metaseqR report

Summary

Analysis summary

Summary: The gene counts table was normalized for inherent systematic or experimental biases (e.g. sequencing depth, gene length, GC content bias etc.) using the Bioconductor package DESeq after removing genes that had zero counts over all the RNA-Seq samples (17247 genes). The output of the normalization algorithm was a table with normalized counts, which can be used for differential expression analysis with statistical algorithms developed specifically for count data. Prior to the statistical testing procedure, the gene read counts were filtered for possible artifacts that could affect the subsequent statistical testing procedures. Genes presenting any of the following were excluded from further analysis: i) genes with length less than 500 (5666 genes), ii) genes whose average reads per 100 bp was less than the 25^th quantile of the total normalized distribution of average reads per 100bp (0 genes with cutoff value 0 average reads per 100 bp), iii) genes with read counts below the median read counts of the total normalized count distribution (9152 genes with cutoff value 2 normalized read counts). The total number of genes excluded due to the application of gene filters was 6393. The total (unified) number of genes excluded due to the application of all filters was 29727. The resulting gene counts table was subjected to differential expression analysis for the contrasts WU versus TU using the Bioconductor package DESeq. The final numbers of differentially expressed genes were (per contrast): for the contrast WU versus TU, 431 (35) statistically significant genes were found with a p-value (FDR or adjusted p-value) threshold of 0.05 and of these, 223 (17) were up-regulated, 186 (17) were down-regulated and 22 (1) were not differentially expressed according to an absolute fold change cutoff value of 1 in log₂ scale. Literature references for all the algorithms used can be found at the end of this report.

Input options

Read counts file: imported custom data frame
Conditions: WU, TU, TH, TR
Samples included: UNK, WU1, WU2, WU3, TU1, TU2, TU3, TH1, TH2, TH3, TR1, TR2, TR3
Samples excluded: none
Requested contrasts: WU_vs_TU
Library sizes: not available
Annotation: embedded
Organism:
Annotation source: Ensembl genomes
Count type: utr
Exon filters: none applied
Gene filters: length, avg.reads, expression, biotype

length
- length: 500

avg.reads
- average.per.bp: 100
- quantile: 0.25

expression
- median: TRUE
- mean: FALSE
- quantile: NA
- known: NA
- custom: NA

biotype
- pseudogene: FALSE
- snRNA: FALSE
- protein_coding: FALSE
- antisense: FALSE
- miRNA: FALSE
- snoRNA: FALSE
- lincRNA: FALSE
- processed_transcript: FALSE
- misc_RNA: FALSE
- rRNA: TRUE
- sense_intronic: FALSE
- sense_overlapping: FALSE
- polymorphic_pseudogene: FALSE
- IG_C_gene: FALSE
- IG_J_gene: FALSE
- IG_D_gene: FALSE
- IG_LV_gene: FALSE
- IG_V_gene: FALSE
- IG_V_pseudogene: TRUE
- TR_V_gene: FALSE
- TR_V_pseudogene: TRUE
- three_prime_overlapping_ncrna: FALSE

Filter application: after normalization
Normalization algorithm: DESeq
Normalization arguments: locfunc

[[list(locfunc = function (x, na.rm = FALSE) standardGeneric("median"))locfunc

Statistical algorithm(s): DESeq
Statistical arguments for DESeq: method, sharingMode, fitType

method: blind
sharingMode: fit-only
fitType: local

Meta-analysis method: no meta-analysis, reported p-values from the first supplied statistical algorithm
Multiple testing correction: Benjamini-Hochberg FDR
p-value threshold: 0.05
Logarithmic tranformation offset: 1
Analysis preset: not available
Quality control plots: multidimensional scaling, biotype detection, biotype counts, sample and biotype saturation, filtered biotypes, correlation heatmap and correlogram, pairwise scatterplots between samples, boxplots, GC-content bias, transcript length bias, mean-difference plot, mean-variance plot, DEG biotype detection, volcano plot, DEG heatmap
Figure format: png, pdf
Output directory: /data/images/proton/run339/www/metaseqr_quantseq_run339_with_UNK
Output data: Annotation, p-value, Adjusted p-value (FDR), Fold change, Statistics, Read counts
Output scale(s): Natural scale, log2 scale, Reads per Gene Model
Output values: Normalized values
Output statistics: Mean
Total run time: 12 minutes 50 seconds

Filtered genes

Number of filtered genes: 29727 which is the union of

Filtered because of zero reads: 17247
Filtered because of exon filters: 0
Filtered because of gene filters: 12480 which is the union of
- length: 5666 genes with filter cutoff value 500
- avg.reads: 516 genes with filter cutoff value 0
- expression: 9152 genes further decomposed to (filter name, filtered genes, filter cutoff):
- - median: 9152 genes with filter cutoff value 2
- biotype: 210 genes with filter cutoff value rRNA, IG_V_pseudogene, TR_V_pseudogene

Differentially expressed genes

Number of differentially expressed genes per contrast:

WU_vs_TU: 431 (35) statistically significant genes of which 223 (17) up regulated, 186 (17) down regulated and 22 (1) not differentially expressed according to a p-value (FDR or adjusted p-value) threshold of 0.05 and an absolute fold change cutoff value of 1 in log₂ scale.

Run log

[ 2017-04-11 19:02:57 ] INFO 2017-04-11 19:02:57: Data processing started...
[ 2017-04-11 19:02:57 ] INFO Read counts file: imported custom data frame
[ 2017-04-11 19:02:57 ] INFO Conditions: WU, TU, TH, TR
[ 2017-04-11 19:02:57 ] INFO Samples to include: UNK, WU1, WU2, WU3, TU1, TU2, TU3, TH1, TH2, TH3, TR1, TR2, TR3
[ 2017-04-11 19:02:57 ] INFO Samples to exclude: none
[ 2017-04-11 19:02:57 ] INFO Requested contrasts: WU_vs_TU
[ 2017-04-11 19:02:57 ] INFO Annotation: embedded
[ 2017-04-11 19:02:57 ] INFO Organism: custom
[ 2017-04-11 19:02:57 ] INFO Reference source: ensembl
[ 2017-04-11 19:02:57 ] INFO Count type: utr
[ 2017-04-11 19:02:57 ] INFO Exon filters: none applied
[ 2017-04-11 19:02:57 ] INFO Gene filters: length, avg.reads, expression, biotype
[ 2017-04-11 19:02:57 ] INFO length:
[ 2017-04-11 19:02:57 ] INFO length: 500
[ 2017-04-11 19:02:57 ] INFO avg.reads:
[ 2017-04-11 19:02:57 ] INFO average.per.bp: 100
[ 2017-04-11 19:02:57 ] INFO quantile: 0.25
[ 2017-04-11 19:02:57 ] INFO expression:
[ 2017-04-11 19:02:57 ] INFO median: TRUE
[ 2017-04-11 19:02:57 ] INFO mean: FALSE
[ 2017-04-11 19:02:57 ] INFO quantile: NA
[ 2017-04-11 19:02:57 ] INFO known: NA
[ 2017-04-11 19:02:57 ] INFO custom: NA
[ 2017-04-11 19:02:57 ] INFO biotype:
[ 2017-04-11 19:02:57 ] INFO pseudogene: FALSE
[ 2017-04-11 19:02:57 ] INFO snRNA: FALSE
[ 2017-04-11 19:02:57 ] INFO protein_coding: FALSE
[ 2017-04-11 19:02:57 ] INFO antisense: FALSE
[ 2017-04-11 19:02:57 ] INFO miRNA: FALSE
[ 2017-04-11 19:02:57 ] INFO snoRNA: FALSE
[ 2017-04-11 19:02:57 ] INFO lincRNA: FALSE
[ 2017-04-11 19:02:57 ] INFO processed_transcript: FALSE
[ 2017-04-11 19:02:57 ] INFO misc_RNA: FALSE
[ 2017-04-11 19:02:57 ] INFO rRNA: TRUE
[ 2017-04-11 19:02:57 ] INFO sense_intronic: FALSE
[ 2017-04-11 19:02:57 ] INFO sense_overlapping: FALSE
[ 2017-04-11 19:02:57 ] INFO polymorphic_pseudogene: FALSE
[ 2017-04-11 19:02:57 ] INFO IG_C_gene: FALSE
[ 2017-04-11 19:02:57 ] INFO IG_J_gene: FALSE
[ 2017-04-11 19:02:57 ] INFO IG_D_gene: FALSE
[ 2017-04-11 19:02:57 ] INFO IG_LV_gene: FALSE
[ 2017-04-11 19:02:57 ] INFO IG_V_gene: FALSE
[ 2017-04-11 19:02:57 ] INFO IG_V_pseudogene: TRUE
[ 2017-04-11 19:02:57 ] INFO TR_V_gene: FALSE
[ 2017-04-11 19:02:57 ] INFO TR_V_pseudogene: TRUE
[ 2017-04-11 19:02:57 ] INFO three_prime_overlapping_ncrna: FALSE
[ 2017-04-11 19:02:57 ] INFO Filter application: postnorm
[ 2017-04-11 19:02:57 ] INFO Normalization algorithm: deseq
[ 2017-04-11 19:02:57 ] INFO Normalization arguments:
[ 2017-04-11 19:02:57 ] INFO locfunc:
[ 2017-04-11 19:02:57 ] INFO [[
[ 2017-04-11 19:02:57 ] INFO list(locfunc = function (x, na.rm = FALSE) standardGeneric("median"))
[ 2017-04-11 19:02:57 ] INFO locfunc
[ 2017-04-11 19:02:57 ] INFO Statistical algorithm: deseq
[ 2017-04-11 19:02:57 ] INFO Statistical arguments:
[ 2017-04-11 19:02:57 ] INFO deseq: blind, fit-only, local
[ 2017-04-11 19:02:57 ] INFO Meta-analysis method: none
[ 2017-04-11 19:02:57 ] INFO Multiple testing correction: BH
[ 2017-04-11 19:02:57 ] INFO p-value threshold: 0.05
[ 2017-04-11 19:02:57 ] INFO Logarithmic transformation offset: 1
[ 2017-04-11 19:02:57 ] INFO Quality control plots: mds, biodetection, countsbio, saturation, readnoise, filtered, correl, pairwise, boxplot, gcbias, lengthbias, meandiff, meanvar, biodist, volcano, deheatmap
[ 2017-04-11 19:02:57 ] INFO Figure format: png, pdf
[ 2017-04-11 19:02:57 ] INFO Output directory: /data/images/proton/run339/www/metaseqr_quantseq_run339_with_UNK
[ 2017-04-11 19:02:57 ] INFO Output data: annotation, p.value, adj.p.value, fold.change, stats, counts, flags
[ 2017-04-11 19:02:57 ] INFO Output scale(s): natural, log2, rpgm
[ 2017-04-11 19:02:57 ] INFO Output values: normalized
[ 2017-04-11 19:02:57 ] INFO Output statistics: mean
[ 2017-04-11 19:02:57 ] INFO Reading custom external gene annotation for from /data/results/tools/rnaseq/metaseqr/mm10/gene_data_mm10.txt.gz...
[ 2017-04-11 19:03:00 ] INFO Checking chromosomes in transcript counts and gene annotation...
[ 2017-04-11 19:03:00 ] INFO Processing transcripts...
[ 2017-04-11 19:03:00 ] INFO Separating transcripts (UTR regions) per gene for UNK...
[ 2017-04-11 19:03:23 ] INFO Separating transcripts (UTR regions) per gene for WU1...
[ 2017-04-11 19:03:51 ] INFO Separating transcripts (UTR regions) per gene for WU2...
[ 2017-04-11 19:04:13 ] INFO Separating transcripts (UTR regions) per gene for WU3...
[ 2017-04-11 19:04:37 ] INFO Separating transcripts (UTR regions) per gene for TU1...
[ 2017-04-11 19:04:59 ] INFO Separating transcripts (UTR regions) per gene for TU2...
[ 2017-04-11 19:05:24 ] INFO Separating transcripts (UTR regions) per gene for TU3...
[ 2017-04-11 19:05:46 ] INFO Separating transcripts (UTR regions) per gene for TH1...
[ 2017-04-11 19:06:13 ] INFO Separating transcripts (UTR regions) per gene for TH2...
[ 2017-04-11 19:06:37 ] INFO Separating transcripts (UTR regions) per gene for TH3...
[ 2017-04-11 19:07:05 ] INFO Separating transcripts (UTR regions) per gene for TR1...
[ 2017-04-11 19:07:37 ] INFO Separating transcripts (UTR regions) per gene for TR2...
[ 2017-04-11 19:08:06 ] INFO Separating transcripts (UTR regions) per gene for TR3...
[ 2017-04-11 19:08:32 ] INFO Saving gene model to /data/images/proton/run339/www/metaseqr_quantseq_run339_with_UNK/data/gene_model.RData
[ 2017-04-11 19:08:36 ] INFO Summarizing count data...
[ 2017-04-11 19:08:45 ] INFO Removing genes with zero counts in all samples...
[ 2017-04-11 19:08:46 ] INFO Normalizing with: deseq
[ 2017-04-11 19:08:47 ] INFO Applying gene filter length...
[ 2017-04-11 19:08:47 ] INFO Threshold below which ignored: 500
[ 2017-04-11 19:08:47 ] INFO Applying gene filter avg.reads...
[ 2017-04-11 19:08:47 ] INFO Threshold below which ignored: 0
[ 2017-04-11 19:08:47 ] INFO Applying gene filter expression...
[ 2017-04-11 19:08:47 ] INFO Threshold below which ignored: 2
[ 2017-04-11 19:08:47 ] INFO Applying gene filter biotype...
[ 2017-04-11 19:08:47 ] INFO Biotypes ignored: rRNA, IG_V_pseudogene, TR_V_pseudogene
[ 2017-04-11 19:08:47 ] INFO 29727 genes filtered out
[ 2017-04-11 19:08:47 ] INFO 19821 genes remain after filtering
[ 2017-04-11 19:08:47 ] INFO Running statistical tests with: deseq
[ 2017-04-11 19:08:48 ] INFO Contrast: WU_vs_TU
[ 2017-04-11 19:09:09 ] INFO Contrast WU_vs_TU: found 431 genes
[ 2017-04-11 19:09:09 ] INFO Building output files...
[ 2017-04-11 19:09:10 ] INFO Contrast: WU_vs_TU
[ 2017-04-11 19:09:10 ] INFO Adding non-filtered data...
[ 2017-04-11 19:09:10 ] INFO binding annotation...
[ 2017-04-11 19:09:12 ] INFO binding p-values...
[ 2017-04-11 19:09:12 ] INFO binding FDRs...
[ 2017-04-11 19:09:12 ] INFO binding natural normalized fold changes...
[ 2017-04-11 19:09:12 ] INFO binding log2 normalized fold changes...
[ 2017-04-11 19:09:13 ] INFO binding normalized mean counts...
[ 2017-04-11 19:09:13 ] INFO binding normalized mean counts...
[ 2017-04-11 19:09:14 ] INFO binding all normalized counts for WU...
[ 2017-04-11 19:09:16 ] INFO binding all normalized counts for TU...
[ 2017-04-11 19:09:17 ] INFO binding filtering flags...
[ 2017-04-11 19:09:18 ] INFO Writing output...
[ 2017-04-11 19:09:18 ] INFO Adding filtered data...
[ 2017-04-11 19:09:18 ] INFO binding annotation...
[ 2017-04-11 19:09:18 ] INFO binding p-values...
[ 2017-04-11 19:09:18 ] INFO binding FDRs...
[ 2017-04-11 19:09:18 ] INFO binding natural normalized fold changes...
[ 2017-04-11 19:09:18 ] INFO binding log2 normalized fold changes...
[ 2017-04-11 19:09:18 ] INFO binding normalized mean counts...
[ 2017-04-11 19:09:19 ] INFO binding normalized mean counts...
[ 2017-04-11 19:09:20 ] INFO binding all normalized counts for WU...
[ 2017-04-11 19:09:20 ] INFO binding all normalized counts for TU...
[ 2017-04-11 19:09:21 ] INFO binding filtering flags...
[ 2017-04-11 19:09:22 ] INFO Writing output...
[ 2017-04-11 19:09:24 ] INFO Creating quality control graphs...
[ 2017-04-11 19:09:24 ] INFO Plotting in png format...
[ 2017-04-11 19:09:24 ] INFO Plotting mds...
[ 2017-04-11 19:09:25 ] INFO Plotting biodetection...
[ 2017-04-11 19:09:27 ] INFO Plotting countsbio...
[ 2017-04-11 19:09:30 ] INFO Plotting saturation...
[ 2017-04-11 19:09:44 ] INFO Plotting readnoise...
[ 2017-04-11 19:09:48 ] INFO Plotting correl...
[ 2017-04-11 19:09:48 ] INFO Plotting pairwise...
[ 2017-04-11 19:10:58 ] INFO Plotting boxplot...
[ 2017-04-11 19:10:58 ] INFO Plotting gcbias...
[ 2017-04-11 19:10:59 ] INFO Plotting lengthbias...
[ 2017-04-11 19:11:01 ] INFO Plotting meandiff...
[ 2017-04-11 19:11:03 ] INFO Plotting meanvar...
[ 2017-04-11 19:11:23 ] INFO Plotting boxplot...
[ 2017-04-11 19:11:23 ] INFO Plotting gcbias...
[ 2017-04-11 19:11:25 ] INFO Plotting lengthbias...
[ 2017-04-11 19:11:26 ] INFO Plotting meandiff...
[ 2017-04-11 19:11:28 ] INFO Plotting meanvar...
[ 2017-04-11 19:11:49 ] INFO Plotting biodist...
[ 2017-04-11 19:11:49 ] INFO Contrast: WU_vs_TU
[ 2017-04-11 19:11:49 ] INFO Plotting volcano...
[ 2017-04-11 19:11:49 ] INFO Contrast: WU_vs_TU
[ 2017-04-11 19:11:50 ] INFO Plotting deheatmap...
[ 2017-04-11 19:11:50 ] INFO Contrast: WU_vs_TU
[ 2017-04-11 19:12:49 ] INFO Plotting filtered...
[ 2017-04-11 19:12:50 ] INFO Plotting in pdf format...
[ 2017-04-11 19:12:50 ] INFO Plotting mds...
[ 2017-04-11 19:12:50 ] INFO Plotting biodetection...
[ 2017-04-11 19:12:51 ] INFO Plotting countsbio...
[ 2017-04-11 19:12:53 ] INFO Plotting saturation...
[ 2017-04-11 19:13:02 ] INFO Plotting readnoise...
[ 2017-04-11 19:13:07 ] INFO Plotting correl...
[ 2017-04-11 19:13:07 ] INFO Plotting pairwise...
[ 2017-04-11 19:13:46 ] INFO Plotting boxplot...
[ 2017-04-11 19:13:46 ] INFO Plotting gcbias...
[ 2017-04-11 19:13:48 ] INFO Plotting lengthbias...
[ 2017-04-11 19:13:50 ] INFO Plotting meandiff...
[ 2017-04-11 19:13:54 ] INFO Plotting meanvar...
[ 2017-04-11 19:14:15 ] INFO Plotting boxplot...
[ 2017-04-11 19:14:15 ] INFO Plotting gcbias...
[ 2017-04-11 19:14:17 ] INFO Plotting lengthbias...
[ 2017-04-11 19:14:19 ] INFO Plotting meandiff...
[ 2017-04-11 19:14:23 ] INFO Plotting meanvar...
[ 2017-04-11 19:14:46 ] INFO Plotting biodist...
[ 2017-04-11 19:14:46 ] INFO Contrast: WU_vs_TU
[ 2017-04-11 19:14:47 ] INFO Plotting volcano...
[ 2017-04-11 19:14:47 ] INFO Contrast: WU_vs_TU
[ 2017-04-11 19:14:47 ] INFO Plotting deheatmap...
[ 2017-04-11 19:14:47 ] INFO Contrast: WU_vs_TU
[ 2017-04-11 19:15:45 ] INFO Plotting filtered...
[ 2017-04-11 19:15:45 ] INFO Creating HTML report...
[ 2017-04-11 19:15:45 ] INFO Compressing figures...

Figures

Multidimensional scaling

The Multi-Dimensional Scaling (MDS) plots comprise a means of visualizing the level of similarity of individual cases of a dataset. It is similar to Principal Component Analysis (PCA), but instead of using the covariance matrix to find similarities among cases, MDS uses absolute distance metrics such as the classical Euclidean distance. Because of the relative linear relations among sequencing samples, it provides a more realistic clustering among samples. MDS serves quality control and it can be interpreted as follows: when the distance among samples of the same biological condition in the MDS space is small, this is an indication of high correlation and reproducibility among them. When this distance is larger or heterogeneous (e.g. the 3rd sample of a triplicate set is further from the other 2), this constitutes an indication of low correlation and reproducibility among samples. It can help exclude poor samples from further analysis.

Biotype detection

The biotype detection bar diagrams are a set of quality control charts that show the percentage of each biotype in the genome (i.e. in the whole set of features provided, for example, protein coding genes, non coding RNAs or pseudogenes) in grey bars, which proportion has been detected in a sample before normalization and after a basic filtering by removing features with zero counts in red lined bars, and the percentage of each biotype within the sample in solid red bars. The difference between grey bars and solid red bars is that the grey bars show the percentage of a feature in the genome while the solid red bars show the percentage in the sample. Thus, the solid red bars may be sometimes higher than the grey bars because certain features (e.g. protein coding genes) may be detected within a sample with a higher proportion relatively to their presence in the genome, as compared with other features. For example, while the percentage of protein coding genes in the whole genome is already higher than other biotypes, this percentage is expected to be even higher in an RNA-Seq experiment where one expects protein-coding genes to exhibit greater abundance. The vertical green line separates the most abundant biotypes (on the left-hand side, corresponding to the left axis scale) from the rest (on the right-hand side, corresponding to the right axis scale). Otherwise, the lower abundance biotypes would be indistinguishable. Unexpected outcomes in this quality control chart (e.g. very low detection of protein coding genes) would signify possible low quality of a sample.

Biotype detection counts

The biotype detection counts boxplots are a set of quality control charts that depict both the biological classification for the detected features and the actual distribution of the read counts for each biological type. The boxplot comprises a means of summarizing the read counts distribution of a sample in the form of a bar with extending lines, as commonly used way of graphically presenting groups of numerical data. A boxplot also indicates which observations, if any, might be considered outliers and is able to visually show different types of populations, without making any assumptions of the underlying statistical distribution. The spacing between the different parts of the box help indicate variance, skewness and identify outliers. The thick bar inside the colored box is the median of the observations while the box extends over the Interquartile Range of the observations. The whiskers extend up (down) to +/-1.5xIQR. Unexpected outcomes (e.g. protein coding read count distribution similar to pseudogene read count distribution) indicates poor sample quality.

Read and biotype saturation

The read and biotype saturation plots are a set of quality control charts that depict the read count saturation levels at several sequencing depths. Thus, they comprise a means of assessing whether the sequencing depth of an RNA-Seq experiment is sufficient in order to detect the biological features under investigation. These quality control charts are separated in two subgroups: the first subgroup (read saturation per biotype for all samples) is a set of plots, one for each biological feature (e.g. protein coding, pseudogene, lincRNA, etc.), that depict the number of detected features in different sequencing depths and for all samples in the same plot. The second subgroup (read saturation per sample for all biotypes) is a set of plots similar to the above, but with, there is one pair of plots with two panels for each sample, presenting all biological features. The left panel depicts the saturation levels for the less abundatnt features, while the right panel, the saturation for the more abundant features, as placing them all together would make the less abundant features indistinguishable. All the saturation plots should be interpreted as follows: if the read counts for a biotype tend to be saturated, the respective curve should tend to reach a plateau at higher depths. Otherwise, more sequencing is needed for the specific biotype.

Read saturation per biotype for all samples

Read saturation per sample for all biotypes

RNA-Seq reads noise

The read noise plots depict the percentage of biological features detected when subsampling the total number of reads. Very steep curves in read noise plots indicate that although the sequencing depth reaches its maximum, a relatively small percentage of total features is detected, indicating that the level of background noise is relatively high. Less steep RNA composition curves, indicate less noise. When a sample's curve deviate from the rest, it could indicate lower or higher quality, depending on the curves of the rest of the samples.

Correlation plots

The sample correlation plots depict the accordance among the RNA-Seq samples, as this is manifested through the read counts table used with the metaseqr pipeline, with two representations that both use the correlation matrix (a matrix which depicts all the pairwise correlations between each pair of samples) of the read counts matrix. The first is a correlation clustered heatmap which depicts the correlations among samples as color-scaled image and the hierarchical clustering tree depicts the grouping of the samples according to their correlation. Samples from the same group that are not clustered together provides an indication that there might be a quality problem with the dataset. The second is a 'correlogram' plot, where again the samples are hierarchically clustered and grouped but this time correlations are presented as ellipses inside each cell. Each cell represents a pairwise comparison and each correlation coefficient is represented by an ellipse whose 'diameter', direction and color depict the accordance for that pair of samples. Highly correlated samples are depicted as ellipses with narrow diameter, while poorly correlated samples are depicted as ellipses with wide diameters. Also, highly correlated samples are depicted as ellipses with a left-to-right upwards direction while poorly correlated samples are depicted as ellipses with a right-to-left upwards direction.

Correlation heatmap

Data correlogram

Pairwise scatterplots

The pairwise comparison plots are split in three parts: the upper diagonal consists of simple scatterplots for all pairwise sample comparisons, together with their Pearson correlation coefficient. It is a simple measure of between sample correlation using all the available data points instead of only the correlation matrix. The lower diagonal consists of mean-difference plots for all pairwise sample comparisons. A mean-difference plot (or a Bland-Altman plots) is a method of data plotting used in analyzing the agreement between two different assays/variables. In this graphical method the differences (or alternatively the ratios) between the two variables are plotted against the averages of the two. Such a plot is useful, for example, for analyzing data with strong correlation between x and y axes, when the (x,y) dots on the plot are close to the diagonal x=y. In this case, the value of the transformed variable X is about the same as x and y and the variable Y shows the difference between x and y. In both represantations, irregular shapes of the red smoother lines are an indication of poor correlation between samples or of other systematic bias sources, which is usually corrected through data normalization.

Boxplots

The boxplot comprises a means of summarizing the read counts distribution of a sample in the form of a bar with extending lines, as a commonly used way of graphically presenting groups of numerical data. A boxplot also indicates which observations, if any, might be considered outliers and is able to visually show different types of populations, without making any assumptions about the underlying statistical distribution. The spacings between the different parts of the box help indicate variance, skewness and identify outliers. The thick bar inside the colored box is the median of the observations while the box extends over the Interquartile Range of the observations. The whiskers extend up (down) to +/-1.5xIQR. Boxplots at similar levels indicate good quality of the normalization. If boxplots remain at different levels after normalization, maybe another normalization algorithm may have to be examined. The un-normalized boxplots show the need for data normalization in order for the data from different samples to follow the same underlying distribution and statistical testing becoming possible.

Boxplot of un-normalized data

Boxplot of normalized data

GC content bias

The GC-content bias plot is a quality control chart that shows the possible dependence of the read counts (in log2 scale) under a gene to the GC content percentage of that gene. In order for the statistical tests to be able to detect statistical significance which occurs due to real biological effects and not by other systematic biases present in the data (e.g. a possible GC-content bias), the latter should be accounted for by the applied normalization algorithm. Although the tests are performed for each gene across biological conditions one could assume that the GC content does not represent a bias, as it is the same for the tested gene across samples and conditions. However, Risso et al. (2011) showed that the GC-content could have an impact in the statistical testing procedure. The GC-content bias plot depicts the dependence of the read counts to the GC content before and after normalization. The smoothing lines for each sample, should be as 'straight' as possible after normalization. In addition, if the smoothing lines differ significantly between biological conditions, this would constitute a possible quality warning.

GC content bias un-normalized

GC content bias normalized

Gene/transcript length bias

The gene/transcript length bias plot is a quality control chart that shows the possible dependence of the read counts (in log2 scale) under a gene to the length that gene (whole gene or sum of exons depending on the analysis). In order for the statistical tests to be able to detect statistical significance which occurs due to real biological effects and not by other systematic biases present in the data (e.g. a possible length bias), the latter should be accounted for by the applied normalization algorithm. Although the tests are performed for each gene across bioogical conditions, one could assume that the gene length does not represent a bias as it's the same for the tested gene across samples and conditions. However, it has been shown in several studies that the gene length could have an impact on the statistical testing procedure. The length bias plot depicts the dependence of the read counts to the gene/transcript length before and after normalization. The smoothing lines for each sample, should be as 'straight' as possible after normalization. In addition, if the smoothing lines differ significantly among biological conditions, this would constitute a possible quality warning.

Gene/transcript length bias un-normalized

Gene/transcript length bias normalized

Mean-difference

A mean-difference plot (or a Bland-Altman plot) is a method of data plotting used in analyzing the agreement between two different assays/variables. In this graphical method the differences (or alternatively the ratios) between the two variables are plotted against the averages of the two. Such a plot is useful, for example, for analyzing data with strong correlation between x and y axes, when the (x,y) dots on the plot are close to the diagonal x=y. In this case, the value of the transformed variable X is about the same as x and y and the variable Y shows the difference between x and y. When the data cloud in a mean difference plot is centered around the horizontal zero line, this is an indication of good data quality and good normalization results. On the other hand, when the data cloud deviates from the center line or has a 'banana' shape, this constitutes an indication of systematic biases present in the data and that either the chosen normalization algorithm has not worked well, or that data are not normalized. The smoothing curve that traverses the data (red curve) summarizes the above trends.

Mean-difference plots for the replicates of WU

Mean-difference plots for the replicates of TU

Mean-difference plots for the replicates of TH

Mean-difference plots for the replicates of TR

Mean-variance

The mean-variance plot comprises a graphical means of displaying a possible relationship between the means of gene expression (counts) values and their variances across replicates of the same biological condition. Thus data can be inspected for possible overdispersion (greater variability in a dataset than would be expected based on a given simple statistical model). In such plots for RNA-Seq data, overdispersion is usually manifested as increasing variance with increasing gene expression (counts) and it is summarized through a smoothing curve (red curve). The following is taken from the EDASeq package vignette: '...although the Poisson distribution is a natural and simple way to model count data, it has the limitation of assuming equality of the mean and variance. For this reason, the negative binomial distribution has been proposed as an alternative when the data show over-dispersion...' If overdispersion is not present, the data cloud is expected to be evenly scattered around the smoothing curve.

Mean-variance plots for the replicates of WU

Mean-variance plots for the replicates of TU

Mean-variance plots for the replicates of TH

Mean-variance plots for the replicates of TR

Chromosome and biotype distribution of filtered genes

The chromosome and biotype distribution of filtered genes is a quality control chart with two rows and four panels: on the left panel of the first row, the bar chart depicts the numbers of filtered genes per chromosome (actual numbers shown above the bars). On the right panel of the first row, the bar chart depicts the numbers of filtered genes per biotype (actual numbers shown above the bars). On the left panel of the second row, the bar chart depicts the fraction of the filtered genes to the total genes per chromosome (actual percentages shown above the bars). On the right panel of the second row, the bar chart depicts the fraction of the filtered genes to the total genes per biotype (actual percentages shown above the bars). This plot should indicate possible quality problems when for example the filtered genes for a specific chromosome (or the fraction) is extremely higher than the rest. Generally, the fractions per chromosome should be uniform and the fractions per biotype should be proportional to the biotype fraction relative to the genome.

Volcano plots

A volcano plot is a scatterplot that is often used when analyzing high-throughput -omics data (e.g. microarray data, RNA-Seq data) to give an overview of interesting genes. The log2 fold change is plotted on the x-axis and the negative log10 p-value is plotted on the y-axis. A volcano plot combines the results of a statistical test (aka, p-values) with the magnitude of the change enabling quick visual identification of those genes that display large-magnitude changes that are also statistically significant. The horizontal dashed line sets the threshold for statistical significance, while the vertical dashed lines set the thresholds for biological significance. It should be noted that the volcano plots become harder to interpret when using more than one statistical algorithm and performing meta-analysis. This happens because the genes that have stronger evidence of being differentially expressed obtain lower p-values while the rest either remain at similar levels or obtain higher p-values. The result is a 'warped' volcano plot, with two main data clouds: one in the upper part of the plot, and one in the lower part of the plot. You can always zoom in when using interacting mode (the default).

Volcano plot for the contrast WU_vs_TU

DEG heatmaps

The Differentially Expressed Genes (DEGs) heatmaps depict how well samples from different conditions cluster together according to their expression values after normalization and statistical testing, for each requested statistical contrast. If samples from the same biological condition do not cluster together, this would constitute a warning sign regarding the quality of the samples. In addition, DEG heatmaps provide an initial view of possible clusters of co-expressed genes.

DEG heatmap for the contrast WU_vs_TU

Chromosome and biotype distributions of DEGs

The chromosome and biotype distributions bar diagram for Differentially Expressed Genes (DEGs) is split in two panels: i) on the left panel DEGs are distributed per chromosome and the percentage of each chromosome in the genome is presented in grey bars, the percentage of DEGs in each chromosome is presented in red lined bars and the percentage of certain chromosomes in the distribution of DEGs is presented in solid red bars. ii) on the right panel, DEGs are distributed per biotype and the percentage of each biotype in the genome (i.e. in the whole set of features provided, for example, protein coding genes, non coding RNAs or pseudogenes) is presented in grey bars, the percentage of DEGs in each biotype is presented in blue lined bars and the percentage of each biotype in DEGs is presented in solid blue lines. The vertical green line separates the most abundant biotypes (on the left-hand side, corresponding to the left axis scale), from the rest (on the right-hand side, corresponding to the right axis scale). Otherwise, the lower abundance, biotypes would be indistinguishable.

Biotype distribution in DEG for the contrast WU_vs_TU

Get all the figures in png, pdf format.

Results

DEG table for the contrast WU_vs_TU

The following table presents the top 10% statistically significant genes (use the download links below the table to retrieve the whole list) for the contrast WU_vs_TU. The fields of the table correspond to the requested features to be exported. The table can be searched using the search field on the top right.

chromosome	start	end	gene_id	gc_content	strand	gene_name	biotype	p-value_deseq	FDR_deseq	natural_normalized_fold_change_WU_vs_TU	log2_normalized_fold_change_WU_vs_TU	natural_normalized_mean_counts_WU	log2_normalized_mean_counts_WU	rpgm_normalized_mean_counts_WU	natural_normalized_mean_counts_TU	log2_normalized_mean_counts_TU	rpgm_normalized_mean_counts_TU	natural_normalized_counts_UNK	natural_normalized_counts_WU1	natural_normalized_counts_WU2	natural_normalized_counts_WU3	log2_normalized_counts_UNK	log2_normalized_counts_WU1	log2_normalized_counts_WU2	log2_normalized_counts_WU3	rpgm_normalized_counts_UNK	rpgm_normalized_counts_WU1	rpgm_normalized_counts_WU2	rpgm_normalized_counts_WU3	natural_normalized_counts_TU1	natural_normalized_counts_TU2	natural_normalized_counts_TU3	log2_normalized_counts_TU1	log2_normalized_counts_TU2	log2_normalized_counts_TU3	rpgm_normalized_counts_TU1	rpgm_normalized_counts_TU2	rpgm_normalized_counts_TU3
chr7	46711998	46715700	ENSMUSG00000040026	0.4807	-	Saa3	protein_coding	1.39e-16	2.75e-12	2.22e+02	7.80e+00	1.20e+01	3.01e+00	2.50e-03	2.78e+03	1.13e+01	6.04e-01	0.00e+00	1.20e+01	1.60e+01	1.80e+01	0.00e+00	3.70e+00	4.09e+00	4.25e+00	0.00e+00	2.61e-03	3.48e-03	3.92e-03	3.88e+03	2.88e+03	1.56e+03	1.19e+01	1.15e+01	1.06e+01	8.46e-01	6.28e-01	3.39e-01
chr1	83116766	83119167	ENSMUSG00000026166	0.3926	+	Ccl20	protein_coding	3.69e-11	3.65e-07	8.70e+01	6.44e+00	7.00e+00	2.55e+00	3.15e-03	7.17e+02	9.38e+00	3.11e-01	0.00e+00	1.20e+01	9.00e+00	8.00e+00	0.00e+00	3.70e+00	3.32e+00	3.17e+00	0.00e+00	5.21e-03	3.90e-03	3.47e-03	1.13e+03	5.29e+02	4.88e+02	1.01e+01	9.05e+00	8.93e+00	4.92e-01	2.30e-01	2.12e-01
chr9	7445822	7455975	ENSMUSG00000043613	0.4079	+	Mmp3	protein_coding	2.99e-10	1.98e-06	3.95e+01	5.30e+00	7.20e+01	4.93e+00	4.68e-02	2.88e+03	1.15e+01	1.87e+00	0.00e+00	7.00e+01	1.08e+02	1.10e+02	0.00e+00	6.15e+00	6.77e+00	6.79e+00	0.00e+00	4.55e-02	7.02e-02	7.15e-02	2.50e+03	3.08e+03	3.07e+03	1.13e+01	1.16e+01	1.16e+01	1.62e+00	2.00e+00	1.99e+00
chr3	83773853	83789956	ENSMUSG00000097250	0.4606	-	Gm26771	antisense	4.12e-08	1.88e-04	1.14e-01	-3.13e+00	1.65e+03	8.30e+00	1.32e+00	1.87e+02	6.71e+00	1.49e-01	0.00e+00	1.89e+03	1.79e+03	2.93e+03	0.00e+00	1.09e+01	1.08e+01	1.15e+01	0.00e+00	1.51e+00	1.43e+00	2.34e+00	4.22e+02	1.18e+02	2.20e+01	8.72e+00	6.89e+00	4.52e+00	3.37e-01	9.41e-02	1.75e-02
chr10	117277334	117282274	ENSMUSG00000069516	0.4710	-	Lyz2	protein_coding	4.74e-08	1.88e-04	1.07e+01	3.42e+00	2.40e+02	7.10e+00	1.17e-01	2.58e+03	1.12e+01	1.26e+00	6.38e+02	3.30e+01	2.14e+02	7.40e+01	9.32e+00	5.09e+00	7.75e+00	6.23e+00	3.13e-01	1.62e-02	1.05e-01	3.63e-02	4.22e+03	1.74e+03	1.77e+03	1.20e+01	1.08e+01	1.08e+01	2.07e+00	8.51e-01	8.68e-01
chr3	83766321	83774316	ENSMUSG00000027996	0.4816	+	Sfrp2	protein_coding	7.77e-08	2.27e-04	1.13e-01	-3.15e+00	1.78e+03	8.38e+00	3.83e-01	2.00e+02	6.99e+00	4.30e-02	0.00e+00	2.04e+03	1.93e+03	3.16e+03	0.00e+00	1.10e+01	1.09e+01	1.16e+01	0.00e+00	4.39e-01	4.15e-01	6.78e-01	4.27e+02	1.41e+02	3.30e+01	8.74e+00	7.15e+00	5.09e+00	9.17e-02	3.03e-02	7.09e-03
chr13	23621755	23622502	ENSMUSG00000051627	0.5989	-	Hist1h1e	protein_coding	8.00e-08	2.27e-04	5.07e-03	-7.62e+00	1.84e+03	3.21e+00	1.70e+00	8.00e+00	1.57e+00	7.70e-03	7.36e+03	0.00e+00	0.00e+00	0.00e+00	1.28e+01	0.00e+00	0.00e+00	0.00e+00	6.80e+00	0.00e+00	0.00e+00	0.00e+00	2.50e+01	0.00e+00	0.00e+00	4.70e+00	0.00e+00	0.00e+00	2.31e-02	0.00e+00	0.00e+00
chr6	30723547	30748465	ENSMUSG00000051855	0.4545	+	Mest	protein_coding	1.31e-07	3.25e-04	4.24e-02	-4.56e+00	8.01e+02	9.56e+00	9.06e-02	3.30e+01	4.91e+00	3.74e-03	4.94e+02	1.01e+03	5.61e+02	1.14e+03	8.95e+00	9.98e+00	9.13e+00	1.02e+01	5.59e-02	1.14e-01	6.35e-02	1.29e-01	3.40e+01	5.10e+01	1.40e+01	5.13e+00	5.70e+00	3.91e+00	3.85e-03	5.77e-03	1.58e-03
chr4	4133531	4138819	ENSMUSG00000045573	0.4386	-	Penk	protein_coding	3.54e-07	7.80e-04	1.34e-01	-2.90e+00	2.98e+03	8.92e+00	1.06e+00	3.97e+02	8.12e+00	1.41e-01	0.00e+00	3.94e+03	2.60e+03	5.36e+03	0.00e+00	1.19e+01	1.13e+01	1.24e+01	0.00e+00	1.40e+00	9.25e-01	1.90e+00	8.34e+02	2.56e+02	1.00e+02	9.71e+00	8.01e+00	6.66e+00	2.96e-01	9.09e-02	3.55e-02
chr2	164940780	164955850	ENSMUSG00000017737	0.5213	+	Mmp9	protein_coding	5.30e-07	1.05e-03	1.21e+01	3.60e+00	1.11e+02	3.66e+00	2.35e-02	1.35e+03	9.21e+00	2.87e-01	0.00e+00	4.00e+00	4.28e+02	1.10e+01	0.00e+00	2.32e+00	8.74e+00	3.58e+00	0.00e+00	8.50e-04	9.09e-02	2.34e-03	1.13e+02	5.29e+02	3.41e+03	6.83e+00	9.05e+00	1.17e+01	2.40e-02	1.12e-01	7.24e-01
chr13	108670603	108671397	ENSMUSG00000059751	0.4704	+	Rps3a3	processed_pseudogene	6.11e-07	1.10e-03	6.11e-02	-4.03e+00	1.07e+03	9.02e+00	8.52e-01	6.40e+01	5.99e+00	5.13e-02	2.88e+02	6.50e+01	1.77e+03	2.15e+03	8.17e+00	6.04e+00	1.08e+01	1.11e+01	2.30e-01	5.18e-02	1.41e+00	1.72e+00	5.40e+01	8.80e+01	5.10e+01	5.78e+00	6.48e+00	5.70e+00	4.31e-02	7.02e-02	4.07e-02
chr3	121250134	121250737	ENSMUSG00000106619	0.5811	-	Gm9353	processed_pseudogene	1.79e-06	2.96e-03	1.53e-03	-9.35e+00	1.09e+03	3.67e+00	8.66e-01	1.00e+00	5.28e-01	5.32e-04	4.34e+03	2.00e+00	0.00e+00	1.00e+00	1.21e+01	1.58e+00	0.00e+00	1.00e+00	3.46e+00	1.59e-03	0.00e+00	7.97e-04	0.00e+00	2.00e+00	0.00e+00	0.00e+00	1.58e+00	0.00e+00	0.00e+00	1.59e-03	0.00e+00
chr4	43631539	43641533	ENSMUSG00000086043	0.5159	-	Gm12473	antisense	2.28e-06	3.47e-03	1.83e-03	-9.09e+00	5.45e+02	2.77e+00	4.35e-01	0.00e+00	0.00e+00	0.00e+00	2.18e+03	0.00e+00	0.00e+00	0.00e+00	1.11e+01	0.00e+00	0.00e+00	0.00e+00	1.74e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr6	30747554	30896794	ENSMUSG00000025607	0.3904	-	Copg2	protein_coding	2.61e-06	3.70e-03	8.25e-02	-3.60e+00	7.34e+02	9.19e+00	7.29e-02	6.00e+01	5.84e+00	5.92e-03	1.65e+02	1.04e+03	5.88e+02	1.15e+03	7.38e+00	1.00e+01	9.20e+00	1.02e+01	1.64e-02	1.03e-01	5.83e-02	1.14e-01	7.40e+01	7.20e+01	3.30e+01	6.23e+00	6.19e+00	5.09e+00	7.34e-03	7.14e-03	3.27e-03
chr19	34473786	34475135	ENSMUSG00000050370	0.4985	-	Ch25h	protein_coding	2.88e-06	3.81e-03	2.60e+01	4.70e+00	1.00e+01	2.43e+00	6.93e-03	2.85e+02	8.11e+00	1.98e-01	0.00e+00	2.00e+00	3.00e+01	8.00e+00	0.00e+00	1.58e+00	4.95e+00	3.17e+00	0.00e+00	1.39e-03	2.08e-02	5.54e-03	3.83e+02	2.69e+02	2.03e+02	8.58e+00	8.08e+00	7.67e+00	2.65e-01	1.86e-01	1.41e-01
chr7	23357125	23358069	ENSMUSG00000109325	0.5587	+	Krt8-ps	processed_pseudogene	3.36e-06	4.16e-03	1.52e-03	-9.36e+00	6.58e+02	2.84e+00	5.25e-01	0.00e+00	0.00e+00	0.00e+00	2.63e+03	0.00e+00	0.00e+00	0.00e+00	1.14e+01	0.00e+00	0.00e+00	0.00e+00	2.10e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr14	56365068	56388551	ENSMUSG00000022229	0.4575	+	Atp12a	protein_coding	4.60e-06	5.36e-03	5.62e-02	-4.15e+00	2.84e+02	6.39e+00	1.51e-01	1.50e+01	3.90e+00	7.96e-03	0.00e+00	2.92e+02	3.31e+02	5.12e+02	0.00e+00	8.19e+00	8.38e+00	9.00e+00	0.00e+00	1.55e-01	1.76e-01	2.72e-01	1.50e+01	2.20e+01	8.00e+00	4.00e+00	4.52e+00	3.17e+00	7.96e-03	1.17e-02	4.25e-03
chr7	75769038	75782099	ENSMUSG00000097415	0.4715	-	AU020206	lincRNA	8.31e-06	9.15e-03	1.44e+01	3.85e+00	2.30e+01	3.72e+00	9.27e-03	3.48e+02	7.98e+00	1.39e-01	0.00e+00	2.90e+01	2.30e+01	4.10e+01	0.00e+00	4.91e+00	4.58e+00	5.39e+00	0.00e+00	1.16e-02	9.17e-03	1.63e-02	1.18e+02	1.81e+02	7.45e+02	6.89e+00	7.51e+00	9.54e+00	4.70e-02	7.22e-02	2.97e-01
chr11	82035571	82037453	ENSMUSG00000035385	0.4376	+	Ccl2	protein_coding	1.14e-05	1.19e-02	1.38e+01	3.79e+00	2.10e+02	6.02e+00	1.37e-01	2.91e+03	1.15e+01	1.91e+00	0.00e+00	4.23e+02	2.54e+02	1.61e+02	0.00e+00	8.73e+00	7.99e+00	7.34e+00	0.00e+00	2.77e-01	1.66e-01	1.05e-01	3.87e+03	2.93e+03	1.94e+03	1.19e+01	1.15e+01	1.09e+01	2.53e+00	1.92e+00	1.27e+00
chr4	136895917	136898803	ENSMUSG00000036887	0.5535	-	C1qa	protein_coding	1.22e-05	1.21e-02	1.12e+01	3.48e+00	4.40e+01	3.92e+00	2.97e-02	5.05e+02	8.49e+00	3.38e-01	0.00e+00	6.00e+00	8.90e+01	8.20e+01	0.00e+00	2.81e+00	6.49e+00	6.38e+00	0.00e+00	4.02e-03	5.97e-02	5.50e-02	1.67e+02	2.51e+02	1.10e+03	7.39e+00	7.98e+00	1.01e+01	1.12e-01	1.68e-01	7.35e-01
chrY	1260771	1286629	ENSMUSG00000069045	0.3678	-	Ddx3y	protein_coding	1.31e-05	1.24e-02	7.11e-01	-4.93e-01	1.09e+02	2.84e+00	5.83e-03	7.70e+01	2.62e+00	4.12e-03	4.32e+02	0.00e+00	0.00e+00	5.00e+00	8.76e+00	0.00e+00	0.00e+00	2.58e+00	2.30e-02	0.00e+00	0.00e+00	2.67e-04	0.00e+00	0.00e+00	2.32e+02	0.00e+00	0.00e+00	7.86e+00	0.00e+00	0.00e+00	1.24e-02
chr1	79434669	79440120	ENSMUSG00000050711	0.3955	-	Scg2	protein_coding	1.38e-05	1.24e-02	1.64e-02	-5.93e+00	1.21e+02	5.42e+00	6.98e-02	1.00e+00	6.67e-01	5.76e-04	0.00e+00	1.07e+02	1.20e+02	2.58e+02	0.00e+00	6.75e+00	6.92e+00	8.02e+00	0.00e+00	6.16e-02	6.91e-02	1.49e-01	0.00e+00	3.00e+00	0.00e+00	0.00e+00	2.00e+00	0.00e+00	0.00e+00	1.73e-03	0.00e+00
chr8	39581685	39642673	ENSMUSG00000025044	0.3639	-	Msr1	protein_coding	1.49e-05	1.26e-02	1.22e+01	3.61e+00	1.70e+01	3.52e+00	1.95e-03	2.22e+02	7.71e+00	2.51e-02	2.10e+01	1.00e+00	3.80e+01	9.00e+00	4.46e+00	1.00e+00	5.29e+00	3.32e+00	2.38e-03	1.13e-04	4.30e-03	1.02e-03	2.26e+02	1.30e+02	3.09e+02	7.83e+00	7.03e+00	8.28e+00	2.56e-02	1.47e-02	3.50e-02
chr17	57203970	57228136	ENSMUSG00000024164	0.5201	-	C3	protein_coding	1.52e-05	1.26e-02	1.13e+01	3.49e+00	6.60e+01	5.80e+00	1.72e-02	7.49e+02	9.49e+00	1.97e-01	2.10e+01	6.10e+01	5.40e+01	1.26e+02	4.46e+00	5.95e+00	5.78e+00	6.99e+00	5.52e-03	1.60e-02	1.42e-02	3.31e-02	4.66e+02	9.00e+02	8.80e+02	8.87e+00	9.82e+00	9.78e+00	1.22e-01	2.37e-01	2.31e-01
chr4	130913125	130936141	ENSMUSG00000028581	0.5309	+	Laptm5	protein_coding	1.83e-05	1.40e-02	1.02e+01	3.35e+00	1.00e+02	4.75e+00	9.60e-03	1.03e+03	9.91e+00	9.87e-02	0.00e+00	2.60e+01	3.13e+02	6.00e+01	0.00e+00	4.75e+00	8.29e+00	5.93e+00	0.00e+00	2.50e-03	3.01e-02	5.77e-03	8.58e+02	6.74e+02	1.55e+03	9.75e+00	9.40e+00	1.06e+01	8.26e-02	6.49e-02	1.49e-01
chr3	96261682	96263311	ENSMUSG00000091405	0.5092	-	Hist2h4	protein_coding	1.84e-05	1.40e-02	9.60e-03	-6.70e+00	7.97e+02	3.16e+00	3.45e-01	7.00e+00	1.46e+00	2.89e-03	3.19e+03	1.00e+00	0.00e+00	0.00e+00	1.16e+01	1.00e+00	0.00e+00	0.00e+00	1.38e+00	4.33e-04	0.00e+00	0.00e+00	2.00e+01	0.00e+00	0.00e+00	4.39e+00	0.00e+00	0.00e+00	8.66e-03	0.00e+00	0.00e+00
chr4	137741733	137796384	ENSMUSG00000028766	0.5122	-	Alpl	protein_coding	2.20e-05	1.60e-02	6.66e-02	-3.91e+00	4.20e+02	7.89e+00	4.42e-02	2.70e+01	3.31e+00	2.85e-03	2.10e+01	7.63e+02	3.41e+02	5.53e+02	4.46e+00	9.58e+00	8.42e+00	9.11e+00	2.21e-03	8.05e-02	3.60e-02	5.83e-02	0.00e+00	6.80e+01	1.30e+01	0.00e+00	6.11e+00	3.81e+00	0.00e+00	7.17e-03	1.37e-03
chr5	118561757	118563101	ENSMUSG00000106912	0.4193	-	Gm43785	TEC	2.26e-05	1.60e-02	2.86e+01	4.84e+00	6.00e+00	1.36e+00	4.39e-03	1.85e+02	3.04e+00	1.47e-01	2.10e+01	1.00e+00	0.00e+00	0.00e+00	4.46e+00	1.00e+00	0.00e+00	0.00e+00	1.67e-02	7.97e-04	0.00e+00	0.00e+00	5.54e+02	0.00e+00	0.00e+00	9.12e+00	0.00e+00	0.00e+00	4.42e-01	0.00e+00	0.00e+00
chr4	134082448	134095082	ENSMUSG00000000682	0.4953	-	Cd52	protein_coding	2.41e-05	1.65e-02	1.36e+01	3.77e+00	2.20e+01	3.13e+00	5.44e-03	3.06e+02	8.12e+00	7.73e-02	0.00e+00	3.00e+00	6.00e+01	2.30e+01	0.00e+00	2.00e+00	5.93e+00	4.58e+00	0.00e+00	7.59e-04	1.52e-02	5.82e-03	3.04e+02	1.54e+02	4.59e+02	8.25e+00	7.28e+00	8.85e+00	7.69e-02	3.90e-02	1.16e-01
chr5	134600022	134615025	ENSMUSG00000040751	0.5223	-	Lat2	protein_coding	2.75e-05	1.82e-02	1.15e+01	3.52e+00	3.00e+01	3.68e+00	4.33e-03	3.57e+02	8.30e+00	5.12e-02	0.00e+00	8.00e+00	7.40e+01	3.90e+01	0.00e+00	3.17e+00	6.23e+00	5.32e+00	0.00e+00	1.15e-03	1.06e-02	5.59e-03	1.72e+02	2.97e+02	6.03e+02	7.43e+00	8.22e+00	9.24e+00	2.46e-02	4.25e-02	8.64e-02
chr6	136801553	136804415	ENSMUSG00000096010	0.4384	-	Hist4h4	protein_coding	5.17e-05	3.31e-02	8.89e-03	-6.81e+00	6.74e+02	3.35e+00	1.92e-01	5.00e+00	1.33e+00	1.42e-03	2.69e+03	0.00e+00	0.00e+00	3.00e+00	1.14e+01	0.00e+00	0.00e+00	2.00e+00	7.67e-01	0.00e+00	0.00e+00	8.54e-04	1.50e+01	0.00e+00	0.00e+00	4.00e+00	0.00e+00	0.00e+00	4.27e-03	0.00e+00	0.00e+00
chr10	117287796	117292868	ENSMUSG00000069515	0.4609	-	Lyz1	protein_coding	6.43e-05	3.98e-02	4.11e+01	5.36e+00	2.00e+00	1.15e+00	8.57e-04	1.12e+02	6.39e+00	5.49e-02	0.00e+00	1.00e+00	5.00e+00	1.00e+00	0.00e+00	1.00e+00	2.58e+00	1.00e+00	0.00e+00	4.90e-04	2.45e-03	4.90e-04	2.35e+02	6.20e+01	3.90e+01	7.88e+00	5.98e+00	5.32e+00	1.15e-01	3.04e-02	1.91e-02
chr8	70373558	70382066	ENSMUSG00000031849	0.5643	+	Comp	protein_coding	7.43e-05	4.46e-02	1.31e-01	-2.94e+00	3.30e+03	9.05e+00	5.33e-01	4.31e+02	8.64e+00	6.96e-02	0.00e+00	4.15e+03	3.38e+03	5.68e+03	0.00e+00	1.20e+01	1.17e+01	1.25e+01	0.00e+00	6.71e-01	5.46e-01	9.17e-01	2.35e+02	6.44e+02	4.13e+02	7.88e+00	9.33e+00	8.69e+00	3.80e-02	1.04e-01	6.67e-02
chr8	70382355	70439579	ENSMUSG00000003575	0.5317	-	Crtc1	protein_coding	7.74e-05	4.51e-02	1.31e-01	-2.93e+00	3.31e+03	1.02e+01	3.76e-01	4.32e+02	8.63e+00	4.91e-02	2.10e+01	4.14e+03	3.38e+03	5.68e+03	4.46e+00	1.20e+01	1.17e+01	1.25e+01	2.39e-03	4.71e-01	3.84e-01	6.47e-01	2.31e+02	6.48e+02	4.16e+02	7.86e+00	9.34e+00	8.70e+00	2.63e-02	7.38e-02	4.74e-02
chr10	51480679	51496613	ENSMUSG00000062593	0.4042	+	Lilrb4a	protein_coding	8.73e-05	4.94e-02	2.06e+01	4.36e+00	8.00e+00	2.57e+00	2.01e-03	1.94e+02	7.41e+00	4.60e-02	0.00e+00	7.00e+00	2.10e+01	6.00e+00	0.00e+00	3.00e+00	4.46e+00	2.81e+00	0.00e+00	1.66e-03	4.97e-03	1.42e-03	3.48e+02	1.09e+02	1.26e+02	8.45e+00	6.78e+00	6.99e+00	8.23e-02	2.58e-02	2.98e-02
chrX	9435252	9487771	ENSMUSG00000015340	0.3972	-	Cybb	protein_coding	1.06e-04	5.83e-02	6.30e+00	2.66e+00	2.50e+01	3.56e+00	1.95e-03	1.61e+02	7.12e+00	1.27e-02	6.20e+01	0.00e+00	2.70e+01	1.00e+01	5.98e+00	0.00e+00	4.81e+00	3.46e+00	4.90e-03	0.00e+00	2.13e-03	7.90e-04	1.37e+02	6.90e+01	2.78e+02	7.11e+00	6.13e+00	8.12e+00	1.08e-02	5.45e-03	2.20e-02
chr16	36934983	36963212	ENSMUSG00000022831	0.4295	+	Hcls1	protein_coding	1.31e-04	7.00e-02	1.93e+01	4.27e+00	5.00e+00	1.70e+00	6.80e-04	1.19e+02	6.61e+00	1.54e-02	0.00e+00	0.00e+00	1.50e+01	6.00e+00	0.00e+00	0.00e+00	4.00e+00	2.81e+00	0.00e+00	0.00e+00	1.94e-03	7.77e-04	8.80e+01	4.50e+01	2.25e+02	6.48e+00	5.52e+00	7.82e+00	1.14e-02	5.83e-03	2.91e-02
chr17	35552128	35557180	ENSMUSG00000039518	0.5375	+	Cdsn	protein_coding	1.44e-04	7.51e-02	1.09e-01	-3.20e+00	5.67e+02	7.17e+00	2.61e-01	6.10e+01	5.04e+00	2.79e-02	0.00e+00	7.62e+02	8.66e+02	6.41e+02	0.00e+00	9.58e+00	9.76e+00	9.33e+00	0.00e+00	3.51e-01	3.99e-01	2.95e-01	5.00e+00	1.34e+02	4.30e+01	2.58e+00	7.08e+00	5.46e+00	2.30e-03	6.17e-02	1.98e-02
chr1	171229572	171234365	ENSMUSG00000058715	0.4864	-	Fcer1g	protein_coding	1.58e-04	8.02e-02	2.53e+01	4.66e+00	9.00e+00	2.61e+00	3.06e-03	2.46e+02	7.37e+00	8.60e-02	0.00e+00	7.00e+00	2.10e+01	7.00e+00	0.00e+00	3.00e+00	4.46e+00	3.00e+00	0.00e+00	2.45e-03	7.35e-03	2.45e-03	5.59e+02	8.30e+01	9.50e+01	9.13e+00	6.39e+00	6.58e+00	1.96e-01	2.91e-02	3.33e-02
chr16	36963460	36990467	ENSMUSG00000047746	0.4247	-	Fbxo40	protein_coding	1.62e-04	8.02e-02	1.89e+01	4.24e+00	5.00e+00	1.70e+00	1.36e-03	1.17e+02	6.59e+00	3.03e-02	0.00e+00	0.00e+00	1.50e+01	6.00e+00	0.00e+00	0.00e+00	4.00e+00	2.81e+00	0.00e+00	0.00e+00	3.88e-03	1.55e-03	8.80e+01	4.50e+01	2.18e+02	6.48e+00	5.52e+00	7.77e+00	2.28e-02	1.16e-02	5.64e-02
chr7	103826534	103828096	ENSMUSG00000052305	0.4651	-	Hbb-bs	protein_coding	1.78e-04	8.60e-02	1.38e-02	-6.18e+00	6.99e+02	2.86e+00	2.59e-01	9.00e+00	1.90e+00	3.21e-03	2.80e+03	0.00e+00	0.00e+00	0.00e+00	1.15e+01	0.00e+00	0.00e+00	0.00e+00	1.04e+00	0.00e+00	0.00e+00	0.00e+00	2.50e+01	1.00e+00	0.00e+00	4.70e+00	1.00e+00	0.00e+00	9.25e-03	3.70e-04	0.00e+00
chr10	93523338	93540033	ENSMUSG00000015890	0.4457	-	Amdhd1	protein_coding	2.14e-04	9.93e-02	1.04e+02	6.70e+00	0.00e+00	2.50e-01	1.07e-04	1.29e+02	2.87e+00	5.53e-02	0.00e+00	1.00e+00	0.00e+00	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	0.00e+00	4.29e-04	0.00e+00	0.00e+00	3.87e+02	0.00e+00	0.00e+00	8.60e+00	0.00e+00	0.00e+00	1.66e-01	0.00e+00	0.00e+00
chr11	112782224	112787760	ENSMUSG00000000567	0.5095	+	Sox9	protein_coding	2.19e-04	9.93e-02	1.62e-01	-2.63e+00	9.89e+02	9.58e+00	2.82e-01	1.59e+02	7.30e+00	4.55e-02	1.85e+02	9.00e+02	1.36e+03	1.51e+03	7.54e+00	9.82e+00	1.04e+01	1.06e+01	5.28e-02	2.57e-01	3.88e-01	4.31e-01	1.37e+02	2.07e+02	1.34e+02	7.11e+00	7.70e+00	7.08e+00	3.91e-02	5.91e-02	3.82e-02
chr3	138341775	138358861	ENSMUSG00000074206	0.4262	+	Adh6b	protein_coding	2.20e-04	9.93e-02	1.16e+02	6.86e+00	0.00e+00	3.96e-01	1.40e-04	1.73e+02	3.01e+00	4.84e-02	0.00e+00	0.00e+00	0.00e+00	2.00e+00	0.00e+00	0.00e+00	0.00e+00	1.58e+00	0.00e+00	0.00e+00	0.00e+00	5.59e-04	5.20e+02	0.00e+00	0.00e+00	9.03e+00	0.00e+00	0.00e+00	1.45e-01	0.00e+00	0.00e+00

Download the DEG result list for WU_vs_TU.
Download the whole result list for WU_vs_TU.

References

Anders, S., and Huber, W. (2010). Differential expression analysis for sequence count data. Genome Biol 11, R106.
Planet, E., Attolini, C.S., Reina, O., Flores, O., and Rossell, D. (2012). htSeqTools: high-throughput sequencing quality control, processing and visualization in R. Bioinformatics 28, 589-590.
Tarazona, S., Garcia-Alcalde, F., Dopazo, J., Ferrer, A., and Conesa, A. (2011). Differential expression in RNA-seq: a matter of depth. Genome Res 21, 2213-2223.
Risso, D., Schwartz, K., Sherlock, G., and Dudoit, S. (2011). GC-content normalization for RNA-Seq data. BMC Bioinformatics 12, 480.
Benjamini, Y., and Hochberg, Y. (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B (Methodological) 57, 289-300.