RNAseq report for PI0001
This report includes summaries of data quality, data processing, and snapshots of results for your RNA-Seq study. This report should assist you to get a general picture of the study, to spot any irregularities in the sample or data, and to explore the most significant results in differential gene expression. Please consult our RNAseq report documentation on how to use this report.
Distances/similarities between samples
This section plots the distances or similarities between samples in the form of heatmap, PCA, and/or MDS plots.
Similarity matrix of samples
The similarities (Pearson correlation coefficient) between samples are visualized here in the form of heatmap. Larger values indicate higher similarity between samples. The similarities were calculated using normalized and 'rlog' transformed read counts of all genes using DESeq2.
Multidimensional scaling analysis of samples
Multidimensional scaling was conducted to visualize the distance/similarity between samples. Top 500 genes with highest variance among samples were used to make this plot.
Top gene expression patterns
Normalized read counts of top genes with highest variance, calculated using DESeq2. Values plotted in Log2 scale after centering per gene. A static version of this figure can be download in the Download data section.
Differential gene expression
DESeq2 calculates the expression levels of genes and conducts statistical analysis of differential gene expression.
Summary table of gene differential expression
General statistics of differentially expressed genes in pairwise comparisons. Genes with adjusted p-values smaller than 0.05 were considered differentially expressed.
| Comparison(cond.1_cond.2) | Higher in condition 1 | Higher in condition 2 | Not differentially expressed | Did not pass filter |
|---|---|---|---|---|
| Tumor_vs_Normal | 0 | 2 | 32201 | 0 |
Scatter plot
Scatter plot is a simple and straightforward way to visualize differential gene expression results. Expression levels of genes in one condition are shown on X-axis while those in other are shown on Y-axis.
The scatter plots here include differentially expressed genes (up to the first 1000 genes) and up to 1000 randomly selected non-differentially expressed genes. You can download the scatter plots with all genes in the Download data section.
Red dots represent differentially expressed genes (adjusted p-values<0.05). Grey dots represent non-differentially expressed genes. Count data transformation was carried out using the 'rlog' method in DESeq2
MA plot
MA plot is a type of visualization of differential gene expression results often used in publications. Expression levels are shown on X-axis while log2 of fold changes are shown on Y-axis.
The MA plots here include differentially expressed genes (up to the first 1000 genes) and up to 1000 randomly selected non-differentially expressed genes. You can download the MA plots with all genes in the Download data section.
Red dots represent differentially expressed genes (adjusted p-values<0.05). Grey dots represent non-differentially expressed genes. Shrinkage of effect size was carried out using the 'normal' method in DESeq2
Top differentially expressed genes in comparison Tumor vs. Normal
Top 50 differentially expressed genes, ranked by FDR, in comparison Tumor vs. Normal. Full DESeq2 results can be downloaded in the Download data section.
Genes with positive Log2 fold changes have higher expression in Tumor. Genes with negative Log2 fold changes have higher expression in Normal.
| Rank | Gene name | Mean counts | Log2 Fold change | False discovery rate |
|---|---|---|---|---|
| 1 | SLC6A14 | 188 | -1.97 | 1.0e-05 |
| 2 | TRIM31 | 198 | -1.82 | 5.8e-05 |
| 3 | NR4A1 | 173 | 2.20 | 9.9e-02 |
| 4 | COL8A1 | 642 | -1.34 | 1.0e-01 |
| 5 | PTGES | 451 | -1.41 | 1.0e-01 |
| 6 | EGR2 | 61 | 2.85 | 1.6e-01 |
| 7 | EGR3 | 137 | 2.28 | 1.6e-01 |
| 8 | EGF | 25 | -2.60 | 1.7e-01 |
| 9 | NFATC2 | 51 | 2.10 | 5.5e-01 |
| 10 | TJP3 | 46 | -1.99 | 5.5e-01 |
| 11 | NR1D1 | 244 | 1.89 | 6.7e-01 |
| 12 | LY6D | 208 | -1.42 | 6.7e-01 |
| 13 | PIGR | 100 | -1.57 | 7.2e-01 |
| 14 | LGALS9 | 206 | -1.71 | 7.8e-01 |
| 15 | FN1 | 1508 | -1.04 | 8.2e-01 |
| 16 | CYP1A1 | 143 | 3.67 | 8.7e-01 |
| 17 | CLEC7A | 39 | -2.36 | 8.9e-01 |
| 18 | AKAP6 | 99 | -0.26 | 1.0e+00 |
| 19 | SRFBP1 | 426 | 0.26 | 1.0e+00 |
| 20 | CSNK1G3 | 548 | 0.06 | 1.0e+00 |
| 21 | TEX30 | 134 | -0.33 | 1.0e+00 |
| 22 | MAGI1 | 88 | 0.34 | 1.0e+00 |
| 23 | EIF4E | 842 | 0.13 | 1.0e+00 |
| 24 | DIP2C | 257 | 0.41 | 1.0e+00 |
| 25 | TWF1 | 2200 | -0.15 | 1.0e+00 |
| 26 | GXYLT1 | 715 | 0.08 | 1.0e+00 |
| 27 | SLC2A13 | 328 | -0.17 | 1.0e+00 |
| 28 | DLG5 | 2066 | 0.17 | 1.0e+00 |
| 29 | PLBD2 | 596 | -0.31 | 1.0e+00 |
| 30 | RAD9B | 14 | 0.10 | 1.0e+00 |
| 31 | DCP1B | 149 | -0.10 | 1.0e+00 |
| 32 | ANK3 | 740 | -0.33 | 1.0e+00 |
| 33 | UBE3B | 609 | -0.10 | 1.0e+00 |
| 34 | BTBD11 | 460 | 0.12 | 1.0e+00 |
| 35 | UEVLD | 278 | -0.20 | 1.0e+00 |
| 36 | OXSM | 131 | -0.15 | 1.0e+00 |
| 37 | NGLY1 | 317 | 0.05 | 1.0e+00 |
| 38 | THRB | 479 | 0.18 | 1.0e+00 |
| 39 | NPAS3 | 11 | 0.13 | 1.0e+00 |
| 40 | GPR158 | 152 | -0.42 | 1.0e+00 |
| 41 | ENKUR | 77 | -0.30 | 1.0e+00 |
| 42 | NOCT | 158 | 0.57 | 1.0e+00 |
| 43 | SLC7A11 | 1210 | -0.37 | 1.0e+00 |
| 44 | ITPR1 | 106 | 0.52 | 1.0e+00 |
| 45 | IPMK | 203 | -0.12 | 1.0e+00 |
| 46 | FAM177A1 | 270 | 0.09 | 1.0e+00 |
| 47 | NUBPL | 206 | -0.06 | 1.0e+00 |
| 48 | EXT2 | 3159 | -0.55 | 1.0e+00 |
| 49 | NR3C2 | 16 | -0.23 | 1.0e+00 |
| 50 | ZNF827 | 363 | -0.03 | 1.0e+00 |
Gene set enrichment analysis
g:Profiler performs functional enrichment analysis also known as gene set enrichment analysis on input gene list. It maps genes to known functional information sources and detects statistically significantly enriched terms.
Summary table of gene set enrichment analysis
General statistics of gene set enrichment analysis in pairwise comparisons. Gene sets with false discovery rate smaller than 0.05 were considered enriched.
| Comparison(cond.1_cond.2) | Higher in condition 1 | Higher in condition 2 | Not enriched |
|---|---|---|---|
| Tumor_vs_Normal | 0 | 2 | 193 |
Top enriched gene sets in comparison Tumor vs. Normal
Top 30 gene sets, ranked by p-value, in comparison Tumor vs. Normal. Full g:Profiler results can be downloaded in the Download data section.
| Rank | Gene set name | Gene set category | Adjusted p-value | Expression pattern |
|---|---|---|---|---|
| 1 | Variant SLC6A14 may confer susceptibility towards obesity | Reactome Pathway | 0.025 | Higher in Normal |
| 2 | beta-alanine transport | GO:Biological Process | 0.050 | Higher in Normal |
| 3 | (R)-carnitine transport | GO:Biological Process | 0.075 | Higher in Normal |
| 4 | (R)-carnitine transmembrane transport | GO:Biological Process | 0.075 | Higher in Normal |
| 5 | carnitine transmembrane transport | GO:Biological Process | 0.175 | Higher in Normal |
| 6 | carnitine transport | GO:Biological Process | 0.199 | Higher in Normal |
| 7 | amino-acid betaine transport | GO:Biological Process | 0.299 | Higher in Normal |
| 8 | aromatic amino acid transport | GO:Biological Process | 0.324 | Higher in Normal |
| 9 | branched-chain amino acid transport | GO:Biological Process | 0.374 | Higher in Normal |
| 10 | Na+/Cl- dependent neurotransmitter transporters | Reactome Pathway | 0.473 | Higher in Normal |
| 11 | alanine transport | GO:Biological Process | 0.474 | Higher in Normal |
| 12 | negative regulation of NLRP3 inflammasome complex assembly | GO:Biological Process | 0.499 | Higher in Normal |
| 13 | quaternary ammonium group transport | GO:Biological Process | 0.723 | Higher in Normal |
| 14 | Amino acid transport across the plasma membrane | Reactome Pathway | 0.797 | Higher in Normal |
| 15 | negative regulation of viral transcription | GO:Biological Process | 0.798 | Higher in Normal |
| 16 | regulation of viral transcription | GO:Biological Process | 0.997 | Higher in Normal |
| 17 | biological regulation | GO:Biological Process | 1.000 | Higher in Normal |
| 18 | establishment of localization | GO:Biological Process | 1.000 | Higher in Normal |
| 19 | regulation of RNA metabolic process | GO:Biological Process | 1.000 | Higher in Normal |
| 20 | positive regulation of RNA metabolic process | GO:Biological Process | 1.000 | Higher in Normal |
| 21 | protein modification by small protein conjugation or removal | GO:Biological Process | 1.000 | Higher in Normal |
| 22 | protein K63-linked ubiquitination | GO:Biological Process | 1.000 | Higher in Normal |
| 23 | response to other organism | GO:Biological Process | 1.000 | Higher in Normal |
| 24 | defense response to virus | GO:Biological Process | 1.000 | Higher in Normal |
| 25 | biological process involved in interaction with host | GO:Biological Process | 1.000 | Higher in Normal |
| 26 | modulation by symbiont of entry into host | GO:Biological Process | 1.000 | Higher in Normal |
| 27 | transmembrane transport | GO:Biological Process | 1.000 | Higher in Normal |
| 28 | regulation of macromolecule metabolic process | GO:Biological Process | 1.000 | Higher in Normal |
| 29 | regulation of molecular function | GO:Biological Process | 1.000 | Higher in Normal |
| 30 | protein-containing complex assembly | GO:Biological Process | 1.000 | Higher in Normal |
Download data
This section contains links to download your original data, and data and/or images generated by various bioinformatics tools. There may be files for each sample, files for all samples, and files for group comparisons. To download individual files, click on the corresponding links. There are also instructions at the bottom of the this section if you want to download everything in batch.
Links in this section expire after 60 days. If you want to download files after that, please contact us.
Files concerning all samples
These files provide an overview of all samples (some of these are already displayed interactively for you in sections above):- Normalized read counts of genes
- MDS plot of samples
- Similarity matrix of samples
- Heatmap of expression of top genes
Comparison level files
Instructions to download all files
- Download a script to download all files. We assume it is in your Downloads folder.
- Find and open Terminal(Mac/Linux) or Windows Powershell(Windows).
- Type
cd ~/Downloadsand Enter. (If your download folder is different, please change accordingly) - Copy and Paste
bash download_links.ps1(Mac/Linux) orPowershell.exe -ExecutionPolicy Bypass -File .\download_links.ps1(Windows) and Enter.
Software Versions
Software versions are collected at run time from the software output. This pipeline is adapted from nf-core RNAseq pipeline.
- RNAseq pipeline
- v2.1.0
- Nextflow
- v21.10.6
- DESeq2
- v1.28.0
- gProfiler
- v1.0.0
Workflow Summary
This section summarizes important parameters used in the pipeline. They were collected when the pipeline was started.
- Genome
- GRCh38
- DESeq2 FDR cutoff
- 0.05
- DESeq2 Log2FC cutoff
- 0.585
- gProfiler FDR cutoff
- 0.05
Report generated on 2023-05-14, 12:59.