x_axis = "Treatment concentration"
selected_studies = ["EUT103"]
pathways = enrichr_output['KEGG_2015'].map(item => {return {
Pathway: item[1],
'Gene symbol': item[5].join(', '),
Rank: item[0],
'p-value': item[2],
'Adj. p-value': item[6],
'Z-score': item[3],
'Combined score': item[4]
}})
enrichr_output = fetch_enrichr(enrich_get_url, {method: 'GET'})
enrich_get_url = fetch_enrichr("https://amp.pharm.mssm.edu/Enrichr/addList", enrichr_post_query_options)
.then(content => content.userListId)
.then(userid => "https://amp.pharm.mssm.edu/Enrichr/enrich?userListId=" + userid + "&backgroundType=KEGG_2015")
formData = {
var f = new FormData()
f.append('list', genes_for_lineplot.join('\n'))
return f}
enrichr_post_query_options = {
return {
method: "POST",
body: formData}
}
function fetch_enrichr(url, query_options) {
return fetch(url, query_options).then(response_dataset => {
if (!response_dataset.ok) throw new Error(response_dataset.status);
return response_dataset.json();})
}
lineplots_charts = function(arr) {
var vl_arr = []
for (let i = 0; i < arr.length; i++) {
var data = arr[i]
var compound = selected_compounds_names[i]
const layer_1 = vl.markLine().data(lineplots_data[i]).title(compound).encode(
vl.x().fieldQ(x_axis).scale({"type": "log"}),
vl.y().fieldQ("logFC"),
vl.color().fieldN("SYMBOL")
).width(width).height(height)
const layer_2 = vl.markPoint().data(lineplots_data[i]).encode(
vl.x().fieldQ(x_axis).scale({"type": "log"}),
vl.y().fieldQ("logFC"),
vl.color().fieldN("SYMBOL")
)
vl_arr.push(vl.layer(layer_1, layer_2)
)
}
return vl.hconcat(vl_arr).render()
}
lineplots_data = {
var arr = []
for (let i=0; i<heatmaps_data.length; i++) {
arr.push(heatmaps_data[i].filter(item => genes_for_lineplot.includes(item.SYMBOL)))
}
return arr
}
lineplot_data = heatmap_data.filter(item => genes_for_lineplot.includes(item.SYMBOL))
keys = ["Control compound", "Control timepoint", "Treatment compound", "Treatment timepoint", "Control concentration", "Treatment concentration"]
plot_heatmaps = function(arr) {
var vl_arr = []
for (let i = 0; i < arr.length; i++) {
var data = arr[i]
var compound = selected_compounds_names[i]
vl_arr.push(
vl.markRect().data(data).title(compound)
.encode(
vl.y().fieldN("SYMBOL"),
vl.x().fieldN(x_axis),
vl.color().fieldQ("logFC").scale({scheme: "lightmulti", reverse: false, zero: false}),
vl.tooltip([
{
"field": "logFC",
"type": "quantitative",
"format": ",.2f"
},
vl.fieldN("SYMBOL"),
vl.fieldN("ENSEMBL"),
vl.fieldN("GENENAME"),
vl.fieldN("Treatment compound"),
vl.fieldQ("Treatment concentration"),
vl.fieldQ("Treatment timepoint"),
vl.fieldN("Control compound"),
vl.fieldQ("Control concentration"),
vl.fieldQ("Control timepoint"),
]),
)
)
}
return vl.hconcat(vl_arr).render()
}
heatmaps_data = {
var arr1 = []
for (let compound_name of selected_compounds_names) {
var logfc_selected_genes_this_compound = logfc_selected_genes.filter((el,i) => compound_dataset_indexes[compound_name].some(j => i === j))
var arr2 = []
for (let i = 0; i < logfc_selected_genes_this_compound.length; i++) {
var dataset_index = compound_dataset_indexes[compound_name][i]
var dataset = filtered_datasets[dataset_index]
var logfc_array = logfc_selected_genes_this_compound[i]
for (let j = 0; j < logfc_array.length; j++) {
var logfc_item = logfc_array[j]
for (var key of keys) {
logfc_item[key] = dataset[key]
}
arr2.push(logfc_item)
}
}
arr1.push(arr2)
}
return arr1
}
heatmap_data = {
var arr = []
for (let i = 0; i < logfc_selected_genes.length; i++) {
var dataset = filtered_datasets[i]
var logfc_array = logfc_selected_genes[i]
for (let j = 0; j < logfc_array.length; j++) {
var logfc_item = logfc_array[j]
for (var key of keys) {
logfc_item[key] = dataset[key]
}
arr.push(logfc_item)
}
}
return arr
}
compound_dataset_indexes = {
var compound_dataset_map = {}
for (let compound of selected_compounds_names) {
var indexes = []
for (let i=0; i < filtered_datasets.length; i++) {
if (filtered_datasets[i]['Treatment compound'] == compound) {
indexes.push(i)
}
}
compound_dataset_map[compound] = indexes
}
return compound_dataset_map
}
logfc_selected_genes = get_datasets_from_promises(
get_promised_fold_changes_datasets(
filtered_datasets.map(item => item["Dataset id"]), query_options_for_fold_changes_of_selected_genes
)
)
query_options_for_fold_changes_of_selected_genes = {
return {
method: 'POST',
body: JSON.stringify({
condition: {
containedIn: [{ column: ["SYMBOL"] }, unique_deregulated_genes],
},
})
}
}
unique_deregulated_genes = Array.from(new Set(deregulated_genes))
deregulated_genes = [].concat(...logfc_arrays).map(item => item.SYMBOL)
filtered_datasets
logfc_arrays = get_datasets_from_promises(
get_promised_fold_changes_datasets(
filtered_datasets.map(item => item["Dataset id"]), query_options_for_fold_changes
)
)
get_datasets_from_promises = function(array_of_promises) {
return Promise.all(array_of_promises).then(item => item.map(dataset => dataset.results.map(row => row.data)))
}
get_promised_fold_changes_datasets = function(array_of_datasets, query) {
return array_of_datasets.map(dataset => {
return fetch(base_url_edelweiss + dataset + "/versions/latest/data", query).then(response_dataset => {
if (!response_dataset.ok) throw new Error(response_dataset.status);
return response_dataset.json();
})
})
}
query_options_for_fold_changes = {
return {
method: 'POST',
body: JSON.stringify({
condition: {
and: [
{lt: [{column: ["padj"]}, padj_threshold]},
{or: [
{lt: [{column: ["logFC"]}, -logfc_threshold]},
{gt: [{column: ["logFC"]}, logfc_threshold]},
]},
]
},
})
}
}
compounds_table = {
var tbl = [];
var i = 0;
for (var compound of selected_compounds) {
tbl.push({
'Name': compound['Name'],
'Molecular formula': svgs[i],
})
i = i + 1
}
return tbl;
};
svgs = Promise.all(promises)
// Copied from PCR analysis notebook
promises = {
var promises = [];
for (var compound of selected_compounds) {
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;
}
filtered_datasets = fetch_dataset_to_array(overview_url, query_options_filter_compound_and_timepoint)
selected_compounds_names = selected_compounds.map(item => item.Name)
query_options_filter_compound_and_timepoint = {
return {
method: 'POST',
body: JSON.stringify({
condition: {
and: [
{containedIn: [{ column: ["Treatment compound"] }, selected_compounds_names]},
{containedIn: [{ column: ["Treatment timepoint"] }, selected_timepoints]},
{containedIn: [{ column: ["Control compound"] }, selected_controls]},
{containedIn: [{ column: ["Study"] }, selected_studies]},
]
}
})
}
}
control_compounds = get_unique_elements(overview_filtered_for_studies_case_studies_compounds, "Control compound")
concentrations = get_unique_elements(overview_filtered_for_studies_case_studies_compounds, "Treatment concentration")
timepoints = get_unique_elements(overview_filtered_for_studies_case_studies_compounds, "Treatment timepoint")
overview_filtered_for_studies_case_studies_compounds = fetch_dataset_to_array(overview_url, query_options_filter_studies_case_studies_compounds)
query_options_filter_studies_case_studies_compounds = {
return {
method: 'POST',
body: JSON.stringify({
condition: {
and: [
{containedIn: [{ column: ["Treatment compound"] }, selected_compounds_names]},
{containedIn: [{ column: ["Case study"] }, selected_case_studies]},
{containedIn: [{ column: ["Study"] }, selected_studies]},
]
}
})
}
}
unique_filtered_compounds = Array.from(new Set(filtered_compounds.map(a => a.Id)))
.map(id => {
return filtered_compounds.find(a => a.Id === id)
})
filtered_compounds = overview_filtered_for_studies_and_case_studies.map(item => {
return {
"Code": item.Code,
"SMILES": item.SMILES.canonical,
"CAS": item["CAS number"],
"Parent compound": item["Parent compound"],
"Name": item["Treatment compound"],
"Id": item["Index in compound list"],
}
})
//compounds = get_unique_elements(overview_filtered_for_studies_and_case_studies, "Treatment compound")
overview_filtered_for_studies_and_case_studies = fetch_dataset_to_array(overview_url, query_options_filter_studies_case_studies)
query_options_filter_studies_case_studies = {
return {
method: 'POST',
body: JSON.stringify({
condition: {
and: [
{containedIn: [{ column: ["Case study"] }, selected_case_studies]},
{containedIn: [{ column: ["Study"] }, selected_studies]},
]
}
})
}
}
Type JavaScript, then Shift-Enter. Ctrl-space for more options. Arrow ↑/↓ to switch modes.
studies = get_unique_elements(overview, "Study")
case_studies = get_unique_elements(overview, "Case study")
get_unique_elements = function(array, parameter) {
return Array.from(new Set(array.map(item => item[parameter])))
}
overview = fetch_dataset_to_array(overview_url, {})
function fetch_dataset_to_array(dataset_url, query_options) {
return fetch(dataset_url, query_options).then(response_dataset => {
if (!response_dataset.ok) throw new Error(response_dataset.status);
return response_dataset.json();}).then(json => json.results.map(x => x.data));
}
overview_url = base_url_edelweiss + overview_id + "/versions/latest/data"
overview_id = "d8922983-1724-4ce8-af07-93f290d9c3c2"
base_url_edelweiss = 'https://api.develop.edelweiss.douglasconnect.com/datasets/'
// Import libraries for data table rendering
import {render_data_table} from '@info474/utilities'
import {select, slider} from "@jashkenas/inputs"
import {Select, Range, Toggle, Table} from "@observablehq/inputs"
import {vl} from "@vega/vega-lite-api"
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