svgbit 0.1.1

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Installation

Install svgbit with pip:

pip install svgbit

Command-line Interface

svgbit has a command line version. Just tape:

svgbit --help

after installation, and you may get a short help massage:

usage: svgbit [-h] [--count_transpose] [--coordinate_transpose] [--k K]
              [--n_genes N_GENES] [--n_gene_clusters N_GENE_CLUSTERS] [--savedir SAVEDIR]
              [--cores CORES]
              count coordinate

Find spatial variable genes for Spatial Trasncriptomics data.

positional arguments:
  count                 path to count matrix file (shape: (spot * gene))
  coordinate            path to coordinate file (shape: (spot * 2))

optional arguments:
  -h, --help            show this help message and exit
  --count_transpose     transpose count matrix if specified
  --coordinate_transpose
                        transpose coordinate file if specified
  --k K                 number of nearest neighbors for KNN network (default: 6)
  --n_svgs N_SVGS       number of SVGs to find clusters (default: 1000)
  --n_svg_clusters N_SVG_CLUSTERS
                        number of SVG clusters to find (default: 8)
  --he_image HE_IMAGE   path to H&E image. Only used for visualization (default: None)
  --savedir SAVEDIR     path to save results (default: .)
  --cores CORES         number of threads to run svgbit (default: 8)

Follow the introduction and results will save to –savedir.

Note

svgbit will use all available CPUs as default. While python’s child process will inherit all resources from parent, this may consume much memory. Specify cores keyword argument to avoid Cannot allocate memory error.

Note

svgbit may consume ~35 Gib memory when running with a 2980 spots, 32285 genes matrix with 8 cores.

Python Interface

svgbit has a set of python API. You may run svgbit through command line or python. We recommend the usage of python API for more feature and convient control of your input data.

Run svgbit with one function

svgbit could load data from Space Ranger output directory:

import svgbit
dataset = svgbit.load_10X("spaceranger_output/outs")

Or load data from csv files:

import svgbit
dataset = svgbit.STDataset(
    count_df="Data/count_df.csv",
    coordinate_df="Data/coor_df.csv",
    count_df_kwargs={"index_col": 0, "header": 0},
    coordinate_df_kwargs={"index_col": 0, "header": 0},
)

svgbit.STDataset also accept pd.DataFrame and np.array as count_df and coordinate_df. If str or pathlib.Path are given, svgbit would try to load data with pandas.

Note

When init STDataset instance in this way, svgbit assume that the shape of count matrix and coordinate file is (spot * gene) and (spot * 2). Specify count_transpose or coordinate_transpose as True when necessary.

After data loading, run:

svgbit.run(dataset)

to perform full pipeline of svgbit. Results will save as attributes of dataset.

Visit our API references for further detail.

Visualization

Draw SVG heatmap with:

svgbit.svg_heatmap(dataset, save_path="heatmap.jpg", he_image="he_image.jpg")

Parameter he_image is optional. If not specified, hotspot discription map will show without morphological information.

Details about svgbit.run()

When you perform svgbit.run(), sevaral steps will be done as below. For further detail of calculation, please refer to our publication.

Acquire weight

To calculate hotspot matrix, svgbit needs a weight network which discribes association across spots. svgbit uses k-nearest neighbors with 6 neighbors as a default. You may pass key word argument k to svgbit.run() to change this behavior.

In this step, svgbit.run() will execute STDataset.acquire_weight() method with given parameters. You may also perform this step by:

dataset.acquire_weight()

Weight will save as attribute weight of STDataset and detailed discription of weight is saved to weight_type attribute. Users may provide a libpysal.weights.W instance as user-specified weight:

dataset.weight = user_specified_weight

Acquire hotspot

Hotspot matrix is estimated by:

dataset.acquire_hotspot()

and save to hotspot_df attribute.

Density

AI and Di value discribed in our paper will be calculate by:

dataset.acquire_density()

and save to AI and Di attribute as pd.Series.

Find SVG clusters

SVG clusters is estimated by:

dataset.find_clusters()

and save to svg_cluster attribute.

For further discription of hotspot, AI, Di and SVG cluster, please refer to our manuscript.

Citation