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Add schema subcommand (#1939)
Add the `cellxgene schema apply` and `cellxgene schema validate` subcommands. The first takes an h5ad file and a yaml with config information and produces a new h5ad that follows the cellxgene data integration schema. The second takes an h5ad and checks if it follows the schema version written into its metadata. Both are currently marked as "experimental" as the primary intended users are still at CZI.
This commit is contained in:
Marcus Kinsella
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#!/bin/bash
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wget "https://s3-us-west-2.amazonaws.com/10x.files/samples/cell/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz"
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tar xf "pbmc3k_filtered_gene_bc_matrices.tar.gz"
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python3 - <<MERGE_GENES
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import os
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from scipy.io import mmread, mmwrite
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import scipy.sparse
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import pandas as pd
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from server.converters.schema import gene_symbol
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mat = mmread("filtered_gene_bc_matrices/hg19/matrix.mtx").todense()
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genes = pd.read_csv("filtered_gene_bc_matrices/hg19/genes.tsv", sep='\t', names=["gene_id", "gene_symbol"])
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upgraded_genes = gene_symbol.get_upgraded_var_index(pd.DataFrame(index=genes["gene_symbol"]))
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df = pd.DataFrame(data=mat, index=upgraded_genes).T
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merged = df.sum(axis=1, level=0, skipna=False)
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os.makedirs("merged")
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merged.columns.to_frame().to_csv("merged/genes.tsv", index=False, header=False)
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mmwrite("merged/matrix.mtx", scipy.sparse.coo_matrix(merged).T)
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MERGE_GENES
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cp "filtered_gene_bc_matrices/hg19/barcodes.tsv" "merged/barcodes.tsv"
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awk '{print $1"\t"$1}' merged/genes.tsv > genes_tmp.tsv; mv genes_tmp.tsv merged/genes.tsv
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echo -e "\n\n\nRunning tutorial on original\n\n\n"
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Rscript - <<TUTORIAL
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library(Seurat)
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pbmc.data <- Read10X(data.dir = "filtered_gene_bc_matrices/hg19/")
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pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k", min.features = 200)
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pbmc <- NormalizeData(pbmc, normalization.method = "LogNormalize", scale.factor = 10000)
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pbmc <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 2000)
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pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")
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all.genes <- rownames(pbmc)
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pbmc <- ScaleData(pbmc, features = all.genes)
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pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))
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pbmc <- FindNeighbors(pbmc, dims = 1:10)
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pbmc <- FindClusters(pbmc, resolution = 0.5)
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pbmc <- RunUMAP(pbmc, dims = 1:10)
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saveRDS(pbmc, file = "./seurat_tutorial.rds")
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TUTORIAL
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echo -e "\n\n\nRunning tutorial on merged\n\n\n"
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Rscript - <<TUTORIAL_MERGED
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library(Seurat)
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pbmc.data <- Read10X(data.dir = "merged/")
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pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k", min.features = 200)
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pbmc <- NormalizeData(pbmc, normalization.method = "LogNormalize", scale.factor = 10000)
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pbmc <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 2000)
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pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")
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all.genes <- rownames(pbmc)
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pbmc <- ScaleData(pbmc, features = all.genes)
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pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))
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pbmc <- FindNeighbors(pbmc, dims = 1:10)
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pbmc <- FindClusters(pbmc, resolution = 0.5)
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pbmc <- RunUMAP(pbmc, dims = 1:10)
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saveRDS(pbmc, file = "./seurat_tutorial_merged.rds")
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TUTORIAL_MERGED
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echo -e "\n\n\nRunning SCTransform on original\n\n\n"
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Rscript - <<SCTRANSFORM
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library(Seurat)
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library(sctransform)
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pbmc.data <- Read10X(data.dir = "filtered_gene_bc_matrices/hg19/")
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pbmc <- CreateSeuratObject(counts = pbmc.data)
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pbmc <- PercentageFeatureSet(pbmc, pattern = "^MT-", col.name = "percent.mt")
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pbmc <- SCTransform(pbmc, vars.to.regress = "percent.mt", verbose = FALSE)
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pbmc <- RunPCA(pbmc, verbose = FALSE)
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pbmc <- RunUMAP(pbmc, dims = 1:30, verbose = FALSE)
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pbmc <- FindNeighbors(pbmc, dims = 1:30, verbose = FALSE)
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pbmc <- FindClusters(pbmc, verbose = FALSE)
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saveRDS(pbmc, file = "./sctransform.rds")
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SCTRANSFORM
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echo -e "\n\n\nRunning SCTransform on merged\n\n\n"
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Rscript - <<SCTRANSFORM_MERGED
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library(Seurat)
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library(sctransform)
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pbmc.data <- Read10X(data.dir = "merged/")
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pbmc <- CreateSeuratObject(counts = pbmc.data)
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pbmc <- PercentageFeatureSet(pbmc, pattern = "^MT-", col.name = "percent.mt")
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pbmc <- SCTransform(pbmc, vars.to.regress = "percent.mt", verbose = FALSE)
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pbmc <- RunPCA(pbmc, verbose = FALSE)
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pbmc <- RunUMAP(pbmc, dims = 1:30, verbose = FALSE)
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pbmc <- FindNeighbors(pbmc, dims = 1:30, verbose = FALSE)
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pbmc <- FindClusters(pbmc, verbose = FALSE)
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saveRDS(pbmc, file = "./sctransform_merged.rds")
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SCTRANSFORM_MERGED
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echo -e "\n\n\nConverting\n\n\n"
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Rscript - <<SCEASY
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library(sceasy)
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srt <- readRDS("seurat_tutorial.rds")
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sceasy::convertFormat(srt,
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outFile = "seurat_tutorial.h5ad",
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from = "seurat",
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to = "anndata",
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assay = "RNA",
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main_layer = "data",
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transfer_layers = c("data", "counts", "scale.data"),
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drop_single_values = FALSE)
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srt <- readRDS("seurat_tutorial_merged.rds")
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sceasy::convertFormat(srt,
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outFile = "seurat_tutorial_merged.h5ad",
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from = "seurat",
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to = "anndata",
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assay = "RNA",
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main_layer = "data",
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transfer_layers = c("data", "counts", "scale.data"),
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drop_single_values = FALSE)
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srt <- readRDS("sctransform.rds")
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sceasy::convertFormat(srt,
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outFile = "sctransform.h5ad",
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from = "seurat",
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to = "anndata",
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assay = "SCT",
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main_layer = "data",
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transfer_layers = c("data", "counts", "scale.data"),
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drop_single_values = FALSE)
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srt <- readRDS("sctransform_merged.rds")
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sceasy::convertFormat(srt,
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outFile = "sctransform_merged.h5ad",
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from = "seurat",
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to = "anndata",
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assay = "SCT",
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main_layer = "data",
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transfer_layers = c("data", "counts", "scale.data"),
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drop_single_values = FALSE)
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SCEASY
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fixup_gene_symbols:
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X: log1p
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obs:
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cell_type_ontology_term_id:
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louvain:
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CD4 T cells: CL:00001
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B cells: CL:00002
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CD14+ Monocytes: CL:00003
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NK cells: CL:00004
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CD8 T cells: CL:00005
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FCGR3A+ Monocytes: CL:00006
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Dendritic cells: CL:00007
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Megakaryocytes: CL:00008
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tissue_ontology_term_id: UBERON:12345
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assay_ontology_term_id: EFO:12345
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disease_ontology_term_id: MONDO:12345
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ethnicity_ontology_term_id: MANCESTRO:12345
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development_stage_ontology_term_id: HsapDv:12345
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sex: other
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uns:
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version:
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corpora_schema_version: 1.0.0
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corpora_encoding_version: 0.1.0
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organism_ontology_term_id: NCBITaxon:9606
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title: Test dataset
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contributors:
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- name: Marcus
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institution: CZI
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layer_descriptions:
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X: raw
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project_links:
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- link_url: https://chanzuckerberg.com/
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link_name: CZI
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link_type: SUMMARY
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fixup_gene_symbols:
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X: log1p
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obs:
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cell_type_ontology_term_id:
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louvain:
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CD4 T cells: CL:00001
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B cells: CL:00002
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CD14+ Monocytes: CL:00003
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NK cells: CL:00004
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CD8 T cells: CL:00005
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FCGR3A+ Monocytes: CL:00006
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Dendritic cells: CL:00007
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Megakaryocytes: CL:00008
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tissue_ontology_term_id: UBERON:12345
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assay_ontology_term_id: EFO:12345
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disease_ontology_term_id: MONDO:12345
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ethnicity_ontology_term_id: HANCESTRO:12345
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development_stage_ontology_term_id: HsapDv:12345
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sex: other
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uns:
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version:
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corpora_schema_version: 1.0.0
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corpora_encoding_version: 0.1.0
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organism_ontology_term_id: NCBITaxon:9606
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title: Test dataset
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contributors:
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- name: Marcus
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institution: CZI
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layer_descriptions:
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X: raw
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project_links:
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- link_url: https://chanzuckerberg.com/
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link_name: CZI
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link_type: SUMMARY
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import os
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import unittest
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import pandas as pd
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from server.test import FIXTURES_ROOT
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from server.converters.schema import gene_symbol
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class TestHGNCSymbolChecker(unittest.TestCase):
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def setUp(self):
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self.test_hgnc_path = os.path.join(FIXTURES_ROOT, "hgnc_example.txt.gz")
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self.hgnc_checker = gene_symbol.HGNCSymbolChecker.from_hgnc_records(self.test_hgnc_path)
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def test_symbol_upgrade(self):
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self.assertEqual(self.hgnc_checker.upgrade_symbol("SEPT1"), "SEPTIN1")
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self.assertEqual(self.hgnc_checker.upgrade_symbol("ADRB2R"), "ADRB2")
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self.assertEqual(self.hgnc_checker.upgrade_symbol("BAR"), "ADRB2")
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self.assertEqual(self.hgnc_checker.upgrade_symbol("sept1"), "SEPTIN1")
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self.assertEqual(self.hgnc_checker.upgrade_symbol("AdRb2R"), "ADRB2")
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self.assertEqual(self.hgnc_checker.upgrade_symbol("bar"), "ADRB2")
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# Strip off seurat endings when appropriate
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self.assertEqual(self.hgnc_checker.upgrade_symbol("SEPT1.1"), "SEPTIN1")
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self.assertEqual(self.hgnc_checker.upgrade_symbol("ADRB2-1"), "ADRB2")
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# DIFF6 is ambiguous so don't upgrade it
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self.assertEqual(self.hgnc_checker.upgrade_symbol("DIFF6"), "DIFF6")
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self.assertEqual(self.hgnc_checker.upgrade_symbol("diff6"), "diff6")
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# ARG1 is approved
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self.assertEqual(self.hgnc_checker.upgrade_symbol("ARG1"), "ARG1")
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self.assertEqual(self.hgnc_checker.upgrade_symbol("arg1"), "ARG1")
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# HAP1 is both approved and withdrawn
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self.assertEqual(self.hgnc_checker.upgrade_symbol("HAP1"), "HAP1")
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self.assertEqual(self.hgnc_checker.upgrade_symbol("hap1"), "HAP1")
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# Leave unknown symbols alone
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self.assertEqual(self.hgnc_checker.upgrade_symbol("NOTASYMBOL"), "NOTASYMBOL")
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self.assertEqual(self.hgnc_checker.upgrade_symbol("notasymbol"), "notasymbol")
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def test_check_symbol(self):
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self.assertEqual(self.hgnc_checker.check_symbol("SEPT1"), gene_symbol.SymbolStatus.UPGRADABLE)
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self.assertEqual(self.hgnc_checker.check_symbol("DIFF6"), gene_symbol.SymbolStatus.AMBIGUOUS)
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self.assertEqual(self.hgnc_checker.check_symbol("NOTASYMBOL"), gene_symbol.SymbolStatus.UNKNOWN)
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# HAP1 is one of the approved and withdrawn symbols
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self.assertEqual(self.hgnc_checker.check_symbol("HAP1"), gene_symbol.SymbolStatus.APPROVED)
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def test_upgrade_index(self):
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index = pd.Index(["SEPT1", "DIFF6", "NOTASYMBOL", "bar", "SEPTIN1"])
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var_df = pd.DataFrame([[0] * len(index)], index=index)
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upgraded_index = gene_symbol.get_upgraded_var_index(var_df, hgnc_path=self.test_hgnc_path)
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self.assertEqual(upgraded_index.tolist(), ["SEPTIN1", "DIFF6", "NOTASYMBOL", "ADRB2", "SEPTIN1"])
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import json
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import unittest
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import unittest.mock
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from server.converters.schema import ontology
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class TestOntologyParsing(unittest.TestCase):
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def setUp(self):
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self.curies = ["UBERON:0002048", "HsapDv:0000174", "NCBITaxon:9606", "EFO:0008995"]
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self.names = ["UBERON", "HsapDv", "NCBITaxon", "EFO"]
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self.values = ["0002048", "0000174", "9606", "0008995"]
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self.iris = [
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"http://purl.obolibrary.org/obo/UBERON_0002048",
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"http://purl.obolibrary.org/obo/HsapDv_0000174",
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"http://purl.obolibrary.org/obo/NCBITaxon_9606",
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"http://www.ebi.ac.uk/efo/EFO_0008995",
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]
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URL_ROOT = "http://www.ebi.ac.uk/ols/api/ontologies/"
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self.urls = [
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URL_ROOT + "UBERON/terms/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FUBERON_0002048",
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URL_ROOT + "HsapDv/terms/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FHsapDv_0000174",
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URL_ROOT + "NCBITaxon/terms/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FNCBITaxon_9606",
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URL_ROOT + "EFO/terms/http%253A%252F%252Fwww.ebi.ac.uk%252Fefo%252FEFO_0008995",
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]
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self.responses = {
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"UBERON:0002048": {
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"iri": "http://purl.obolibrary.org/obo/UBERON_0002048",
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"description": ["Respiration organ that develops as an outpocketing of the esophagus."],
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"label": "lung",
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},
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"HsapDv:0000174": {
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"iri": "http://purl.obolibrary.org/obo/HsapDv_0000174",
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"description": ["Infant stage that refers to an infant who is over 1 and under 2 months old."],
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"label": "1-month-old human stage",
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},
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"NCBITaxon:9606": {
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"iri": "http://purl.obolibrary.org/obo/NCBITaxon_9606",
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"description": None,
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"label": "Homo sapiens",
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},
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"EFO:0008995": {
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"iri": "http://www.ebi.ac.uk/efo/EFO_0008995",
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"description": [
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(
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'10X is a "synthetic long-read" technology and works by capturing a barcoded oligo-coated '
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"gel-bead and 0.3x genome copies into a single emulsion droplet, processing the equivalent "
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"of 1 million pipetting steps. Successive versions of the 10x chemistry use different "
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"barcode locations to improve the sequencing yield and quality of 10x experiments."
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)
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],
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"label": "10X sequencing",
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},
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}
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def test_ontololgy_name(self):
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for curie, expected_name in zip(self.curies, self.names):
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self.assertEqual(ontology._ontology_name(curie), expected_name)
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def test_ontololgy_value(self):
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for curie, expected_value in zip(self.curies, self.values):
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self.assertEqual(ontology._ontology_value(curie), expected_value)
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def test_iri(self):
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for curie, expected_iri in zip(self.curies, self.iris):
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self.assertEqual(ontology._iri(curie), expected_iri)
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def test_ontology_info_url(self):
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for curie, expected_url in zip(self.curies, self.urls):
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self.assertEqual(ontology._ontology_info_url(curie), expected_url)
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def test_empty_ontology_info_url(self):
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self.assertEqual(ontology._ontology_info_url(""), "")
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class TestOntologyLookup(unittest.TestCase):
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def setUp(self):
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self.responses = {
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"UBERON:0002048": {
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"iri": "http://purl.obolibrary.org/obo/UBERON_0002048",
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"description": ["Respiration organ that develops as an outpocketing of the esophagus."],
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"label": "lung",
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},
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"HsapDv:0000174": {
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"iri": "http://purl.obolibrary.org/obo/HsapDv_0000174",
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"description": ["Infant stage that refers to an infant who is over 1 and under 2 months old."],
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"label": "1-month-old human stage",
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},
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"NCBITaxon:9606": {
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"iri": "http://purl.obolibrary.org/obo/NCBITaxon_9606",
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"description": None,
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"label": "Homo sapiens",
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},
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"EFO:0008995": {
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"iri": "http://www.ebi.ac.uk/efo/EFO_0008995",
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"description": [
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('10X is a "synthetic long-read" technology and works by capturing a barcoded oligo-coated '
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'gel-bead and 0.3x genome copies into a single emulsion droplet, processing the equivalent '
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'of 1 million pipetting steps. Successive versions of the 10x chemistry use different barcode '
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'locations to improve the sequencing yield and quality of 10x experiments.')
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],
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"label": "10X sequencing",
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||||
},
|
||||
}
|
||||
|
||||
self.labels = {
|
||||
"UBERON:0002048": "lung",
|
||||
"HsapDv:0000174": "1-month-old human stage",
|
||||
"NCBITaxon:9606": "Homo sapiens",
|
||||
"EFO:0008995": "10X sequencing",
|
||||
}
|
||||
|
||||
@unittest.mock.patch("requests.get")
|
||||
def test_lookup_label(self, mock_get):
|
||||
|
||||
for curie, response in self.responses.items():
|
||||
mock_get.return_value.content = json.dumps(response)
|
||||
mock_get.return_value.json.return_value = response
|
||||
mock_get.return_value.status_code = 200
|
||||
|
||||
label = ontology.get_ontology_label(curie)
|
||||
self.assertEqual(label, self.labels[curie])
|
||||
@@ -0,0 +1,257 @@
|
||||
import json
|
||||
import os
|
||||
import unittest
|
||||
import unittest.mock
|
||||
|
||||
import anndata
|
||||
import numpy
|
||||
import pandas as pd
|
||||
import scanpy as sc
|
||||
|
||||
from server.converters.schema import remix
|
||||
|
||||
PROJECT_ROOT = os.popen("git rev-parse --show-toplevel").read().strip()
|
||||
|
||||
|
||||
class TestApplySchema(unittest.TestCase):
|
||||
|
||||
def setUp(self):
|
||||
self.source_h5ad_path = f"{PROJECT_ROOT}/server/test/fixtures/pbmc3k-CSC-gz.h5ad"
|
||||
self.output_h5ad_path = f"{PROJECT_ROOT}/server/test/fixtures/test_remix.h5ad"
|
||||
self.config_path = f"{PROJECT_ROOT}/server/test/fixtures/test_config.yaml"
|
||||
self.bad_config_path = f"{PROJECT_ROOT}/server/test/fixtures/test_bad_config.yaml"
|
||||
|
||||
def tearDown(self):
|
||||
try:
|
||||
os.remove(self.output_h5ad_path)
|
||||
except OSError:
|
||||
pass
|
||||
|
||||
@unittest.mock.patch("server.converters.schema.ontology.get_ontology_label")
|
||||
def test_apply_schema(self, mock_get_ontology_label):
|
||||
mock_get_ontology_label.return_value = "test label"
|
||||
remix.apply_schema(self.source_h5ad_path, self.config_path, self.output_h5ad_path)
|
||||
new_adata = sc.read_h5ad(self.output_h5ad_path)
|
||||
|
||||
self.assertIn("cell_type", new_adata.obs.columns)
|
||||
self.assertListEqual(["test label"], new_adata.obs["cell_type"].unique().tolist())
|
||||
self.assertListEqual(
|
||||
["CL:00001", "CL:00002", "CL:00003", "CL:00004", "CL:00005", "CL:00006", "CL:00007", "CL:00008"],
|
||||
sorted(new_adata.obs["cell_type_ontology_term_id"].unique().tolist())
|
||||
)
|
||||
|
||||
self.assertIn("version", new_adata.uns_keys())
|
||||
|
||||
@unittest.mock.patch("server.converters.schema.ontology.get_ontology_label")
|
||||
def test_apply_bad_schema(self, mock_get_ontology_label):
|
||||
mock_get_ontology_label.return_value = "test label"
|
||||
remix.apply_schema(self.source_h5ad_path, self.bad_config_path, self.output_h5ad_path)
|
||||
new_adata = sc.read_h5ad(self.output_h5ad_path)
|
||||
|
||||
# Should refuse to write the version
|
||||
self.assertNotIn("version", new_adata.uns_keys())
|
||||
|
||||
class TestFieldParsing(unittest.TestCase):
|
||||
|
||||
def test_is_curie(self):
|
||||
self.assertTrue(remix.is_curie("EFO:00001"))
|
||||
self.assertTrue(remix.is_curie("UBERON:123456"))
|
||||
self.assertTrue(remix.is_curie("HsapDv:0001"))
|
||||
self.assertFalse(remix.is_curie("UBERON"))
|
||||
self.assertFalse(remix.is_curie("UBERON:"))
|
||||
self.assertFalse(remix.is_curie("123456"))
|
||||
|
||||
def test_is_ontology_field(self):
|
||||
self.assertTrue(remix.is_ontology_field("tissue_ontology_term_id"))
|
||||
self.assertTrue(remix.is_ontology_field("cell_type_ontology_term_id"))
|
||||
self.assertFalse(remix.is_ontology_field("cell_ontology"))
|
||||
self.assertFalse(remix.is_ontology_field("method"))
|
||||
|
||||
def test_get_label_field_name(self):
|
||||
self.assertEqual("tissue", remix.get_label_field_name("tissue_ontology_term_id"))
|
||||
self.assertEqual("cell_type", remix.get_label_field_name("cell_type_ontology_term_id"))
|
||||
|
||||
def test_split_suffix(self):
|
||||
self.assertEqual(("UBERON:1234", " (organoid)"), remix.split_suffix("UBERON:1234 (organoid)"))
|
||||
self.assertEqual(("UBERON:1234", " (cell culture)"), remix.split_suffix("UBERON:1234 (cell culture)"))
|
||||
self.assertEqual(("UBERON:1234", ""), remix.split_suffix("UBERON:1234"))
|
||||
self.assertEqual(("UBERON:1234 (something)", ""), remix.split_suffix("UBERON:1234 (something)"))
|
||||
|
||||
@unittest.mock.patch("server.converters.schema.ontology.get_ontology_label")
|
||||
def test_get_curie_and_label(self, mock_get_ontology_label):
|
||||
mock_get_ontology_label.return_value = "test label"
|
||||
self.assertEqual(
|
||||
remix.get_curie_and_label("UBERON:1234"),
|
||||
("UBERON:1234", "test label")
|
||||
)
|
||||
self.assertEqual(
|
||||
remix.get_curie_and_label("UBERON:1234 (cell culture)"),
|
||||
("UBERON:1234 (cell culture)", "test label (cell culture)")
|
||||
)
|
||||
self.assertEqual(
|
||||
remix.get_curie_and_label("whatever"),
|
||||
("", "whatever")
|
||||
)
|
||||
|
||||
|
||||
class TestManipulateAnndata(unittest.TestCase):
|
||||
|
||||
def setUp(self):
|
||||
|
||||
self.cell_count = 20
|
||||
self.gene_count = 200
|
||||
X = numpy.random.randint(0, 1000, (self.cell_count, self.gene_count))
|
||||
uns = {"organism": "monkey", "experiment": "monkey experiment"}
|
||||
obs = pd.DataFrame(
|
||||
index=[f"Cell{d}" for d in range(self.cell_count)],
|
||||
columns=["tissue", "CellType"],
|
||||
data=[["lung", "epithelial"]] * (self.cell_count // 2) + [["lung", "endothelial"]] * (self.cell_count // 2)
|
||||
)
|
||||
var = pd.DataFrame(index=[f"SEPT{d}" for d in range(self.gene_count)])
|
||||
|
||||
self.adata = anndata.AnnData(X=X, obs=obs, var=var, uns=uns)
|
||||
|
||||
def test_safe_add_field(self):
|
||||
|
||||
remix.safe_add_field(self.adata.obs, "tissue", ["monkey lung"] * self.cell_count)
|
||||
self.assertEqual(self.adata.obs["tissue_original"].tolist(), ["lung"] * self.cell_count)
|
||||
self.assertEqual(self.adata.obs["tissue"].tolist(), ["monkey lung"] * self.cell_count)
|
||||
|
||||
remix.safe_add_field(self.adata.uns, "contributors", [{"name": "contributor1"}, {"name": "contributor2"}])
|
||||
self.assertEqual(
|
||||
self.adata.uns["contributors"],
|
||||
json.dumps([{"name": "contributor1"}, {"name": "contributor2"}])
|
||||
)
|
||||
|
||||
@unittest.mock.patch("server.converters.schema.ontology.get_ontology_label")
|
||||
def test_remix_uns(self, mock_get_ontology_label):
|
||||
mock_get_ontology_label.return_value = "Pan troglodytes"
|
||||
uns_config = {
|
||||
"version": {
|
||||
"corpora_schema_version": "1.0.0",
|
||||
"corpora_encoding_version": "0.1.0"
|
||||
},
|
||||
"organism_ontology_term_id": "NCBITaxon:9598",
|
||||
"contributors": [
|
||||
{
|
||||
"name": "scientist",
|
||||
"email": "scientist@science.com"
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
remix.remix_uns(self.adata, uns_config)
|
||||
|
||||
self.assertEqual(
|
||||
sorted(self.adata.uns_keys()),
|
||||
sorted(["organism_original", "organism", "organism_ontology_term_id",
|
||||
"contributors", "version", "experiment"])
|
||||
)
|
||||
|
||||
self.assertEqual(self.adata.uns['organism'], "Pan troglodytes")
|
||||
self.assertEqual(self.adata.uns['organism_original'], "monkey")
|
||||
self.assertEqual(self.adata.uns['organism_ontology_term_id'], "NCBITaxon:9598")
|
||||
self.assertEqual(self.adata.uns['contributors'],
|
||||
json.dumps([{"name": "scientist", "email": "scientist@science.com"}]))
|
||||
|
||||
@unittest.mock.patch("server.converters.schema.ontology.get_ontology_label")
|
||||
def test_remix_obs(self, mock_get_ontology_label):
|
||||
mock_get_ontology_label.return_value = "lung (in a monkey)"
|
||||
obs_config = {
|
||||
"tissue_ontology_term_id": {
|
||||
"tissue": {
|
||||
"lung": "UBERON:00000"
|
||||
}
|
||||
},
|
||||
"cell_color": {
|
||||
"CellType": {
|
||||
"epithelial": "fuschia",
|
||||
"endothelial": "khaki"
|
||||
}
|
||||
},
|
||||
"sex": "male"
|
||||
}
|
||||
|
||||
remix.remix_obs(self.adata, obs_config)
|
||||
self.assertEqual(
|
||||
sorted(self.adata.obs_keys()),
|
||||
sorted(["tissue", "tissue_ontology_term_id", "tissue_original", "CellType", "cell_color", "sex"])
|
||||
)
|
||||
|
||||
self.assertTrue(all(v == "lung" for v in self.adata.obs.tissue_original))
|
||||
self.assertTrue(all(v == "UBERON:00000" for v in self.adata.obs.tissue_ontology_term_id))
|
||||
self.assertTrue(all(v == "lung (in a monkey)" for v in self.adata.obs.tissue))
|
||||
self.assertTrue(all(v == "male" for v in self.adata.obs.sex))
|
||||
self.assertTrue(all(v in (("epithelial", "fuschia"), ("endothelial", "khaki"))
|
||||
for v in zip(self.adata.obs.CellType, self.adata.obs.cell_color)))
|
||||
|
||||
|
||||
class TestFixupGeneSymbols(unittest.TestCase):
|
||||
|
||||
def setUp(self):
|
||||
self.seurat_path = f"{PROJECT_ROOT}/server/test/fixtures/schema_test_data/seurat_tutorial.h5ad"
|
||||
self.seurat_merged_path = f"{PROJECT_ROOT}/server/test/fixtures/schema_test_data/seurat_tutorial_merged.h5ad"
|
||||
self.sctransform_path = f"{PROJECT_ROOT}/server/test/fixtures/schema_test_data/sctransform.h5ad"
|
||||
self.sctransform_merged_path = f"{PROJECT_ROOT}/server/test/fixtures/schema_test_data/sctransform_merged.h5ad"
|
||||
|
||||
# There's lots of MALAT1, but it doesn't collide with any other names,
|
||||
# so it shouldn't change during merging.
|
||||
self.stable_gene = "MALAT1"
|
||||
|
||||
def test_fixup_gene_symbols_seurat(self):
|
||||
|
||||
if not os.path.isfile(self.seurat_path):
|
||||
return unittest.skip(
|
||||
"Skipping gene symbol conversion tests because test h5ads are not present. To create them, "
|
||||
"run server/test/fixtures/schema_test_data/generate_test_data.sh"
|
||||
)
|
||||
|
||||
original_adata = sc.read_h5ad(self.seurat_path)
|
||||
merged_adata = sc.read_h5ad(self.seurat_merged_path)
|
||||
|
||||
fixup_config = {"X": "log1p", "counts": "raw", "scale.data": "log1p"}
|
||||
|
||||
fixed_adata = remix.fixup_gene_symbols(original_adata, fixup_config)
|
||||
|
||||
self.assertEqual(
|
||||
merged_adata.layers["counts"][:, merged_adata.var.index == self.stable_gene].sum(),
|
||||
fixed_adata.raw.X[:, fixed_adata.var.index == self.stable_gene].sum()
|
||||
)
|
||||
self.assertAlmostEqual(
|
||||
merged_adata.X[:, merged_adata.var.index == self.stable_gene].sum(),
|
||||
fixed_adata.X[:, fixed_adata.var.index == self.stable_gene].sum()
|
||||
)
|
||||
|
||||
self.assertAlmostEqual(
|
||||
merged_adata.layers["scale.data"][:, merged_adata.var.index == self.stable_gene].sum(),
|
||||
fixed_adata.layers["scale.data"][:, fixed_adata.var.index == self.stable_gene].sum()
|
||||
)
|
||||
|
||||
def test_fixup_gene_symbols_sctransform(self):
|
||||
|
||||
if not os.path.isfile(self.sctransform_path):
|
||||
return unittest.skip(
|
||||
"Skipping gene symbol conversion tests because test h5ads are not present. To create them, "
|
||||
"run server/test/fixtures/schema_test_data/generate_test_data.sh"
|
||||
)
|
||||
|
||||
original_adata = sc.read_h5ad(self.sctransform_path)
|
||||
merged_adata = sc.read_h5ad(self.sctransform_merged_path)
|
||||
|
||||
fixup_config = {"X": "log1p", "counts": "raw"}
|
||||
|
||||
fixed_adata = remix.fixup_gene_symbols(original_adata, fixup_config)
|
||||
|
||||
# sctransform does a bunch of stuff, including slightly modifying the
|
||||
# raw counts. So we can't assert for exact equality the way we do with
|
||||
# the vanilla seurat tutorial. But, the results should still be very
|
||||
# close.
|
||||
merged_raw_stable = merged_adata.layers["counts"][:, merged_adata.var.index == self.stable_gene].sum()
|
||||
fixed_raw_stable = fixed_adata.raw.X[:, fixed_adata.var.index == self.stable_gene].sum()
|
||||
self.assertLess(abs(merged_raw_stable - fixed_raw_stable), .001 * merged_raw_stable)
|
||||
|
||||
self.assertAlmostEqual(
|
||||
merged_adata.X[:, merged_adata.var.index == self.stable_gene].sum(),
|
||||
fixed_adata.X[:, fixed_adata.var.index == self.stable_gene].sum(),
|
||||
0
|
||||
)
|
||||
@@ -0,0 +1,435 @@
|
||||
import json
|
||||
import os
|
||||
import unittest
|
||||
|
||||
import pandas as pd
|
||||
import scanpy as sc
|
||||
|
||||
from server.converters.schema import validate
|
||||
|
||||
PROJECT_ROOT = os.popen("git rev-parse --show-toplevel").read().strip()
|
||||
|
||||
|
||||
class TestFieldValidation(unittest.TestCase):
|
||||
|
||||
def test_validate_stringified_list_of_dicts(self):
|
||||
|
||||
good = json.dumps([{"a": 1}, {2: "x", "z": "y"}])
|
||||
not_stringified = [{"a": 1}, {2: "x", "z": "y"}]
|
||||
not_a_list = json.dumps({"bad": "dict"})
|
||||
not_json = "oh hey!"
|
||||
|
||||
self.assertTrue(validate._validate_stringified_list_of_dicts(good))
|
||||
|
||||
self.assertFalse(validate._validate_stringified_list_of_dicts(not_stringified))
|
||||
self.assertFalse(validate._validate_stringified_list_of_dicts(not_a_list))
|
||||
self.assertFalse(validate._validate_stringified_list_of_dicts(not_json))
|
||||
|
||||
def test_validate_human_readable_string(self):
|
||||
|
||||
good = "oh hey!"
|
||||
curie = "EFO:0001"
|
||||
ensg = "ENSG000001234"
|
||||
enst = "ENST000005678"
|
||||
|
||||
self.assertTrue(validate._validate_human_readable_string(good))
|
||||
|
||||
self.assertFalse(validate._validate_human_readable_string(curie))
|
||||
self.assertFalse(validate._validate_human_readable_string(ensg))
|
||||
self.assertFalse(validate._validate_human_readable_string(enst))
|
||||
|
||||
def test_validate_curie(self):
|
||||
|
||||
self.assertTrue(validate._validate_curie("UBERON:00001", ["UBERON", "EFO"]))
|
||||
self.assertTrue(validate._validate_curie("HsapDv:00002", ["HsapDv"]))
|
||||
|
||||
self.assertFalse(validate._validate_curie("HsapDv:00002", ["UBERON", "EFO"]))
|
||||
self.assertFalse(validate._validate_curie("EFO:00002 (organoid)", ["UBERON", "EFO"]))
|
||||
self.assertFalse(validate._validate_curie("EFO:00002 extra", ["UBERON", "EFO"]))
|
||||
self.assertFalse(validate._validate_curie("UBERON:ABCD", ["UBERON", "EFO"]))
|
||||
self.assertFalse(validate._validate_curie("Uberon:00002", ["UBERON", "EFO"]))
|
||||
self.assertFalse(validate._validate_curie("UBERON:", ["UBERON", "EFO"]))
|
||||
self.assertFalse(validate._validate_curie("UBERON", ["UBERON", "EFO"]))
|
||||
|
||||
def test_validate_suffixed_curie(self):
|
||||
|
||||
self.assertTrue(validate._validate_suffixed_curie("EFO:00001", ["UBERON", "EFO"]))
|
||||
self.assertTrue(validate._validate_suffixed_curie("UBERON:00001 (cell culture)", ["UBERON", "EFO"]))
|
||||
|
||||
self.assertFalse(validate._validate_suffixed_curie("HsapDv:00002 (organoid)", ["UBERON", "EFO"]))
|
||||
self.assertFalse(validate._validate_suffixed_curie("HsapDv:00002(organoid)", ["UBERON", "EFO"]))
|
||||
self.assertFalse(validate._validate_suffixed_curie("HsapDv:00002", ["UBERON", "EFO"]))
|
||||
self.assertFalse(validate._validate_suffixed_curie("EFO:00002 extra", ["UBERON", "EFO"]))
|
||||
self.assertFalse(validate._validate_suffixed_curie("UBERON:ABCD", ["UBERON", "EFO"]))
|
||||
self.assertFalse(validate._validate_suffixed_curie("Uberon:00002", ["UBERON", "EFO"]))
|
||||
self.assertFalse(validate._validate_suffixed_curie("UBERON:", ["UBERON", "EFO"]))
|
||||
self.assertFalse(validate._validate_suffixed_curie("UBERON", ["UBERON", "EFO"]))
|
||||
|
||||
|
||||
class TestColumnValidation(unittest.TestCase):
|
||||
|
||||
def test_validate_unique(self):
|
||||
unique = pd.DataFrame([["abc", "def"], ["ghi", "jkl"], ["mnop", "qrs"]],
|
||||
index=["X", "Y", "Z"], columns=["col1", "col2"])
|
||||
duped = pd.DataFrame([["abc", "def"], ["ghi", "qrs"], ["abc", "qrs"]],
|
||||
index=["X", "Y", "X"], columns=["col1", "col2"])
|
||||
|
||||
schema_def = {"unique": True}
|
||||
|
||||
errors = validate._validate_column(unique.index, "index", "unique_df", schema_def)
|
||||
self.assertFalse(errors)
|
||||
|
||||
errors = validate._validate_column(duped.index, "index", "duped_df", schema_def)
|
||||
self.assertEqual(len(errors), 1)
|
||||
self.assertIn("is not unique", errors[0])
|
||||
|
||||
errors = validate._validate_column(unique["col1"], "col1", "unique_df", schema_def)
|
||||
self.assertFalse(errors)
|
||||
|
||||
errors = validate._validate_column(duped["col1"], "col1", "duped_df", schema_def)
|
||||
self.assertEqual(len(errors), 1)
|
||||
self.assertIn("is not unique", errors[0])
|
||||
|
||||
schema_def = {"unique": False}
|
||||
errors = validate._validate_column(duped["col1"], "col1", "duped_df", schema_def)
|
||||
self.assertFalse(errors)
|
||||
|
||||
def test_validate_nullable(self):
|
||||
non_null = pd.DataFrame([["abc", "def"], ["ghi", "jkl"], ["mnop", "qrs"]],
|
||||
index=["X", "Y", "Z"], columns=["col1", "col2"])
|
||||
has_null = pd.DataFrame([["abc", "", None], ["ghi", "jkl", 1], ["mnop", "qrs", 2]],
|
||||
index=["X", "Y", "Z"], columns=["col1", "col2", "col3"])
|
||||
|
||||
schema_def = {"nullable": False}
|
||||
errors = validate._validate_column(non_null["col1"], "col1", "nonnull_df", schema_def)
|
||||
self.assertFalse(errors)
|
||||
errors = validate._validate_column(has_null["col1"], "col1", "hasnull_df", schema_def)
|
||||
self.assertFalse(errors)
|
||||
|
||||
errors = validate._validate_column(has_null["col2"], "col2", "hasnull_df", schema_def)
|
||||
self.assertEqual(len(errors), 1)
|
||||
self.assertIn("contains empty values", errors[0])
|
||||
errors = validate._validate_column(has_null["col3"], "col3", "hasnull_df", schema_def)
|
||||
self.assertEqual(len(errors), 1)
|
||||
self.assertIn("contains empty values", errors[0])
|
||||
|
||||
schema_def = {"nullable": True}
|
||||
errors = validate._validate_column(has_null["col2"], "col2", "hasnull_df", schema_def)
|
||||
self.assertFalse(errors)
|
||||
|
||||
def test_human_readable(self):
|
||||
hr_df = pd.DataFrame(
|
||||
[["for you, a human", "UBERON:12345", "UBERON:1234 (thundercat)"],
|
||||
["hope you're well", "bit of lungs", "brain"]],
|
||||
index=["ENSG00001", "ENSG00002"],
|
||||
columns=["good", "curie", "suffixed_curie"])
|
||||
|
||||
schema_def = {"type": "human-readable string"}
|
||||
errors = validate._validate_column(hr_df["good"], "good", "hr", schema_def)
|
||||
self.assertFalse(errors)
|
||||
|
||||
errors = validate._validate_column(hr_df["curie"], "curie", "hr", schema_def)
|
||||
self.assertEqual(len(errors), 1)
|
||||
self.assertIn("non-human-readable", errors[0])
|
||||
|
||||
errors = validate._validate_column(hr_df["suffixed_curie"], "suffixed_curie", "hr", schema_def)
|
||||
self.assertEqual(len(errors), 1)
|
||||
self.assertIn("non-human-readable", errors[0])
|
||||
|
||||
errors = validate._validate_column(hr_df.index, "ensg", "hr", schema_def)
|
||||
self.assertEqual(len(errors), 1)
|
||||
self.assertIn("non-human-readable", errors[0])
|
||||
|
||||
def test_curie(self):
|
||||
|
||||
curie_df = pd.DataFrame(
|
||||
[["EFO:00001", "HsapDv:00001 (cell culture)", "EFO:", "MONDO:0001 cell culture"],
|
||||
["UBERON:00002", "HsapDv:00002 (organoid)", "EFO:12345", "MONDO:0002 (baba yaga)"],
|
||||
["EFO:0000000005", "HsapDv:000004 (humanzee)", "EFO:000002", "MONDO:0004 (TMNT)"]],
|
||||
index=["X", "Y", "Z"],
|
||||
columns=["good", "good_suffix", "bad", "bad_suffix"])
|
||||
|
||||
# Good
|
||||
schema_def = {"type": "curie", "prefixes": ["EFO", "UBERON"]}
|
||||
errors = validate._validate_column(curie_df["good"], "good", "curie_df", schema_def)
|
||||
self.assertFalse(errors)
|
||||
|
||||
# Good suffix
|
||||
schema_def = {"type": "suffixed curie", "prefixes": ["HsapDv", "WHATEVER"]}
|
||||
errors = validate._validate_column(curie_df["good_suffix"], "good_suffix", "curie_df", schema_def)
|
||||
self.assertFalse(errors)
|
||||
|
||||
# Bad prefix
|
||||
schema_def = {"type": "curie", "prefixes": ["EFO"]}
|
||||
errors = validate._validate_column(curie_df["good"], "good", "curie_df", schema_def)
|
||||
self.assertEqual(len(errors), 1)
|
||||
self.assertIn("invalid ontology", errors[0])
|
||||
self.assertIn("must be curies from one of these", errors[0])
|
||||
|
||||
# Bad curies
|
||||
schema_def = {"type": "curie", "prefixes": ["EFO"]}
|
||||
errors = validate._validate_column(curie_df["bad"], "bad", "curie_df", schema_def)
|
||||
self.assertEqual(len(errors), 1)
|
||||
self.assertIn("invalid ontology", errors[0])
|
||||
|
||||
# Bad suffixes
|
||||
schema_def = {"type": "suffixed curie", "prefixes": ["EFO"]}
|
||||
errors = validate._validate_column(curie_df["bad_suffix"], "bad_suffix", "curie_df", schema_def)
|
||||
self.assertEqual(len(errors), 1)
|
||||
self.assertIn("invalid ontology", errors[0])
|
||||
|
||||
def test_enum(self):
|
||||
enum_df = pd.DataFrame(
|
||||
[["abc", "ghi"],
|
||||
["def", "jkl"]],
|
||||
index=["X", "Y"],
|
||||
columns=["col1", "col2"])
|
||||
|
||||
# All match
|
||||
schema_def = {"type": "string", "enum": ["abc", "def", "xyz"]}
|
||||
errors = validate._validate_column(enum_df["col1"], "col1", "enum_df", schema_def)
|
||||
self.assertFalse(errors)
|
||||
|
||||
# Missing value
|
||||
schema_def = {"type": "string", "enum": ["abc", "xyz"]}
|
||||
errors = validate._validate_column(enum_df["col1"], "col1", "enum_df", schema_def)
|
||||
self.assertEqual(len(errors), 1)
|
||||
self.assertIn("unpermitted values", errors[0])
|
||||
|
||||
|
||||
class TestDictValidations(unittest.TestCase):
|
||||
|
||||
|
||||
def test_key_presence(self):
|
||||
|
||||
schema_def = {"keys": {"abc": None, "def": None}}
|
||||
|
||||
dict_ = {"abc": "123", "def": "456"}
|
||||
errors = validate._validate_dict(dict_, "d", schema_def)
|
||||
self.assertFalse(errors)
|
||||
|
||||
# Missing keys are bad
|
||||
dict_ = {"abc": "123"}
|
||||
errors = validate._validate_dict(dict_, "d", schema_def)
|
||||
self.assertEqual(len(errors), 1)
|
||||
self.assertIn("missing key", errors[0])
|
||||
|
||||
# Extra keys are okay
|
||||
dict_ = {"abc": "123", "def": "456", "xyz": "789"}
|
||||
errors = validate._validate_dict(dict_, "d", schema_def)
|
||||
self.assertFalse(errors)
|
||||
|
||||
# Better not be empty come on
|
||||
dict_ = {}
|
||||
errors = validate._validate_dict(dict_, "d", schema_def)
|
||||
self.assertEqual(len(errors), 2)
|
||||
|
||||
def test_nullable(self):
|
||||
|
||||
schema_def = {"keys": {"abc": {"type": "string", "nullable": False},
|
||||
"def": {"type": "string", "nullable": True}}}
|
||||
|
||||
dict_ = {"abc": "xyz", "def": ""}
|
||||
errors = validate._validate_dict(dict_, "d", schema_def)
|
||||
self.assertFalse(errors)
|
||||
|
||||
dict_ = {"abc": "", "def": ""}
|
||||
errors = validate._validate_dict(dict_, "d", schema_def)
|
||||
self.assertEqual(len(errors), 1)
|
||||
self.assertIn("empty value", errors[0])
|
||||
|
||||
def test_recurse(self):
|
||||
|
||||
schema_def = {
|
||||
"keys": {
|
||||
"subdict": {
|
||||
"type": "dict",
|
||||
"keys": {
|
||||
"subdict_key1": None,
|
||||
"subdict_key2": None
|
||||
}
|
||||
},
|
||||
"ontology": {
|
||||
"type": "curie",
|
||||
"prefixes": ["ONTOLOGY"]
|
||||
},
|
||||
"blob": {
|
||||
"type": "stringified list of dicts"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
dict_ = {
|
||||
"subdict": {"subdict_key1": "any", "subdict_key2": "any"},
|
||||
"ontology": "ONTOLOGY:123456",
|
||||
"blob": json.dumps([{"abc": 123}, {"def": 456}])
|
||||
}
|
||||
errors = validate._validate_dict(dict_, "d", schema_def)
|
||||
self.assertFalse(errors)
|
||||
|
||||
dict_ = {
|
||||
"subdict": {"subdict_key1": "any"},
|
||||
"ontology": "ONTOLOGY:123456",
|
||||
"blob": json.dumps([{"abc": 123}, {"def": 456}])
|
||||
}
|
||||
errors = validate._validate_dict(dict_, "d", schema_def)
|
||||
self.assertEqual(len(errors), 1)
|
||||
self.assertIn("missing key", errors[0])
|
||||
|
||||
dict_ = {
|
||||
"subdict": {"subdict_key1": "any", "subdict_key2": "any"},
|
||||
"ontology": "oh no not an ontology term",
|
||||
"blob": json.dumps([{"abc": 123}, {"def": 456}])
|
||||
}
|
||||
errors = validate._validate_dict(dict_, "d", schema_def)
|
||||
self.assertEqual(len(errors), 1)
|
||||
self.assertIn("invalid ontology", errors[0])
|
||||
|
||||
dict_ = {
|
||||
"subdict": {"subdict_key1": "any", "subdict_key2": "any"},
|
||||
"ontology": "ONTOLOGY:123456",
|
||||
"blob": [{"abc": 123}, {"def": 456}]
|
||||
}
|
||||
errors = validate._validate_dict(dict_, "d", schema_def)
|
||||
self.assertEqual(len(errors), 1)
|
||||
self.assertIn("JSON-encoded list of dicts", errors[0])
|
||||
|
||||
# Multiple errors
|
||||
dict_ = {
|
||||
"subdict": {"subdict_key1": "any"},
|
||||
"ontology": "oh no not an ontology term",
|
||||
"blob": json.dumps([{"abc": 123}, {"def": 456}])
|
||||
}
|
||||
errors = validate._validate_dict(dict_, "d", schema_def)
|
||||
self.assertEqual(len(errors), 2)
|
||||
|
||||
|
||||
class TestDataframeValidation(unittest.TestCase):
|
||||
|
||||
def test_column_presence(self):
|
||||
df = pd.DataFrame(
|
||||
[["abc", "EFO:123"],
|
||||
["def", "UBERON:456"]],
|
||||
columns=["hr_string", "ontology"],
|
||||
index=["X", "Y"]
|
||||
)
|
||||
|
||||
schema_def = {
|
||||
"columns": {
|
||||
"hr_string": {"type": "human-readable string"},
|
||||
"ontology": {"type": "curie", "prefixes": ["EFO", "UBERON"]}
|
||||
}
|
||||
}
|
||||
errors = validate._validate_dataframe(df, "df", schema_def)
|
||||
self.assertFalse(errors)
|
||||
|
||||
schema_def = {
|
||||
"columns": {
|
||||
"hr_string": {"type": "human-readable string"},
|
||||
"another_hr_string": {"type": "human-readable string"},
|
||||
"ontology": {"type": "curie", "prefixes": ["EFO", "UBERON"]}
|
||||
}
|
||||
}
|
||||
errors = validate._validate_dataframe(df, "df", schema_def)
|
||||
self.assertEqual(len(errors), 1)
|
||||
self.assertIn("missing column", errors[0])
|
||||
|
||||
# Extra is okay
|
||||
df = pd.DataFrame(
|
||||
[["abc", "EFO:123", "extra"],
|
||||
["def", "UBERON:456", "extra"]],
|
||||
columns=["hr_string", "ontology", "extra"],
|
||||
index=["X", "Y"]
|
||||
)
|
||||
schema_def = {
|
||||
"columns": {
|
||||
"hr_string": {"type": "human-readable string"},
|
||||
"ontology": {"type": "curie", "prefixes": ["EFO", "UBERON"]}
|
||||
}
|
||||
}
|
||||
errors = validate._validate_dataframe(df, "df", schema_def)
|
||||
self.assertFalse(errors)
|
||||
|
||||
|
||||
def test_index(self):
|
||||
df = pd.DataFrame(
|
||||
[["abc", "123"],
|
||||
["def", "456"]],
|
||||
columns=["col1", "col2"],
|
||||
index=["ENSG0001", "ENSG0002"]
|
||||
)
|
||||
|
||||
schema_def = {"index": {"unique": True}}
|
||||
errors = validate._validate_dataframe(df, "df", schema_def)
|
||||
self.assertFalse(errors)
|
||||
|
||||
schema_def = {"index": {"type": "human-readable string"}}
|
||||
errors = validate._validate_dataframe(df, "df", schema_def)
|
||||
self.assertEqual(len(errors), 1)
|
||||
self.assertIn("non-human-readable", errors[0])
|
||||
|
||||
df = pd.DataFrame(
|
||||
[["abc", "123"],
|
||||
["def", "456"]],
|
||||
columns=["col1", "col2"],
|
||||
index=["ENSG0001", "ENSG0001"]
|
||||
)
|
||||
schema_def = {"index": {"unique": True}}
|
||||
errors = validate._validate_dataframe(df, "df", schema_def)
|
||||
self.assertEqual(len(errors), 1)
|
||||
self.assertIn("is not unique", errors[0])
|
||||
|
||||
def test_recurse(self):
|
||||
|
||||
df = pd.DataFrame(
|
||||
[["abc", "HsapDv:0001"],
|
||||
["EFO:123", "UBERON:456"]],
|
||||
columns=["hr_string", "ontology"],
|
||||
index=["X", "Y"]
|
||||
)
|
||||
schema_def = {
|
||||
"columns": {
|
||||
"hr_string": {"type": "human-readable string"},
|
||||
"ontology": {"type": "curie", "prefixes": ["EFO", "UBERON"]}
|
||||
}
|
||||
}
|
||||
errors = validate._validate_dataframe(df, "df", schema_def)
|
||||
self.assertEqual(len(errors), 2)
|
||||
self.assertEqual(len([e for e in errors if "non-human-readable" in e]), 1)
|
||||
self.assertEqual(len([e for e in errors if "invalid ontology" in e]), 1)
|
||||
|
||||
|
||||
class TestGetSchema(unittest.TestCase):
|
||||
|
||||
def test_get_schema(self):
|
||||
self.assertIsInstance(validate.get_schema_definition("1.0.0"), dict)
|
||||
|
||||
with self.assertRaises(ValueError):
|
||||
validate.get_schema_definition("10.1.5")
|
||||
|
||||
|
||||
class TestValidate(unittest.TestCase):
|
||||
|
||||
def setUp(self):
|
||||
self.source_h5ad_path = f"{PROJECT_ROOT}/server/test/fixtures/pbmc3k-CSC-gz.h5ad"
|
||||
|
||||
def test_shallow(self):
|
||||
|
||||
adata = sc.read_h5ad(self.source_h5ad_path)
|
||||
self.assertFalse(validate.validate_adata(adata, True))
|
||||
|
||||
adata.uns["version"] = {
|
||||
"corpora_schema_version": "1.0.0",
|
||||
"corpora_encoding_version": "0.1.0"
|
||||
}
|
||||
self.assertTrue(validate.validate_adata(adata, True))
|
||||
|
||||
def test_deep(self):
|
||||
adata = sc.read_h5ad(self.source_h5ad_path)
|
||||
self.assertFalse(validate.validate_adata(adata, False))
|
||||
|
||||
adata.uns["version"] = {
|
||||
"corpora_schema_version": "1.0.0",
|
||||
"corpora_encoding_version": "0.1.0"
|
||||
}
|
||||
self.assertFalse(validate.validate_adata(adata, False))
|
||||
Reference in New Issue
Block a user