|Normalization of imported RNA-seq data|
|RNASeq analysis sometimes produces e.g. .tsv files with several different normalizations. Which normalization should I use for analysis in Qlucore?|
* Raw Counts: Raw (integer) counts per gene and sample.
* Normalized Counts: Normalized (real) counts per gene and sample scaled to the sequencing depth by DESeq2.
* VST Blind Counts: Counts on a log2 scale per gene and sample after variance stabilizing transformation (VST) disregarding the generalized linear model design.
* VST Model Counts: Counts on a log2 scale per gene and sample after variance stabilizing transformation (VST) considering the generalized linear model design.
* FPKMs: Fragments Per Kilobase Per Million.
* Raw Counts if you want to use the normalizations available in Qlucore (FPKM/TPM/TMM).
* VST Blind Counts if you want to use the VST normalization from DESeq2.
* VST Model Counts if you know which model was used with DESeq2 and want to use that. An alternative approach is to use VST Blind Counts normalization + eliminated factors in Qlucore.
* FPKM requires log transform of data. It is preferable to instead use the raw counts + the FPKM in Qlucore since the software then will handle the log transformation.
- How to import data (RNA-seq, Illumina, Affymetrix, 10x, Agilent, Wizard, tab separated, csv, txt, bam)
- Analyzing Flow Cytometry data
- Convert from R to Qlucore data file format
- Import Affymetrix data
- Import annotations
- Save RNA-seq or Affymetrix files as .gedata
- Two colored arrays
- Agilent arrays
- Analyze data from Array Express
- Data pre-processing for expression data
- Data with many samples
- File extensions
- Gene lengths for count data
- 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?
- How to import 10X single cell data
- How To Import TCGA Data
- Loading and creating annotations
- Low expression levels RNA-seq data filtering
- Manual import of data and annotations from GEO
- 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
- Quantile normalization and eliminated factors
- RNAseq and array technologies
- Supported data types
- TPM values for RNASeq
- Use .sra files
- Working with RNA-seq data (bam)
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