DGE and DTE analysis of Salmon/Kallisto inputs using Sleuth
Analysis and result presented was performed with Salmon counts, Code snippet to import Kallisto counts is also provided
DGE analysis
cd/home/USER/SSAPs/analysis/dgeR
Load required libraries
library(data.table)library(sleuth)
Import clinical data
Load clinical information and define file path
dir="../../"sampleData=paste0(dir,"clinical.txt")sampleData=fread(sampleData)rownames(sampleData) =sampleData$ENA_RUNsampleData$individual=as.factor(sampleData$individual)sampleData$paris_classification=as.factor(sampleData$paris_classification)# Use relevel() to set adjacent normal samples as referencesampleData$paris_classification=relevel(sampleData$paris_classification,ref="normal")
Construct t2g data.frame
Load the annoData data.frame that contain per-transcript annotation derived from GENCODE GTF file and constrct a transcript-to-gene data.frame t2g in the specified format by Sleuth
Construct s2c data.frame
Salmon
Create a sample_to_covariates data.frame s2c that contains the required information in the specified format by Sleuth
Sleuth will compare all conditions against the reference level (i.e. first level in the factor variable). Use the levels() function to check the order of the condition and userelevel() function to reassign reference level if necessary
Kallisto
Create a sample_to_covariates data.frame s2c that contains the required information in the specified format by Sleuth
Construct Sleuth object
Construct the Sleuth object so with sleuth_prep. This object will store the metadata, model to be used for differential testing, transcript-to-gene and bootstrap information
Use aggregation_column = "ens_gene" to allow summarize the data on the gene level and gene_mode = TRUE to do counts aggregation at the gene level for normalization, transformation, and downstream steps.
The default transformation of counts is natural log, i.e. log(x+0.5). We change this to log base 2 by assigning an appropriate transformation_function
Differential expression analysis
The likelihood ratio test is performed with a 3-step procedure. First, fit the full model by fit_name = "full", then performs a second fit to a “reduced” model that presumes abundances are equal in the two conditions. Finally, Sleuth will perform LRT to identify transcripts with a significantly better fit with the “full” model
The models that have been fit can be examined with the models() function.
Retrieve results of the test
Exporting results
We construct a normData data.frame to store per-group normalised mean transcripts per million (TPM) and normalised TPM of all samples, and a deData data.frame to store the Sleuth LRT results. We merge both data.frame with a annoData data.frame that contain per-transcript annotation derived from GENCODE GTF file before exporting the results in tabular format
Exploring results
Principal component plot of the samples
PCA
Plot sample heatmap using the Jensen-Shannon divergence
Lower divergence values represent samples that are more similar to each other
Plot clustered transcript heatmap
Create expression heatmap for top 40 genes with lowest q-values
Plot the normalized bootstraps across all samples
Plot expression with bootstrap variation for selected gene
Session info
DTE analysis
Load required libraries
Import clinical data
Load clinical information and define file path
Construct t2g data.frame
Load the annoData data.frame that contain per-transcript annotation derived from GENCODE GTF file and constrct a transcript-to-gene data.frame t2g in the specified format by Sleuth
Construct s2c data.frame
Salmon
Create a data.frame s2c that contains the required information in the specified format by Sleuth
Sleuth will compare all conditions against the reference level (i.e. first level in the factor variable). Use the levels() function to check the order of the condition and userelevel() function to reassign reference level if necessary
Kallisto
Create a sample_to_covariates data.frame s2c that contains the required information in the specified format by Sleuth
Construct Sleuth object
Construct the Sleuth object so with sleuth_prep. This object will store the metadata, model to be used for differential testing, transcript-to-gene and bootstrap information.
The default transformation of counts is natural log, i.e. log(x+0.5). We change this to log base 2 by assigning an appropriate transformation_function
Differential expression analysis
The likelihood ratio test is performed with a 3-step procedure. First, fit the full model by fit_name = "full", then performs a second fit to a “reduced” model that presumes abundances are equal in the two conditions. Finally, Sleuth will perform LRT to identify transcripts with a significantly better fit with the “full” model
The models that have been fit can be examined with the models() function.
Retrieve results of the test
Exporting results
We construct a normData data.frame to store per-group normalised mean transcripts per million (TPM) and normalised TPM of all samples, and a deData data.frame to store the Sleuth LRT results. We merge both data.frame with a annoData data.frame that contain per-transcript annotation derived from GENCODE GTF file before exporting the results in tabular format
Exploring results
Principal component plot of the samples
PCA
Plot sample heatmap using the Jensen-Shannon divergence
Lower divergence values represent samples that are more similar to each other
Sample heatmap
Plot clustered transcript heatmap
Create expression heatmap for top 40 transcripts with lowest q-values
Transcript heatmap
Plot the normalized bootstraps across all samples
Plot expression with bootstrap variation for selected transcript
