The cellranger vdj pipeline can be used to analyze sequencing data produced from Chromium Next GEM Single Cell 5' V(D)J libraries. It takes FASTQ files for V(D)J libraries and performs sequence assembly and paired clonotype calling. The pipeline uses the Chromium Cell Barcodes (also called 10x Barcodes) and UMIs to assemble V(D)J transcripts per cell. Clonotypes and CDR3 sequences are output as a .vloupe file which can be loaded into Loupe V(D)J Browser. Visit the What is Cell Ranger page to learn more about Cell Ranger for Immune Profiling. (10X Genomics)

Pseudotime analysis provides a transformative lens into cellular dynamics, offering an avenue to chart the developmental journey of individual cells. This primer introduces the novice to the realm of pseudotime and its significance in the intricate landscape of cell differentiation and gene expression. Utilizing Monocle, a pioneering tool in this domain, the article elucidates how cellular trajectories are constructed from single-cell RNA-sequencing data. The comparison of Monocle with its contemporaries highlights its robustness in handling complex trajectories and its unparalleled flexibility. As the biological world delves deeper into cellular intricacies, tools like Monocle stand as indispensable allies in unearthing the secrets of cellular progression. This article serves as a beacon for those navigating the vast ocean of single-cell analysis.

Single-cell sequencing technologies, notably scRNA-Seq and scATAC-Seq, offer unparalleled insights into gene expression and chromatin accessibility at the cellular level. However, integrating these distinct datasets presents a challenge due to their inherent differences. This article delves into the process of transforming scATAC-Seq data from genomic regions to gene-centric information and subsequently integrating it with scRNA-Seq data using shared latent spaces. By leveraging tools and methods that identify underlying patterns across datasets, researchers can achieve a comprehensive view of cellular states, bridging gene expression with chromatin dynamics.

scRNA-Seq: makers explore

Single-cell RNA sequencing (scRNA-seq) offers unparalleled insights into cellular heterogeneity. However, integrating datasets from diverse sources poses challenges, especially for newcomers. This guide provides a concise walkthrough of scRNA-seq data integration using the Seurat package, coupled with essential tips for beginners. From preprocessing to downstream analysis, we cover the key steps to ensure effective data harmonization, aiming to empower researchers to derive meaningful insights from integrated datasets.

In single-cell RNA sequencing (scRNA-seq) analysis, batch effects—non-biological variations from different sample processing—are pervasive challenges. Without correction, they can obscure genuine biological signals. This article elucidates the importance of batch effect removal and presents a comparative overview of three popular correction methods within Seurat: Harmony, fastMNN, and SCTransform. Choosing an apt method ensures accurate and unbiased biological insights, highlighting the significance of vigilant batch correction in scRNA-seq studies.

RNA-Seq stands for RNA sequencing, a revolutionary technique that helps scientists understand the expression of genes within a cell. In traditional RNA-Seq, we study the averaged gene expression of thousands of cells, but this approach has its limitations. It’s like trying to understand the flavor profile of a smoothie by tasting it – you know the overall taste, but you can’t pinpoint the individual fruits that contribute to it.

Alternative splicing research is important for understanding the diversity of gene expression and regulation, as it enables the production of multiple protein isoforms from a single gene, allowing cells to generate complex functional diversity and adapt to changing environmental conditions. Who said this?

RNA-seq expression normalization is the process of adjusting the raw gene expression counts to account for differences in sequencing depth and other technical factors. It is important to perform normalization to enable comparisons between samples and increase the accuracy and reproducibility of downstream analyses. Common normalization methods include TPM, FPKM, and DESeq. Who sad this?

Single Cell RNA-Seq Notes from Seminal

Bioinformatics: scRNA-seq data processing practices, protocol from seurat

Delete low maped reads from count or expression matrix

Tophat

transdecoder for translate your mRNA to protein

Trimmomatic is a java tool for cut and filter reads