Input and output files description

Input files

A example meta data file is provided in Zenodo. The meta data file looks like the following:

Cell_ID	Sample	Cell_Type	x	y
E12_E1S3_100034	E12_E1S3	Fibro	15940	18584
E12_E1S3_100035	E12_E1S3	Fibro	15942	18623
...	...	...	...	...
E16_E2S7_326412	E16_E2S7	Fibro	32990.5	14475

Cell_ID

The name of each cell. It should be Cell_ID for cell-level data and Spot_ID for low resolution data. Warning: Duplicated Cell_IDs within the same sample are not permitted. In the event of duplicated Cell_IDs across samples, the sample name will be prefixed to Cell_ID.
Sample

The name of sample which each cell belongs to.
Cell_Type

Cell type for each cell. This column is not required for low resolution data.
x

X coordinate for each cell.
y

Y coordinate for each cell.

A example output is provided in Zenodo.

Previouly named as preprocessing-dir.

meta_data.csv.gz

Processed meta data file.
samples.yaml

File contains the required files information for GNN training.
{sample name}_CellTypeComposition.csv.gz

Files contain the cell type composition information for each niche.
{sample name}_Coordinates.csv

Files contain the spatial information of anchoring cell for each niche.
{sample name}_EdgeIndex.csv.gz

Files contain the niche index of edges among niche graph.
{sample name}_NeighborIndicesMatrix.csv.gz

Files contain the neighborhood index of each niche for niche graph.
{sample name}_NicheWeightMatrix.npz

Files contain the weights between cells and niches.
cell_type_code.csv

File contains the mapping of cell type name to integer.
spotxcelltype.csv.gz

Deconvolution methods outputed cell type composition for each spot. This file doesn't exist when using cell level dataset as input.

cell_level_niche_cluster.csv.gz

Files conatains the probabilistic assignment of a cell to niche cluster.
cell_level_max_niche_cluster.csv.gz

Files conatains the niche cluster with maximum probability to each cell.
niche_level_niche_cluster.csv.gz

Files conatains the probabilistic assignment of a niche to niche cluster.
niche_level_max_niche_cluster.csv.gz

Files conatains the niche cluster with maximum probability to each niche.
{sample name}_out.csv.gz

Files contain the features for each niche cluster in each sample.
{sample name}_out_adj.csv.gz

Files contain the adjancy information between niche clusters in each sample.
{sample name}_s.csv.gz

Files contain the projection probabilities from niche to niche clusters in each sample.
{sample name}_z.csv.gz

Files contain the embeddings of each niche in each sample.
consolidate_out.csv.gz

File contains the features for each niche cluster.
consolidate_out_adj.csv.gz

File contains the adjancy information between niche clusters.
consolidate_s.csv.gz

File contains the projection probabilities from niche to niche clusters.
model_state_dict.pt

File contains the trained parameters for model.
epoch_0.pt

File contains the initial parameters for model.
epoch_X.pt

File contains the intermediate parameters for model.

Previouly named as NTScore-dir.

{sample name}_NTScore.csv.gz

Files contain the niche- and cell-level NT score for each niche/cell.
NTScore.csv.gz

File contains niche- and cell-level NT score for all samples.
niche_cluster_score.csv.gz

File contains NT score for each niche cluster.
cell_NTScore.csv.gz

File contains cell-level NT score for all samples. Warning: the number of rows were expanded to same for paralle processing using pytorch. Do not use this file directly.
niche_NTScore.csv.gz

File contains niche-level NT score for all samples. Warning: the number of rows were expanded to same for paralle processing using pytorch. Do not use this file directly.