32:T6bb,text2 = md`The notebook provides information on the transcriptomic profile of the 2D HepaRG cell line as a function of time (Day 8, Day 9, Day 10 and Day 13 after cell seeding and being respectively, 24h, 48h, 72h and 144h in induction media). The cells were seeded as according to the official HepaRG user guide (https://saferworldbydesign.com/saferliver/heparg/tutorials-and-support/protocols/). In brief, following cell thawing and counting, the HepaRG™ HPR116 cells are seeded into flat bottom multi-well plate using HepaRG™ Thaw, Plate & General Purpose Medium. Twenty four hours after plating, the HepaRG™ Thaw, Plate & General Purpose Medium was removed, and replaced with the HepaRG™ Maintenance/Metabolism Medium. The cells were kept for 7 days in HepaRG® Maintenance and Metabolism Medium 620 media (media change occurred every 48h). This is done in order to regain CYP activities which normally decrease after cell seeding as the cells reconstitute the monolayer and then regain gradually to reach a peak on Day 7. On Day 7 the cells are normally ready to be exposed to test substrate and therefore the HepaRG™ Induction Medium was used. For the gene expression profiling experiment, the control non-exposed cells were maintained in HepaRG™ Induction Medium and samples were taken at Day 8, Day 9, Day 10 and Day 13 after cell seeding and respectively, 24h, 48h, 72h and 144h in induction media. The visualization represents what happens with the cells as a function of time in the HepaRG™ Induction Medium. The transcriptomic profile might be explored by selection of individual gene or by pre-selection of cluster of genes involved in different pathways as well as toxicological categories.` 33:T53c,query_options_for_gene_list = { let condition; if (toxicologicalCategory === "") { if (majorPathwayDropdown !== "" || majorMinorPathwayDropdown !== "") { condition = ({ and: [ ({ fuzzySearch: [({"column": ["Pathway"]}), majorPathwayDropdown]}), ({ fuzzySearch: [({"column": ["Pathway"]}), majorMinorPathwayDropdown]}), ] }) } else { let conditionFragments = [] if (gene_fragments.length > 0) { conditionFragments = [ ({ or: _.concat((gene_fragments.map(item => ({fuzzySearch: [({ "column": ["SYMBOL"] }), item]}))), (gene_fragments.map(item => ({fuzzySearch: [({ "column": ["All_Gene_names"] }), item]})))) }) ] } if (pathwaySearch !== "") { conditionFragments.push(({fuzzySearch: [({ "column": ["Pathway"] }), pathwaySearch]})) } condition = ({ and: conditionFragments }) } } else { condition = ({ exactSearch: [({"column": ["Toxicological.categories"]}), toxicologicalCategory] }) } return { method: 'POST', body: JSON.stringify({ //columns: ["Ensembl_IDs", "SYMBOL"], condition: condition, orderBy: [{ expression: { column: ["SYMBOL"] }, ordering: "ascending" }], }) } }34:[] 30:{"title":"Dynamic gene expression profiling of the HepaRG cell line","cells":[{"id":0,"value":"text1 = md`# Dynamic gene expression profiling of the HepaRG cell line`","pinned":false,"hidden":false,"mode":"ojs"},{"id":502,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":464,"value":"text3 = md`## Step 1a: Individual gene selection\n\nIn this section you can search individual genes of interest and study their expression as a function of time. The search is by gene symbols. Please type in a gene name fragment ( e.g. CYP if you want to search for CYP 1A1) and press \"Add item to list\". By executing this command, a preselection of all genes containing the fragment of interest (e.g. CYP) will be displayed on the table below (Step 2). If you want to explore the expression of several CYPs at a time, please select the checkboxes of the respective genes. The total gene selection will be visualized as a heatmap as well as a line chart.\n\nIn case you want to explore the expression over time of a gene symbols containing different gene fragments (e.g. CYP and SCL) then following the already described steps add several items to list using the \"Add item to list\" icon.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":391,"value":"viewof newItemInput = Text({label: \"Enter the fragment of a gene to be included:\", value: \"CYP\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":396,"value":"// The button has an onclick handler that mutates the target. Since\n// in this case the target is an array we make a copy, add the current newItemInput \n// value and set the items array to the new value\nupdate = {\n const button = html``;\n button.onclick = () => {\n let newItems = [...mutable gene_fragments, newItemInput];\n mutable gene_fragments = newItems;\n };\n return button;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":400,"value":"// Here we show a message that the list is empty or otherwise\n// return a ul tag with one nested li tag for each item\n// Each item gets an a tag with an onclick handler that\n// triggers setting items to a copy of items with the item at its index\n// filtered out.\ndisplayGeneList = { if (gene_fragments.length === 0) {\n return html`

There are no items in the list of gene name fragments

`\n }\n else {\n return html`

Use the input field below to add new items and the bin icon to remove items

${item} \n (mutable gene_fragments = gene_fragments.filter(\n (item, filterindex) => filterindex != index\n ))}>🗑

\n`\n}\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":386,"value":"text4 = md`## Step 1b: Select a toxicological category\n\nHere you can select a toxicological category of interest and follow the expression of the genes belonging to the toxicological category as a function of time.\nNote: If you select an item from the dropdown meny in this section , then the search term in the previous steps are automatically ignored.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":423,"value":"viewof toxicologicalCategory = Select(toxicologicalCategoryVocuabulary, {label: \"Tox Category\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":532,"value":"text5 = md`## Step 1c: Select a pathway of interest\n\nHere you can select a pathway of interested and see how the genes belonging to the pathway as according to the Uniprot database are expressed over time. \nNote: If you select an item from the dropdown here, then the search terms in previous steps will be ignored.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":608,"value":"viewof majorPathwayDropdown = Select(major_pathways, {label: \"Major pathway filter\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":673,"value":"viewof majorMinorPathwayDropdown = Select(major_minor_pathways, {label: \"Major and minor pathway filter\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":566,"value":"//\nviewof pathwaySearch = Text({label: \"Pathway search (applies only if both pathway filters are empty):\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":364,"value":"text6 = md`## Step 2: Select list of genes to include in visualisation\n\nBelow are all the genes selected in step 1 (a,b or c). The search for those genes returned a total of **${gene_list.length}** genes. You can refine the set of genes which will be displayed in the heatmap here:`","pinned":false,"hidden":false,"mode":"ojs"},{"id":338,"value":"viewof selected_gene_objects = {\n const preselected = toxicologicalCategory === \"\" ? gene_objects_with_names.slice(0, 5) : gene_objects_with_names\n return Table(gene_objects_with_names, {value: preselected, columns: [\"SYMBOL\", \"All_Gene_names\", \"Pathway\", \"D8\", \"D9\", \"D10\", \"D13\"]})\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":212,"value":"viewof width2 = Range([350, width], {step: 20, value: 350, label: \"Heatmap width\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":215,"value":"viewof height = Range([250, 650], {step: 20, value: 250, label: \"Heatmap height\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":729,"value":"viewof fontSize = Range([8, 20], {step: 1, value: 11, label: \"Heatmap font size\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":314,"value":"viewof rescaled_counts = Toggle({label: \"Rescaled counts:\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":232,"value":"text7 = md`### Average read counts as a heatmap`","pinned":false,"hidden":false,"mode":"ojs"},{"id":134,"value":"average_counts_heatmap = {\n var color_column;\n \n if (rescaled_counts) {\n color_column = \"RESCALED_COUNT\";\n } else {\n color_column = \"COUNT\";\n }\n\n return vl.markRect().data(reshaped_rnaseq_data_days)\n .encode(\n vl.y().axis({labelFontSize: fontSize, titleFontSize: fontSize}).fieldN(\"SAMPLE\"),\n vl.x().axis({labelFontSize: fontSize, titleFontSize: fontSize}).fieldN(\"TIMEPOINT\").sort(timepoints),\n vl.color().fieldQ(color_column)\n .scale({type: \"linear\", scheme: \"pinkyellowgreen\", zero: false, nice: true})\n .legend({titleOrient: 'right', tickCount: 5, titleFontSize: fontSize, labelFontSize: fontSize}),\n vl.tooltip([\n vl.fieldN(\"SYMBOL\"),\n vl.fieldN(\"ENSEMBL\"),\n vl.fieldN(\"PATHWAY\"),\n vl.fieldN(\"TIMEPOINT\"),\n vl.fieldQ(\"COUNT\"),\n vl.fieldQ(\"RESCALED_COUNT\")]),\n ).width(width2).height(height).render()\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":293,"value":"text8 = md`### Average read counts as a line chart`","pinned":false,"hidden":false,"mode":"ojs"},{"id":263,"value":"viewof linechart = {\n const layer_1 = vl.markLine().data(reshaped_rnaseq_data_days)\n .encode(\n vl.x().axis({labelFontSize: fontSize, titleFontSize: fontSize}).fieldN(\"TIMEPOINT\").sort(timepoints),\n vl.y().axis({labelFontSize: fontSize, titleFontSize: fontSize}).fieldQ(\"COUNT\"),\n vl.color().fieldN(\"SAMPLE\").legend({titleFontSize: fontSize, labelFontSize: fontSize})\n ).width(width2).height(height)\n \n const layer_2 = vl.markPoint().data(reshaped_rnaseq_data)\n .encode(\n vl.x().fieldN(\"TIMEPOINT\").sort(timepoints),\n vl.y().fieldQ(\"COUNT\"),\n vl.color().fieldN(\"SAMPLE\")\n )\n \n return vl.layer(layer_1, layer_2).render()\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":64,"value":"text11 = html`Selected genes:

${selected_gene_objects.map(item => html`

${item.NAME}`)}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":489,"value":"md`# Source code\n\nBelow is the source code of this notebook\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":752,"value":"reshaped_normalized_rnaseq_data_days = reshaped_normalized_rnaseq_data.map(obj => {\n const obj2 = obj;\n if (obj.TIMEPOINT == \"24h\") { obj2.TIMEPOINT = \"Day8\"}\n if (obj.TIMEPOINT == \"48h\") { obj2.TIMEPOINT = \"Day9\"}\n if (obj.TIMEPOINT == \"72h\") { obj2.TIMEPOINT = \"Day10\"}\n if (obj.TIMEPOINT == \"144h\") { obj2.TIMEPOINT = \"Day13\"}\n return obj2;\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":753,"value":"reshaped_rnaseq_data_days = reshaped_rnaseq_data.map(obj => {\n const obj2 = obj;\n if (obj.TIMEPOINT == \"24h\") { obj2.TIMEPOINT = \"Day8\"}\n if (obj.TIMEPOINT == \"48h\") { obj2.TIMEPOINT = \"Day9\"}\n if (obj.TIMEPOINT == \"72h\") { obj2.TIMEPOINT = \"Day10\"}\n if (obj.TIMEPOINT == \"144h\") { obj2.TIMEPOINT = \"Day13\"} return obj2;\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":126,"value":"reshaped_rnaseq_data = reshape_rnaseq_data(rnaseq_data)","pinned":true,"hidden":false,"mode":"ojs"},{"id":245,"value":"reshaped_normalized_rnaseq_data = reshape_normalized_rnaseq_data(normalized_rnaseq_data)","pinned":true,"hidden":false,"mode":"ojs"},{"id":114,"value":"timepoints = [\"D8\", \"D9\", \"D10\", \"D13\"]","pinned":true,"hidden":false,"mode":"ojs"},{"id":113,"value":"reshape_rnaseq_data = function(data) {\n let i, j;\n const arr = [];\n //const processedEnsemblIds = new Set()\n \n for (i = 0; i < data.length; i++) {\n //const ensemblId = data[i].Ensembl_IDs\n //if (!processedEnsemblIds.has(ensemblId)) {\n //processedEnsemblIds.add(ensemblId)\n for (j = 0; j < timepoints.length; j++) {\n const item = data[i];\n const timepoint = timepoints[j];\n const count_column_name = timepoint;\n\n var new_item = {\n \"SYMBOL\": item.SYMBOL,\n \"ENSEMBL\": item.Ensembl_IDs,\n \"PATHWAY\": item.Pathway,\n \"TIMEPOINT\": timepoint,\n \"COUNT\": item[count_column_name],\n \"RESCALED_COUNT\": item[count_column_name] / max_count,\n \"SAMPLE\": item.SYMBOL + \", \" + item.Ensembl_IDs,\n \"ALL_GENE_NAMES\": item.All_Gene_names,\n };\n\n arr.push(new_item); \n }\n //}\n }\n \n return arr;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":240,"value":"replicates = [1, 2, 3]","pinned":true,"hidden":false,"mode":"ojs"},{"id":230,"value":"reshape_normalized_rnaseq_data = function(data) {\n var i, j, k;\n var arr = [];\n \n for (i = 0; i < data.length; i++) {\n for (j = 0; j < timepoints.length; j++) {\n var item = data[i];\n var timepoint = timepoints[j];\n \n var average_count = 0;\n \n for (k = 0; k < replicates.length; k++) {\n var count_column_name = timepoint + \"_Replicate\" + replicates[k];\n average_count = average_count + item[count_column_name];\n }\n \n average_count = average_count / (replicates.length);\n\n var new_item = {\n \"SYMBOL\": item.SYMBOL,\n \"ENSEMBL\": item.Ensembl_IDs,\n \"PATHWAY\": item.Pathway,\n \"TIMEPOINT\": timepoint,\n \"COUNT\": average_count,\n \"SAMPLE\": item.SYMBOL + \", \" + item.Ensembl_IDs,\n \"ALL_GENE_NAMES\": item.All_Gene_names,\n };\n\n arr.push(new_item); \n }\n }\n \n return arr;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":228,"value":"normalized_rnaseq_data = fetch_dataset_to_array(normalized_dataset_url, query_options_for_rnaseq_data)","pinned":true,"hidden":false,"mode":"ojs"},{"id":110,"value":"rnaseq_data = fetch_dataset_to_array(dataset_url, query_options_for_rnaseq_data)","pinned":true,"hidden":false,"mode":"ojs"},{"id":104,"value":"query_options_for_rnaseq_data = {\n return {\n method: 'POST',\n body: JSON.stringify({\n condition: {\n containedIn: [\n {column: [\"Ensembl_IDs\"]}, selected_gene_objects.map(item => item.Ensembl_IDs),\n ]\n },\n \n })\n }\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":77,"value":"//selected_gene_objects = gene_objects_with_names.filter(item => selected_genes.includes(item.NAME))","pinned":true,"hidden":false,"mode":"ojs"},{"id":201,"value":"gene_list = gene_objects_with_names.map(item => item.NAME)","pinned":true,"hidden":false,"mode":"ojs"},{"id":70,"value":"gene_objects_with_names = _.chain(gene_objects).uniqBy(item => item.Ensembl_IDs).map(item => {item.NAME = item.SYMBOL + \", \" + item.Ensembl_IDs; return item}).value()","pinned":true,"hidden":false,"mode":"ojs"},{"id":301,"value":"max_count = {\n var maximums = [];\n var i;\n \n for (i = 0; i < timepoints.length; i++) {\n var timepoint = timepoints[i];\n maximums.push(\n Math.max(...gene_objects.map(item => item[timepoint]))\n ) \n }\n\n return Math.max(...maximums);\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":14,"value":"gene_objects = fetch_dataset_to_array(dataset_url, query_options_for_gene_list)","pinned":true,"hidden":false,"mode":"ojs"},{"id":50,"value":"function dynamicSort(property) {\n var sortOrder = 1;\n if(property[0] === \"-\") {\n sortOrder = -1;\n property = property.substr(1);\n }\n return function (a,b) {\n /* next line works with strings and numbers, \n * and you may want to customize it to your needs\n */\n var result = (a[property] < b[property]) ? -1 : (a[property] > b[property]) ? 1 : 0;\n return result * sortOrder;\n }\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":4,"value":"function fetch_dataset_to_array(dataset_url, query_options) {\n return fetch(dataset_url, query_options).then(response_dataset => {\n if (!response_dataset.ok) throw new Error(response_dataset.status);\n return response_dataset.json();}).then(json => json.results.map(x => x.data));\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":394,"value":"mutable gene_fragments = [\"CYP\"]","pinned":true,"hidden":false,"mode":"ojs"},{"id":16,"value":"$33","pinned":true,"hidden":false,"mode":"ojs"},{"id":591,"value":"pathways_dropdown = unique_pathways.unshift(\"\", \"NA\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":556,"value":"unique_pathways = Array.from(new Set(all_pathways.map(item => item.Pathway)))","pinned":true,"hidden":false,"mode":"ojs"},{"id":548,"value":"all_pathways = fetch_dataset_to_array(dataset_url, {\n method: 'POST',\n body: JSON.stringify({\n columns: [\"Pathway\"],\n condition: {\n \"not\": [\n { \"exactSearch\": [{ \"column\": [\"Pathway\"] }, \"NA\"] }\n ]\n },\n orderBy: [{ expression: { column: [\"Pathway\"] }, ordering: \"ascending\" }],\n })\n })","pinned":true,"hidden":false,"mode":"ojs"},{"id":425,"value":"toxicologicalCategoryVocuabulary = [\n\"\",\n\"Efflux transporter(hepatocyte to bile canaliculi)\",\n\"Uptake transporters (Blood to hepatocytes)\",\n\"Important liver CYPs (Less abundant)\",\n\"Efflux transporters (Hepatocytes to blood)\",\n\"Important liver CYPs (Most abundant)\"\n]","pinned":true,"hidden":false,"mode":"ojs"},{"id":225,"value":"normalized_dataset_url = api_url + dataset_id + \"/versions/3/data\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":5,"value":"dataset_url = api_url + dataset_id + \"/versions/10/data\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":7,"value":"api_url = \"https://api.edelweissdata.com/datasets/\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":10,"value":"dataset_id = \"7bff53eb-7c1a-4875-b6b1-cd002e7e9c4c\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":683,"value":"major_pathways.unshift(\"\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":686,"value":"major_minor_pathways.unshift(\"\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":657,"value":"major_pathways = major_pathways_records.map(item => item[\"Major pathways\"])","pinned":true,"hidden":false,"mode":"ojs"},{"id":665,"value":"major_minor_pathways = major_minor_pathways_records.map(item => item[\"Maroj and Minor Pathways\"])","pinned":true,"hidden":false,"mode":"ojs"},{"id":661,"value":"major_minor_pathways_records = fetch_dataset_to_array(major_minor_pathway_dataset_url, {\n method: 'POST',\n body: JSON.stringify({\n columns: [\"Maroj and Minor Pathways\"],\n orderBy: [{ expression: { column: [\"Maroj and Minor Pathways\"] }, ordering: \"ascending\" }],\n })\n })","pinned":true,"hidden":false,"mode":"ojs"},{"id":648,"value":"major_pathways_records = fetch_dataset_to_array(major_pathway_dataset_url, {\n method: 'POST',\n body: JSON.stringify({\n columns: [\"Major pathways\"],\n orderBy: [{ expression: { column: [\"Major pathways\"] }, ordering: \"ascending\" }],\n })\n })","pinned":true,"hidden":false,"mode":"ojs"},{"id":652,"value":"major_minor_pathway_dataset_url = api_url + major_minor_pathway_dataset_id + \"/versions/latest/data\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":650,"value":"major_pathway_dataset_url = api_url + major_pathway_dataset_id + \"/versions/latest/data\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":644,"value":"major_pathway_dataset_id = \"7f87c58f-be80-4f91-8c86-57493e1f1fa6\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":646,"value":"major_minor_pathway_dataset_id = \"962c1d9c-ae72-4f1f-aa57-435d3097a2cd\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":35,"value":"import {Select, Range, Toggle, Search, Table, Text} from \"@observablehq/inputs\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":460,"value":"_ = require(\"lodash\");","pinned":true,"hidden":false,"mode":"ojs"},{"id":135,"value":"import {vl} from \"@vega/vega-lite-api\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":405,"value":"import {html} from \"@observablehq/htl\"","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W34","stdlib":"1","titling":"auto"}