Installation

Installing using python

To use TARDIS (python version), first install its dependencies using pip or conda.

Attention

To run TARDIS with cluster dependent modules, you must specify cluster fields. One recommended spatial clustering method is Cellcharter [1].

Alternatively, we provide a Non-negative Matrix Factorization (NMF) model for simple analysis.

You can simply install TARDIS dependencies using conda through:

(.venv) $ conda create -n tardis_env python>=3.8
(.venv) $ conda activate tardis_env
(.venv) $ conda install numpy pandas scipy scikit-learn anndata scanpy
(.venv) $ pip install tardis_spac

Note

Both python and R version requires python to run Cluster dependent analysis. since they require Cellcharter to perform clustering.

If other clustering method is performed, follow the instructions in Build your first project to perform downstream analysis.

Installing using R

To install TARDIS (R version), first download the package from GitHub

Note

Compatible with R >= 4.2. Uses Seurat for count matrices and guide-level metadata.

The Python CellCharter module is not included in this port.

Install with devtools

Install core dependencies first (recommended), then install the package:

install.packages(c("devtools", "Matrix", "ggplot2"))
# Seurat: install from CRAN or https://satijalab.org/seurat/articles/install.html
install.packages("Seurat")

devtools::install("/path/to/stereoseq/tardis_r")
# If dependency download fails, use:
# devtools::install("/path/to/stereoseq/tardis_r", dependencies = FALSE)

Alternative without devtools:

install.packages("/path/to/stereoseq/tardis_r", repos = NULL, type = "source")

For h5ad files (e.g. filtered_guide_bc_matrix.h5), see _Tutorial for more details. Also set Python with anndata:

install.packages("reticulate")
Sys.setenv(RETICULATE_PYTHON = "/path/to/python/with/anndata")
reticulate::use_python(Sys.getenv("RETICULATE_PYTHON"), required = TRUE)

Optional: NMF, dbscan, ks, transport, hdf5r.

Quick start

library(tardisSpac)

guide <- read_guide_h5("filtered_guide_bc_matrix.h5")

guide <- filter_guide_reads_h5(
  guide,
  guide_prefix = "sg",
  filter_threshold = 10,
  assign_pattern = "max"
)

guide <- kl_divergence(guide, reference_guide = "sgTgfbr2_1", copy = TRUE)
plot_top_kde(guide, result_field = "kl_div")

Python ↔ R API

Python (`tardis_spac`)	R (`tardisSpac`)
`AnnData`	Seurat object
`.var[metric]`	`object[["RNA"]]@meta.features[[metric]]`
`.obs`	`object@meta.data`
`obsm['spatial']`	`object@misc$spatial`
`read_guide_h5` / `sc.read_h5ad`	`read_guide_h5()`
`stats.kl_divergence`	`kl_divergence()`
`stats.wasserstein_distance`	`wasserstein_distance()`
`stats.aitchison_distance`	`aitchison_distance()`
`stats.permanova`	`permanova()`
`utils.filter_guide_reads`	`filter_guide_reads()`
`utils.filter_guide_reads_h5`	`filter_guide_reads_h5()`
`utils.load_bin`	`load_bin()`
`external.cluster_cellcharter`	(not ported)