The single-cell RNA sequencing data analysis was performed on the Maxwell High-Performance Cluster at the University of Houston. The analytical program used was the Cell Ranger 2.1.1 Single Cell Analysis Pipelines (10X Genomics). Raw base call files generated by Nextseq 500 were demultiplexed using “cellranger mkfastq” pipeline to FASTQ files. FASTQ files were aligned to hg38 human reference genome using “cellranger count” which used STAR aligner (20 (link)). Gene expression matrix was reduced using Principal Components Analysis (PCA) and visualized in 2-d space by passing PCA data into t-distributed stochastic neighbor embedding (t-SNE), a nonlinear dimensionality reduction method (23 ). Graph-based hierarchical clustering algorithm operating in PCA space was used to cluster cells based on the similarity of expression. Differentially expressed genes between clusters were found using sSeq method (24 ). The top 100 differentially expressed genes from cluster-1 were used for gene ontology. The gene list was analyzed using Gene MANIA package on Cytoscape.
Cell ranger 2.1.1 single cell analysis pipelines
Manufactured by 10x Genomics
Sourced in United States
Cell Ranger 2.1.1 is a software pipeline for analyzing single cell RNA sequencing data. It provides tools for sample demultiplexing, barcode processing, alignment, and gene counting.
Automatically generated - may contain errors
Lab products found in correlation
2 protocols using cell ranger 2.1.1 single cell analysis pipelines
1
Single-Cell RNA-Seq Data Analysis Workflow
2
Single-Cell Transcriptome Profiling Pipeline
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Single cell sequencing data downstream analysis was performed on the Maxwell Cluster high-performance research computing center at University of Houston, Houston, Texas, while using the analytical program, Cell Ranger 2.1.1 Single Cell Analysis Pipelines (10X Genomics, Pleasanton, CA, USA). Raw base call files that were generated by Nextseq 500 were demultiplexed using the “cellranger mkfastq” pipeline to FASTQ files. FASTQ files were aligned to the hg38 human reference genome using “cellranger count” using the STAR aligner [54 (link)]. The dimensionality of the gene-expression data was reduced to two-dimensions (2-d) using t-Stochastic Neighbor Embedding (tSNE), a nonlinear dimensionality reduction method [55 ]. A graph-based hierarchical clustering algorithm operating in this two-dimensional space was then used to cluster cells based on similarity of expression. These clusters were then associated with specific sub-type of T-cells (see Results) while using marker genes that were identified from the heat map of relative gene expression values [56 (link)].
About PubCompare
Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.
We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.
However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.
Ready to get started?
Sign up for free.
Registration takes 20 seconds.
Available from any computer
No download required
Revolutionizing how scientists
search and build protocols!