The goal of pysc is to integrate Miscellaneous Python functions for
Single Cell analysis.
You can install the development version of pysc from
GitHub with:
if (!requireNamespace("pak")) {
install.packages("pak",
repos = sprintf(
"https://r-lib.github.io/p/pak/devel/%s/%s/%s",
.Platform$pkgType, R.Version()$os, R.Version()$arch
)
)
}
pak::pkg_install("Yunuuuu/pysc")library(pysc)This is a basic example which shows you how to solve a common problem:
data <- reticulate::py_to_r(sccoda_datasets("haber"))
data$Condition <- gsub("_[0-9]$", "", data$Mouse)
data <- data[data$Condition %in% c("Control", "Salm")]
data <- sccoda_data(
data, c("Mouse", "Condition"),
setdiff(names(data), c("Mouse", "Condition"))
)
sccoda_out <- sccoda(data, "Condition")
pysc_tidy(sccoda_out)
#> Intercept terms Intercept Final Parameter Intercept HDI 3%
#> <char> <num> <num>
#> 1: Endocrine 0.858 0.157
#> 2: Enterocyte 1.838 1.023
#> 3: Enterocyte.Progenitor 2.204 1.511
#> 4: Tuft 0.495 -0.226
#> 5: Endocrine 0.858 0.157
#> 6: Enterocyte 1.838 1.023
#> 7: Enterocyte.Progenitor 2.204 1.511
#> 8: Tuft 0.495 -0.226
#> 9: Endocrine 0.858 0.157
#> 10: Enterocyte 1.838 1.023
#> 11: Enterocyte.Progenitor 2.204 1.511
#> 12: Tuft 0.495 -0.226
#> Intercept HDI 97% Intercept SD Intercept Expected Sample
#> <num> <num> <num>
#> 1: 1.495 0.357 49.60943
#> 2: 2.622 0.431 132.18216
#> 3: 2.902 0.373 190.60076
#> 4: 1.162 0.374 34.50765
#> 5: 1.495 0.357 49.60943
#> 6: 2.622 0.431 132.18216
#> 7: 2.902 0.373 190.60076
#> 8: 1.162 0.374 34.50765
#> 9: 1.495 0.357 49.60943
#> 10: 2.622 0.431 132.18216
#> 11: 2.902 0.373 190.60076
#> 12: 1.162 0.374 34.50765
#> Effect Final Parameter Effect HDI 3% Effect HDI 97% Effect SD
#> <num> <num> <num> <num>
#> 1: 0.000000 0.000 0.000 0.000
#> 2: 0.000000 -1.714 0.106 0.566
#> 3: 0.000000 -0.496 1.139 0.299
#> 4: 0.000000 -0.155 1.853 0.588
#> 5: 0.000000 0.000 0.000 0.000
#> 6: 0.000000 -1.236 0.161 0.346
#> 7: 0.000000 -0.707 0.489 0.208
#> 8: 0.000000 -0.413 1.111 0.317
#> 9: 0.000000 0.000 0.000 0.000
#> 10: 1.390214 0.516 2.181 0.473
#> 11: 0.000000 -0.988 0.773 0.259
#> 12: 0.000000 -1.417 0.578 0.395
#> Effect Inclusion probability Effect Expected Sample Effect log2-fold change
#> <num> <num> <num>
#> 1: 0.0000000 49.60943 0.0000000
#> 2: 0.7786000 132.18216 0.0000000
#> 3: 0.4519333 190.60076 0.0000000
#> 4: 0.6890667 34.50765 0.0000000
#> 5: 0.0000000 49.60943 0.0000000
#> 6: 0.5237333 132.18216 0.0000000
#> 7: 0.4362667 190.60076 0.0000000
#> 8: 0.4873333 34.50765 0.0000000
#> 9: 0.0000000 25.05959 -0.9852517
#> 10: 0.9821333 268.12969 1.0204035
#> 11: 0.3207333 96.27961 -0.9852517
#> 12: 0.4284000 17.43111 -0.9852517