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Single-Cell Web Tools

Single Cell Web Tools


The Single Cell Web tool provides single-cell lncRNA-target annotation based on 31 single-cell RNA sequencing (scRNA-seq) datasets related to 21 human cancers (>500 cells). It provides interactive and customizable functions including two powerful modules:
(1) pan-cancer analysis module: graphically display the differences/similarities of lncRNA-target regulations among cancer types at the single-cell level.
(2) cancer-specific analysis module: reveal the cancer cell sub-populations specificity of lncRNA-target regulations and intra-tumor heterogeneity at the single-cell level.
In summary, from the perspective of pan-cancer and cancer-specific analysis, Single Cell Web Tools characterize lncRNA-target regulations from scRNA-seq datasets, including differential lncRNA-target regulations analysis, differential expression analysis, relevant functional states analysis, lncRNA-target networks, cell clustering maps, distribution in diverse cell populations and in sub-populations. View our Help page for further help.

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Pan-cancer analysis module
Differential regulations in diverse cell populations

This function provides the differential analysis of different lncRNA-target regulation activity scores in 31 single-cell datasets containing 21 human diseases. Differential lncRNA-target regulation in a given single-cell dataset was identified by comparison with other single-cell datasets.

Differential expression analysis

This function provides the landscape of differential expression of lncRNAs and target genes across 31 single-cell datasets with 21 human cancers.

Functional states analysis

This function provides the landscape of differential lncRNA-target regulations associated with a given cell state across 31 single-cell datasets with 21 human cancers. Differential lncRNA-target regulations with a given cell state in a given single-cell data set were identified by comparing with other single-cell data sets.

Cancer-specific analysis module
Differential regulations in sub-populations

This function provides the differences in the distribution of lncRNA-target regulation activity scores among cancer cell sub-populations. It will help to understand the roles of lncRNA-target regulations in intra-tumor heterogeneity at the single-cell level.

Differential expression analysis

This function provides the differential expression of lncRNAs and target genes across cancer cell sub-populations.

Functional states analysis

This function provides the activity scores of differential lncRNA-target regulations associated with a given cell state among cancer cell sub-populations in a single-cell data sets.

Information of single cell datasets
ID Cancer Type Cell Number lncRNA-Target number Source Accession Cell Type
(UM)GSE139829 Uveal melanoma 42230 2040 Tissue: Eye GSE139829 Neurons,Tissue_stem_cells,T_cells,Macrophage
(NSCLC)GSE143423_Met1 Non-small Cell Lung Cancer 13382 952 Tissue: Brain GSE143423 Epithelial_cells,Macrophage,NK_cell
(UCEC)GSE139555_Uterus Endometrial adenocarcinoma 12758 48 Tissue: Endometrium GSE139555 Pre-B_cell_CD34-,Epithelial_cells,T_cells
(CRC)GSE139555_Colon Colorectal cancer 10112 378 Tissue: Colon GSE139555 B_cell,T_cells,NK_cell,CMP,Monocyte,Neurons,Chondrocytes
(NSCLC)GSE143423_Met2 Non-small Cell Lung Cancer 9595 804 Tissue: Brain GSE143423 Epithelial_cells,Macrophage,Endothelial_cells,Tissue_stem_cells,Astrocyte
(PAAD)GSE112845 Pancreatic cancer 8321 1198 Cell Line: KLM1 GSE112845 MT-ND2+ Epithelial_cells,FOXM1+ Epithelial_cells,LRRC75A+ Epithelial_cells,SPP1+ Epithelial_cells,NUPR1+ Epithelial_cells,DHRS2+ Epithelial_cells,UPP1+ Epithelial_cells,KRTAP3-1+ Epithelial_cells,UBE2T+ Epithelial_cells
(PDAC)PRJCA001063_IB Pancreatic Ductal Adenocarcinoma 8202 804 Tissue: Head PRJCA001063 Tissue_stem_cells,Endothelial_cells,T_cells,Monocyte,B_cell,Epithelial_cells
(ARMS)GSE113660 Alveolar rhabdomyosarcoma 6875 887 Cell Line: Rh41 GSE113660 Neurons,MSC
(PDAC)PRJCA001063_IIA Pancreatic Ductal Adenocarcinoma 6289 958 Tissue: Head PRJCA001063 Epithelial_cells,Monocyte,Tissue_stem_cells,Chondrocytes,Pre-B_cell_CD34-,T_cells
(HNSC)GSE103322 Head and neck cancer 5902 2151 Tissue: Lymph node GSE103322 Monocyte,T_cells,Endothelial_cells,Epithelial_cells,Fibroblasts,Chondrocytes,Tissue_stem_cells,Astrocyte,B_cell
(ATC)GSE148673 Anaplastic Thyroid Cancer 5861 583 Tissue: Fresh tumor GSE148673 T_cells,B_cell,NK_cell,Monocyte,Fibroblasts
(OV)E-MTAB-8559_IIIB Ovarian Carcinoma 5846 1507 Tissue: Peritoneal fluid E-MTAB-8559 Smooth_muscle_cells,Neurons,Fibroblasts,Tissue_stem_cells,MSC,Endothelial_cells
(MPAL)GSE139369 Mixed-phenotype Acute Leukemia 5643 385 Tissue: Bone Marrow GSE139369 Monocyte,CMP,Platelets
(PDAC)PRJCA001063_IIB Pancreatic Ductal Adenocarcinoma 5263 1046 Tissue: Body and tail PRJCA001063 Chondrocytes,T_cells,Macrophage,Monocyte,Epithelial_cells,Fibroblasts,Tissue_stem_cells,NK_cell,Smooth_muscle_cells,Endothelial_cells
(NSCLC)GSE143423_Met3 Non-small Cell Lung Cancer 5169 1350 Tissue: Brain GSE143423 Macrophage,Tissue_stem_cells,Epithelial_cells,Monocyte,T_cells,NK_cell,Astrocyte
(OV)E-MTAB-8559_IIIC Ovarian Carcinoma 5046 1588 Tissue: Peritoneal fluid E-MTAB-8559 MSC,Smooth_muscle_cells,Fibroblasts,Tissue_stem_cells,Embryonic_stem_cells,Epithelial_cells
(SARC)GSE146221_CHLA9 Ewing sarcoma 4865 1421 Cell Line: CHLA9 GSE146221 AL844908.1+ Neurons,AC103706.1+ Neurons,TAGLN+ Neurons,PIF1+ Neurons,FAM111B+ Neurons,HDAC10+ Neurons
(SKCM)GSE72056 Melanoma 4645 2021 Tissue: Skin GSE72056 B_cell,T_cells,Epithelial_cells,Neurons,Tissue_stem_cells,Endothelial_cells,Monocyte
(SARC)GSE146221_CHLA10 Ewing sarcoma 4632 1033 Cell Line: CHLA10 GSE146221 Neuroepithelial_cell,iPS_cells,MSC,Neurons
(BLCA)GSE130001 Bladder cancer 4129 718 Tissue: Bladder GSE130001 Endothelial_cells,Epithelial_cells,Keratinocytes,Chondrocytes,Tissue_stem_cells
(CHOL)GSE125449 Intrahepatic cholangiocarcinoma 3835 703 Tissue: Liver GSE125449 T_cells,Monocyte,Endothelial_cells,Hepatocytes,B_cell,Tissue_stem_cells
(SARC)GSE146221_TC71 Ewing sarcoma 3673 1240 Cell Line: TC71 GSE146221 Neurons,iPS_cells
(NSCLC)E-MTAB-6149 Non-small cell lung cancer 3524 910 Tissue: Lung E-MTAB-6149 Epithelial_cells,Embryonic_stem_cells,T_cells,iPS_cells
(NET)GSE140312 Gastrointestinal Neuroendocrine Cancer 3158 364 Tissue: Liver GSE140312 Endothelial_cells,Fibroblasts,Hepatocytes,Neurons,Monocyte
(HGG)GSE103224 High-grade glioma 2772 1035 Tissue: Brain GSE103224 Neuroepithelial_cell,Chondrocytes,Astrocyte
(OV)GSE118828 Ovarian cancer 1909 945 Tissue: Ovary GSE118828 NK_cell,Epithelial_cells,Monocyte,Tissue_stem_cells,Fibroblasts
(CML)GSE76312-Tissue Bone marrow Chronic myelogenous leukemia 1877 1908 Tissue: Bone marrow GSE76312 CMP,Pro-B_cell_CD34+
(AML)GSE110499 Acute myeloid leukemia 1184 592 Tissue: Bone marrow GSE110499 B_cell,Monocyte,T_cells,MEP
(GBM)GSE84465 Glioblastoma 991 2099 Tissue: Brain GSE84465 Astrocyte,Tissue_stem_cells
(GBM)GSE57872-Tissue Brain Glioblastoma 623 2010 Tissue: Brain GSE57872 ZMAT5+ Astrocyte,OLIG2+ Astrocyte,TOP2A+ Astrocyte,LMX1B+ Astrocyte,SMYD2+ Astrocyte,SAA2+ Astrocyte,NNAT+ Astrocyte
(MEL)GSE99330 Melanoma 575 1669 Cell Line: WM989 GSE99330 Neurons,T_cells
(BRCA)GSE77308 Breast cancer 369 2712 PDX GSE77308 MSC,Epithelial_cells
(BRCA)GSE75688 Breast cancer 317 2885 Tissue: Breast GSE75688 Epithelial_cells,B_cell
(ESCC)GSE81812 Esophageal squamous cell carcinoma 314 2818 Cell Line: KYSE-180 GSE81812 RSPH10B+ Epithelial_cells,CYP24A1+ Epithelial_cells,LINC00954+ Epithelial_cells,CTA-292E10.8+ Epithelial_cells,PIF1+ Epithelial_cells
(MEL)GSE81383 Melanoma 307 2403 Tissue: Skin GSE81383 iPS_cells,Neurons
(CRC)GSE81861-Tissue Colon Colorectal cancer 290 1639 Tissue: Colon GSE81861 PDCD11+ Epithelial_cells,SLITRK6+ Epithelial_cells,TNFRSF11B+ Epithelial_cells,CENPQ+ Epithelial_cells
(CML)GSE98734 Chronic myelogenous leukemia 267 2273 Cell Line: K562 GSE98734 CTH+ BM & Prog.,USO1+ BM & Prog.,CCNO+ BM & Prog.,RP11-474I11.8+ BM & Prog.,ATP8B2+ BM & Prog.,BAHD1+ BM & Prog.
(LUAD)DRP001358 Lung adenocarcinoma 245 2660 Cell Line: LC-2/ad DRP001358 H19+ Epithelial_cells,HMMR+ Epithelial_cells,AGAP1-IT1+ Epithelial_cells,C8orf47+ Epithelial_cells
(LUAD)DRP003337 Lung adenocarcinoma 218 1923 Cell Line: A549 DRP003337 Epithelial_cells,Smooth_muscle_cells
(GBM)GSE57872 Glioblastoma 177 3487 Cell Line: GSC/DGC GSE57872 LA16c-366D1.3+ Astrocyte,UBB+ Astrocyte,LSM11+ Astrocyte,CYTL1+ Astrocyte
(NSCLC)DRP003981-Cell Line PC-9 Non-small cell lung cancer 164 2661 Cell Line: PC-9 DRP003981 RAB3IL1+ Epithelial_cells,NUPR1+ Epithelial_cells,CEP152+ Epithelial_cells
(PC)GSE99795 Prostate cancer 144 2381 Cell Line: LNCaP GSE99795 iPS_cells,Epithelial_cells
(CCA)ERP020478 Cervix cancer 126 3360 Cell Line: Hela ERP020478 MSC,Epithelial_cells
(LUAD)GSE69405-PDX Lung adenocarcinoma 126 2458 PDX GSE69405 Epithelial_cells,Endothelial_cells
(CRC)GSE51254 Colorectal cancer 99 2271 Cell Line: HCT-116 GSE51254 Epithelial_cells,iPS_cells
(BCA)DRP003981 Bronchoalveolar carcinoma 96 1872 Cell Line: H1650 DRP003981 Epithelial_cells
(NSCLC)DRP003981-Cell Line H1975 Non-small cell lung cancer 96 1929 Cell Line: H1975 DRP003981 TFPI2+ Epithelial_cells,HLA-DPA1+ Epithelial_cells
(NSCLC)DRP003981-Cell Line H2228 Non-small cell lung cancer 96 1967 Cell Line: H2228 DRP003981 RACGAP1+ Epithelial_cells,DYNLL1+ Epithelial_cells
(LUAD)DRP003981-Cell Line II18 Lung adenocarcinoma 96 2302 Cell Line: II18 DRP003981 S100A9+ Epithelial_cells,DGKA+ Epithelial_cells,CTD-2636A23.2+ Epithelial_cells
(MLPS)E-MTAB-6142 Myxoid liposarcoma 96 1769 Cell Line: MLS 1765-92 E-MTAB-6142 AURKA+ MSC,RIBC2+ MSC
(PDAC)GSE99305 Pancreatic ductal adenocarcinoma 96 1998 Cell Line: Pancreatic ductal adenocarcinoma cell lines GSE99305 NUF2+ Epithelial_cells,TP53INP2+ Epithelial_cells
(CML)GSE76312 Chronic myelogenous leukemia 91 1714 Cell Line: K562 GSE76312 LRRC16A+ BM & Prog.,AHSP+ BM & Prog.
(MEL)GSE97681-Cell Line WM983B Melanoma 86 3358 Cell Line: WM983B GSE97681 CTB-151G24.1+ Neurons,RP11-542A14.2+ Neurons,POSTN+ Neurons
(RCC)GSE73121 Renal cell carcinoma 83 2403 PDX GSE73121 PAEP+ Epithelial_cells,G0S2+ Epithelial_cells
(LUAD)GSE81861-Cell Line A549 Lung adenocarcinoma 74 1422 Cell Line: A549 GSE81861 Epithelial_cells
(CML)GSE81861 Chronic myelogenous leukemia 73 1831 Cell Line: K562 GSE81861 BM & Prog.,MEP
(BRCA)GSE75367 Breast cancer 70 2389 CTC GSE75367 Epithelial_cells,Platelets
(HCC)GSE103866 Hepatocellular carcinoma 55 2031 Cell Line: HuH-7 GSE103866 iPS_cells
(CRC)GSE81861-Colorectal cancer Colorectal cancer 51 1679 Cell Line: HCT-116 GSE81861 Embryonic_stem_cells
(CML)GSE69405 Chronic myelogenous leukemia 50 2168 Cell Line: K562 GSE94979 Epithelial_cells
(LUAD)GSE94979 Lung adenocarcinoma 50 1081 Cell Line: H358 GSE69405 BM & Prog.
(NSCLC)GSE81861-Cell Line H1437 Non-small cell lung cancer 47 1238 Cell Line: H1437 GSE81861 Neuroepithelial_cell,BM & Prog.
(LUAD)DRP001358-Cell Line PC-9 Lung adenocarcinoma 46 2411 Cell Line: PC-9 DRP001358 Epithelial_cells
(LUAD)DRP001358-Cell Line VMRC-LCD Lung adenocarcinoma 46 2008 Cell Line: VMRC-LCD DRP001358 iPS_cells
(BRCA)GSE91395 Breast cancer 45 422 Cell Line: MCF-7 GSE91395 HMGB1+ Epithelial_cells,DHRS2+ Epithelial_cells
(HCC)GSE103866-Cell Line HuH-1 Hepatocellular carcinoma 43 1927 Cell Line: HuH-1 GSE103866 Hepatocytes
(NB)GSE90683 Neuroblastoma 36 3014 Cell Line: Neuroblastoma cell line GSE90683 Neurons
(MEL)GSE97681-Cell Line WM989 Melanoma 36 3289 Cell Line: WM989 GSE97681 Neurons,MSC
(RCC)GSE73121-Tissue Kidney Renal cell carcinoma 35 2050 Tissue: Kidney GSE73121 Epithelial_cells
(BRCA)GSE91395-Cell Line MDA-MB-231 Breast cancer 33 374 Cell Line: MDA-MB-231 GSE91395 MSC
(HCC)GSE65364 Hepatocellular carcinoma 26 778 Tissue: Liver GSE65364 Hepatocytes
(AML)E-MTAB-2672 Acute myeloid leukemia 24 2587 Tissue: Bone marrow E-MTAB-2672 GMP
(ALL)GSE100694 Acute lymphoblastic leukemia 24 3394 Cell Line: LOUCY/JURKAT GSE100694 Pro-B_cell_CD34+
Differential regulations in diverse cell populations

This function provides the landscape of the proportion of cells with high activity scores (> 0.5*max (AUC scores)) for differential lncRNA-target regulations in cells across 31 single-cell datasets with 21 human cancers. Wilcoxon rank sum test was used to identify differential lncRNA-target regulations by comparing the activity scores between a given scRNA-seq and other scRNA-seq datasets.

scRNA-seq Dataset: Select a cancer single-cell data set to obtain differential lncRNA-target regulations.

LncRNA/Gene Symbol: Select a lncRNA to obtain lncRNA-target regulations for analysis.



The landscape of the cell proportion of differential lncRNA-target regulations
Analysis of differences between single-cell datasets
Table Result of associated lncRNA-target regulations
Differential expression analysis

This function provides the landscape of differential expression of lncRNAs and target genes across 31 single-cell datasets with 21 human cancers. Wilcoxon rank sum test was used to identify differential lncRNA and genes by comparing expression levels between a given scRNA-seq and other scRNA-seq datasets.

scRNA-seq Dataset: Select a cancer single-cell data set for differential expression analysis.

LncRNA/Gene Symbol: Select a lncRNA for differential expression analysis.



The landscape of differential expression of lncRNAs and target genes
Table Result of associated Expression
Differential expression of lncRNAs and target genes
Table Result of associated lncRNA-target regulations
Functional states analysis

This function provides the landscape of the proportion of cells with high activity scores (> 0.5*max (AUC scores)) for differential lncRNA-target regulations associated with a given cell state (such as stemness, epithelial-mesenchymal transition, angiogenesis and inflammation) across 31 single-cell datasets with 21 human cancers. Wilcoxon rank sum test was used to identify differential lncRNA-target regulations by comparing the activity scores between a given cell functional state and other cell functional states.

scRNA-seq Dataset: Select a cancer single-cell data set for differential regulation analysis.

Cell functional states: Select a cancer single-cell data set for differential regulation analysis.

LncRNA/Gene Symbol: Select a lncRNA for drawing the point diagram.



The landscape of the cell proportion of differential lncRNA-target regulations associated with cell state
Analysis of differences between single-cell datasets
Table Result of associated lncRNA-target regulations
Differential regulations in sub-populations

This function provides the differences in the distribution of lncRNA-target regulation activity scores among cancer cell sub-populations and shows the proportion of cells with high activity scores (> 0.5*max (AUC scores)). It will help to understand the roles of lncRNA-target regulations in intra-tumor heterogeneity at the single-cell level. Wilcoxon rank sum test was used to identify differential lncRNA-target regulations by comparing the activity scores between a given cancer cell sub-population and other cancer cell sub-populations.

scRNA-seq Dataset: Select a cancer single-cell data set for analysis.

Cancer cell sub-populations: Select a cancer cell sub-population for differential regulations analysis.

LncRNA/Gene Symbol: Select a lncRNA to obtain key lncRNA-target regulations for analysis.



The landscape of the cell proportion of differential lncRNA-target regulations
Analysis of differences between cell subtypes
Table Result of associated lncRNA-target regulations
Differential expression analysis

This function provides the differential expression of lncRNAs and target genes across cancer cell sub-populations. Wilcoxon rank sum test was used to analyze the differential expression of lncRNA and its target genes in different cancer cell sub-populations.

LncRNA: Select a lncRNA for differential expression analysis.

scRNA-seq Dataset: Select a cancer single-cell data set for analysis.

Cancer cell sub-populations: Select a cancer cell sub-population for differential regulations analysis.

LncRNA/Gene Symbol: Select a lncRNA for differential expression analysis.



The landscape of differential expression of lncRNAs and target genes
Table Result of associated genes
Differential expression box-plot
Table Result of associated lncRNA-target regulations
Functional states analysis

This function provides the cell proportion with high activity scores (> 0.5*max (AUC scores)) of differential lncRNA-target regulations associated with a given cell state (such as stemness, epithelial-mesenchymal transition, angiogenesis and inflammation) among cancer cell sub-populations in a single-cell data sets. Wilcoxon rank sum test was used to identify differential lncRNA-target regulations by comparing the activity scores between a given cell functional state and other cell functional states.

scRNA-seq Dataset: Select a cancer single-cell data set for analysis.

Cell functional states: Select a cell functional state for analysis.

LncRNA/Gene Symbol: Select a lncRNA to obtain key lncRNA-target regulations for analysis.



The landscape of the cell proportion of differential lncRNA-target regulations associated with cell state
Analysis of differences between cell subtypes
Table Result of associated lncRNA-target regulations

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