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BioMark RT-qPCR Analysis

Single Cell RNA-Seq Analysis

I am starting with...



fastq files

Align and quantify before analyzing.

Designed for RNA-seq.


gene expression matrix

Already have quantifications? Jump ahead to the analysis.

Designed for RT-qPCR data.

Align and Quantify

Alignment using the BrowserGenome alignment algorithm.

1) Choose a genome model (?):

Homo Sapiens:
Mus Musculus:
Custom genome model (.BrowGenModel format):

1. Enter the species name:
2. Choose chomosomes to be included (?):
3. Load genome sequence file (.2bit format) (?):
4.
5.
6.
7.
8. Load gene model (GENCODE .gtf format, takes several minutes) (?):
9. Save the genome model in .BrowGenModel format (?):

2) Map deep sequencing data (64-bit browser required):

1.Current genome model:
2. Load genome sequence
3. Upload all FASTQ files (?):
4. Optional: What is the position and sequence of an internal barcode? (?)
5. At what position in your reads should the 25-mer mapping start? (?)
6.

7. How much memory can BrowserGenome use? (?)


Start the mapping (?):

Loaded tracks:

3) Export quantified counts of annotated genes: (optional)

1.
2.
3.
4. Export the data (?):

Gene expression matrix

Gene expression matrix with rows as samples and columns as genes. Sample Ct values for BioMark data from Lawson et al. are provided as an example. Check the Transpose option if your data is rows as genes and columns as samples.
Transform data:
LoD (for LoD method):
FAIL:
Transpose?:



Quality Control

Distribution of Expression Values

Distribution of Ct values prior to transformation to help you choose the optimal LoD Ct value. Current LoD Ct value is marked by the red dotted line. All values beyond the limit of detection, highlighted in red, will be set to the limit such that after transformation becomes 0.

% Samples Detected Per Gene

Histogram summarizing the distribution of and barplot visualizing the percentage of successes (non-failures) per gene. Failed genes identified as having fewer percentage successes than a user-defined threshold, highlighted in red, are removed from downstream analysis. Use the threshold slider to adjust the threshold.
Percentage Threshold:

% Genes Detected Per Sample

Histogram summarizing the distribution of and barplot visualizing the percentage of genes detected per sample. Failed samples identified as having fewer percentage genes detected than a user-defined threshold, highlighted in red, are removed from downstream analysis. Use the threshold slider to adjust the threshold.
Percentage Threshold:

Clustering

Hierarchical Clustering Heatmap

Unbiased row-wise (sample) hierarchical clustering with heatmap visualization. Heatmap colors are column-scaled. Use options to specify the distance metric and linkage criterion. Columns (genes) are ordered in the same order as in the Differential Expression panel.
Linkage criterion: Distance metric:

Principal Component Analysis

Principal component analysis with K-means clustering. For principal component analysis, to ensure an invertible matrix, more rows than columns are needed. Data will be trimmed to the necessary dimensions for PCA as needed. Differential expression and other downstream analyses are calculated according to the first 2 subpopulation group labels.
K:

Differential Expression

Differential Expression

Barplot showing distribution of p-values with significant hits (p-value < 0.05 / # of tests) highlighted in red. P-values calculated by Wilcoxon rank-sum test. Likewise, the barplot of fold-changes is also plotted. A volcano plot shows p-values vs. fold-change with significant hits highlighted in red. Use options to sort by fold-change or p-value and update barplots. Sorting influenced column-ordering in the Heatmap map and the ranking used in the Gene Set Enrichment panel.
Group 1: Group 2: Sort by:
Volcano Plot
Distribution of -log10(P-value)

Distribution of log2(fold change)

Gene set enrichment

Gene set enrichment testing by hyper-geometric test.
Gene set:

Gene expression

Plot overall distribution of gene expression or compare between 2 main groups. Use options to select mode of visualization. Additional gene information is also provided.
Gene: Plot type:
Overall
By Group
Information