voyagerpy 0.1.1

Python library for Voyager, the geo-spatialist R library.

VoyagerPy

This repo manages the VoyagerPy Python package, a Python implementation of the R package Voyager

Installation

To install the latest release of VoyagerPy, you can install it via pip:

pip install voyagerpy

Clone the repo

Clone this repo either using SSH:

git clone [e-mail]:pmelsted/voyagerpy.git

or HTTPS:

git clone https://github.com/pmelsted/voyagerpy.git.

To get the bleeding edge version, change your branch to dev by running

git checkout dev

once inside the voyagerpy directory.

Install using pip

To install VoyagerPy, run

pip install .

Some users may experince problems with installing GeoPandas, which VoyagerPy depends on. We refer to the GeoPandas installation page if this is the case.

Structure of VoyagerPy

VoyagerPy uses AnnData as its internal datastructure. An AnnData object, adata, holds the following attributes:

• adata.X: the main data matrix of size $N_{obs} \times N_{vars}$. It holds the count data for each observation and feature (e.g. barcodes x genes), which may have gone under some transformation. Data type may be a scipy.sparse.csr_matrix, numpy.ndarray, or numpy.matrix. This is yet to be set in stone.

• adata.layers: A dictionary-like data structure with the values being matrices of the same shape as adata.X. These can hold transformations of adata.X, such as log-normalized counts.

• adata.obs: A pandas.DataFrame object where the rows represent the barcodes, and the columns are features of the barcodes.

• adata.obsp: This is a dictionary-based object, where each value is a pandas.DataFrame of size $N_{obs}\times N_{obs}$, representing a pairwise metric on the observation. For instance, adata.obsp["distances"] can hold the pairwise distances between the positions of origin for the barcodes. This can be handy to store graphs over the barcodes.

• adata.obsm: This is a dictionary-based object where each value is a pandas.DataFrame or geopandas.GeoDataFrame. The number of rows in these data frames must be $N_{obs}$. Example of data frames to be stored here:

  • geometry: a geopandas.GeoDataFrame where each column is of geopandas.GeoSeries or pandas.Series, used for plotting spatial objects, such as points or polygons. To use GeoPandas for plotting a column, it must be a geopandas.GeoSeries. These will represent the geometries of the barcodes.
  • local_*: a pandas.DataFrame which contains spatial results over features in obs. These can be e.g. local Moran's I, local spatial heteroscedasticity (LOSH) over some features x, y, z. The columns of local_moran and local_losh would then be x, y, z.

• adata.var: A pandas.DataFrame object where the rows represent the features from the columns of X (e.g. genes), and the columns are features of the genes (or whatever the columns of X represent).

• adata.varp, adata.varm: These are not used for the time being, but these objects can be used similarly to adata.obsp and adata.obsm but for feature (gene) data.

• adata.uns: This is a dictionary containing data that cannot be stored in the above objects:

  • config: These can contain config or metadata about this object. By using VoyagerPy to read the scRNA-seq data, this dictionary has the following items by default:
    • "var_names": "gene_ids", meaning that the index of the variables (genes) are the standardized ENSG gene IDs.
    • "secondary_var_names": "symbol", meaning that the column "symbol" in adata.var contains the symbol names for the genes.
  • spatial: This dictionary contains various spatial data, including:
    • img: a dictionary with key-values as resolution-image
    • scale: a dictionary describing the scales of the images.
    • transform: metadata that describes the transforms applied to the images (rotation, mirror) such that the originals can be recovered.
    • local_results: This is a dictionary which can contain Monte-Carlo simulations of spatial autocorrelation statistics, such as for local Moran statistics.