_0.3.1 listenv_0.8.0
[5] distances_0.1.8 lattice_0.20-45 vctrs_0.3.8 generics_0.1.1
[9] htmltools_0.5.2 yaml_2.2.1 utf8_1.2.2 rlang_0.4.12
[13] R.oo_1.24.0 jquerylib_0.1.4 later_1.3.0 pillar_1.6.4
[17] glue_1.4.2 DBI_1.1.1 R.utils_2.11.0 lifecycle_1.0.1
[21] R.methodsS3_1.8.1 codetools_0.2-18 evaluate_0.14 knitr_1.36
[25] fastmap_1.1.0 httpuv_1.6.3 parallel_4.1.1 fansi_0.5.0
[29] furrr_0.2.3 Rcpp_1.0.7 filelock_1.0.2 promises_1.2.0.1
[33] jsonlite_1.7.2 deldir_1.0-6 parallelly_1.28.1 fs_1.5.0
[37] png_0.1-7 digest_0.6.28 stringi_1.7.5 rlist_0.4.6.2
[41] grid_4.1.1 rprojroot_2.0.2 tools_4.1.1 magrittr_2.0.1
[45] sass_0.4.0 tibble_3.1.5 crayon_1.4.1 whisker_0.4
[49] pkgconfig_2.0.3 ellipsis_0.3.2 Matrix_1.3-4 data.table_1.14.2
[53] assertthat_0.2.1 rmarkdown_2.11 R6_2.5.1 globals_0.14.0
[57] git2r_0.28.0 compiler_4.1.1