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CommPath webtool tutorials

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CommPath: inference and analysis of intercellular communications by pathway analysis

Overview

Here, we introduce CommPath, an open source R package and a webserver, to infer and visualize the LR associations and signaling pathway-driven cell-cell communications from scRNA-seq data.

Key Features

CommPath has two key features:

(i) it manually curates a comprehensive signaling molecule interaction database of LR interactions, as well as their currently accessible pathway annotations, including KEGG pathways, WikiPathways, reactome pathways, and GO terms;

(ii) it prioritizes both LR pairs among cell types and cell type specific signaling pathways mediating cell-cell communications.

Interface

0.Register and login

CommPath needs to be registered. And through your CommPath account, you can easily get the analysis states and the results of your tasks.

1. Upload input files

CommPath requires input of an expression matrix of gene × cell produced from scRNA-seq experiments and a label vector indicating cell clusters.

For example, here are N genes * M cells for P clusters (one row = one cell) with headers:

Cells Gene1 Gene2 …… GeneN Clusters
Cell1 Cluster1
Cell2 Cluster1
Cell3 Cluster2
…… ……
CellM ClusterP

An example of input file is “HCC.tumor.3k.csv”.

2. Run your task

To run a task, four parameters should be selected. These parameters are:

(1) Pvalue. Default = 0.05;
(2) Fold Change. Default = 3;
(3) Species. Now CommPath supports L-R pairs in 3 species, including human (hsapiens), mouse (mmusculus), and rat (rnorvegicus). More model organisms will be supported soon.
(4) Data files, which is your input file in “1. Upload input files” section.

And, click “START CommPath CALCULATION” to run a task.

Then, CommPath will automatically jump to “Task list” section, and the top row shows the task status in real time.

When the task status changed from “In progression” to “Completed”, you can click “VIEW RESULT” and browse the results.

In the “Task list” section, you can also review the results of previously completed tasks.

3. Results

Results for all clusters

Default “Results” page (“Circos” tab page) shows circos of all clusters. The widths of lines indicate the counts (Left plot) or the overall interaction intensity (Right plot) of LR pairs among clusters.

In the above circos plot, the directions of lines indicate the associations from ligands to receptors, and the widths of lines represent the counts of LR pairs among clusters.

Pathway enrichment analysis

CommPath conducts pathway analysis to identify dysregulated signaling pathways containing the marker ligands and receptors for each cluster.

The “GSVA” tab page, shows differentially activated pathways for each cluster.

There are 7 columns stored in the variable ident.up.dat/ident.down.dat:

Columns cell.from, cell.to, ligand, receptor show the upstream and dowstream clusters and the specific ligands and receptors for the LR associations;

Columns log2FC.LR, P.val.LR, P.val.adj.LR show the interaction intensity (measured by the product of log2FCs of ligands and receptors) and the corresponding original and adjusted P values for hypothesis tests of the LR pairs or the overall interaction intensity among clusters.

Results for specific cluster

When you focus on some specific cluster (eg. Endothelial), you can select “Endothelial” on the “Cluster” drop-down menu. Then, the circos plot of “Endothelial” will update in “Circos” tab page, as well as L-R associations and signaling pathway-driven cell-cell communications (“LR pairs” tab page).

In the “Circos” tab page, users would highlight the interaction of specific clusters. Here we take the Endothelial cells as an example:

Then network graph tools are to visualize the pathways and associated functional LR interactions.

In the above network graph, the pie charts represent the activated pathways in the selected cells (here Endothelial cells) and the scatter points represent the LR pairs of which the receptors are included in the genesets of the linked pathways. Colors of scatter points indicate the upstream clusters releasing the corresponding ligands. Sizes of pie charts indicate their total in-degree and the proportions indicate the in-degree from different upstream clusters.

The legend of the above network graph is generally the same to that of the previous network plot, except that: (i) the scatter points represent the LR pairs of which the ligands are included in the genesets of the linked pathways; (ii) colors of scatter points indicate the downstream clusters expressing the corresponding receptors; (iii) sizes of pie charts indicate their total out-degree and the proportions indicate the out-degree to different downstream clusters.

dot plot to investigate the upstream and downstream LR pairs involved in the specific pathways in the selected clusters:

pathway-mediated cell-cell communication chain

For a specific cell cluster, here named as B for demonstration, CommPath identifies the upstream cluster A sending signals to B, the downstream cluster C receiving signals from B, and the significantly activated pathways in B to mediate the A-B-C communication chain. More exactly, through LR and pathways analysis described above, CommPath is able to identify LR pairs between A and B, LR pairs between B and C, and pathways activated in B. Then CommPath screens for pathways in B which involve both the receptors to interact with A and ligands to interact with C.

Left

In the above line plot, the widths of lines between Upstream cluster and Receptor represent the overall interaction intensity between the upstream cluster and Endothelial cells via the specific receptors; the sizes and colors of dots in the Receptor column represent the average log2FC and -log10(P) from differential expression tests comparing the receptor expression in Endothelial cells to that in all other cells; the lengths and colors of bars in the Pathway annotation column represent the mean difference and -log10(P) form differential activation tests comparing the pathway scores in Endothelial cells to those in all other cells.

Comparison to another object

“Comparison to another object” tab page, provide useful utilities to compare cell-cell interactions between two conditions, namely case group and control group, such as disease and control. The case group is the current task, and the control group is defined as another task by click “Comparion to another object” drop-down menu. Then, differentially activated signaling pathway-driven cell-cell communications between two CommPath objects is showed.

Here we, for example, use CommPath to compare the cell-cell communication between cells from HCC tumor and normal tissues. We have pre-created the CommPath object for the normal samples following the above steps.

In the above line plot, the widths of lines between Upstream cluster and Receptor represent the overall interaction intensity between the upstream clusters and Endothelial cells via the specific receptors, and the colors indicate the interaction intensity is upregulated (red) or downregulated (blue) in tumor tissues (object.1) compared to that in normal tissues (object.2); the sizes and colors of dots in the Receptor column represent the average log2FC and -log10(P) of expression of receptors in Endothelial cells compared to all other cells in tumor tissues; the lengths and colors of bars in the Pathway annotation column represent the mean difference and -log10(P) of pathway scores of Endothelial cells in tumor tissues compared to that in normal tissues.

4. Download the results

All the results, provided as a zipped package, can be freely downloaded by clicking “Download” buttom on “Task list” section.

Commpath R package instructions

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CommPath

CommPath is an R package for inference and analysis of ligand-receptor interactions from single cell RNA sequencing data.

Installation

CommPath R package can be easily installed from Github using devtools:

devtools::install_github("yingyonghui/CommPath")
library(CommPath)

Dependencies

Tutorials

In this vignette we show CommPath’s steps and functionalities for inference and analysis of ligand-receptor interactions by applying it to a scRNA-seq data (GEO accession number: GSE156337) on cells from hepatocellular carcinoma (HCC) patients.

Brief description of CommPath object

We start CommPath analysis by creating a CommPath object, which is a S4 object and consists of six slots including
(i) data, a matrix containing the normalized expression values by gene * cell;
(ii) cell.info, a data frame contain the information of cells;
(iii) meta.info, a list containing some important parameters used during the analysis;
(iv) LR.marker, a data.frame containing the result of differential expression test of ligands and receptors;
(v) interact, a list containing the information of LR interaction among clusters;
(vi) pathway, a list containing the information of pathways related to the ligands and receptors.

CommPath input

The expression matrix and cell indentity information are required for CommPath input. We downloaded the processed HCC scRNA-seq data from Mendeley data. For a fast review and illustration of CommPath’s functionalities, we randomly selected the expression data of 3000 cells across the top 5000 highly variable genes from the tumor and normal tissues, respectively. The example data are available in figshare.
We here illustrate the CommPath steps for date from the tumor tissues. And analysis for data from the normal tissues would be roughly in the same manner.

# load(url("https://figshare.com/ndownloader/files/33926126"))
load("path_to_download/HCC.tumor.3k.RData")

This dataset consists of 2 varibles which are required for CommPath input:
tumor.expr : expression matrix of gene * cell. Expression values are required to be first normalized by the library-size and log-transformed;
tumor.label : a vector of lables indicating identity classes of cells in the expression matrix, and the order of lables should match the order of cells in the expression matrix; usrs may also provide a data frame containing the meta infomation of cells with the row names matching the cells in the expression matrix and a column named as Cluster must be included to indicate identity classes of cells.

Identification of marker ligands and receptors

We start CommPath analysis by creating a CommPath object:

# Classify the species of the scRNA-seq experiment by the species parameter
# CommPath now enable the analysis of scRNA-seq experiment from human (hsapiens) and mouse (mmusculus).
tumor.obj <- createCommPath(expr.mat = tumor.expr, 
        cell.info = tumor.label, 
        species = 'hsapiens')

Firstly we’re supposed to identify marker ligands and receptors (ligands and receptors that are significantly highly expressed) for each identity class of cells in the expression matrix. CommPath provide findLRmarker to identify these markers by t.test or wilcox.test.

tumor.obj <- findLRmarker(object = tumor.obj, method = 'wilcox.test')

Identification of ligand-receptor (L-R) associations

# find significant L-R pairs
tumor.obj <- findLRpairs(object = tumor.obj,
        logFC.thre = 0, 
        p.thre = 0.05)

The counts of significant LR pairs and overall interaction intensity among cell clusters are then stored in tumor.obj@interact[[‘InteractNumer’]]，and the detailed information of each LR pair is stored in tumor.obj@interact[[‘InteractGeneUnfold’]].

Then you can visualize the interaction through a circos plot:

# Plot interaction for all cluster
circosPlot(object = tumor.obj)

In the above circos plot, the directions of lines indicate the associations from ligands to receptors, and the widths of lines indicate the counts of LR pairs among clusters.

# Plot interaction for all cluster
circosPlot(object = tumor.obj)

Now the widths of lines indicate the overall interaction intensity among clusters.

# Highlight the interaction of specific cluster
# Here we take the endothelial cell as an example
ident = 'Endothelial'
circosPlot(object = tumor.obj, ident = ident)

For a specific cluster of interest, CommPath provides function findLigand (findReceptor) to find the upstream (downstream) cluster and the corresponding ligand (receptor) for specific cluster and receptor (ligand):

# For the selected cluster and selected receptor, find the upstream cluster
select.ident = 'Endothelial'
select.receptor = 'ACKR1'

ident.up.dat <- findLigand(object = tumor.obj, 
    select.ident = select.ident, 
    select.receptor = select.receptor)
head(ident.up.dat)

# For the selected cluster and selected ligand, find the downstream cluster
select.ident = 'Endothelial'
select.ligand = 'CXCL12'

ident.down.dat <- findReceptor(object = tumor.obj, 
    select.ident = select.ident, 
    select.ligand = select.ligand)
head(ident.down.dat)

There are 7 columns stored in the variable ident.up.dat/ident.down.dat:
Columns Cell.From, Cell.To, Ligand, Receptor show the upstream and dowstream clusters and the specific ligands and receptors in the LR associations;
Columns Log2FC.LR, P.val.LR, P.val.adj.LR show the interaction intensity (measured by the product of Log2FCs of ligands and receptors)and the corresponding original and adjusted p value for hypothesis test of one pair of LR.

CommPath also provides dot plots to investigate its upstream clusters which release specific ligands and its downstream clusters which expressed specific receptors:

# Investigate the upstream clusters which release specific ligands to the interested cluster
dotPlot(object = tumor.obj, receptor.ident = ident)


# Investigate the downstream clusters which expressed specific receptors for the interested cluster
dotPlot(object = tumor.obj, ligand.ident = ident)

Pathway analysis

CommPath conducts pathway analysis to identify signaling pathways involving the marker ligands and receptors for each cluster.

# Find pathways in which genesets show overlap with the marker ligands and receptors in the example dataset
# CommPath provides pathway annotations from KEGG pathways, WikiPathways, reactome pathways, and GO terms
tumor.obj <- findLRpath(object = tumor.obj, category = 'kegg')

Now genesets showing overlap with the marker ligands and receptors are stored in tumor.obj@interact[[‘pathwayLR’]]. Then we score the pathways to measure the activation levels for each pathway in each cell.

# Compute pathway activation score by the gsva algorithm or an average manner
# For more information about gsva algorithm, see the GSVA package
tumor.obj <- scorePath(object = tumor.obj, method = 'gsva', min.size = 10, parallel.sz = 4)

After that CommPath provide diffAllPath to perform pathway differential activation analysis for cells in each identity class and find the receptor and ligand in the pathway:

# get significantly up-regulated pathways in each identity class
acti.path.dat <- diffAllPath(object = tumor.obj, only.posi = TRUE, only.sig = TRUE)
head(acti.path.dat)

There are several columns stored in the variable acti.path.dat:
Columns mean.diff, mean.1, mean.2, t, df, p.val, p.val.adj show the statistic result; description shows the name of pathway;
Columns cell.up and ligand.up show the upstream identity classes which would release specific ligands to interact with the receptors from the current identity class;
Column receptor.in.path shows the marker receptors expressed by the current identity class and these receptors are included in the current pathway;
Column ligand.in.path shows the marker ligands released by the current identity class and these ligands are also included in the current pathway.

Then we use pathHeatmap to plot a heatmap of those differentially activated pathways for each cluster to display the highly variable pathways:

pathHeatmap(object = tumor.obj,
       acti.path.dat = acti.path.dat,
       top.n.pathway = 10,
       sort = "p.val.adj")

Cell-cell interaction flow via pathways

For a specific cell cluster, which here we name it as B for demonstration, CommPath identify the upstream cluster A sending signals to B, the downstream cluster C receiving signals from B, and the significantly activated pathways in B to mediate the A-B-C communication flow. More exactly, through LR and pathways analysis described above, CommPath is able to identify LR pairs between A and B, LR pairs between B and C, and pathways activated in B. Then CommPath screens for pathways in B which involve both the receptors to interact with A and ligands to interact with C.

# Identification and visualization of the identified pathways
# Plot to identify receptors and the associated activated pathways for a specific cluster
select.ident = 'Endothelial'
pathPlot(object = tumor.obj, 
    select.ident = select.ident, 
    acti.path.dat = acti.path.dat)


# Plot to identify receptors, the associated activated pathways, and the downstream clusters
pathInterPlot(object = tumor.obj, 
    select.ident = select.ident, 
    acti.path.dat = acti.path.dat)

Compare cell-cell interactions between two conditions

CommPath also provide useful utilities to compare cell-cell interactions between two conditions such as disease and control. Here we, for example, used CommPath to compare the cell-cell interactions between cells from HCC tumor and normal tissues. The example data from normal tissues are also available in figshare.

# load(url("https://figshare.com/ndownloader/files/33926129"))
load("path_to_download/HCC.normal.3k.RData")

We have pre-created the CommPath object for the normal samples following the above steps. This dataset consists of 3 varibles:
normal.expr : expression matrix for cells from normal tissues;
normal.label : indentity lables for cells from normal tissues;
normal.obj : CommPath object created from normal.expr and normal.label, and processed by CommPath steps described above.

To compare 2 CommPath object, we shall first identify the differentially expressed ligands and receptors, and differentially activated pathways between the same cluster of cells in the two object.

# Take endothelial cells as example
# Identification of differentially expressed ligands and receptors 
diff.marker.dat <- diffCommPathMarker(object.1 = tumor.obj, object.2 = normal.obj, select.ident = 'Endothelial')

# Identification of differentially activated pathways 
diff.path.dat <- diffCommPathPath(object.1 = tumor.obj, object.2 = normal.obj, select.ident = 'Endothelial', parallel.sz = 4)

Then we compare the differentially activated pathways and the cell-cell communication flow mediated by those pathways.

# To compare differentially activated pathways and the involved receptors between the selected clusters of two CommPath object
pathPlot.compare(object.1 = tumor.obj, object.2 = normal.obj, select.ident = 'Endothelial', diff.marker.dat = diff.marker.dat, diff.path.dat = diff.path.dat)


# To compare the pathway mediated cell-cell communication flow for a specific cluster between 2 CommPath object
pathInterPlot.compare(object.1 = tumor.obj, object.2 = normal.obj, select.ident = 'Endothelial', diff.marker.dat = diff.marker.dat, diff.path.dat = diff.path.dat)

sessionInfo()

R version 4.0.3 (2020-10-10)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /home/luh/miniconda3/envs/seurat4/lib/libopenblasp-r0.3.17.so

locale:
 [1] LC_CTYPE=zh_CN.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=zh_CN.UTF-8        LC_COLLATE=zh_CN.UTF-8    
 [5] LC_MONETARY=zh_CN.UTF-8    LC_MESSAGES=zh_CN.UTF-8   
 [7] LC_PAPER=zh_CN.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=zh_CN.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] GSVA_1.38.2     ggplot2_3.3.5   dplyr_1.0.7     reshape2_1.4.4 
[5] circlize_0.4.13 CommPath_0.1.0 

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.7                  lattice_0.20-44            
 [3] digest_0.6.27               assertthat_0.2.1           
 [5] utf8_1.2.2                  R6_2.5.1                   
 [7] GenomeInfoDb_1.26.7         plyr_1.8.6                 
 [9] stats4_4.0.3                RSQLite_2.2.8              
[11] httr_1.4.2                  pillar_1.6.2               
[13] zlibbioc_1.36.0             GlobalOptions_0.1.2        
[15] rlang_0.4.11                annotate_1.68.0            
[17] blob_1.2.2                  S4Vectors_0.28.1           
[19] Matrix_1.3-4                labeling_0.4.2             
[21] BiocParallel_1.24.1         stringr_1.4.0              
[23] RCurl_1.98-1.4              bit_4.0.4                  
[25] munsell_0.5.0               DelayedArray_0.16.3        
[27] compiler_4.0.3              pkgconfig_2.0.3            
[29] BiocGenerics_0.36.1         shape_1.4.6                
[31] tidyselect_1.1.1            SummarizedExperiment_1.20.0
[33] tibble_3.1.3                GenomeInfoDbData_1.2.4     
[35] IRanges_2.24.1              matrixStats_0.60.1         
[37] XML_3.99-0.7                fansi_0.5.0                
[39] crayon_1.4.1                withr_2.4.2                
[41] bitops_1.0-7                grid_4.0.3                 
[43] xtable_1.8-4                GSEABase_1.52.1            
[45] gtable_0.3.0                lifecycle_1.0.0            
[47] DBI_1.1.1                   magrittr_2.0.1             
[49] scales_1.1.1                graph_1.68.0               
[51] stringi_1.7.4               cachem_1.0.6               
[53] farver_2.1.0                XVector_0.30.0             
[55] ellipsis_0.3.2              generics_0.1.0             
[57] vctrs_0.3.8                 tools_4.0.3                
[59] bit64_4.0.5                 Biobase_2.50.0             
[61] glue_1.4.2                  purrr_0.3.4                
[63] MatrixGenerics_1.2.1        parallel_4.0.3             
[65] fastmap_1.1.0               AnnotationDbi_1.52.0       
[67] colorspace_2.0-2            GenomicRanges_1.42.0       
[69] memoise_2.0.0

CommPath ChangeLog

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2022.2.22

  • Add Google Analytics.
  • Fix errors when submit a new task with a new file.

2022.2.9

  • Add annotations for each plot.
  • Change appname to CommPath.
  • Remove Cluster2 in the result page.
  • Fix label errors for comparison-object.

2022.2.8

  • Fix width-height imbalance.

2022.2.7

  • On line.

scRNA-HCC instructions

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A Single-Cell Atlas of the Multicellular Ecosystem of Primary and Metastatic Hepatocellular Carcinoma

SUMMARY

Hepatocellular carcinoma (HCC) represents a paradigm of the relation between tumor microenvironment (TME) and tumor development. Here, we generated > 70,000 single-cell transcriptomes for 10 HCC patients from four relevant sites: primary tumor, portal vein tumor thrombus (PVTT), metastatic lymph node and non-tumor liver. We discovered a cluster of antitumor central memory T (TCM) cells enriched in intratumoral tertiary lymphoid structures (TLSs) of HCC. We found chronic HBV/HCV infection increases the infiltration of CD8+ T cells in tumors but aggravates the exhaustion of tumor-infiltrating lymphocytes. We identified CD11b+ macrophages to be terminally differentiated tumor-associated macrophages (TAMs) and two distinct differentiation trajectories are related to their accumulation. We further demonstrated CD11b+ TAMs promote HCC cells invasion and migration, and angiogenesis. Our data also revealed the heterogeneous population of malignant hepatocytes and their potential multifaceted roles in shaping the immune microenvironment of HCC. Finally, we identified seven TME subtypes of HCC that can predict patient prognosis. Collectively, this large-scale, single-cell atlas deepens our understanding of the ecosystem in primary and metastatic HCCs, might facilitating the development of new immune therapy strategies for this malignancy.

Droplet-based scRNA-seq and gene expression quantification

Single-cell suspensions were converted to barcoded scRNA-seq libraries by using the Chromium Single Cell 3’ Library, Gel Bead & Multiplex Kit and Chip Kit (10x Genomics), aiming for an estimated 5,000 cells per library and following the manufacturer’s instructions. Samples were processed using kits pertaining to V2 barcoding chemistry of 10x Genomics. Single samples are always processed in a single well of a PCR plate, allowing all cells from a sample to be treated with the same master mix and in the same reaction vessel. For each patient, all samples (NTL, PT, PVTT and MLN) were processed in parallel in the same thermal cycler. The generated scRNA-seq libraries were sequenced on a NovaSeq sequencer (Illumina). The Cell Ranger software (version 2.2.0; 10x Genomics) was used to perform sample demultiplexing, barcode processing and single-cell 3’ counting. Cell Ranger’s mkfastq function was used to demultiplex raw base call files from the sequencer, into sample-specific fastq files. Afterward, fastq files for each sample were processed with Cell Ranger’s count function, which was used to align reads to human genome (build hg38) and quantify gene expression levels in single cells.

Quality control and batch correction

To filter out low-quality cells and doublets (two cells encapsulated in a single droplet), for each sample, cells were removed that had either fewer than 200 unique molecular identifiers (UMIs), over 8,000 or below 200 expressed genes. To filter out dead or dying cells, cells were further removed that had over 10% UMIs derived from mitochondrial genome. This resulted in a total of 71,915 high-quality single-cell transcriptomes in all samples.

To further merge samples across tissues and patients, we run a canonical correlation analysis (CCA) for batch correction using the RunMultiCCA function in R package Seurat v2. To calculate canonical correlation vectors (CCVs), variably expressed genes were selected for each sample as having a normalized expression between 0.125 and 3, and a quantile-normalized variance exceeding 0.5, and then combined across all samples. The resulting 2,773 non-redundant variable genes were summarized by CCA, and the first 15 CCVs were aligned to combine raw gene expression matrices generated per sample. The aligned CCVs were also used for tSNE dimensionality reduction using the RunTSNE function in Seurat.

Cell clustering

For cell clustering, we used the FindClusters function in Seurat v2 that implements shared nearest neighbor (SNN) modularity optimization-based clustering algorithm on 30 aligned CCVs with resolution 1–4, leading to 26–61 clusters. A resolution of 3 was chosen for the analysis and a final of 53 clusters were obtained.

RegVar tutorials

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Brief introduction

RegVar is a deep neural network-based computational server for prioritizing tissue-specific regulatory impact of human noncoding SNPs on their potential target genes. RegVar integrates the sequential, epigenetic and evolutionary conservation profiles of SNPs and their potential target genes in 17 human tissues, and give tissue-specific predictions of regulatory probabilities of the provided SNPs on provided genes.

Input

Upload a file containing a list of SNPs and genes

A text file containing a list of SNP IDs and their possible target genes is required to be uploaded to the server for batch analysis.

The result will be generated based on all pairwise combinations of SNPs and genes. SNPs and genes lacking annotations are excluded and pairs of SNPs and genes that are located on different chromosomes are removed. The remaining pairs are referred to as valid pairs and RegVar would accept no more than 10,000 valid pairs.
Click here to see an example file

Or type SNP ID(s) and gene(s) in the corresponding search boxes

SNP ID(s) (indels are currently not supported) and their possible target gene(s) are accepted as input in the SNP and Gene search box, respectively. Multiple SNP IDs or genes should be delimited by commas, spaces or tabs, and if so, the result will be generated based on all pairwise combinations of SNPs and genes.

Output

All results will be listed in the result page, including the basic information of your query data (the positions of the input SNPs and TSSs of genes and the genomic distance between them, in GRCh37/hg19 genome coordinates) (positions of TSSs are annotated from GTEx eGene list, v7 release), and the regulatory probabilities calculated by RegVar.

Raw probability scores come straight from the tissue-specific model, and are interpretable as the extent to which the SNP is likely to have an effect on the regulation of the corresponding gene in your selected tissue.

A result file containing the same information will be sent to your email address, if you have it input.

Model selection

The RegVar website computes RegVar scores based on the DHS-filtered models trained on GTEx datasets. Besides, We also provide the scripts to train non-DHS-filtered models (or full models) on GTEx datasets and to train pathogenic RegVar models on HGMD dataset. Click the following download link for more information.

Software download

The datasets and source code to run RegVar locally are freely available at the download page.

3dsnp v2.0 Data Download

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All data from 3dsnp including high-order modified predictions can be accessed through FTP.

You could also click links in the following table.

PS: If you have any questions or would like to access additional data, please leave a message.

Data Format Link
dbSNP154 Vcf example: chr1
HGSVC2 Vcf pangenie_merged_bi_nosnvs.integrated_callset.hg19
dbSNP153 BigBed dbSnp153Common.bb
Gene annotations GFF GCF_000001405.25_GRCh37.p13_genomic
Gene annotations RefSeq ncbiRefSeq
Assembly Fasta hg19.fa
ENCODE BigWig example: Gm12878 H3k27ac
RepeatMasker BigBed repeats
Fixation index Bed example: chr1 AMR
xp-NSL Bed example: chr1 AMR
ClinVar BigBed clinvarCnv
clinvarMain
ClinGen BigBed clinGenHaplo
clinGenTriplo
clinGenGeneDisease
scATAC-fetal BigWig example: thymus_vascular_endothelial_cells
HiC loops loop raw: Ventricle_Right
mod: Ventricle_Right
target: chr17-42337882-DEL-540 Ventricle_Right

3dsnp v1.0 API

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3dsnp for developers

3DSNP provides a more powerful way for users to access the data through the use of API. SNP data can be accessed by two means: SNP ids or Chromatin position.

Overview

URL

http://cbportal.org/3dsnp/api.do

Format supported

JSON/XML

HTTP request method

GET/POST

Login required

No

Data access restrictions

Frequency limit: No

Request

Request parameters

Required Type Information
id/position true string Represents the SNP ID or genomic position, at least one of them is required,
multiple SNP IDs or positions should separated by comma ‘,’.
Dash symbol ‘-‘
The format of parameter ‘position’
should be ‘1000000-1000100’.
chrom false string Represents the chromosome of queried position and is required when parameter ‘position’ is used.
When there are more than one positions, the corresponding chromosomes should also be separated by ‘,’.
type true string Data type for searching, multiple types should be separated by comma ‘,’.
Available types are listed below.
format true string Represents data types returns. Json and XML formats are supported.

Request data type

DataType Description
basic Basic information of SNP, including sequential facts and phenotype from 1000G project.
chromhmm Chromatin state information generated by the core 15-state ChromHMM models trained
across a variety of cell types.
motif Transcription factor binding motifs altered by SNP.
tfbs Transcription factor binding sites in a variety of cell types.
eqtl Expression quantitative trait loci (eQTL).
3dgene Genes that interact the query SNP through chromatin loops.
3dsnp SNPs that interact the query SNP through chromatin loops. Not available for the query of position.
phylop PhyloP scores of genomic region surrounding the query SNP.

Response

Response parameters

Type DataType Description
id string basic SNP ID
chr string basic Chromosome name
position string basic Location of the query
MAF string basic Minor allele frequency
Ref string basic Reference Allele
Alt string basic Alternative Allele
EAS string basic Allele frequency in the EAS populations
AMR string basic Allele frequency in the AMR populations
AFR string basic Allele frequency in the AFR populations
EUR string basic Allele frequency in the EUR populations
SAS string basic Allele frequency in the SAS populations
linearClosestGene string basic Linear cloest genes
data_gene JsonArray basic listed below in JsonArray Parameters
chromhmm string chromhmm Chromatin state from ChromHMM core 15-state model
data_chromhmm JsonArray chromhmm listed below in JsonArray Parameters
motif string motif Sequence motif altered by the query SNP
data_motif JsonArray motif listed below in JsonArray Parameters
tfbs string tfbs Transcription factor binding sites the query locates
data_tfbs JsonArray tfbs listed below in JsonArray Parameters
eqtl string eqtl Expression quantitative trait loci
data_eqtl JsonArray eqtl listed below in JsonArray Parameters
data_loop_gene JsonArray 3dgene listed below in JsonArray Parameters
data_loop_snp JsonArray 3dsnp listed below in JsonArray Parameters
physcores string physcores PhyloP scores of the query SNP and its +/-10 bp adjacent regions

JsonArray Parameters

Type JsonArray Description
geneID string data_gene RefSeq Gene ID
geneName string data_gene Official gene symbol
geneRelativePosition string data_gene Relative position of the closest gene to the query
geneDescription string data_gene Gene description
chromhmmCell string data_chromhmm Cell type of the corresponding chromatin state
chromhmmName string data_chromhmm Short name of chromatin state
chromhmmFullName string data_chromhmm Full name of chromatin state
chromhmmCellDescription string data_chromhmm Cell type description
chromhmmTissue string data_chromhmm Tissue of the cell type
motif string data_motif Motif ID in TRANSFAC or JASPAR
motifStrand string data_motif Strand of the motif
motifSource string data_motif Database source of the motif
motifMatchedSequence string data_motif Matched sequence for the motif
motifMatchedSequencePos string data_motif Relative position of the query to the sequence
motifRef string data_motif Reference allele
motifAlt string data_motif Alternative allel
tfbsCell string data_tfbs Cell type of the corresponding TFBS
tfbsFactor string data_tfbs Name of the transcription factor
tfbsCellTissue string data_tfbs Tissue of the cell type
tfbsDNAAccessibility string data_tfbs DNA accessibility of the TFBS
tfbsCellDescription string data_tfbs Description for the cell type
eqtlGene string data_eqtl Related gene of the eQTL
eqtlPValue string data_eqtl P-value of the eQTL
eqtlTissue string data_eqtl Tissue in which the eQTL identified
eqtlEffect string data_eqtl Effect size of the eQTL
loopGene string data_loop_gene Genes interacting the query SNP through chromatin loops
loopGeneID string data_loop_gene RefSeq Gene ID
loopGeneDescription string data_loop_gene Gene description
loopCell string data_loop_gene/data_loop_snp Cell type in which the chromatin loop was identified
loopCellTissue string data_loop_gene/data_loop_snp Tissue of the cell type
loopCellDescription string data_loop_gene/data_loop_snp Cell type description
loopStart string data_loop_gene/data_loop_snp Start genomic position of the chromatin loop
loopEnd string data_loop_gene/data_loop_snp End genomic position of the chromatin loop
loopType string data_loop_gene/data_loop_snp Type of the chromatin loop: “Within Loop” or “Anchor-to-Anchor”
loopSNP string data_loop_snp SNPs interacting with the query and in the same LD block through chromatin loops
loopLD string data_loop_snp r^2 in LD
loopPopulation string data_loop_snp Continental population (AFR, AMR, ASN, EUR and SAS)

Request with id

URL example1 : single snp and single data type in json format

Request URL :

http://3dsnp.cbportal.org/api.do?id=rs1000&format=json&type=basic

Response format :

[{
"id":"rs1000",
"position":"32153894",
"chrom":"chr6",
"AFR":"",
"AMR":"",
"Alt":"",
"EAS":"",
"EUR":"",
"Ref":"",
"SAS":"",
"MAF":"",
"linearClosestGene":"AGER,177,upstream-variant-2KB;PBX2,5089,utr-variant-3-prime",
"data_gene": [
    {
    "geneID":"177",
    "geneName":"AGER",
    "geneRelativePosition":"upstream-variant-2KB",
    "geneDescription":"advanced glycosylation end product-specific receptor"
    },
    {
    "geneID":"5089",
    "geneName":"PBX2",
    "geneRelativePosition":"utr-variant-3-prime",
    "geneDescription":"pre-B-cell leukemia homeobox 2"
    }
]}]

URL example2 : mutilple snps and mutilple data types in xml format

Request URL :

http://3dsnp.cbportal.org/api.do?id=rs1000,rs10&format=xml&type=basic,eqtl,motif

Response format :

<?xml version="1.0" encoding="utf-8"?>
<a>
<e class="object">
<AFR type="string" />
<AMR type="string" />
<Alt type="string" />
<EAS type="string" />
<EUR type="string" />
<MAF type="string" />
<Ref type="string" />
<SAS type="string" />
<chrom type="string">chr6</chrom>
<data_gene class="array">
<e class="object">
<geneDescription type="string">advanced glycosylation end product-specific receptor</geneDescription>
<geneID type="string">177</geneID>
<geneName type="string">AGER</geneName>
<geneRelativePosition type="string">upstream-variant-2KB</geneRelativePosition>
</e>
<e class="object">
<geneDescription type="string">pre-B-cell leukemia homeobox 2</geneDescription>
<geneID type="string">5089</geneID>
<geneName type="string">PBX2</geneName>
<geneRelativePosition type="string">utr-variant-3-prime</geneRelativePosition>
</e>
</data_gene>
<eqtl type="string" />
<id type="string">rs1000</id>
<linearClosestGene type="string">AGER,177,upstream-variant-2KB;PBX2,5089,utr-variant-3-prime</linearClosestGene>
<motif type="string" />
<position type="number">32153894</position>
</e>
<e class="object">
<AFR type="string">0.997</AFR>
<AMR type="string">0.9524</AMR>
<Alt type="string">C</Alt>
<EAS type="string">1</EAS>
<EUR type="string">0.9453</EUR>
<MAF type="string">A,0.019369</MAF>
<Ref type="string">A</Ref>
<SAS type="string">0.9949</SAS>
<chrom type="string">chr7</chrom>
<data_gene class="array">
<e class="object">
<geneDescription type="string">cyclin-dependent kinase 6</geneDescription>
<geneID type="string">1021</geneID>
<geneName type="string">CDK6</geneName>
<geneRelativePosition type="string">intron-variant</geneRelativePosition>
</e>
</data_gene>
<eqtl type="string" />
<id type="string">rs10</id>
<linearClosestGene type="string">CDK6,1021,intron-variant</linearClosestGene>
<motif type="string" />
<position type="number">92383887</position>
</e>
</a>

Request with position

URL example3 : single position and single data type in json format

Request URL :

http://3dsnp.cbportal.org/api.do?position=1000000-1100000&chrom=chr11&format=json&type=basic

Response format :

[{
"id":"rs544411125",
"position":"1000017",
"chrom":"chr11",
"AFR":"0",
"AMR":"0",
"Alt":"A",
"EAS":"0",
"EUR":"0",
"Ref":"G",
"SAS":"0.001",
"MAF":"A,0.000199681",
"linearClosestGene":"AP2A2,161,intron-variant",
"data_gene":[
    {
    "geneID":"161",
    "geneName":"AP2A2",
    "geneRelativePosition":"intron-variant",
    "geneDescription":"adaptor related protein complex 2 alpha 2 subunit"
    }]
},
{
"id":"rs561110574",
"position":"1000027",
"chrom":"chr11",
"AFR":"0.0015",
"AMR":"0",
"Alt":"T",
"EAS":"0",
"EUR":"0",
"Ref":"G",
"SAS":"0",
"MAF":"T,0.000399361",
"linearClosestGene":"AP2A2,161,intron-variant",
"data_gene":[
    {
    "geneID":"161",
    "geneName":"AP2A2",
    "geneRelativePosition":"intron-variant",
    "geneDescription":"adaptor related protein complex 2 alpha 2 subunit"
    }]}
]

URL example4 : single position and mutilple data types in xml format

Request URL :

http://3dsnp.cbportal.org/api.do?position=100000-1000100&chrom=chr1&format=xml&type=eqtl,motif

Response format :

<?xml version="1.0" encoding="utf-8"?>
<a>
<e class="object">
<chrom type="string">chr1</chrom>
<data_motif class="array">
<e class="object">
<motif type="string">HEN1_02</motif>
<motifAlt type="string">G</motifAlt>
<motifMatchedSequence type="string">CAGGAAAGCAGCTGGGGGTCCA</motifMatchedSequence>
<motifMatchedSequencePos type="string">21</motifMatchedSequencePos>
<motifRef type="string">A</motifRef>
<motifSource type="string">Transfac</motifSource>
<motifStrand type="string">+</motifStrand>
</e>
</data_motif>
<eqtl type="string" />
<id type="string">rs537152617</id>
<motif type="string">Transfac,HEN1_02,+,CAGGAAAGCAGCTGGGGGTCCA,21</motif>
<position type="number">1000036</position>
</e>
<e class="object">
<chrom type="string">chr1</chrom>
<data_motif class="array">
<e class="object">
<motif type="string">MUSCLE_INI_B</motif>
<motifAlt type="string">T</motifAlt>
<motifMatchedSequence type="string">TCCCGTGGCCATTCAGGCGCC</motifMatchedSequence>
<motifMatchedSequencePos type="string">4</motifMatchedSequencePos>
<motifRef type="string">C</motifRef>
<motifSource type="string">Transfac</motifSource>
<motifStrand type="string">-</motifStrand>
</e>
<e class="object">
<motif type="string">MINI19_B</motif>
<motifAlt type="string">T</motifAlt>
<motifMatchedSequence type="string">TCCCGTGGCCATTCAGGCGCC</motifMatchedSequence>
<motifMatchedSequencePos type="string">4</motifMatchedSequencePos>
<motifRef type="string">C</motifRef>
<motifSource type="string">Transfac</motifSource>
<motifStrand type="string">-</motifStrand>
</e>
<e class="object">
<motif type="string">MINI20_B</motif>
<motifAlt type="string">T</motifAlt>
<motifMatchedSequence type="string">TCCCGTGGCCATTCAGGCGCC</motifMatchedSequence>
<motifMatchedSequencePos type="string">4</motifMatchedSequencePos>
<motifRef type="string">C</motifRef>
<motifSource type="string">Transfac</motifSource>
<motifStrand type="string">-</motifStrand>
</e>
</data_motif>
<eqtl type="string" />
<id type="string">rs573794673</id>
<motif type="string">Transfac,MUSCLE_INI_B,-,TCCCGTGGCCATTCAGGCGCC,4;Transfac,MINI19_B,-,TCCCGTGGCCATTCAGGCGCC,4;Transfac,MINI20_B,-,TCCCGTGGCCATTCAGGCGCC,4</motif>
<position type="number">1000090</position>
</e>
<e class="object">
<chrom type="string">chr11</chrom>
<eqtl type="string" />
<id type="string">rs544411125</id>
<motif type="string" />
<position type="number">1000017</position>
</e>
<e class="object">
<chrom type="string">chr11</chrom>
<eqtl type="string" />
<id type="string">rs561110574</id>
<motif type="string" />
<position type="number">1000027</position>
</e>
</a>

Sample code for java developers

JSON-lib is required for the example, a java library for transforming beans, maps, collections, java arrays and XML to JSON and back again to beans and DynaBeans. You could download it in https://sourceforge.net/projects/json-lib/

public static void main(String[] args) {

    String res_str = MyHttpRequest.sendPost("http://cbportal.org/3dsnp/api.do", "id=rs900012&format=json&type=basic,eqtl,motif");
    JSONArray res_array = JSONArray.fromObject(res_str);
    
    StringBuilder builder = new StringBuilder();
    
    for (int index_snp = 0; index_snp < res_array.size();index_snp++){
    
    JSONObject res_obj = res_array.getJSONObject(index_snp);
    builder.append("id ： "+ res_obj.getString("id")+" , chrom : " +res_obj.getString("chrom") + " , num_eqtl : "+res_obj.getJSONArray("data_eqtl").size()+" , num_motif : " + res_obj.getJSONArray("data_motif").size()+"\n");
    }
    
    System.out.println(builder.toString());
}

MyHttpRequest.class is used to send HTTP request.

public class MyHttpRequest {

public static String sendGet(String url, String param) {
    String result = "";
    BufferedReader in = null;
    try {
        String urlNameString = url + "?" + param;
        URL realUrl = new URL(urlNameString);
        
        URLConnection connection = realUrl.openConnection();
        
        connection.setRequestProperty("accept", "*/*");
        connection.setRequestProperty("connection", "Keep-Alive");
        connection.setRequestProperty("user-agent",
        "Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1;SV1)");
        
        connection.connect();
        
        Map<String, List<String>> map = connection.getHeaderFields();
        
        for (String key : map.keySet()) {
            System.out.println(key + "--->" + map.get(key));
        }

        in = new BufferedReader(new InputStreamReader(connection.getInputStream(),"UTF-8"));
        String line;
        while ((line = in.readLine()) != null) {
            result += line;
        }
    } catch (Exception e) {
        e.printStackTrace();
    } finally {
        try {
            if (in != null) {
                in.close();
            }
        } catch (Exception e2) {
            e2.printStackTrace();
        }
    }
    return result;
}

public static String sendPost(String url, String param) {
    PrintWriter out = null;
    BufferedReader in = null;
    String result = "";
    try {
        URL realUrl = new URL(url);
        URLConnection conn = realUrl.openConnection();
        
        conn.setRequestProperty("accept", "*/*");
        conn.setRequestProperty("connection", "Keep-Alive");
        conn.setRequestProperty("user-agent",
        "Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1;SV1)");
        
        conn.setDoOutput(true);
        conn.setDoInput(true);
        
        out = new PrintWriter(conn.getOutputStream());
        out.print(param);
        out.flush();
        
        in = new BufferedReader(
        new InputStreamReader(conn.getInputStream(),"UTF-8"));
        String line;
        while ((line = in.readLine()) != null) {
            result += line;
        }
    } catch (Exception e) {
        e.printStackTrace();
    } finally {
        try{
            if(out!=null){
                out.close();
            }
            if(in!=null){
                in.close();
            }
        } catch(IOException ex){
            ex.printStackTrace();
        }
    }
}