Public
Edited
Feb 6, 2022
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metadata_table = {
var arr = []
for (const field of metadata_fields) {
arr.push({'Field': field, 'Value': metaraw_data['Assay'][field]})
}
return arr
}
metadata_fields = ['Cell name', 'Cell type', 'Organ', 'Organism']
metaraw_data = fetch(metadataset_url)
.then(response => {
if (!response.ok) throw new Error(response.status);
return response.json();})
// Copied from PCR analysis notebook
promises = {
var promises = [];
for (var compound of metaraw_data.Compound) {
const smiles = compound.SMILES;
const safeSmiles = encodeURIComponent(smiles);
const svg_url = "https://chemidconvert.cloud.douglasconnect.com/v1/asSvg?width=200&height=200&smiles=" + safeSmiles;
const svg = fetch(svg_url).then(response => response.text());
promises.push(svg);
}
return promises;
}
svgs = Promise.all(promises)
compounds_table = {
var tbl = [];
var i = 0;
for (var compound of metaraw_data.Compound) {
tbl.push({
//'Compound': compound['Compound name of actual form used'], // removed, as several entries seem faulty. Which column represents the compound name in the form it has in the real data, form which the triplets have been created??
'Chem name': compound['CHEM_NAME'],
'Compound code': compound['Compound code'],
'Mol': svgs[i],
})
i = i + 1
}
return tbl;
};
current_selected_compound_code=selected_triplet.split(', ')[1]
current_mol=compounds_table.filter(c => {
return c['Compound code'] == current_selected_compound_code
});
// Import libraries for data table rendering
import {render_data_table} from '@info474/utilities'
item_data_with_cell_info = item_data.map(item => {
item["Cell"] = item["Row"] + item["Column"];
return item
})
item_data_selected_timepoint_with_cell_info = item_data_selected_timepoint.map(item => {
item["Cell"] = item["Row"] + item["Column"];
return item
})
item_data = fetch_dataset_to_array(dataset_url, query_options_filter_item)
item_data_selected_timepoint = fetch_dataset_to_array(dataset_url, query_options_filter_item_and_timepoint)
selected_item = selected_triplet.split(",")[0]
query_options_filter_item = {
return {
method: 'POST',
body: JSON.stringify({
columns: ["Item number", "Concentration", "Endpoint 1 measure timepoint", "Endpoint 1 normalized data", "Endpoint 1 data", "Row", "Column"],
condition: {
and: [
{exactSearch: [{ column: ["Item number"] }, selected_item]},
]
}
})
}
}
query_options_filter_item_and_timepoint = {
return {
method: 'POST',
body: JSON.stringify({
columns: ["Item number", "Concentration", "Endpoint 1 measure timepoint", "Endpoint 1 normalized data", "Endpoint 1 data", "Row", "Column"],
condition: {
and: [
{exactSearch: [{ column: ["Item number"] }, selected_item]},
{containedIn: [{ column: ["Endpoint 1 measure timepoint"] }, selected_timepoints]}
]
}
})
}
}
unique_exposures = get_unique_elements(all_conditions, "Endpoint 1 measure timepoint")
unique_concentrations = get_unique_elements(all_conditions, "Concentration")
unique_triplets = Array.from(new Set(compound_triplets)).sort()
unique_timepoints = get_unique_elements(all_conditions, "Endpoint 1 measure timepoint")
unique_items = get_unique_elements(all_conditions, "Item number").sort()
compound_triplets = all_conditions.filter(function(el) {
return el["Compound"] != null;
}).map(item => item["Item number"] + ", " + item["Compound code"] + ", " + item["Compound"]).sort()
all_conditions = fetch_dataset_to_array(dataset_url, query_options)
get_unique_elements = function(array, parameter) {
return Array.from(new Set(all_conditions.map(item => item[parameter])))
}
query_options = {
return {
method: 'POST',
body: JSON.stringify({
columns: ["Item number", "Compound code", "Compound", "Concentration", "Endpoint 1 measure timepoint"]
})
}
}
// Create function to fetch data. Query options defined in cell above
// Pass in an optional variable 'meta' to the function to make it request the metadata!
function fetch_dataset_to_array(dataset_url, query_options, datatype) {
return fetch(dataset_url, query_options).then(response_dataset => {
if (!response_dataset.ok) throw new Error(response_dataset.status);
if (datatype == null)
return response_dataset.json().then(json => json.results.map(x => x.data));
else if (datatype == 'meta' )
return response_dataset.json().then(json => json.results);
})
}
// Based on selected experiment, create unique url for request from the database
dataset_url = base_url_edelweiss + dataset_id + "/versions/latest/data"
metadataset_url = base_url_edelweiss + dataset_id + "/versions/latest/metadata"
// Manually add the different unique identificators for
dataset_id = {
if (selected_experiment == 'Exp 3, GLO') {return "d2292a34-1605-4b60-9792-27244ddcb05a"}
if (selected_experiment == 'Exp 3, FLUO') {return "a428a89f-1c95-45c3-ba61-8da13933cf47"}
if (selected_experiment == 'Exp 2, GLO') {return "08142397-6e7f-408a-9b85-4e4419f0b9ed"}
if (selected_experiment == 'Exp 2, FLUO') {return "cb4ed15f-c6ef-4fc5-81d9-07bde53d3dfd"}
if (selected_experiment == 'Exp 1, GLO') {return "786a972b-a35b-440d-953b-1a90b1f94178"}
if (selected_experiment == 'Exp 1, FLUO') {return "f125c292-a42b-40ea-b56f-55b7bb41e0e0"}
}
summary_dataset_with_fitted_data = {
const arr = []
for (const obj of summary_dataset) {
const obj2 = Object.create(obj)
const obj3 = Object.create(obj)
obj2['type'] = 'Data'
arr.push(obj2)
obj3['IC50'] = obj2['K'] * Math.pow(obj2['Timepoint'], -obj2['n'])
obj3['type'] = 'Haber'
arr.push(obj3)
}
return arr
}
summary_dataset_formatted = {
var arr = []
for (let index = 0; index < summary_dataset.length; ++index) {
var obj = summary_dataset[index]
var formatted_obj = {}
for (const property in obj) {
var value = obj[property]
var new_value = value
if (typeof(value)==='number') {new_value = value.toFixed(2)}
formatted_obj[property] = new_value
}
arr.push(formatted_obj)
}
return arr
}
summary_dataset = fetch_dataset_to_array(summary_dataset_url, summary_query_options)
selected_compound_code = selected_triplet.split(", ")[1]
summary_query_options = {
return {
method: 'POST',
body: JSON.stringify({
condition: {exactSearch: [{ column: ["Compound code"] }, selected_compound_code]}
})
}
}
summary_dataset_url = base_url_edelweiss + summary_dataset_id + "/versions/latest/data"
// Manually add the different unique identificators for
summary_dataset_id = {
/*
if (selected_experiment == 'Exp 3, GLO') {return "27d3bb96-96f2-47ed-9127-69c1ff923e44"}
if (selected_experiment == 'Exp 3, FLUO') {return "e9d7040f-c497-41c2-9220-18178edb5eb6"}
if (selected_experiment == 'Exp 2, GLO') {return "f21c89f2-810c-410f-a9f7-f0c44e47b8c5"}
if (selected_experiment == 'Exp 2, FLUO') {return "0fe5c09e-591e-4964-bf6b-187107f3fef8"}
if (selected_experiment == 'Exp 1, GLO') {return "547169c5-d933-4c3a-819a-a007f2264696"}
if (selected_experiment == 'Exp 1, FLUO') {return "bb089d46-d2cf-428b-9c62-8b8325123c1c"}
*/
if (selected_experiment == 'Exp 3, GLO') {return "3babcd94-1252-4cfe-be9f-3c9ef55ea708"}
if (selected_experiment == 'Exp 3, FLUO') {return "d7800540-6484-4e47-9dfc-d3c679cab896"}
if (selected_experiment == 'Exp 2, GLO') {return "7796d14c-081e-4046-bc14-a96c18679b70"}
if (selected_experiment == 'Exp 2, FLUO') {return "8e817a36-49aa-4c80-8cde-0b6aaa383728"}
if (selected_experiment == 'Exp 1, GLO') {return "edbc750c-12a2-4203-af5c-541a25908e25"}
if (selected_experiment == 'Exp 1, FLUO') {return "7f8026d2-de41-4c62-aff4-2795caf10cb0"}
}
// Set url for data base
base_url_edelweiss = 'https://api.develop.edelweiss.douglasconnect.com/datasets/'
// Import selection, range and toggle tools from inputs notebook
import {Select, Range, Toggle} from "@observablehq/inputs"
// Import vega-lite visualization library
import {vl} from "@vega/vega-lite-api"
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Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
