smart seq icell8 cds (TaKaRa)
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Smart Seq Icell8 Cds, supplied by TaKaRa, used in various techniques. Bioz Stars score: 93/100, based on 4 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Average 93 stars, based on 4 article reviews
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1) Product Images from "A Comprehensive Multi-Center Cross-platform Benchmarking Study of Single-cell RNA Sequencing Using Reference Samples"
Article Title: A Comprehensive Multi-Center Cross-platform Benchmarking Study of Single-cell RNA Sequencing Using Reference Samples
Journal: bioRxiv
doi: 10.1101/2020.03.27.010249
Figure Legend Snippet: (a) . Schematic overview of the study design. Two well-characterized reference cell lines (sample A of a breast cancer cell line & sample B of a matched control normal B lymphocyte cell line) were used to generate scRNA-seq data across four platforms (10X Genomics, Fluidigm C1, Fluidigm C1 HT, and WaferGen), five testing sites (10X_LLU, 10X_NCI, C1_FDA_HT, C1_LLU, and WaferGen) using standard manufactures’ protocols. At 10X_LLU and 10X_NCI sites, mixed singe-cell capture and library constructions were also prepared with either 10% or 5% cancer cells spiked into B lymphocytes. At the NCI site, single-cell capture and library construction was also performed in fixed and mixed cells (5% cancer cell spiked into B lymphocytes). One set of 10X scRNA libraries from NCI was also sequenced using a shorter modified sequencing method. Bulk cell level RNA-seq data were also obtained from these cell lines, each in triplicate. All scRNA-seq data were subject to 3 different pre-processing pipelines for either 10X or C1/WaferGen technologies, respectively. We evaluated seven normalization methods such as Scran Deconvolution, CPM, LogCPM, TMM, DESeq, Quantile, and linnorm and seven batch effect correction algorithms including CCA, MNN, Scanorama, BBKNN, Harmony, limma, and ComBat. The cross-platform and cross-center performances were evaluated further by t-SNE, UMAP, modified alignment score, and both dot and feature plotting on certain selected marker genes. Abbreviations and notations for Fig. 1a : 10X_LLU , single cells were captured using 10X Genomics Chromium controller and scRNA-seq were sequenced at LLU Center for Genomics using the standard 10X Genomics protocol (26×98 bp); 10X_NCI_M , 10X Genomics scRNA-seq libraries were prepared and sequenced at NCI sequencing facility using a modified 10X sequencing protocol (26×56 bp); 10X_NCI , the same 10X Genomics scRNA-seq libraries were prepared at the NCI sequencing facility but sequenced at LLU using the standard 10X sequencing protocol (26×98 bp); C1_FDA_HT , single cells were captured using Fluidigm C1 HT IFC and the scRNA-seq libraries were sequenced at the FDA/CBER sequencing facility (75×2 bp); C1_LLU , single cells were captured using Fluidigm C1 IFC chip and the scRNA-seq libraries were sequenced at the LLU Center for Genomics (150×2 bp, ∼4-4.77M reads/cell); WaterGen_PE , single cells were captured using the ICELL8 chip (Takara Bio) and scRNA-seq libraries were sequenced at paired ends (75×2 bp) at Takara Bio; WaterGen_SE , the same scRNA-seq libraries generated at Takara Bio were sequenced at the LLU Center for Genomics (150×1 bp, ∼1M reads/cell). See Table 1 for detail on the numbers of single cells captured and sequencing read depths in each platform and each site. (b). For both the breast cancer cell line (A) and normal B lymphocyte cell line (B) across 7 data sets, percentage of reads mapped to the exonic region (blue), non-exonic region (orange), or not mapped to the human genome (gray). For UMI methods (10X genomics platform), dark blue indicates the exonic reads with UMIs. (c). Median number of genes detected per cell at different sequencing read depth. Solid line represents the breast cancer cell line (A). Dashed line represents the normal B lymphocyte cell line (B).
Techniques Used: Modification, Sequencing, RNA Sequencing Assay, Marker, Generated