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Input and output files description

Input files

A example input file is provided in examples/stereo_seq_brain/original_data.csv. The input 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. 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.

  • x

    X coordinate for each cell.

  • y

    Y coordinate for each cell.

Output files

preprocessing-dir

  • 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.

GNN-dir

  • 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.

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.