DAC

Peking University BIOPIC Data Access Committee (PUBDAC).The DATA ACCESS AGREEMENT is provided at https://github.com/zhangyybio/single-T-cell-data-access. Applicants can request access to the data by directly downloading it or by sending an email to cancerpku@pku.edu.cn. The process that is used to approve an application includes verifying the institution, participants and research purposes of the application. In general this process will take about two weeks. In principal, any academic research institutions complying with the laws and bioethic regulation policies of China will be approved.

Dac ID Contact Person Email Access Information
EGAC00001000551 Xianwen Ren renxwise [at] pku [dot] edu [dot] cn No additional information is available

This DAC controls 10 datasets:

Dataset ID Description Technology Samples
EGAD00001003337 T cells isolated from peripheral blood, tumors and adjacent normal tissues from six hepatocellular carcinoma patients. SmartSeq2 and Tang2009 protocol were used to amplify RNA from single T cells. High depth enables simultaneously expression profiling and TCR assembling. Illumina HiSeq 2500,Illumina HiSeq 4000 5063
EGAD00001003910 Deep single-cell RNA sequencing data for 11,138 T cells from tumour, adjacent normal tissue and peripheral blood of treatment-naive CRC patients. The DATA ACCESS AGREEMENT is provided at https://github.com/zhangyybio/single-T-cell-data-access. Applicants can request access to the data by directly downloading it or by sending an email to cancerpku@pku.edu.cn. The process that is used to approve an application includes verifying the institution, participants and research purposes of the application. In general this process will take about two weeks. In principal, any academic research institutions complying with the laws and bioethic regulation policies of China will be approved. Illumina HiSeq 4000 11138
EGAD00001003999 Deep single-cell RNA sequencing data for 12346 T cells from tumour, adjacent normal tissue and peripheral blood of treatment-naïve NSCLC patients Illumina HiSeq 2500,Illumina HiSeq 4000 12346
EGAD00001004148 bulk RNA-seq data of the 5 HCC patinets. Single cell RNA seq data of these patients was under the accession number EGAD00001003337 Illumina HiSeq 4000 5
EGAD00001004149 bulk Exome-seq data of the 5 HCC patinets. Single cell RNA seq data of these patients was under the accession number EGAD00001003337 Illumina HiSeq 4000 10
EGAD00001005365 Single-cell sequencing of human pancreatic cells on 10X 5' platform. Illumina HiSeq 4000 8
EGAD00001005373 In this study, we performed systematic comparative analysis of seven widely-used SNV-calling methods, including SAMtools, the GATK Best Practices pipeline, CTAT, FreeBayes, MuTect2, Strelka2 and VarScan2, on both simulated and real single-cell RNA-seq datasets. We generated SMART-seq2 data for 70 CD45- single cells, which were derived from two colorectal cancer patients (P0411 and P0413). The average sequencing depths of these cells were 1.4 million reads per cell. We also generated tumor and adjacent normal bulk WES data, as well as tumor bulk RNA-seq data for these patients. Illumina HiSeq 4000 75
EGAD00001005448 Single cell RNA sequencing (scRNA-seq) is widely used for profiling transcriptomes of individual cells. The droplet-based 10X Genomics Chromium (10X) approach and the plate-based Smart-seq2 full-length method are two frequently-used scRNA-seq platforms, yet there are only a few thorough and systematic comparisons of their advantages and limitations. Here, by directly comparing the scRNA-seq data by the two platforms from the same samples of CD45- cells, we systematically evaluated their features using a wide spectrum of analysis. Smart-seq2 detected more genes in a cell, especially low abundance transcripts as well as alternatively spliced transcripts, but captured higher proportion of mitochondrial genes. The composite of Smart-seq2 data also resembled bulk RNA-seq data better. For 10X-based data, we observed higher noise for mRNA in the low expression level. Despite the poly(A) enrichment, approximately 10-30% of all detected transcripts by both platforms were from non-coding genes, with lncRNA accounting for a higher proportion in 10X. 10X-based data displayed more severe dropout problem, especially for genes with lower expression levels. However, 10X-data can better detect rare cell types given its ability to cover a large number of cells. In addition, each platform detected different sets of differentially expressed genes between cell clusters, indicating the complementary nature of these technologies. Our comprehensive benchmark analysis offers the basis for selecting the optimal scRNA-seq strategy based on the objectives of each study. Illumina HiSeq 4000 78
EGAD00001005960 The immune microenvironment of hepatocellular carcinoma (HCC) is poorly characterized. Combining two single-cell RNA sequencing technologies, we produced transcriptomes of CD45+ immune cells for HCC patients from five immune-relevant sites: tumor, adjacent liver, hepatic lymph node (LN), blood, and ascites. This dataset is part of Smartseq2 data Illumina HiSeq 4000 17
EGAD00001005961 The immune microenvironment of hepatocellular carcinoma (HCC) is poorly characterized. Combining two single-cell RNA sequencing technologies, we produced transcriptomes of CD45+ immune cells for HCC patients from five immune-relevant sites: tumor, adjacent liver, hepatic lymph node (LN), blood, and ascites. This is the droplet data of this study Illumina HiSeq 4000 19