Public
Edited
Nov 26, 2024
vl_neg = vl.markRect().data(data_neg_filter).title("Non-steatotic")
.encode(
vl.y().fieldN("Protein"),
vl.x().fieldN("CAS (Group ID)"),
vl.color().fieldQ("log2FoldChange").scale({scheme: "redblue", reverse: false, zero: false, domainMin: -4, domainMax: 4}),
vl.tooltip([
{
"field": "log2FoldChange",
"type": "quantitative",
"format": ",.2f"
},
vl.fieldN("GENE"),
vl.fieldN("Organism"),
vl.fieldN("Organ"),
vl.fieldN("Cell type"),
vl.fieldN("Cell name"),
vl.fieldN("Treatment modality"),
vl.fieldN("Exposure time"),
vl.fieldN("Compound"),
vl.fieldQ("Concentration"),
vl.fieldN("accession"),
]),
)
vl_pos = vl.markRect().data(data_pos_filter).title("Steatotic")
.encode(
vl.y().fieldN("Protein"),
vl.x().fieldN("CAS (Group ID)"),
vl.color().fieldQ("log2FoldChange").scale({scheme: "redblue", reverse: false, zero: false, domainMin: -4, domainMax: 4}),
vl.tooltip([
{
"field": "log2FoldChange",
"type": "quantitative",
"format": ",.2f"
},
vl.fieldN("GENE"),
vl.fieldN("Organism"),
vl.fieldN("Organ"),
vl.fieldN("Cell type"),
vl.fieldN("Cell name"),
vl.fieldN("Treatment modality"),
vl.fieldN("Exposure time"),
vl.fieldN("Compound"),
vl.fieldQ("Concentration"),
vl.fieldN("accession"),
]),
)
data_avg_filter = avg_data.filter(data =>
checkboxes_treatment.includes(data['Treatment modality']) &&
checkboxes_exposure.includes(data['Exposure time']) &&
checkboxes_organ.includes(data['Organ']) &&
checkboxes_cell_name.includes(data['Cell name']) &&
checkboxes_cell_type.includes(data['Cell type'])
)
data_neg_filter = data_neg.filter(data =>
checkboxes_treatment.includes(data['Treatment modality']) &&
checkboxes_exposure.includes(data['Exposure time']) &&
checkboxes_organ.includes(data['Organ']) &&
checkboxes_cell_name.includes(data['Cell name']) &&
checkboxes_cell_type.includes(data['Cell type']) &&
data['padj'] < threshold_padj
)
data_pos_filter = data_pos.filter(data =>
checkboxes_treatment.includes(data['Treatment modality']) &&
checkboxes_exposure.includes(data['Exposure time']) &&
checkboxes_organ.includes(data['Organ']) &&
checkboxes_cell_name.includes(data['Cell name']) &&
checkboxes_cell_type.includes(data['Cell type']) &&
data['padj'] < threshold_padj
)
data_neg = newData.filter(data => data.Steatotic === false)
data_pos = newData.filter(data => data.Steatotic === true)
cell_type = get_unique_elements(data, "Cell type")
cell_name = get_unique_elements(data, "Cell name")
organisms = get_unique_elements(data, "Organism")
organs = get_unique_elements(data, "Organ")
treatment_modalities = get_unique_elements(data, "Treatment modality")
exposure_times = get_unique_elements(data, "Exposure time")
get_unique_elements(data, "Steatotic")
get_unique_elements = function(array, parameter) {
return Array.from(new Set(array.map(item => item[parameter])))
}
newData = data.map(item => ({
...item,
"CAS (Group ID)": `${item["CAS number"]} (${item["BMD condition"]})`
}));
data = fetch_dataset_to_array(dataset_url, {})
avg_data = fetch_dataset_to_array(dataset_avg_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));
}
dataset_url = api_url + dataset_id + "/versions/latest/data"
api_url = "https://api.edelweissdata.com/datasets/"
dataset_id = "4e5e9cc9-2934-43fd-a17f-f106fd1d1708"
dataset_avg_url = api_url + dataset_avg_id + "/versions/latest/data"
dataset_avg_id = "6a97d1ae-1f12-48c5-9aab-15eb51be1b5b"
import {vl} from "@vega/vega-lite-api"
import {ForceGraph} from "@d3/force-directed-graph"
Purpose-built for displays of data
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.
