metaseqR report

Summary

Analysis summary

Summary: The raw bam files, one for each RNA-Seq sample, were summarized to a gene read counts table, using the Bioconductor package GenomicRanges. In the final read counts table, each row represented one gene, each column one RNA-Seq sample and each cell, the corresponding read counts associated with each row and column.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 (3157 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 (125 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 0.02309 average reads per 100 bp), iii) genes with read counts below the 25^th quantile of the normalized counts distribution (2581 genes with cutoff value 4 normalized read counts). The total number of genes excluded due to the application of gene filters was 2078. The total (unified) number of genes excluded due to the application of all filters was 6771. The resulting gene counts table was subjected to differential expression analysis for the contrasts G versus U using the Bioconductor package DESeq. The final numbers of differentially expressed genes were (per contrast): for the contrast G versus U, 252 (14) statistically significant genes were found with a p-value (FDR or adjusted p-value) threshold of 0.05 and of these, 231 (14) were up-regulated, 21 (0) were down-regulated and 0 (0) 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 sam/bam/bed files
Conditions: T, U, G
Samples included: T1, T2, T3, U1, U2, U3, G1, G2, G3
Samples excluded: none
Requested contrasts: G_vs_U
Library sizes:

T1: 4436547
T2: 4231987
T3: 3912463
U1: 3928975
U2: 10192862
U3: 5030237
G1: 3727842
G2: 4880135
G3: 4956222

Annotation: download
Organism: mouse (Mus musculus), genome version alias mm10
Annotation source: RefSeq database
Count type: gene
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: FALSE
- mean: FALSE
- quantile: 0.25
- known: NA
- custom: NA

biotype

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, correlation heatmap and correlogram, pairwise scatterplots between samples, boxplots, volcano plot, DEG biotype detection, DEG heatmap
Figure format: png, pdf
Output directory: /data/images/proton/run296/www/metaseqr_G_vs_U
Output data: Annotation, p-value, Adjusted p-value (FDR), Fold change, Statistics, Read counts
Output scale(s): log2 scale
Output values: Normalized values
Output statistics: Mean, Median, Standard deviation, Median Absolute Deviation (MAD), Coefficient of Variation, Robust Coefficient of Variation
Total run time: 07 minutes 29 seconds

Filtered genes

Number of filtered genes: 6771 which is the union of

Filtered because of zero reads: 3157
Filtered because of exon filters: 0
Filtered because of gene filters: 3614 which is the union of
- length: 125 genes with filter cutoff value 500
- avg.reads: 1952 genes with filter cutoff value 0.02309462
- expression: 2581 genes further decomposed to (filter name, filtered genes, filter cutoff):
- - quantile: 2581 genes with filter cutoff value 4

Differentially expressed genes

Number of differentially expressed genes per contrast:

G_vs_U: 252 (14) statistically significant genes of which 231 (14) up regulated, 21 (0) down regulated and 0 (0) 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

[ 2016-07-06 15:25:06 ] INFO 2016-07-06 15:25:06: Data processing started...
[ 2016-07-06 15:25:06 ] INFO Read counts file: imported sam/bam/bed files
[ 2016-07-06 15:25:06 ] INFO Conditions: T, U, G
[ 2016-07-06 15:25:06 ] INFO Samples to include: T1, T2, T3, U1, U2, U3, G1, G2, G3
[ 2016-07-06 15:25:06 ] INFO Samples to exclude: none
[ 2016-07-06 15:25:06 ] INFO Requested contrasts: G_vs_U
[ 2016-07-06 15:25:06 ] INFO Annotation: download
[ 2016-07-06 15:25:06 ] INFO Organism: mm10
[ 2016-07-06 15:25:06 ] INFO Reference source: refseq
[ 2016-07-06 15:25:06 ] INFO Count type: gene
[ 2016-07-06 15:25:06 ] INFO Exon filters: none applied
[ 2016-07-06 15:25:06 ] INFO Gene filters: length, avg.reads, expression, biotype
[ 2016-07-06 15:25:06 ] INFO length:
[ 2016-07-06 15:25:06 ] INFO length: 500
[ 2016-07-06 15:25:06 ] INFO avg.reads:
[ 2016-07-06 15:25:06 ] INFO average.per.bp: 100
[ 2016-07-06 15:25:07 ] INFO quantile: 0.25
[ 2016-07-06 15:25:07 ] INFO expression:
[ 2016-07-06 15:25:07 ] INFO median: FALSE
[ 2016-07-06 15:25:07 ] INFO mean: FALSE
[ 2016-07-06 15:25:07 ] INFO quantile: 0.25
[ 2016-07-06 15:25:07 ] INFO known: NA
[ 2016-07-06 15:25:07 ] INFO custom: NA
[ 2016-07-06 15:25:07 ] INFO biotype:
[ 2016-07-06 15:25:07 ] INFO Filter application: postnorm
[ 2016-07-06 15:25:07 ] INFO Normalization algorithm: deseq
[ 2016-07-06 15:25:07 ] INFO Normalization arguments:
[ 2016-07-06 15:25:07 ] INFO locfunc:
[ 2016-07-06 15:25:07 ] INFO [[
[ 2016-07-06 15:25:07 ] INFO list(locfunc = function (x, na.rm = FALSE) standardGeneric("median"))
[ 2016-07-06 15:25:07 ] INFO locfunc
[ 2016-07-06 15:25:07 ] INFO Statistical algorithm: deseq
[ 2016-07-06 15:25:07 ] INFO Statistical arguments:
[ 2016-07-06 15:25:07 ] INFO deseq: blind, fit-only, local
[ 2016-07-06 15:25:07 ] INFO Meta-analysis method: none
[ 2016-07-06 15:25:07 ] INFO Multiple testing correction: BH
[ 2016-07-06 15:25:07 ] INFO p-value threshold: 0.05
[ 2016-07-06 15:25:07 ] INFO Logarithmic transformation offset: 1
[ 2016-07-06 15:25:07 ] INFO Quality control plots: mds, biodetection, countsbio, saturation, readnoise, correl, pairwise, boxplot, volcano, biodist, deheatmap
[ 2016-07-06 15:25:07 ] INFO Figure format: png, pdf
[ 2016-07-06 15:25:07 ] INFO Output directory: /data/images/proton/run296/www/metaseqr_G_vs_U
[ 2016-07-06 15:25:07 ] INFO Output data: flags, annotation, p.value, adj.p.value, fold.change, stats, counts
[ 2016-07-06 15:25:07 ] INFO Output scale(s): log2
[ 2016-07-06 15:25:07 ] INFO Output values: normalized
[ 2016-07-06 15:25:07 ] INFO Output statistics: mean, median, sd, mad, cv, rcv
[ 2016-07-06 15:25:07 ] INFO Downloading gene annotation for mm10...
[ 2016-07-06 15:25:31 ] INFO Converting annotation to GenomicRanges object...
[ 2016-07-06 15:25:31 ] INFO Getting DNA sequences...
[ 2016-07-06 15:25:36 ] INFO Getting GC content...
[ 2016-07-06 15:25:38 ] INFO Converting annotation to GenomicRanges object...
[ 2016-07-06 15:25:38 ] INFO Reading bam file GK3R1-T1.bam for sample with name T1. This might take some time...
[ 2016-07-06 15:25:38 ] WARN Information regarding strandedness of the reads is not correctly provided! Assuming unstranded...
[ 2016-07-06 15:25:38 ] INFO Reading bam file GK3R2-T2.bam for sample with name T2. This might take some time...
[ 2016-07-06 15:25:38 ] WARN Information regarding strandedness of the reads is not correctly provided! Assuming unstranded...
[ 2016-07-06 15:25:38 ] INFO Reading bam file GK3R3-T3.bam for sample with name T3. This might take some time...
[ 2016-07-06 15:25:38 ] WARN Information regarding strandedness of the reads is not correctly provided! Assuming unstranded...
[ 2016-07-06 15:25:38 ] INFO Reading bam file GK3R4-U1.bam for sample with name U1. This might take some time...
[ 2016-07-06 15:25:38 ] WARN Information regarding strandedness of the reads is not correctly provided! Assuming unstranded...
[ 2016-07-06 15:25:44 ] INFO Counting reads overlapping with given annotation...
[ 2016-07-06 15:25:45 ] INFO ...for single-end reads...
[ 2016-07-06 15:25:45 ] INFO ...ignoring strandedness...
[ 2016-07-06 15:25:45 ] INFO Counting reads overlapping with given annotation...
[ 2016-07-06 15:25:45 ] INFO ...for single-end reads...
[ 2016-07-06 15:25:45 ] INFO ...ignoring strandedness...
[ 2016-07-06 15:25:45 ] INFO Counting reads overlapping with given annotation...
[ 2016-07-06 15:25:45 ] INFO ...for single-end reads...
[ 2016-07-06 15:25:45 ] INFO ...ignoring strandedness...
[ 2016-07-06 15:25:45 ] INFO Counting reads overlapping with given annotation...
[ 2016-07-06 15:25:45 ] INFO ...for single-end reads...
[ 2016-07-06 15:25:45 ] INFO ...ignoring strandedness...
[ 2016-07-06 15:26:15 ] INFO Reading bam file GK3R8-G2.bam for sample with name G2. This might take some time...
[ 2016-07-06 15:26:15 ] WARN Information regarding strandedness of the reads is not correctly provided! Assuming unstranded...
[ 2016-07-06 15:26:16 ] INFO Reading bam file GK3R7-G1.bam for sample with name G1. This might take some time...
[ 2016-07-06 15:26:16 ] WARN Information regarding strandedness of the reads is not correctly provided! Assuming unstranded...
[ 2016-07-06 15:26:17 ] INFO Reading bam file GK3R6-U3.bam for sample with name U3. This might take some time...
[ 2016-07-06 15:26:17 ] WARN Information regarding strandedness of the reads is not correctly provided! Assuming unstranded...
[ 2016-07-06 15:26:18 ] INFO Reading bam file GK3R5-U2.bam for sample with name U2. This might take some time...
[ 2016-07-06 15:26:18 ] WARN Information regarding strandedness of the reads is not correctly provided! Assuming unstranded...
[ 2016-07-06 15:26:22 ] INFO Counting reads overlapping with given annotation...
[ 2016-07-06 15:26:22 ] INFO ...for single-end reads...
[ 2016-07-06 15:26:22 ] INFO ...ignoring strandedness...
[ 2016-07-06 15:26:22 ] INFO Counting reads overlapping with given annotation...
[ 2016-07-06 15:26:23 ] INFO ...for single-end reads...
[ 2016-07-06 15:26:23 ] INFO ...ignoring strandedness...
[ 2016-07-06 15:26:25 ] INFO Counting reads overlapping with given annotation...
[ 2016-07-06 15:26:25 ] INFO ...for single-end reads...
[ 2016-07-06 15:26:25 ] INFO ...ignoring strandedness...
[ 2016-07-06 15:26:34 ] INFO Counting reads overlapping with given annotation...
[ 2016-07-06 15:26:34 ] INFO ...for single-end reads...
[ 2016-07-06 15:26:34 ] INFO ...ignoring strandedness...
[ 2016-07-06 15:27:42 ] INFO Reading bam file GK3R9-G3.bam for sample with name G3. This might take some time...
[ 2016-07-06 15:27:42 ] WARN Information regarding strandedness of the reads is not correctly provided! Assuming unstranded...
[ 2016-07-06 15:27:49 ] INFO Counting reads overlapping with given annotation...
[ 2016-07-06 15:27:49 ] INFO ...for single-end reads...
[ 2016-07-06 15:27:49 ] INFO ...ignoring strandedness...
[ 2016-07-06 15:28:19 ] INFO Exporting raw read counts table to /data/images/proton/run296/www/metaseqr_G_vs_U/lists/raw_counts_table.txt.gz
[ 2016-07-06 15:28:19 ] INFO Saving gene model to /data/images/proton/run296/www/metaseqr_G_vs_U/data/gene_model.RData
[ 2016-07-06 15:28:20 ] INFO Removing genes with zero counts in all samples...
[ 2016-07-06 15:28:20 ] INFO Normalizing with: deseq
[ 2016-07-06 15:28:20 ] INFO Applying gene filter length...
[ 2016-07-06 15:28:20 ] INFO Threshold below which ignored: 500
[ 2016-07-06 15:28:20 ] INFO Applying gene filter avg.reads...
[ 2016-07-06 15:28:20 ] INFO Threshold below which ignored: 0.0230946219723108
[ 2016-07-06 15:28:20 ] INFO Applying gene filter expression...
[ 2016-07-06 15:28:20 ] INFO Threshold below which ignored: 4
[ 2016-07-06 15:28:20 ] INFO Applying gene filter biotype...
[ 2016-07-06 15:28:20 ] INFO 6771 genes filtered out
[ 2016-07-06 15:28:20 ] INFO 15548 genes remain after filtering
[ 2016-07-06 15:28:20 ] INFO Running statistical tests with: deseq
[ 2016-07-06 15:28:20 ] INFO Contrast: G_vs_U
[ 2016-07-06 15:28:40 ] INFO Contrast G_vs_U: found 252 genes
[ 2016-07-06 15:28:40 ] INFO Exporting and compressing normalized read counts table to /data/images/proton/run296/www/metaseqr_G_vs_U/lists/normalized_counts_table.txt
[ 2016-07-06 15:28:40 ] INFO Building output files...
[ 2016-07-06 15:28:40 ] INFO Contrast: G_vs_U
[ 2016-07-06 15:28:40 ] INFO Adding non-filtered data...
[ 2016-07-06 15:28:40 ] INFO binding annotation...
[ 2016-07-06 15:28:41 ] INFO binding p-values...
[ 2016-07-06 15:28:41 ] INFO binding FDRs...
[ 2016-07-06 15:28:42 ] INFO binding log2 normalized fold changes...
[ 2016-07-06 15:28:42 ] INFO binding normalized mean counts...
[ 2016-07-06 15:28:42 ] INFO binding normalized median counts...
[ 2016-07-06 15:28:43 ] INFO binding normalized count sds...
[ 2016-07-06 15:28:44 ] INFO binding normalized count MADs...
[ 2016-07-06 15:28:46 ] INFO binding normalized count CVs...
[ 2016-07-06 15:28:47 ] INFO binding normalized counts RCVs...
[ 2016-07-06 15:28:50 ] INFO binding normalized mean counts...
[ 2016-07-06 15:28:50 ] INFO binding normalized median counts...
[ 2016-07-06 15:28:52 ] INFO binding normalized count sds...
[ 2016-07-06 15:28:53 ] INFO binding normalized count MADs...
[ 2016-07-06 15:28:54 ] INFO binding normalized count CVs...
[ 2016-07-06 15:28:56 ] INFO binding normalized counts RCVs...
[ 2016-07-06 15:28:59 ] INFO binding all normalized counts for G...
[ 2016-07-06 15:28:59 ] INFO binding all normalized counts for U...
[ 2016-07-06 15:28:59 ] INFO binding filtering flags...
[ 2016-07-06 15:29:00 ] INFO Writing output...
[ 2016-07-06 15:29:00 ] INFO Adding filtered data...
[ 2016-07-06 15:29:00 ] INFO binding annotation...
[ 2016-07-06 15:29:00 ] INFO binding p-values...
[ 2016-07-06 15:29:00 ] INFO binding FDRs...
[ 2016-07-06 15:29:00 ] INFO binding log2 normalized fold changes...
[ 2016-07-06 15:29:00 ] INFO binding normalized mean counts...
[ 2016-07-06 15:29:00 ] INFO binding normalized median counts...
[ 2016-07-06 15:29:01 ] INFO binding normalized count sds...
[ 2016-07-06 15:29:01 ] INFO binding normalized count MADs...
[ 2016-07-06 15:29:02 ] INFO binding normalized count CVs...
[ 2016-07-06 15:29:02 ] INFO binding normalized counts RCVs...
[ 2016-07-06 15:29:04 ] INFO binding normalized mean counts...
[ 2016-07-06 15:29:04 ] INFO binding normalized median counts...
[ 2016-07-06 15:29:04 ] INFO binding normalized count sds...
[ 2016-07-06 15:29:05 ] INFO binding normalized count MADs...
[ 2016-07-06 15:29:05 ] INFO binding normalized count CVs...
[ 2016-07-06 15:29:06 ] INFO binding normalized counts RCVs...
[ 2016-07-06 15:29:07 ] INFO binding all normalized counts for G...
[ 2016-07-06 15:29:07 ] INFO binding all normalized counts for U...
[ 2016-07-06 15:29:07 ] INFO binding filtering flags...
[ 2016-07-06 15:29:07 ] INFO Writing output...
[ 2016-07-06 15:29:08 ] INFO Creating quality control graphs...
[ 2016-07-06 15:29:08 ] INFO Plotting in png format...
[ 2016-07-06 15:29:08 ] INFO Plotting mds...
[ 2016-07-06 15:29:09 ] INFO Plotting biodetection...
[ 2016-07-06 15:29:10 ] INFO Plotting countsbio...
[ 2016-07-06 15:29:12 ] INFO Plotting saturation...
[ 2016-07-06 15:29:17 ] INFO Plotting readnoise...
[ 2016-07-06 15:29:19 ] INFO Plotting correl...
[ 2016-07-06 15:29:19 ] INFO Plotting pairwise...
[ 2016-07-06 15:30:48 ] INFO Plotting boxplot...
[ 2016-07-06 15:30:48 ] INFO Plotting boxplot...
[ 2016-07-06 15:30:48 ] INFO Plotting volcano...
[ 2016-07-06 15:30:48 ] INFO Contrast: G_vs_U
[ 2016-07-06 15:30:50 ] INFO Plotting biodist...
[ 2016-07-06 15:30:50 ] INFO Contrast: G_vs_U
[ 2016-07-06 15:30:50 ] INFO Plotting deheatmap...
[ 2016-07-06 15:30:50 ] INFO Contrast: G_vs_U
[ 2016-07-06 15:31:33 ] INFO Plotting in pdf format...
[ 2016-07-06 15:31:33 ] INFO Plotting mds...
[ 2016-07-06 15:31:33 ] INFO Plotting biodetection...
[ 2016-07-06 15:31:34 ] INFO Plotting countsbio...
[ 2016-07-06 15:31:35 ] INFO Plotting saturation...
[ 2016-07-06 15:31:39 ] INFO Plotting readnoise...
[ 2016-07-06 15:31:41 ] INFO Plotting correl...
[ 2016-07-06 15:31:42 ] INFO Plotting pairwise...
[ 2016-07-06 15:31:52 ] INFO Plotting boxplot...
[ 2016-07-06 15:31:53 ] INFO Plotting boxplot...
[ 2016-07-06 15:31:53 ] INFO Plotting volcano...
[ 2016-07-06 15:31:53 ] INFO Contrast: G_vs_U
[ 2016-07-06 15:31:53 ] INFO Plotting biodist...
[ 2016-07-06 15:31:53 ] INFO Contrast: G_vs_U
[ 2016-07-06 15:31:53 ] INFO Plotting deheatmap...
[ 2016-07-06 15:31:53 ] INFO Contrast: G_vs_U
[ 2016-07-06 15:32:35 ] INFO Creating HTML report...
[ 2016-07-06 15:32:35 ] 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

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 G_vs_U

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 G_vs_U

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 G_vs_U

Get all the figures in png, pdf format.

Results

DEG table for the contrast G_vs_U

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 G_vs_U. 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	log2_normalized_fold_change_G_vs_U	log2_normalized_mean_counts_G	log2_normalized_median_counts_G	log2_normalized_sd_counts_G	log2_normalized_mad_counts_G	log2_normalized_cv_counts_G	log2_normalized_rcv_counts_G	log2_normalized_mean_counts_U	log2_normalized_median_counts_U	log2_normalized_sd_counts_U	log2_normalized_mad_counts_U	log2_normalized_cv_counts_U	log2_normalized_rcv_counts_U	log2_normalized_counts_G1	log2_normalized_counts_G2	log2_normalized_counts_G3	log2_normalized_counts_U1	log2_normalized_counts_U2	log2_normalized_counts_U3
chr7	141789010	141819135	NM_010844	0.5135	+	Muc5ac	protein_coding	6.07e-20	9.43e-16	9.463524	0.333	0.00	0.57735	0.00000	1.73205	NaN	5.903	6.29	5.72091	7.61640	0.969207	1.211759	0.00	0.00	1.00	11.42	0.00	6.29
chr17	78653963	78736560	NM_011500	0.4029	-	Strn	protein_coding	9.03e-12	7.02e-08	6.160618	4.153	4.00	0.26525	0.00000	0.06387	0.000000	7.092	4.86	4.16327	0.49581	0.586997	0.102061	4.00	4.00	4.46	11.90	4.52	4.86
chr1	130635920	130661618	NM_007576	0.4028	-	C4bp	protein_coding	4.31e-10	2.23e-06	5.934905	2.359	2.32	0.79313	1.09263	0.33622	0.470568	5.108	3.32	4.29004	1.95989	0.839827	0.589986	3.17	1.58	2.32	10.00	2.00	3.32
chr2	105076537	105126510	NR_015462	0.4425	-	Wt1os	antisense	2.60e-09	1.01e-05	6.491853	1.195	1.00	0.33773	0.00000	0.28262	0.000000	4.289	2.00	4.32968	0.61533	1.009402	0.307667	1.00	1.58	1.00	9.28	2.00	1.58
chr14	33967069	33978764	NM_001013777	0.4749	-	Zfp488	protein_coding	1.11e-06	3.44e-03	4.619351	3.459	3.81	1.00913	0.65319	0.29173	0.171561	6.004	4.39	3.31097	0.86727	0.551465	0.197451	2.32	3.81	4.25	9.81	4.39	3.81
chr4	145837218	145837992	NR_033612	0.5265	+	Gm13238	processed_pseudogene	1.70e-06	4.41e-03	6.022368	0.333	0.00	0.57735	0.00000	1.73205	NaN	3.002	1.00	4.36212	1.48260	1.453133	1.482600	0.00	0.00	1.00	8.01	0.00	1.00
chr3	117575226	117689508	NM_001305451	0.3907	+	Plppr5	protein_coding	3.53e-06	7.36e-03	4.641450	2.918	2.58	0.57735	0.00000	0.19784	0.000000	5.779	5.04	3.04298	2.77909	0.526593	0.550926	3.58	2.58	2.58	9.12	3.17	5.04
chr19	41083980	41206774	NM_011904	0.4766	-	Tll2	protein_coding	4.22e-06	7.36e-03	4.564149	3.298	3.00	0.69021	0.28562	0.20926	0.095205	4.861	3.46	4.15715	2.77909	0.855287	0.803337	3.00	2.81	4.09	9.54	3.46	1.58
chr7	4074124	4120728	NR_033568	0.4410	-	9430041J12Rik	processed_transcript	4.64e-06	7.36e-03	4.897240	1.831	2.32	1.64108	1.25724	0.89646	0.541464	4.121	2.58	4.11026	2.34987	0.997390	0.909052	3.17	0.00	2.32	8.78	2.58	1.00
chr5	95305922	95343575	NM_001270456	0.4007	+	Gm3259	processed_transcript	4.74e-06	7.36e-03	4.960002	2.252	2.00	0.82190	0.61533	0.36502	0.307667	4.664	4.09	3.98333	4.57747	0.854099	1.119881	2.00	1.58	3.17	8.90	4.09	1.00
chr1	166483612	166497588	NM_001164306	0.4096	-	Gm4846	protein_coding	7.55e-06	1.07e-02	5.369234	0.667	0.00	1.15470	0.00000	1.73205	NaN	2.979	1.00	4.32256	1.48260	1.451069	1.482600	2.00	0.00	0.00	7.94	0.00	1.00
chr3	154786290	155055407	NM_177066	0.4180	-	Tnni3k	protein_coding	8.52e-06	1.10e-02	4.245928	3.623	3.70	0.42036	0.44413	0.11601	0.120020	5.918	4.75	3.08345	1.73453	0.521044	0.364789	4.00	3.17	3.70	9.41	4.75	3.58
chr9	123636906	123678832	NM_139142	0.4370	-	Slc6a20a	protein_coding	1.71e-05	2.04e-02	4.741467	2.195	2.58	1.05549	0.61533	0.48086	0.238044	4.450	2.32	3.68663	0.00000	0.828381	0.000000	3.00	1.00	2.58	8.71	2.32	2.32
chr3	108940556	108960776	NM_001078646	0.4285	+	Henmt1	protein_coding	3.54e-05	3.93e-02	4.231221	3.107	3.00	1.16468	1.48260	0.37482	0.494200	5.473	4.95	3.43953	3.90260	0.628484	0.787736	2.00	3.00	4.32	9.14	4.95	2.32
chr11	58421110	58426023	NM_027239	0.4697	+	1810065E05Rik	protein_coding	7.72e-05	7.65e-02	-3.269410	5.300	2.32	6.33773	1.95989	1.19578	0.844079	5.989	6.99	3.59453	2.94829	0.600223	0.421866	12.58	1.00	2.32	6.99	2.00	8.98
chr14	54970687	54994549	NM_080728	0.5078	-	Myh7	protein_coding	7.87e-05	7.65e-02	4.799975	1.000	1.00	1.00000	1.48260	1.00000	1.482600	2.862	1.00	4.12095	1.48260	1.440059	1.482600	2.00	0.00	1.00	7.58	1.00	0.00
chrX	147383477	147429192	NM_008356	0.3576	-	Il13ra2	protein_coding	1.04e-04	9.55e-02	4.081388	3.187	3.17	0.38910	0.53755	0.12207	0.169577	4.939	3.17	3.38269	0.53755	0.684917	0.169577	3.58	3.17	2.81	8.84	3.17	2.81
chr6	141805439	141856171	NM_030687	0.3810	-	Slco1a4	protein_coding	1.25e-04	1.08e-01	4.533979	1.969	2.00	0.36946	0.47729	0.18764	0.238645	3.696	2.00	3.83600	1.48260	1.037929	0.741300	2.32	1.58	2.00	8.09	2.00	1.00
chr7	24818794	24840275	NR_045096	0.4651	+	Gm5893	antisense	1.38e-04	1.13e-01	4.918863	0.333	0.00	0.57735	0.00000	1.73205	NaN	2.957	1.00	3.38926	0.00000	1.146263	0.000000	1.00	0.00	0.00	6.87	1.00	1.00
chr8	125995101	126030688	NM_008430	0.4745	+	Kcnk1	protein_coding	1.66e-04	1.29e-01	3.039840	3.727	2.32	2.71660	0.47729	0.72897	0.205558	6.791	7.30	3.24364	3.62409	0.477617	0.496193	6.86	2.00	2.32	9.75	3.32	7.30
chr18	34207774	34221772	NR_040534	0.4502	-	Gm10548	transcribed_processed_pseudogene	2.38e-04	1.72e-01	4.517848	1.774	2.00	0.68934	0.47729	0.38858	0.238645	3.318	1.00	4.01501	0.00000	1.210045	0.000000	1.00	2.00	2.32	7.95	1.00	1.00
chr6	121300295	121337718	NM_144512	0.4769	+	Slc6a13	protein_coding	2.43e-04	1.72e-01	4.398549	1.774	2.00	0.68934	0.47729	0.38858	0.238645	4.126	2.58	3.18998	0.86727	0.773055	0.335504	2.00	1.00	2.32	7.79	2.58	2.00
chr7	103550367	103555504	NM_147100	0.3585	+	Olfr613	protein_coding	2.97e-04	2.01e-01	3.316766	3.043	3.32	0.93538	0.71969	0.30738	0.216649	5.966	5.78	1.60739	1.95989	0.269410	0.339002	2.00	3.32	3.81	5.78	7.66	4.46
chr2	52038112	52056681	NM_009398	0.4097	+	Tnfaip6	protein_coding	3.52e-04	2.28e-01	3.478578	9.852	9.62	0.50083	0.17505	0.05084	0.018189	11.877	10.48	2.57658	0.27859	0.216930	0.026571	10.43	9.62	9.51	14.85	10.30	10.48
chr2	149830782	150004392	NM_001085521	0.4180	+	Syndig1	protein_coding	4.42e-04	2.75e-01	3.137504	4.337	4.58	1.23198	1.24731	0.28406	0.272045	6.344	5.49	2.55613	1.73453	0.402952	0.315837	5.43	3.00	4.58	9.22	4.32	5.49
chr4	96042659	96077540	NM_145548	0.4176	-	Cyp2j13	protein_coding	5.08e-04	2.95e-01	4.301170	2.000	2.00	1.00000	1.48260	0.50000	0.741300	4.319	2.58	3.23376	0.38997	0.748744	0.150863	1.00	2.00	3.00	8.05	2.32	2.58

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

Download the raw read counts table for the experiment.
Download the normalized read counts table for the experiment.

References

Statham, A.L., Strbenac, D., Coolen, M.W., Stirzaker, C., Clark, S.J., Robinson, M.D. (2010). Repitools: an R package for the analysis of enrichment-based epigenomic data. Bioinformatics 26(13), 1662-1663.
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.
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.