{
"@context": [
"https://www.w3.org/ns/activitystreams",
"https://purl.org/coar/notify"
],
"id": "urn:uuid:f973121f-4507-49ac-8f68-f6ec7f7b7244",
"type": [
"Offer",
"coar-notify:ReviewAction"
],
"actor": {
"id": "https://datalake.inria.fr",
"type": "Service",
"name": "Inria DataLake"
},
"origin": {
"id": "https://datalake.inria.fr",
"type": "Service",
"inbox": "https://prod-datadcis-api.inria.fr/coar/inbox"
},
"target": {
"id": "https://inria.hal.science",
"type": "Service",
"inbox": "https://inbox.hal.science"
},
"object": {
"id": "https://hal.science/hal-04750857v1",
"ietf:cite-as": null,
"sorg:citation": {
"@context": "https://doi.org/10.5063/schema/codemeta-2.0",
"type": "SoftwareSourceCode",
"name": "GSEA",
"codeRepository": null,
"referencePublication": null
},
"mentionType": "software",
"mentionContext": [
"For GSEA based on transcriptome expression data, we prepared a pre-ranked gene list according to the descending ordered log 2 FoldChange value derived from differential expression analysis; we then leveraged the R package clusterProfiler (v4.6.0) to determine functional enrichment based on the Molecular Signature Database (MSigDB) 67,68 .",
"Individual regulon activity was estimated using a two-sided GSEA.",
"The corresponding enrichment score was calculated using a single-sample GSEA (ssGSEA) approach through the R package GSVA (v1.46.0) 70 .",
"Using Hallmark and Reactome associated gene sets, we performed gene set enrichment analysis (GSEA) and found that p53 (normalized enrichment score [NES] = -2.00,"
]
}
}
2026-04-05 06:47:26
urn:uuid:f75cb2b4-ebae-4fb2-839e-a741a09d7669
https://datalake.inria.fr
https://inria.hal.science
["Offer","coar-notify:ReviewAction"]
{
"@context": [
"https://www.w3.org/ns/activitystreams",
"https://purl.org/coar/notify"
],
"id": "urn:uuid:f75cb2b4-ebae-4fb2-839e-a741a09d7669",
"type": [
"Offer",
"coar-notify:ReviewAction"
],
"actor": {
"id": "https://datalake.inria.fr",
"type": "Service",
"name": "Inria DataLake"
},
"origin": {
"id": "https://datalake.inria.fr",
"type": "Service",
"inbox": "https://prod-datadcis-api.inria.fr/coar/inbox"
},
"target": {
"id": "https://inria.hal.science",
"type": "Service",
"inbox": "https://inbox.hal.science"
},
"object": {
"id": "https://hal.science/hal-04750857v1",
"ietf:cite-as": null,
"sorg:citation": {
"@context": "https://doi.org/10.5063/schema/codemeta-2.0",
"type": "SoftwareSourceCode",
"name": "R package GSVA",
"codeRepository": null,
"referencePublication": null
},
"mentionType": "software",
"mentionContext": [
"The corresponding enrichment score was calculated using a single-sample GSEA (ssGSEA) approach through the R package GSVA (v1.46.0) .",
"We used gene set variation analysis on these gene sets to generate enrichment scores for each cell by using the R package GSVA (v1.46.0)."
]
}
}
2026-04-05 06:47:26
urn:uuid:f687b0b8-614d-48de-85c0-d63803780333
https://datalake.inria.fr
https://inria.hal.science
["Offer","coar-notify:ReviewAction"]
{
"@context": [
"https://www.w3.org/ns/activitystreams",
"https://purl.org/coar/notify"
],
"id": "urn:uuid:f687b0b8-614d-48de-85c0-d63803780333",
"type": [
"Offer",
"coar-notify:ReviewAction"
],
"actor": {
"id": "https://datalake.inria.fr",
"type": "Service",
"name": "Inria DataLake"
},
"origin": {
"id": "https://datalake.inria.fr",
"type": "Service",
"inbox": "https://prod-datadcis-api.inria.fr/coar/inbox"
},
"target": {
"id": "https://inria.hal.science",
"type": "Service",
"inbox": "https://inbox.hal.science"
},
"object": {
"id": "https://hal.science/hal-04750857v1",
"ietf:cite-as": null,
"sorg:citation": {
"@context": "https://doi.org/10.5063/schema/codemeta-2.0",
"type": "SoftwareSourceCode",
"name": "MOSAIK",
"codeRepository": null,
"referencePublication": null
},
"mentionType": "software",
"mentionContext": [
"MOSAIK works with paired-end reads from Illumina HiSeq 2000 and uses both a hashing scheme and the Smith-Waterman algorithm to produce gapped optimal alignments and to map exon junction-spanning reads with a local alignment option for RNA-seq.",
"The raw, paired-end reads were aligned to the human reference genome, GRCh38/hg38, using MOSAIK (v1.1.0021)",
"After raw paired-end reads from WES were aligned/mapped to the human genome reference (hg38) and PCR duplicate reads were removed by MOSAIK aligner, we then analyzed the resulting alignments using the Bayesian model-based software GigaBayes/FreeBayes that enables the efficient analysis of billions of aligned short-read sequences."
]
}
}
{
"@context": [
"https://www.w3.org/ns/activitystreams",
"https://purl.org/coar/notify"
],
"id": "urn:uuid:ea20fc8b-2695-4cc8-9914-816ad4247c76",
"type": [
"Offer",
"coar-notify:ReviewAction"
],
"actor": {
"id": "https://datalake.inria.fr",
"type": "Service",
"name": "Inria DataLake"
},
"origin": {
"id": "https://datalake.inria.fr",
"type": "Service",
"inbox": "https://prod-datadcis-api.inria.fr/coar/inbox"
},
"target": {
"id": "https://inria.hal.science",
"type": "Service",
"inbox": "https://inbox.hal.science"
},
"object": {
"id": "https://hal.science/hal-04750857v1",
"ietf:cite-as": null,
"sorg:citation": {
"@context": "https://doi.org/10.5063/schema/codemeta-2.0",
"type": "SoftwareSourceCode",
"name": "MCPcounter",
"codeRepository": null,
"referencePublication": null
},
"mentionType": "software",
"mentionContext": [
"We modified two gene signatures, LM22 and MCPcounter (v1.2.0) , to construct our compendium .",
"As LM22 does not contain signatures related to fibroblasts and endothelial cells, extra 40 genes were added to account for these cells (32 genes for endothelial cells and 8 genes for fibroblasts) from MCPcounter to our compendium, which consisted of 364 genes representing 24 microenvironment cell types (Supplementary Data 5)."
]
}
}
2026-04-05 06:47:26
urn:uuid:e3ebabf9-75a5-45a7-94ee-dfb002808a0b
https://datalake.inria.fr
https://inria.hal.science
["Offer","coar-notify:ReviewAction"]
{
"@context": [
"https://www.w3.org/ns/activitystreams",
"https://purl.org/coar/notify"
],
"id": "urn:uuid:e3ebabf9-75a5-45a7-94ee-dfb002808a0b",
"type": [
"Offer",
"coar-notify:ReviewAction"
],
"actor": {
"id": "https://datalake.inria.fr",
"type": "Service",
"name": "Inria DataLake"
},
"origin": {
"id": "https://datalake.inria.fr",
"type": "Service",
"inbox": "https://prod-datadcis-api.inria.fr/coar/inbox"
},
"target": {
"id": "https://inria.hal.science",
"type": "Service",
"inbox": "https://inbox.hal.science"
},
"object": {
"id": "https://hal.science/hal-04750857v1",
"ietf:cite-as": null,
"sorg:citation": {
"@context": "https://doi.org/10.5063/schema/codemeta-2.0",
"type": "SoftwareSourceCode",
"name": "R package pRRophetic",
"codeRepository": null,
"referencePublication": null
},
"mentionType": "software",
"mentionContext": [
"We then employed the R package pRRophetic (v0.5) to predict the chemotherapeutic sensitivity for each WT case with default settings ; the estimated IC 50 of each sample treated with a specific chemotherapy drug was obtained by ridge regression, and prediction accuracy was measured through tenfold cross-validation with the GDSC training set."
]
}
}