Developed by UTAP team
Bioinformatics unit at Life Sciences Core Facilities (LSCF)
Weizmann Institute of Science
Bioinformatics unit at Life Sciences Core Facilities (LSCF)
Weizmann Institute of Science
This analysis ran DESeq2 with batches. This analysis ran DESeq2 with the : contrasts.
Figure 1: Histogram showing the number of reads for each sample in raw data. Median, mean and standard deviation of the sequencing depth of all samples were 4170450, 4504656, 1695691 reads.
Figure 2:
Heatmap plotting the highly-expressed genes.
The highest fraction of counts from a single gene is 3.5%. The figure below presents the fraction of reads from the genes with the most counts.
Figure 3:
Heatmap of Pearson distances between samples using normalized log2 gene expression values.
Distances between samples are calculated as 1- r (r = Pearson correlation coefficient).
Figure 4 :
Distances between samples are calculated according to Pearson distances and then clustered according to Ward’s minimum variance agglomerative method.
Download samples dendrogram without batch correction table
Download samples dendrogram with batch correction table
Figure 5:
PCA analysis: a. Histogram of explained variance percentage for each PC component.
Download figure5a with batch correction as table
Download figure5a without batch correction as table
b. PCA plot of PC1 vs PC2 c. PCA plot of PC1 vs PC3
Table 1: Differentially expressed (DE) genes for each comparison
Differential expression analysis was performed using DESeq2.
Thresholds for significant DE genes (per comparison):
| Comparison | Padj corrected by fdrtool | Plots | MA plot | DE Genes |
|---|---|---|---|---|
| VP24_NT_vs_VP24_24h | FALSE | link | link | link |
Table 2: DE genes for functional analysis
To perform functional enrichments, you can try one or more of the following websites: Intermine, Reactome, GeneAnalytics from the GeneCards(R) Suite(R) or STRING.You can also use the relevant Send to Intemine buttons below to send the differentially expressed genes directly to Intermine.
Normalization of the counts and differential expression analysis was performed using DESeq2 (DOI: 10.1186/s13059-014-0550-8) with the parameters: betaPrior=True, cooksCutoff=FALSE, independentFiltering=FALSE. Raw P values were adjusted for multiple testing using the procedure of Benjamini and Hochberg (DOI: 10.1111/j.2517-6161.1995.tb02031.x).
We recommend looking at the p-values distribution plots for each pairwise comparison in the Differential Expression Analysis section of the report. The plots can be reached by clicking on the word link in the General Plots column of Table 1. The p-values distribution plots should be used to evaluate the need of correcting the adjusted p-value with fdrtools.
Interactive MA plots for each pairwise comparison were done using Glimma (see the link in the “Plots” column of Table 1 under the section Differential Expression Analysis). A dot plot representation of the normalized gene counts per condition can be found in the link in the " DE Genes" column of Table 1. The pipeline was constructed using Snakemake (DOI: 10.1093/bioinformatics/bts480).
Results: raw counts, normalized counts and ComBat (log normalized counts after batch correction; combat values were calculated using the “sva” package of R and are batch corrected normalized log2 count values), and pairwise DESeq2 statistics can be downloaded as txt here or xlsx here files.
Normalized counts can be downloaded from: here.
R packages versions can be found at: sessionInfo.txt
Samples comparison and their batch details can be found at: ~/example_and_data_for_testing_DESeq_from_counts_matrix/20210303_220413_demo_DESeq2_from_counts_matrix/pheno_data-20210303_220413.tsv
General information about the run can be found at: ~/UTAP-data/reports/20210303_220413_demo_analysis_parameters.yaml
Input counts matrix: ~/example_and_data_for_testing_DESeq_from_counts_matrix/01.raw_counts_VP24.txt
Output folder: ~/example_and_data_for_testing_DESeq_from_counts_matrix/20210303_220413_demo_DESeq2_from_counts_matrix
Report output folder: ~/example_and_data_for_testing_DESeq_from_counts_matrix/20210303_220413_demo_DESeq2_from_counts_matrix/demo__20210303_220413
Citing UTAP:
Kohen R, Barlev J, Hornung G, Stelzer G, Feldmesser E, Kogan K, Safran M, Leshkowitz D: UTAP: User-friendly Transcriptome Analysis Pipeline. BMC Bioinformatics 2019, 20(1):154 (PMID: 30909881).