scTWASdb for developers

Underdeveloping

Underdeveloping

2023.7.1

• Add Google Analytics.
• Fix errors in displaying the position of a target SNP.

2023.6.28

• scTWAS v1.0 online

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.

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.

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.

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.

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

Request data type

Response

Response parameters

JsonArray Parameters

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();
        }
    }
}

2023.7.26

• Update the source of database (both the url of 3dsnp.omic.tech and www.omic.tech/3dsnpv2) to local server through frp proxy

2023.5.16

• Fix errors in display LD information in the IGV browser;
• Fix errors in display summary information in the detail page.

2023.5.15

• Update the format of Circos-plot to SVG;
• Update the source of database (only accessed by 3dsnp.omic.tech) to local server through frp proxy;
• Fix errors in display allele and score information for SNPs without HiC interactions.

2022.4.11

• Fix link to 3dsnp v1.0 (cbportal is outdated).

2022.3.31

• Fix export errors for excel.
• Add an secure alternative link 3dsnp.omic.tech

2021.12.19

• Fix export errors for igvtools.

2021.12.18

• Add allele frequency information of major populations in the export data for the main table.
• 3dsnp v2.0 have been published on NAR. So citing information in the pagefoot is updated.
• Fix excel export button for the main table.

2021.10.6

• Add a pie chart for the nearest scATAC peaks
• Add Zoom functions, point labels, and borders to the Umap of the nearest scATAC peaks

2021.10.4

• Add all tables for SVs from ClinVar.
• Add annotations about ClinVar in the 3dsnp v2 tutorials.
• Update documentation links to the 3dsnp v2 tutorials and API.
• Update scores for SVs from HGSVC.

2021.10.3

• Add SV-data from ClinVar.
• Add Pathogenicity-data from ClinVar for dbSNP v155.
• Add Pathogenicity-track in the IGVtools.

2021.09.24

• Add LD-data for SVs.
• Add LD-data for SNPs in AFR population.
• Add SNP affected loops for each tissue.
• Fix circos plot errors: no LD snps.

2021.09.19

• Update documentation links.
• Add 3dnps v2.0 documentation.
• Add wordpress documentation for omic.tech.

2021.09.18

• Add IGV tracks for Fst and xpNSL per each major population in IGSR 1000 genomes.
• Add pagings for each table in the details page.

2021.09.16

• Add LD-data check when user click the LD-detail button.
• Fix loading picture error.
• Fix picture saving error of the scATAC-plot.

2021.09.15

• Add IGVtools.
• Add HGSVC2 structural variations and Hi-C structure predictions.
• Add scATAC data.
• Add cCRE scores from scRNA-seq data.
• Add statisticss of population genetics.
• Update snp collections to dbSNP v154.
• Fix foot location error.