add grn inference page

results/grn_inference/data/dataset_info.json

@@ -0,0 +1,52 @@
+[
+  {
+    "dataset_id": "op",
+    "dataset_name": "OPSCA",
+    "dataset_summary": "scRNA-seq data with 146 (originally) perturbations with chemical compounds on PBMCs. Multiome data available for the control compound.",
+    "dataset_description": "Novel single-cell perturbational dataset in human peripheral blood mononuclear cells (PBMCs). 144 compounds were selected from the Library of Integrated Network-Based Cellular Signatures (LINCS) Connectivity Map dataset (PMID: 29195078) and measured single-cell gene expression profiles after 24 hours of treatment. The experiment was repeated in three healthy human donors, and the compounds were selected based on diverse transcriptional signatures observed in CD34+ hematopoietic stem cells (data not released). This experiment was performed in human PBMCs because the cells are commercially available with pre-obtained consent for public release and PBMCs are a primary, disease-relevant tissue that contains multiple mature cell types (including T-cells, B-cells, myeloid cells, and NK cells) with established markers for annotation of cell types. To supplement this dataset, joint scRNA and single-cell chromatin accessibility measurements were measured from the baseline compound using the 10x Multiome assay.",
+    "data_reference": null,
+    "data_url": null,
+    "date_created": "19-02-2025",
+    "file_size": 10781372
+  },
+  {
+    "dataset_id": "norman",
+    "dataset_name": "Norman",
+    "dataset_summary": "Single cell RNA-seq data with 231 perturbations (activation) on K562 cells.",
+    "dataset_description": "How cellular and organismal complexity emerges from combinatorial expression of genes is a central question in biology. High-content phenotyping approaches such as Perturb-seq (single-cell RNA-seq pooled CRISPR screens) present an opportunity for exploring such genetic interactions (GIs) at scale. Here, we present an analytical framework for interpreting high-dimensional landscapes of cell states (manifolds) constructed from transcriptional phenotypes. We applied this approach to Perturb-seq profiling of strong GIs mined from a growth-based, gain-of-function GI map. Exploration of this manifold enabled ordering of regulatory pathways, principled classification of GIs (e.g. identifying suppressors), and mechanistic elucidation of synergistic interactions, including an unexpected synergy between CBL and CNN1 driving erythroid differentiation. Finally, we apply recommender system machine learning to predict interactions, facilitating exploration of vastly larger GI manifolds.",
+    "data_reference": null,
+    "data_url": null,
+    "date_created": "19-02-2025",
+    "file_size": 10781372
+  },
+  {
+    "dataset_id": "adamson",
+    "dataset_name": "Adamson",
+    "dataset_summary": "Single cell RNA-seq data with 82 perturbations (KD) on K562 cells.",
+    "dataset_description": "Functional genomics efforts face tradeoffs between number of perturbations examined and complexity of phenotypes measured. We bridge this gap with Perturb-seq, which combines droplet-based single-cell RNA-seq with a strategy for barcoding CRISPR-mediated perturbations, allowing many perturbations to be profiled in pooled format. We applied Perturb-seq to dissect the mammalian unfolded protein response (UPR) using single and combinatorial CRISPR perturbations. Two genome-scale CRISPR interference (CRISPRi) screens identified genes whose repression perturbs ER homeostasis. Subjecting ∼100 hits to Perturb-seq enabled high-precision functional clustering of genes. Single-cell analyses decoupled the three UPR branches, revealed bifurcated UPR branch activation among cells subject to the same perturbation, and uncovered differential activation of the branches across hits, including an isolated feedback loop between the translocon and IRE1α. These studies provide insight into how the three sensors of ER homeostasis monitor distinct types of stress and highlight the ability of Perturb-seq to dissect complex cellular responses.",
+    "data_reference": null,
+    "data_url": null,
+    "date_created": "19-02-2025",
+    "file_size": 10781372
+  },
+  {
+    "dataset_id": "replogle",
+    "dataset_name": "Reologle",
+    "dataset_summary": "Single cell RNA-seq data with 9722 perturbations (KO) on K562 cells.",
+    "dataset_description": "A central goal of genetics is to define the relationships between genotypes and phenotypes. High-content phenotypic screens such as Perturb-seq (CRISPR-based screens with single-cell RNA-sequencing readouts) enable massively parallel functional genomic mapping but, to date, have been used at limited scales. Here, we perform genome-scale Perturb-seq targeting all expressed genes with CRISPR interference (CRISPRi) across >2.5 million human cells. We use transcriptional phenotypes to predict the function of poorly characterized genes, uncovering new regulators of ribosome biogenesis (including CCDC86, ZNF236, and SPATA5L1), transcription (C7orf26), and mitochondrial respiration (TMEM242). In addition to assigning gene function, single-cell transcriptional phenotypes allow for in-depth dissection of complex cellular phenomena-from RNA processing to differentiation. We leverage this ability to systematically identify genetic drivers and consequences of aneuploidy and to discover an unanticipated layer of stress-specific regulation of the mitochondrial genome. Our information-rich genotype-phenotype map reveals a multidimensional portrait of gene and cellular function.",
+    "data_reference": null,
+    "data_url": null,
+    "date_created": "19-02-2025",
+    "file_size": 10781372
+  },
+  {
+    "dataset_id": "nakatake",
+    "dataset_name": "Nakatake",
+    "dataset_summary": "RNA-seq data with 463 perturbations (overexpression) on SEES3 cells",
+    "dataset_description": "Transcription factors (TFs) play a pivotal role in determining cell states, yet our understanding of the causative relationship between TFs and cell states is limited. Here, we systematically examine the state changes of human pluripotent embryonic stem cells (hESCs) by the large-scale manipulation of single TFs. We establish 2,135 hESC lines, representing three clones each of 714 doxycycline (Dox)-inducible genes including 481 TFs, and obtain 26,998 microscopic cell images and 2,174 transcriptome datasets-RNA sequencing (RNA-seq) or microarrays-48 h after the presence or absence of Dox. Interestingly, the expression of essentially all the genes, including genes located in heterochromatin regions, are perturbed by these TFs. TFs are also characterized by their ability to induce differentiation of hESCs into specific cell lineages. These analyses help to provide a way of classifying TFs and identifying specific sets of TFs for directing hESC differentiation into desired cell types.",
+    "data_reference": null,
+    "data_url": null,
+    "date_created": "19-02-2025",
+    "file_size": 10781372
+  }
+]

results/grn_inference/data/method_info.json

@@ -0,0 +1,210 @@
+[
+  {
+    "task_id": "control_methods",
+    "method_id": "pearson_corr",
+    "method_name": "pearson_corr",
+    "method_summary": "Baseline based on correlation",
+    "method_description": "Baseline GRN inference method using Pearson correlation.\n",
+    "is_baseline": true,
+    "references_doi": null,
+    "references_bibtex": null,
+    "code_url": "https://github.com/openproblems-bio/task_grn_inference",
+    "documentation_url": null,
+    "image": "https://ghcr.io/openproblems-bio/task_grn_inference/control_methods/pearson_corr:dev",
+    "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/control_methods/pearson_corr",
+    "code_version": "dev",
+    "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592"
+  },
+  {
+    "task_id": "control_methods",
+    "method_id": "negative_control",
+    "method_name": "Negative control",
+    "method_summary": "Source-target links based on random assignment",
+    "method_description": "Randomly assigns regulatory links to tf-target links with a given tf and target list. This is to perform near random.\n",
+    "is_baseline": true,
+    "references_doi": null,
+    "references_bibtex": null,
+    "code_url": "https://github.com/openproblems-bio/task_grn_inference",
+    "documentation_url": null,
+    "image": "https://ghcr.io/openproblems-bio/task_grn_inference/control_methods/negative_control:dev",
+    "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/control_methods/negative_control",
+    "code_version": "dev",
+    "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592"
+  },
+  {
+    "task_id": "control_methods",
+    "method_id": "positive_control",
+    "method_name": "positive_control",
+    "method_summary": "Baseline based on correlation",
+    "method_description": "Baseline model based on Pearson correlation that uses both inference and evaluation dataset to infer the GRN.\n",
+    "is_baseline": true,
+    "references_doi": null,
+    "references_bibtex": null,
+    "code_url": "https://github.com/openproblems-bio/task_grn_inference",
+    "documentation_url": null,
+    "image": "https://ghcr.io/openproblems-bio/task_grn_inference/control_methods/positive_control:dev",
+    "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/control_methods/positive_control",
+    "code_version": "dev",
+    "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592"
+  },
+  {
+    "task_id": "grn_methods",
+    "method_id": "portia",
+    "method_name": "portia",
+    "method_summary": "GRN inference using PORTIA",
+    "method_description": "GRN inference using PORTIA.\n",
+    "is_baseline": false,
+    "references_doi": null,
+    "references_bibtex": null,
+    "code_url": "https://github.com/openproblems-bio/task_grn_inference",
+    "documentation_url": null,
+    "image": "https://ghcr.io/openproblems-bio/task_grn_inference/methods/single_omics/portia:dev",
+    "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/methods/single_omics/portia",
+    "code_version": "dev",
+    "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592"
+  },
+  {
+    "task_id": "grn_methods",
+    "method_id": "ppcor",
+    "method_name": "ppcor",
+    "method_summary": "GRN inference using PPCOR",
+    "method_description": "GRN inference using PPCOR.\n",
+    "is_baseline": false,
+    "references_doi": null,
+    "references_bibtex": null,
+    "code_url": "https://github.com/openproblems-bio/task_grn_inference",
+    "documentation_url": null,
+    "image": "https://ghcr.io/openproblems-bio/task_grn_inference/methods/single_omics/ppcor:dev",
+    "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/methods/single_omics/ppcor",
+    "code_version": "dev",
+    "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592"
+  },
+  {
+    "task_id": "grn_methods",
+    "method_id": "scenic",
+    "method_name": "scenic",
+    "method_summary": "GRN inference using scenic",
+    "method_description": "GRN inference using Scenic pipeline.\n",
+    "is_baseline": false,
+    "references_doi": null,
+    "references_bibtex": null,
+    "code_url": "https://github.com/openproblems-bio/task_grn_inference",
+    "documentation_url": null,
+    "image": "https://ghcr.io/openproblems-bio/task_grn_inference/methods/single_omics/scenic:dev",
+    "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/methods/single_omics/scenic",
+    "code_version": "dev",
+    "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592"
+  },
+  {
+    "task_id": "grn_methods",
+    "method_id": "scenicplus",
+    "method_name": "scenicplus",
+    "method_summary": "GRN inference using scenicplus",
+    "method_description": "GRN inference using scenicplus.\n",
+    "is_baseline": false,
+    "references_doi": null,
+    "references_bibtex": null,
+    "code_url": "https://github.com/openproblems-bio/task_grn_inference",
+    "documentation_url": null,
+    "image": "https://ghcr.io/openproblems-bio/task_grn_inference/methods/multi_omics/scenicplus:dev",
+    "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/methods/multi_omics/scenicplus",
+    "code_version": "dev",
+    "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592"
+  },
+  {
+    "task_id": "grn_methods",
+    "method_id": "scprint",
+    "method_name": "scprint",
+    "method_summary": "GRN inference using scPRINT",
+    "method_description": "GRN inference using scPRINT.\n",
+    "is_baseline": false,
+    "references_doi": null,
+    "references_bibtex": null,
+    "code_url": "https://github.com/openproblems-bio/task_grn_inference",
+    "documentation_url": null,
+    "image": "https://ghcr.io/openproblems-bio/task_grn_inference/methods/single_omics/scprint:dev",
+    "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/methods/single_omics/scprint",
+    "code_version": "dev",
+    "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592"
+  },
+  {
+    "task_id": "grn_methods",
+    "method_id": "grnboost2",
+    "method_name": "grnboost2",
+    "method_summary": "GRN inference using GRNBoost2",
+    "method_description": "GRN inference using GRNBoost2.\n",
+    "is_baseline": false,
+    "references_doi": null,
+    "references_bibtex": null,
+    "code_url": "https://github.com/openproblems-bio/task_grn_inference",
+    "documentation_url": null,
+    "image": "https://ghcr.io/openproblems-bio/task_grn_inference/methods/single_omics/grnboost2:dev",
+    "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/methods/single_omics/grnboost2",
+    "code_version": "dev",
+    "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592"
+  },
+  {
+    "task_id": "grn_methods",
+    "method_id": "scglue",
+    "method_name": "scglue",
+    "method_summary": "GRN inference using scglue",
+    "method_description": "GRN inference using scglue. \n",
+    "is_baseline": false,
+    "references_doi": null,
+    "references_bibtex": null,
+    "code_url": "https://github.com/openproblems-bio/task_grn_inference",
+    "documentation_url": null,
+    "image": "https://ghcr.io/openproblems-bio/task_grn_inference/methods/multi_omics/scglue:dev",
+    "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/methods/multi_omics/scglue",
+    "code_version": "dev",
+    "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592"
+  },
+  {
+    "task_id": "grn_methods",
+    "method_id": "granie",
+    "method_name": "granie",
+    "method_summary": "GRN inference using GRaNIE",
+    "method_description": "GRN inference using GRaNIE\n",
+    "is_baseline": false,
+    "references_doi": null,
+    "references_bibtex": null,
+    "code_url": "https://github.com/openproblems-bio/task_grn_inference",
+    "documentation_url": null,
+    "image": "https://ghcr.io/openproblems-bio/task_grn_inference/methods/multi_omics/granie:dev",
+    "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/methods/multi_omics/granie",
+    "code_version": "dev",
+    "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592"
+  },
+  {
+    "task_id": "grn_methods",
+    "method_id": "figr",
+    "method_name": "figr",
+    "method_summary": "GRN inference using figr",
+    "method_description": "GRN inference using figr.\n",
+    "is_baseline": false,
+    "references_doi": null,
+    "references_bibtex": null,
+    "code_url": "https://github.com/openproblems-bio/task_grn_inference",
+    "documentation_url": null,
+    "image": "https://ghcr.io/openproblems-bio/task_grn_inference/methods/multi_omics/figr:dev",
+    "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/methods/multi_omics/figr",
+    "code_version": "dev",
+    "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592"
+  },
+  {
+    "task_id": "grn_methods",
+    "method_id": "celloracle",
+    "method_name": "celloracle",
+    "method_summary": "GRN inference using celloracle",
+    "method_description": "GRN inference using celloracle.\n",
+    "is_baseline": false,
+    "references_doi": null,
+    "references_bibtex": null,
+    "code_url": "https://github.com/openproblems-bio/task_grn_inference",
+    "documentation_url": "https://morris-lab.github.io/CellOracle.documentation/",
+    "image": "https://ghcr.io/openproblems-bio/task_grn_inference/methods/multi_omics/celloracle:dev",
+    "implementation_url": "git@github.com:openproblems-bio/task_grn_inference.git/blob/40e4051728e992753049c0e15af22a99b8e9c592/src/methods/multi_omics/celloracle",
+    "code_version": "dev",
+    "commit_sha": "40e4051728e992753049c0e15af22a99b8e9c592"
+  }
+]