The process of single-cell transcriptomics involves several steps. First, individual cells are isolated and captured using various techniques such as microfluidics or droplet-based methods. These techniques ensure that each cell is isolated and processed separately.
Once the cells are isolated, their RNA is extracted and converted into complementary DNA (cDNA). This cDNA is then amplified and sequenced using high-throughput sequencing technologies. The resulting sequencing data provides a snapshot of the genes that are active within each individual cell.
Analyzing single-cell transcriptomic data can be complex but provides valuable information. Researchers use bioinformatics tools and computational algorithms to identify and compare gene expression patterns across different cells. They can classify cells into distinct types or subtypes based on their gene expression profiles, allowing them to uncover cellular heterogeneity within tissues or organs. It is accelerating our understanding of cellular heterogeneity, dynamic cellular states, and disease mechanisms. It has applications in diverse fields such as developmental biology, immunology, cancer research, and neuroscience.
Our comprehensive guide provides an in-depth overview of this cutting-edge technology, enabling you to dissect gene expression patterns at a single-cell level.
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The input for single-cell transcriptomics pre-processing is typically raw sequencing data, which is obtained from technologies such as RNA-seq or single-cell RNA-seq (scRNA-seq). This data is usually provided in the form of FASTQ files containing short sequence reads.
The raw data is converted into .fastq format for its quality check and is tested on different parameters to identify sequence purity.
Once you've obtained your single-cell RNA-seq data, the first thing you need to do with it is check the quality of the reads you have sequenced. For this task, today we will be using a tool called FastQC. FastQC is a quality control tool for sequencing data, which can be used for both bulk and single-cell RNA-seq data. FastQC takes sequencing data as input and returns a report on read quality.
On the basis of sequence quality it is trimmed and adapter or any mismatch pair are removed. Popular tools for trimming include Trimmomatic, Cutadapt, or fastp.
Once the raw data has been quality checked and trimmed, indexing is performed to build an index of the reference genome or transcriptome. This index allows for efficient alignment of the sequencing reads to the reference during the alignment step. Tools like Bowtie2, STAR, or HISAT2 are commonly used for genome or transcriptome indexing.
The sample is mapped to reference based on the index generated, to identify the reads mapped to a given gene in the reference.. The alignment algorithm takes into account factors like sequencing errors, splice junctions (in the case of RNA-seq), and unique mapping. Popular alignment tools include STAR, HISAT2, or TopHat2.
It searches a given directory for analysis logs and compiles a HTML report.
Normalization focusses on removing technical biases so any differences in comparing expression profiles are due to biological occurrences. In case of scrna-seq, library size normalization is sufficient enough where the aim to identify clusters and the top markers that define each cluster. During feature selection we want to select genes that contain useful information about the biology of the system while removing genes that contain noise.
The simplest approach to feature selection is to select the most variable genes based on their expression across the population.
