SkinDB — 免费皮肤病转录组数据库与在线分析平台 | SkinDB: A Curated Pan-Dermatological Transcriptomic Database

User Manual Datasets Comparison Modes
全AI聚合: SogenPort
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SkinDB Data:
6 skin diseases · 220 analysis datasets (157 GEO series) · 11,283 samples
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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 · 220 analysis datasets · 157 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
分类：

SkinDB (https://skindb.grswsci.top) is a free, no-code, online transcriptomic analysis platform dedicated to skin diseases. It integrates 220 curated datasets comprising 11,283 samples from 9,864 independent subjects across six major dermatological conditions: systemic lupus erythematosus, atopic dermatitis, scleroderma, psoriasis, dermatomyositis, and vitiligo — all analyzable through a web browser without programming.

Data content: 220 curated analysis datasets from 157 GEO series, manually verified against the original records, with per-dataset tissue source, clinical grouping and subject-level metadata (subject, tissue state, timepoint and pair relationships) preserved for every analysis subset.

Analysis capabilities: design-aware differential expression (paired, repeated-measures and mixed-model procedures selected automatically from dataset metadata), gene-gene correlation on independent subjects, GSEA / GO / KEGG pathway enrichment, LASSO feature selection and logistic regression with subject-partitioned cross-validation, and ssGSEA custom gene-set scoring, with publication-ready figures and downloadable raw statistics.

SkinDB brings the centralized, analysis-ready data infrastructure that oncology platforms such as TCGA-based tools provide to dermatological research, lowering the barrier to transcriptomic data reuse for clinicians and researchers without bioinformatics training.