光热生物 — 免费癌症多组学在线分析平台 | GRSWSci Cancer Multi-omics Analysis Platform

User Manual Datasets Comparison Modes
全AI聚合: SogenPort
Modules: 0 Features: 0
SkinDB Data:
6 skin diseases · 165 GEO series · 11,283 samples
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Analysis Help
SkinDB User Manual
TCGA 组学类型说明
方法全称说明与特点
转录组数据 (RNA-seq)
TPMTranscripts Per Million每百万转录本计数。推荐用于样本间比较，已校正基因长度和测序深度，同一样本内所有基因TPM总和为100万
FPKMFragments Per Kilobase Million每千碱基每百万片段。经典标准化方法，校正基因长度和测序深度，适合单样本内基因表达比较
FPKM_UQFPKM Upper QuartileFPKM上四分位数标准化。使用75%分位数进行标准化，对极端值更稳健，适合样本间比较
RSEMRNA-Seq by Expectation-Maximization期望最大化算法估计值。处理多重比对reads，提供更准确的转录本定量，TCGA默认方法
COUNTS_UNSTRANDEDUnstranded Counts非链特异性原始计数。未标准化的reads计数，适合差异表达分析（如DESeq2）
COUNTS_STRANDED_FIRSTFirst Strand Counts第一链特异性计数。保留第一链信息的原始计数，用于链特异性文库
COUNTS_STRANDED_SECONDSecond Strand Counts第二链特异性计数。保留第二链信息的原始计数，用于反向链特异性文库
TCGA 临床变量说明

不同肿瘤类型可用的临床变量不同，以下列出本平台已清洗并支持的全部变量及其适用范围。小写肿瘤名为亚型（如 brca_tnbc = 三阴性乳腺癌）。

病理分级分期

变量名称中文名肿瘤数适用肿瘤类型
histological_grade组织学分级28BLCA, CESC, cesc_cscc, CHOL, ESCA, esca_eac, esca_escc, GIC, GLIOMA, HNSC, hnsc_lscc, hnsc_oscc, KIRC, KRCC, LGG, lgg_astrocytoma, lgg_oligoastrocytoma, lgg_oligodendroglioma, LIHC, OV, PAAD, paad_padc, STAD, stad_dga, stad_srcc, UCEC, ucec_eea, ucec_sea
ajcc_pathologic_stage_clean病理分期42ACC, BLCA, BRCA, brca_idc, brca_ilc, brca_tnbc, CHOL, COAD, coad_lcc, coad_mac, coad_rcc, CRC, ESCA, esca_eac, esca_escc, GIC, HNSC, hnsc_lscc, hnsc_oscc, KICH, KIRC, KIRP, KRCC, LIHC, LUAD, LUSC, MESO, NSCLC, PAAD, paad_padc, READ, SKCM, skcm_mcm, skcm_pcm, STAD, stad_dga, stad_srcc, TGCT, tgct_seminoma, THCA, thca_cptc, UVM
ajcc_pathologic_t_clean原发肿瘤T分期45ACC, BLCA, BRCA, brca_idc, brca_ilc, brca_tnbc, CESC, cesc_cscc, CHOL, COAD, coad_lcc, coad_mac, coad_rcc, CRC, ESCA, esca_eac, esca_escc, GIC, HNSC, hnsc_lscc, hnsc_oscc, KICH, KIRC, KIRP, KRCC, LIHC, LUAD, LUSC, MESO, NSCLC, PAAD, paad_padc, PRAD, READ, SKCM, skcm_mcm, skcm_pcm, STAD, stad_dga, stad_srcc, TGCT, tgct_seminoma, THCA, thca_cptc, UVM
ajcc_pathologic_n_clean淋巴结N分期45ACC, BLCA, BRCA, brca_idc, brca_ilc, brca_tnbc, CESC, cesc_cscc, CHOL, COAD, coad_lcc, coad_mac, coad_rcc, CRC, ESCA, esca_eac, esca_escc, GIC, HNSC, hnsc_lscc, hnsc_oscc, KICH, KIRC, KIRP, KRCC, LIHC, LUAD, LUSC, MESO, NSCLC, PAAD, paad_padc, PRAD, READ, SKCM, skcm_mcm, skcm_pcm, STAD, stad_dga, stad_srcc, TGCT, tgct_seminoma, THCA, thca_cptc, UVM
ajcc_pathologic_m_clean远处转移M分期43BLCA, BRCA, brca_idc, brca_ilc, brca_tnbc, CESC, cesc_cscc, CHOL, COAD, coad_lcc, coad_mac, coad_rcc, CRC, ESCA, esca_eac, esca_escc, GIC, HNSC, hnsc_lscc, hnsc_oscc, KICH, KIRC, KIRP, KRCC, LIHC, LUAD, LUSC, MESO, NSCLC, PAAD, paad_padc, READ, SKCM, skcm_mcm, skcm_pcm, STAD, stad_dga, stad_srcc, TGCT, tgct_seminoma, THCA, thca_cptc, UVM
ajcc_clinical_stage_clean临床分期12ESCA, esca_eac, esca_escc, GIC, HNSC, hnsc_lscc, hnsc_oscc, KIRP, KRCC, TGCT, tgct_seminoma, UVM
ajcc_clinical_t_clean临床T分期14BLCA, ESCA, esca_eac, esca_escc, GIC, HNSC, hnsc_lscc, hnsc_oscc, KIRP, KRCC, PRAD, TGCT, tgct_seminoma, UVM
ajcc_clinical_n_clean临床N分期12ESCA, esca_eac, esca_escc, GIC, HNSC, hnsc_lscc, hnsc_oscc, KIRP, KRCC, TGCT, tgct_seminoma, UVM
ajcc_clinical_m_clean临床M分期16ACC, ESCA, esca_eac, esca_escc, GIC, HNSC, hnsc_lscc, hnsc_oscc, KICH, KIRC, KIRP, KRCC, PRAD, TGCT, tgct_seminoma, UVM
药物名称对照表

GDSC1 药物列表

Drug NameInput Name in Sparkle
SkinDB Datasets (6 skin diseases · 165 GEO series · 11,283 samples)
Comparison Modes: Case vs Control & High vs Low

Every SkinDB module compares two sample groups. The module name tells you how those two groups are defined — by one of two schemes:

Case vs Control

Groups: diseased skin (lesional / disease samples) vs Healthy controls.

How it works (from the code): within each GEO dataset, samples are labelled by condition; the disease group(s) are compared against the Healthy group on the target gene (z-scored expression) using a t-test (≤ 16 samples) or Wilcoxon test.

Answers: is my gene up- or down-regulated in the disease versus normal skin?

High vs Low

Groups: diseased samples only (controls excluded), split by the target gene's own expression — top expressers (High) vs bottom expressers (Low).

How it works (from the code): diseased samples are ranked by the gene; the top and bottom fraction (default 30%; the middle is dropped) form the High and Low groups; limma differential expression is run between them. The fraction is adjustable via the cutoff argument (e.g. 0.3).

Answers: which genes / pathways co-vary with my gene within the disease — what changes when it is highly vs lowly expressed?

Case vs Control High vs Low
The two groupsDisease vs HealthyGene-high vs Gene-low
Healthy controlsIncludedExcluded
Split byDisease statusTarget-gene expression (top/bottom 30%, adjustable)
Statistical testt-test / Wilcoxonlimma
QuestionAltered in disease?Associated with the gene within disease?
💡 Both schemes also come in Divided (each dataset analyzed separately) and Consensus (combined across all datasets of the disease) variants — see the User Manual.
特殊功能基因列表

转录因子 (Transcription Factors)

共 1575 个转录因子，来源于 9 个数据库。T = 数据库中存在　- = 不存在

TFKnockTFTRRUSTENCODEFIMO_JASPARCHEAGTRDChIP_AtlasPWMEnrichhTFtarget
细胞列表
📝 快速记事本
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光热生物 (GRSWSci, https://www.grswsci.top) is a free, no-code, online bioinformatics platform for cancer multi-omics data analysis. Researchers can analyze gene expression, survival, immune infiltration, drug sensitivity, single-cell transcriptomics, spatial transcriptomics, proteomics, and more — all through a web browser without programming.

Supported databases: TCGA (33 cancer types, pan-cancer RNA-seq, miRNA, lincRNA, clinical annotations), GEO (400+ curated datasets), CPTAC (clinical proteomics, total protein and phosphoprotein), TISCH2 (tumor immune single-cell hub, UMAP, violin, pseudobulk DEG), Visium Spatial Transcriptomics (18+ cancer types, 250+ tissue slices), HPA (Human Protein Atlas, immunohistochemistry and immunofluorescence staining), GDSC/PRISM/CTRP (drug sensitivity, CMap connectivity), BioGRID/comPPI (protein-protein interaction networks), KnockTF/CistromeDB (transcription factor regulation), miRTarBase (miRNA target prediction), ICB cohorts (immunotherapy response prediction, TIDE analysis).

Analysis capabilities: differential gene expression (tumor vs normal, single/multi gene, single/multi cancer), Kaplan-Meier survival analysis with Cox regression (OS, DSS, DFI, PFI, optimal and median cutoff), gene-gene correlation with GSEA pathway enrichment (Pearson, Spearman), tumor microenvironment immune cell infiltration scoring, immunotherapy response prediction (TIDE, ICB, easier), drug sensitivity correlation (GDSC, PRISM, CTRP, CMap, CTD2), single-cell RNA-seq visualization (UMAP, violin, gene scoring, pseudobulk), spatial transcriptomics expression mapping, clinical proteomics differential expression and survival, protein-protein interaction network construction, transcription factor regulation analysis (TF correlation, KnockTF, CistromeDB, limma pan-cancer), post-transcriptional regulation (miRNA targeting, RNA editing, alternative splicing, splicing survival), clinical variable association (chi-square, grade, stage, TNM, gender).

GRSWSci is comparable to tools such as GEPIA2, TIMER2.0, cBioPortal, UALCAN, LinkedOmics, and TISIDB, but uniquely integrates all these analysis types into a single unified platform with AI-powered natural language query support.