然后分别加载后熟悉一下: ## Bulk RNA-seq data #benchmark.eset = readRDS("https://xuranw.github.io/MuSiC/data/bulk-eset.rds") benchmark.eset = readRDS("bulk-eset.rds") benchmark.eset table(benchmark.eset$group) bulk.control.mtx = exprs(benchmark.eset)[, benchmark.eset$group == 'he...
This result showed high reproducibility of sbRNA-seq. Since the protocol of sbRNA-seq is based on the original Drop-seq1, the improvement of microbeads and reaction conditions used in this method would further increase gene detection efficiency. Scientific Reports | Vol:.(1234567890) (2022) 12...
For single-cell RNA-sequencing (scRNA-seq), cells were individually sorted into 8-Strip PCR tubes containing the lysis buffer. For bulk RNA-seq, 100 cells (P100) were sorted into one PCR tube as a biological replicates. The batch information for cell sorting was included in Online-only Ta...
RNA-sequencing has become a standard tool for analyzing gene activity in bulk samples and at the single-cell level. By increasing sample sizes and cell counts, this technique can uncover substantial information about cellular transcriptional states. Beyond quantification of gene expression, RNA-seq ca...
## Bulk RNA-seq data #benchmark.eset = readRDS("https://xuranw./MuSiC/data/bulk-eset.rds") benchmark.eset = readRDS("bulk-eset.rds") benchmark.eset table(benchmark.eset$group) bulk.control.mtx = exprs(benchmark.eset)[, benchmark.eset$group =='healthy'] ...
In this study, we systematically integrated both scRNA-seq and bulk RNA-seq data to identify TIME-related lncRNAs. To further explore the clinical significance of TIME-related lncRNAs, we applied ten popular machine learning algorithms to generate an optimal TIME-related lncRNA signature (TRLS). ...
We found that, with the same processed RNA, the SCRB-seq protocol uncovered ~ 20% less total differential expressed (DE) genes than the 1M downsampled TruSeq (Fig. 1c, 10 random downsampling). More importantly, the downsampled TruSeq was able to uncover ~ 35% more DE genes that ...
The RNA-seq data of IER3 were obtained from TCGA (https://portal.gdc.cancer.gov/) and CGGA databases and transformed into log2(TPM + 1)-normalized profiles [16]. The microarray data of IER3 in the GSE16011 set were downloaded from the Gene Expression Omnibus (GEO) database (http...
## Bulk RNA-seq data #benchmark.eset = readRDS("https://xuranw.github.io/MuSiC/data/bulk-eset.rds") benchmark.eset = readRDS("bulk-eset.rds") benchmark.eset table(benchmark.eset$group) bulk.control.mtx = exprs(benchmark.eset)[, benchmark.eset$group == 'healthy'] bulk.case.mtx ...
To ensure the consistency of the data, we applied the “limma” package to screen the scRNA-seq and bulk RNA-seq data for common significantly different genes, and then selected 20 genes for in-depth analysis (Fig.4D,E). Subsequently, we classified the 20 CRCSCs-related genes, which were...