|  | | Qlucore Licenses | | | Qlucore Omics Explorer | | | Analysis | - 2D PCA plot
- 2-way or two-way Anova
- Absent/present calls
- Anova in Qlucore
- BAM file sort order
- Biomarker Workbench
- Calculation of Fold change and Difference
- ChIP-seq and ATAC-seq
- Computation of R/R2 statistics
- Create a subset of variables based on PCA and use it to model.
- dCT values
- Distances
- Eliminate the time factor in data recorded at several time points
- Experiment designs (ANOVA, t-test, limma,...)
- Few or limited number of samples
- Find favorite genes, proteins or variables using the search function
- Fold change and difference
- Gene fusion analysis
- Hierarchial clustering algorithm
- Hierarchical clustering memory usage
- How do I know how much I should filter by variance or by ANOVA
- How does Qlucore calculate the length of the gene to do the normalization?
- How is the normalization (mean=0, Var=1) done in Omics Explorer?
- Identifying subgroups, patterns and structure
- Linkage criterion
- Log functionality and logging analysis steps
- Low expression levels RNA-seq data filtering
- Methylation data support
- Missing values and reconstruction
- Most important PCA variables
- Network and graphs
- Normalization of RNAseq data in Qlucore Omics Explorer
- Normalization using housekeeping genes
- Normalization Z-score (mean=0, var=1) calculations
- Normalize RNA-seq data in R
- Normalizing PCR (QPCR or Q-PCR) data
- Open interface to R (API)
- Paired data
- Pathway analysis and gene sets
- Proteomic data analysis
- Quantile normalization and eliminated factors
- RNA-seq normalization for BAM files
- Selecting Affymetrix probesets using Jetset or collapse
- Statistical tests for the Extended option using the Open R API: Welch, Wilcoxon, Limma, Mann-Whitney
- Statistsics
- Supported classifier methods
- The signal-to-noise (SNR) metric
- Tips for single cell data analysis
- Topological data analysis
- TPM values for RNASeq
- Two-color loop designs
- Unsupervised clustering
- Variable list and matches
- Variables differentiating and/or discriminating subgroups. Finding discrminating variables.
- Weighted correlation network analysis (WGCNA)
- What data is used in the Two group comparison (t-test)
- What is projection score and how do I use it.
- Working with multiple data sets
- Working with RNA-seq data (bam)
| | Data Import | | | NGS Module | | | Other | |
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