To perform a differential gene expression (DGE) analysis on the platform at luxbio.net, you’ll primarily be using its integrated suite of tools designed for RNA-seq data processing, statistical testing, and visualization. The process is streamlined but powerful, allowing researchers to go from raw sequencing reads to a list of statistically significant differentially expressed genes (DEGs) with detailed annotations. The core of the analysis hinges on their proprietary implementation of established algorithms, ensuring both accuracy and computational efficiency. Let’s break down the entire workflow, from data upload to interpretation, with a focus on the specific parameters and data types you’ll encounter.
Uploading and Managing Your Data
Your first step is to navigate to the ‘My Projects’ dashboard after logging in. Here, you can create a new project, which acts as a container for all your data and analyses. Luxbio.net supports a wide array of file formats for upload. For raw data, this includes FASTQ files (both single-end and paired-end), with compression formats like .gz being automatically recognized to save on storage space and upload time. The platform typically accepts uploads up to 50 GB per project for standard accounts, with options for larger quotas. A critical component of the upload is the metadata file. This must be a comma-separated values (CSV) file that maps each of your FASTQ files to its experimental conditions. For example, a simple metadata table for a control vs. treated experiment would look like this:
Table 1: Example Metadata for a DGE Analysis Project
| Sample_ID | File_Name | Condition | Replicate |
|---|---|---|---|
| Control_1 | SRR123_control_1.fastq.gz | Control | 1 |
| Control_2 | SRR124_control_2.fastq.gz | Control | 2 |
| Treated_1 | SRR125_treated_1.fastq.gz | Treated | 1 |
| Treated_2 | SRR126_treated_2.fastq.gz | Treated | 2 |
Accurate metadata is non-negotiable, as it’s the foundation for all subsequent statistical comparisons. The platform’s upload wizard includes a validation step that checks for common errors like missing files or inconsistent naming conventions.
The Core Processing Pipeline: Alignment and Quantification
Once your data is uploaded, you initiate the primary DGE workflow. This is an automated but configurable pipeline. The first stage is quality control and adapter trimming. Luxbio.net uses a customized version of FastQC for quality assessment and Trimmomatic for trimming, providing you with a summary report that includes metrics like per-base sequence quality, adapter content, and GC distribution. You have control over parameters such as the sliding window size for quality trimming (default is 4:20) and the minimum acceptable read length (default is 36 bp).
The next stage is read alignment. The platform offers a choice of aligners optimized for RNA-seq, including STAR and HiSAT2. For most users, the default STAR alignment with standard genomic indexes (e.g., GRCh38 for human) is recommended due to its high accuracy and speed. Key alignment parameters that are set by default include –outSAMtype BAM SortedByCoordinate, which generates a sorted BAM file ready for the next step. The alignment summary statistics, such as overall alignment rate and uniquely mapped reads percentage, are presented in a digestible format. A successful run should typically yield a unique mapping rate above 70-80% for a standard eukaryotic transcriptome.
The final step in the core pipeline is transcript abundance quantification. This is where the platform’s efficiency truly shines. It employs a highly optimized wrapper for featureCounts (for gene-level counts) and Salmon (for transcript-level abundance). For standard DGE analysis, the gene-level count matrix is the required output. The quantification is performed against a pre-loaded reference annotation (e.g., GENCODE or Ensembl). The output is a raw count matrix, where each row represents a gene and each column represents a sample. This matrix is the fundamental input for the statistical analysis stage.
Statistical Analysis for Identifying DEGs
This is the heart of the DGE analysis. Luxbio.net provides a dedicated interface for the statistical testing phase, built around the widely-used R package DESeq2. The process begins with the platform automatically importing the raw count matrix and your metadata. The first internal step is normalization. DESeq2 uses its median of ratios method to account for differences in library size and RNA composition, which is far superior to simple counts-per-million (CPM) normalization for DGE testing.
You then define your statistical model. For a simple two-group comparison (Control vs. Treated), the model is automatically generated as ~ Condition. For more complex designs (e.g., including a batch effect), you can specify a more complex formula like ~ Batch + Condition. The platform then performs the statistical testing, which involves:
- Estimating gene-wise dispersions (variability).
- Fitting a negative binomial generalized linear model (GLM) for each gene.
- Calculating Wald test statistics and p-values for the coefficients of interest (e.g., the effect of ‘Treated’ vs. ‘Control’).
- Applying a multiple testing correction using the Benjamini-Hochberg procedure to control the False Discovery Rate (FDR).
The primary results are presented as a table of all genes tested. The key columns you’ll focus on are:
- baseMean: The mean of normalized counts for all samples.
- log2FoldChange: The log2-transformed fold change between conditions.
- lfcSE: The standard error of the log2FoldChange.
- stat: The Wald statistic.
- pvalue: The raw p-value.
- padj: The adjusted p-value (FDR).
The standard threshold for significance is an adjusted p-value (padj) of less than 0.05. However, you can also apply a fold-change threshold (e.g., |log2FoldChange| > 1, which is a 2-fold change) to focus on biologically meaningful changes. The platform allows you to dynamically filter this results table and download it as a CSV file for further analysis.
Visualization and Functional Interpretation
Identifying a list of DEGs is only the beginning. Luxbio.net integrates several visualization tools to help you interpret the results. The most critical plot is the MA-Plot, which displays the relationship between the average expression level of a gene (baseMean on the x-axis) and the log2 fold change (y-axis). Statistically significant DEGs are typically highlighted in a different color, allowing you to see if up- and down-regulated genes are distributed across expression levels.
Another essential visualization is the Volcano Plot. This plot shows the statistical significance (-log10(padj)) against the magnitude of change (log2FoldChange). It’s excellent for visualizing the trade-off between effect size and statistical significance, helping you identify genes with large and highly significant changes. For quality control, the PCA (Principal Component Analysis) Plot is generated from the normalized count data. This plot shows how your samples cluster based on their overall gene expression profiles. You want to see clear separation between your experimental conditions (e.g., Control samples clustering together away from Treated samples), which validates the experimental design.
Beyond visualizations, the platform offers integrated functional enrichment analysis. With a single click, you can submit your list of significant DEGs (usually both up and down-regulated) to tools like g:Profiler or clusterProfiler, which run against databases like Gene Ontology (GO), KEGG, and Reactome. The results are presented in a table showing the enriched terms, their p-values, and the genes from your list associated with each term. For example, an enrichment of “inflammatory response” GO terms in your upregulated gene list would provide immediate biological context for your treatment’s effect.
Advanced Features and Customization
For power users, the platform offers several advanced features. You can export the normalized count matrix and the DESeq2DataSet (dds) object as RData files, allowing you to continue the analysis in your local R environment for custom visualizations or more complex modeling. There is also a beta feature for time-course analysis using likelihood ratio tests (LRT) within DESeq2, which is essential for experiments with multiple time points.
Furthermore, the platform provides a batch effect correction module. If your PCA plot shows clustering by an unwanted variable (like sequencing date), you can use the ComBat-seq algorithm (integrated from the sva package) to adjust the raw counts before re-running the DESeq2 analysis. This is a more robust approach than including batch in the model for some datasets. All analyses are version-controlled, meaning you can revisit any prior run, see the exact parameters used, and duplicate or modify the analysis, ensuring full reproducibility of your bioinformatics workflow.