metaseqR report

Summary

Analysis summary

Summary: The raw bam files, one for each RNA-Seq sample, were summarized to read counts table, using the Bioconductor package GenomicRanges. In the final read counts table, each row represented 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 (33439 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 (3242 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 0.08361 average reads per 100 bp), iii) genes with read counts below the median read counts of the total normalized count distribution (11449 genes with cutoff value 12 normalized read counts). The total number of genes excluded due to the application of gene filters was 9426. The total (unified) number of genes excluded due to the application of all filters was 45736. The resulting gene counts table was subjected to differential expression analysis for the contrasts Cplus versus vectorhuh75 using the Bioconductor package DESeq. The final numbers of differentially expressed genes were (per contrast): for the contrast Cplus versus vectorhuh75, 542 (49) statistically significant genes were found with a p-value (FDR or adjusted p-value) threshold of 0.05 and of these, 242 (27) were up-regulated, 175 (22) were down-regulated and 125 (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: Cplus, vectorhuh75
Samples included: YK3R12-Cplus-shorthuh75, YK3R13-vectorhuh75
Samples excluded: none
Requested contrasts: Cplus_vs_vectorhuh75
Library sizes:

YK3R12-Cplus-shorthuh75: 2157912
YK3R13-vectorhuh75: 2259053

Annotation: download
Organism: human (Homo sapiens), genome version alias hg19
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
- lincRNA: FALSE
- protein_coding: FALSE
- antisense: FALSE
- processed_transcript: FALSE
- snRNA: FALSE
- sense_intronic: FALSE
- miRNA: FALSE
- misc_RNA: FALSE
- snoRNA: FALSE
- rRNA: TRUE
- polymorphic_pseudogene: FALSE
- sense_overlapping: FALSE
- three_prime_overlapping_ncrna: FALSE
- TR_V_gene: FALSE
- TR_V_pseudogene: TRUE
- TR_D_gene: FALSE
- TR_J_gene: FALSE
- TR_C_gene: FALSE
- TR_J_pseudogene: TRUE
- IG_C_gene: FALSE
- IG_C_pseudogene: TRUE
- IG_J_gene: FALSE
- IG_J_pseudogene: TRUE
- IG_D_gene: FALSE
- IG_V_gene: FALSE
- IG_V_pseudogene: TRUE

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, DEG biotype detection, volcano plot, DEG heatmap
Figure format: png, pdf
Output directory: /data/images/proton2/run342/www/metaseqr_run_run342c
Output data: Annotation, p-value, Adjusted p-value (FDR), Fold change, Read counts
Output scale(s): log2 scale, Reads per Gene Model
Output values: Normalized values
Output statistics: Mean, Median, Standard deviation, Median Absolute Deviation (MAD), Coefficient of Variation, Robust Coefficient of Variation
Total run time: 09 minutes 58 seconds

Filtered genes

Number of filtered genes: 45736 which is the union of

Filtered because of zero reads: 33439
Filtered because of exon filters: 0
Filtered because of gene filters: 12297 which is the union of
- length: 3242 genes with filter cutoff value 500
- avg.reads: 6124 genes with filter cutoff value 0.08361204
- expression: 11449 genes further decomposed to (filter name, filtered genes, filter cutoff):
- - median: 11449 genes with filter cutoff value 12
- biotype: 59 genes with filter cutoff value rRNA, TR_V_pseudogene, TR_J_pseudogene, IG_C_pseudogene, IG_J_pseudogene, IG_V_pseudogene

Differentially expressed genes

Number of differentially expressed genes per contrast:

Cplus_vs_vectorhuh75: 542 (49) statistically significant genes of which 242 (27) up regulated, 175 (22) down regulated and 125 (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

[ 2017-05-02 20:45:59 ] INFO 2017-05-02 20:45:59: Data processing started...
[ 2017-05-02 20:45:59 ] INFO Read counts file: imported sam/bam/bed files
[ 2017-05-02 20:45:59 ] INFO Conditions: Cplus, vectorhuh75
[ 2017-05-02 20:45:59 ] INFO Samples to include: YK3R12-Cplus-shorthuh75, YK3R13-vectorhuh75
[ 2017-05-02 20:45:59 ] INFO Samples to exclude: none
[ 2017-05-02 20:45:59 ] INFO Requested contrasts: Cplus_vs_vectorhuh75
[ 2017-05-02 20:45:59 ] INFO Annotation: download
[ 2017-05-02 20:45:59 ] INFO Organism: hg19
[ 2017-05-02 20:45:59 ] INFO Reference source: ensembl
[ 2017-05-02 20:45:59 ] INFO Count type: utr
[ 2017-05-02 20:45:59 ] INFO Exon filters: none applied
[ 2017-05-02 20:45:59 ] INFO Gene filters: length, avg.reads, expression, biotype
[ 2017-05-02 20:45:59 ] INFO length:
[ 2017-05-02 20:45:59 ] INFO length: 500
[ 2017-05-02 20:45:59 ] INFO avg.reads:
[ 2017-05-02 20:45:59 ] INFO average.per.bp: 100
[ 2017-05-02 20:45:59 ] INFO quantile: 0.25
[ 2017-05-02 20:45:59 ] INFO expression:
[ 2017-05-02 20:45:59 ] INFO median: TRUE
[ 2017-05-02 20:45:59 ] INFO mean: FALSE
[ 2017-05-02 20:45:59 ] INFO quantile: NA
[ 2017-05-02 20:45:59 ] INFO known: NA
[ 2017-05-02 20:45:59 ] INFO custom: NA
[ 2017-05-02 20:45:59 ] INFO biotype:
[ 2017-05-02 20:45:59 ] INFO pseudogene: FALSE
[ 2017-05-02 20:45:59 ] INFO lincRNA: FALSE
[ 2017-05-02 20:45:59 ] INFO protein_coding: FALSE
[ 2017-05-02 20:45:59 ] INFO antisense: FALSE
[ 2017-05-02 20:45:59 ] INFO processed_transcript: FALSE
[ 2017-05-02 20:45:59 ] INFO snRNA: FALSE
[ 2017-05-02 20:45:59 ] INFO sense_intronic: FALSE
[ 2017-05-02 20:45:59 ] INFO miRNA: FALSE
[ 2017-05-02 20:45:59 ] INFO misc_RNA: FALSE
[ 2017-05-02 20:45:59 ] INFO snoRNA: FALSE
[ 2017-05-02 20:45:59 ] INFO rRNA: TRUE
[ 2017-05-02 20:45:59 ] INFO polymorphic_pseudogene: FALSE
[ 2017-05-02 20:45:59 ] INFO sense_overlapping: FALSE
[ 2017-05-02 20:45:59 ] INFO three_prime_overlapping_ncrna: FALSE
[ 2017-05-02 20:45:59 ] INFO TR_V_gene: FALSE
[ 2017-05-02 20:45:59 ] INFO TR_V_pseudogene: TRUE
[ 2017-05-02 20:45:59 ] INFO TR_D_gene: FALSE
[ 2017-05-02 20:45:59 ] INFO TR_J_gene: FALSE
[ 2017-05-02 20:45:59 ] INFO TR_C_gene: FALSE
[ 2017-05-02 20:45:59 ] INFO TR_J_pseudogene: TRUE
[ 2017-05-02 20:45:59 ] INFO IG_C_gene: FALSE
[ 2017-05-02 20:45:59 ] INFO IG_C_pseudogene: TRUE
[ 2017-05-02 20:45:59 ] INFO IG_J_gene: FALSE
[ 2017-05-02 20:45:59 ] INFO IG_J_pseudogene: TRUE
[ 2017-05-02 20:45:59 ] INFO IG_D_gene: FALSE
[ 2017-05-02 20:45:59 ] INFO IG_V_gene: FALSE
[ 2017-05-02 20:45:59 ] INFO IG_V_pseudogene: TRUE
[ 2017-05-02 20:45:59 ] INFO Filter application: postnorm
[ 2017-05-02 20:45:59 ] INFO Normalization algorithm: deseq
[ 2017-05-02 20:45:59 ] INFO Normalization arguments:
[ 2017-05-02 20:45:59 ] INFO locfunc:
[ 2017-05-02 20:45:59 ] INFO [[
[ 2017-05-02 20:45:59 ] INFO list(locfunc = function (x, na.rm = FALSE) standardGeneric("median"))
[ 2017-05-02 20:45:59 ] INFO locfunc
[ 2017-05-02 20:45:59 ] INFO Statistical algorithm: deseq
[ 2017-05-02 20:45:59 ] INFO Statistical arguments:
[ 2017-05-02 20:45:59 ] INFO deseq: blind, fit-only, local
[ 2017-05-02 20:45:59 ] INFO Meta-analysis method: none
[ 2017-05-02 20:45:59 ] INFO Multiple testing correction: BH
[ 2017-05-02 20:45:59 ] INFO p-value threshold: 0.05
[ 2017-05-02 20:45:59 ] INFO Logarithmic transformation offset: 1
[ 2017-05-02 20:45:59 ] INFO Quality control plots: mds, biodetection, countsbio, saturation, readnoise, filtered, correl, pairwise, boxplot, gcbias, lengthbias, biodist, volcano, deheatmap
[ 2017-05-02 20:45:59 ] INFO Figure format: png, pdf
[ 2017-05-02 20:45:59 ] INFO Output directory: /data/images/proton2/run342/www/metaseqr_run_run342c
[ 2017-05-02 20:45:59 ] INFO Output data: annotation, p.value, adj.p.value, fold.change, counts, flags
[ 2017-05-02 20:45:59 ] INFO Output scale(s): log2, rpgm
[ 2017-05-02 20:45:59 ] INFO Output values: normalized
[ 2017-05-02 20:45:59 ] INFO Downloading gene annotation for hg19...
[ 2017-05-02 20:46:08 ] INFO Downloading transcript annotation for hg19...
[ 2017-05-02 20:49:05 ] INFO Converting annotation to GenomicRanges object...
[ 2017-05-02 20:49:05 ] INFO Merging transcript 3' UTRs to create unique gene/transcript models...
[ 2017-05-02 20:53:07 ] INFO Flanking merged transcript 3' UTRs per 500bp...
[ 2017-05-02 20:53:12 ] INFO Reading bam file YK3R12-Cplus-shorthuh75.bam for sample with name YK3R12-Cplus-shorthuh75. This might take some time...
[ 2017-05-02 20:53:12 ] WARN Information regarding strandedness of the reads is not correctly provided! Assuming unstranded...
[ 2017-05-02 20:53:12 ] INFO Reading bam file YK3R13-vectorhuh75.bam for sample with name YK3R13-vectorhuh75. This might take some time...
[ 2017-05-02 20:53:12 ] WARN Information regarding strandedness of the reads is not correctly provided! Assuming unstranded...
[ 2017-05-02 20:53:17 ] INFO Counting reads overlapping with given annotation...
[ 2017-05-02 20:53:17 ] INFO ...for single-end reads...
[ 2017-05-02 20:53:17 ] INFO ...ignoring strandedness...
[ 2017-05-02 20:53:17 ] INFO Counting reads overlapping with given annotation...
[ 2017-05-02 20:53:17 ] INFO ...for single-end reads...
[ 2017-05-02 20:53:17 ] INFO ...ignoring strandedness...
[ 2017-05-02 20:53:31 ] INFO Exporting raw read counts table to /data/images/proton2/run342/www/metaseqr_run_run342c/lists/raw_counts_table.txt.gz
[ 2017-05-02 20:53:32 ] INFO Checking chromosomes in transcript counts and gene annotation...
[ 2017-05-02 20:53:32 ] INFO Processing transcripts...
[ 2017-05-02 20:53:32 ] INFO Separating transcripts (UTR regions) per gene for YK3R12-Cplus-shorthuh75...
[ 2017-05-02 20:54:00 ] INFO Separating transcripts (UTR regions) per gene for YK3R13-vectorhuh75...
[ 2017-05-02 20:54:30 ] INFO Saving gene model to /data/images/proton2/run342/www/metaseqr_run_run342c/data/gene_model.RData
[ 2017-05-02 20:54:31 ] INFO Summarizing count data...
[ 2017-05-02 20:54:39 ] INFO Removing genes with zero counts in all samples...
[ 2017-05-02 20:54:39 ] INFO Normalizing with: deseq
[ 2017-05-02 20:54:39 ] INFO Applying gene filter length...
[ 2017-05-02 20:54:39 ] INFO Threshold below which ignored: 500
[ 2017-05-02 20:54:39 ] INFO Applying gene filter avg.reads...
[ 2017-05-02 20:54:39 ] INFO Threshold below which ignored: 0.0836120401337793
[ 2017-05-02 20:54:39 ] INFO Applying gene filter expression...
[ 2017-05-02 20:54:39 ] INFO Threshold below which ignored: 12
[ 2017-05-02 20:54:39 ] INFO Applying gene filter biotype...
[ 2017-05-02 20:54:39 ] INFO Biotypes ignored: rRNA, TR_V_pseudogene, TR_J_pseudogene, IG_C_pseudogene, IG_J_pseudogene, IG_V_pseudogene
[ 2017-05-02 20:54:40 ] INFO 45736 genes filtered out
[ 2017-05-02 20:54:40 ] INFO 12000 genes remain after filtering
[ 2017-05-02 20:54:40 ] INFO Running statistical tests with: deseq
[ 2017-05-02 20:54:40 ] WARN No replication detected! There is a possibility that DESeq will fail to estimate dispersions...
[ 2017-05-02 20:54:40 ] INFO Contrast: Cplus_vs_vectorhuh75
[ 2017-05-02 20:54:43 ] INFO Contrast Cplus_vs_vectorhuh75: found 542 genes
[ 2017-05-02 20:54:43 ] INFO Exporting and compressing normalized read counts table to /data/images/proton2/run342/www/metaseqr_run_run342c/lists/normalized_counts_table.txt
[ 2017-05-02 20:54:43 ] INFO Building output files...
[ 2017-05-02 20:54:51 ] INFO Contrast: Cplus_vs_vectorhuh75
[ 2017-05-02 20:54:51 ] INFO Adding non-filtered data...
[ 2017-05-02 20:54:51 ] INFO binding annotation...
[ 2017-05-02 20:54:52 ] INFO binding p-values...
[ 2017-05-02 20:54:52 ] INFO binding FDRs...
[ 2017-05-02 20:54:52 ] INFO binding log2 normalized fold changes...
[ 2017-05-02 20:54:52 ] INFO binding all normalized counts for Cplus...
[ 2017-05-02 20:54:52 ] INFO binding all normalized counts for vectorhuh75...
[ 2017-05-02 20:54:52 ] INFO binding filtering flags...
[ 2017-05-02 20:54:53 ] INFO Writing output...
[ 2017-05-02 20:54:53 ] INFO Adding filtered data...
[ 2017-05-02 20:54:53 ] INFO binding annotation...
[ 2017-05-02 20:54:53 ] INFO binding p-values...
[ 2017-05-02 20:54:53 ] INFO binding FDRs...
[ 2017-05-02 20:54:54 ] INFO binding log2 normalized fold changes...
[ 2017-05-02 20:54:54 ] INFO binding all normalized counts for Cplus...
[ 2017-05-02 20:54:54 ] INFO binding all normalized counts for vectorhuh75...
[ 2017-05-02 20:54:54 ] INFO binding filtering flags...
[ 2017-05-02 20:54:55 ] INFO Writing output...
[ 2017-05-02 20:54:56 ] INFO Creating quality control graphs...
[ 2017-05-02 20:54:56 ] INFO Plotting in png format...
[ 2017-05-02 20:54:56 ] INFO Plotting mds...
[ 2017-05-02 20:54:56 ] WARN MDS plot cannot be created with less than 3 samples! Skipping...
[ 2017-05-02 20:54:56 ] INFO Plotting biodetection...
[ 2017-05-02 20:54:57 ] INFO Plotting countsbio...
[ 2017-05-02 20:54:58 ] INFO Plotting saturation...
[ 2017-05-02 20:54:59 ] INFO Plotting readnoise...
[ 2017-05-02 20:54:59 ] INFO Plotting correl...
[ 2017-05-02 20:55:00 ] INFO Plotting pairwise...
[ 2017-05-02 20:55:00 ] INFO Plotting boxplot...
[ 2017-05-02 20:55:00 ] INFO Plotting gcbias...
[ 2017-05-02 20:55:00 ] INFO Plotting lengthbias...
[ 2017-05-02 20:55:01 ] INFO Plotting boxplot...
[ 2017-05-02 20:55:01 ] INFO Plotting gcbias...
[ 2017-05-02 20:55:01 ] INFO Plotting lengthbias...
[ 2017-05-02 20:55:01 ] INFO Plotting biodist...
[ 2017-05-02 20:55:01 ] INFO Contrast: Cplus_vs_vectorhuh75
[ 2017-05-02 20:55:02 ] INFO Plotting volcano...
[ 2017-05-02 20:55:02 ] INFO Contrast: Cplus_vs_vectorhuh75
[ 2017-05-02 20:55:02 ] INFO Plotting deheatmap...
[ 2017-05-02 20:55:02 ] INFO Contrast: Cplus_vs_vectorhuh75
[ 2017-05-02 20:55:27 ] INFO Plotting filtered...
[ 2017-05-02 20:55:27 ] INFO Plotting in pdf format...
[ 2017-05-02 20:55:27 ] INFO Plotting mds...
[ 2017-05-02 20:55:27 ] WARN MDS plot cannot be created with less than 3 samples! Skipping...
[ 2017-05-02 20:55:27 ] INFO Plotting biodetection...
[ 2017-05-02 20:55:27 ] INFO Plotting countsbio...
[ 2017-05-02 20:55:28 ] INFO Plotting saturation...
[ 2017-05-02 20:55:30 ] INFO Plotting readnoise...
[ 2017-05-02 20:55:30 ] INFO Plotting correl...
[ 2017-05-02 20:55:30 ] INFO Plotting pairwise...
[ 2017-05-02 20:55:30 ] INFO Plotting boxplot...
[ 2017-05-02 20:55:30 ] INFO Plotting gcbias...
[ 2017-05-02 20:55:31 ] INFO Plotting lengthbias...
[ 2017-05-02 20:55:31 ] INFO Plotting boxplot...
[ 2017-05-02 20:55:31 ] INFO Plotting gcbias...
[ 2017-05-02 20:55:31 ] INFO Plotting lengthbias...
[ 2017-05-02 20:55:32 ] INFO Plotting biodist...
[ 2017-05-02 20:55:32 ] INFO Contrast: Cplus_vs_vectorhuh75
[ 2017-05-02 20:55:32 ] INFO Plotting volcano...
[ 2017-05-02 20:55:32 ] INFO Contrast: Cplus_vs_vectorhuh75
[ 2017-05-02 20:55:32 ] INFO Plotting deheatmap...
[ 2017-05-02 20:55:32 ] INFO Contrast: Cplus_vs_vectorhuh75
[ 2017-05-02 20:55:57 ] INFO Plotting filtered...
[ 2017-05-02 20:55:57 ] INFO Creating HTML report...
[ 2017-05-02 20:55:57 ] INFO Compressing figures...

Figures

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

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 Cplus_vs_vectorhuh75

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 Cplus_vs_vectorhuh75

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 Cplus_vs_vectorhuh75

Get all the figures in png, pdf format.

Results

DEG table for the contrast Cplus_vs_vectorhuh75

The following table presents the top 5% statistically significant genes (use the download links below the table to retrieve the whole list) for the contrast Cplus_vs_vectorhuh75. 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_Cplus_vs_vectorhuh75	log2_normalized_counts_YK3R12-Cplus-shorthuh75	rpgm_normalized_counts_YK3R12-Cplus-shorthuh75	log2_normalized_counts_YK3R13-vectorhuh75	rpgm_normalized_counts_YK3R13-vectorhuh75
chr14	106173457	106175002	ENSG00000211895	0.6481	-	IGHA1	IG_C_gene	2.85e-53	3.42e-49	-11.30720	1.13e+01	2.04e+00	0.00e+00	0.00e+00
chr5	137801179	137805004	ENSG00000120738	0.5656	+	EGR1	protein_coding	1.15e-24	6.90e-21	-6.76377	8.35e+00	1.34e-01	1.58e+00	8.23e-04
chr4	91759652	91760263	ENSG00000187653	0.4542	-	TMSB4XP8	pseudogene	1.65e-16	6.60e-13	-3.59895	9.87e+00	3.90e-01	6.27e+00	3.18e-02
chr14	106053226	106054732	ENSG00000211890	0.6496	-	IGHA2	IG_C_gene	1.90e-13	5.70e-10	-6.64386	6.64e+00	7.96e-02	0.00e+00	0.00e+00
chr4	155484108	155492238	ENSG00000171564	0.3509	+	FGB	protein_coding	2.39e-13	5.73e-10	6.62936	0.00e+00	0.00e+00	6.63e+00	1.13e-02
chr19	12902310	12904124	ENSG00000171223	0.6507	+	JUNB	protein_coding	7.73e-13	1.55e-09	-3.11221	9.20e+00	3.93e-01	6.09e+00	4.48e-02
chr4	74262831	74287129	ENSG00000163631	0.3490	+	ALB	protein_coding	8.09e-12	1.39e-08	4.28011	2.81e+00	3.79e-04	7.09e+00	8.53e-03
chr7	68796821	68798619	ENSG00000229031	0.4397	-	RP11-666G4.1	pseudogene	1.37e-10	2.05e-07	-4.62936	6.63e+00	8.19e-02	2.00e+00	2.51e-03
chr2	154333852	154335322	ENSG00000177519	0.5962	-	RPRM	protein_coding	3.22e-10	4.29e-07	3.22973	4.39e+00	1.05e-02	7.62e+00	1.03e-01
chr9	131104432	131105049	ENSG00000223551	0.4304	+	TMSB4XP4	pseudogene	1.74e-09	2.08e-06	-3.70044	6.87e+00	9.70e-02	3.17e+00	6.69e-03
chr12	52416616	52453291	ENSG00000123358	0.5601	+	NR4A1	protein_coding	4.03e-09	4.39e-06	-2.81340	7.90e+00	1.68e-02	5.09e+00	2.33e-03
chr6	86370710	86388451	ENSG00000203875	0.4095	-	SNHG5	processed_transcript	1.33e-08	1.33e-05	2.27224	8.32e+00	7.63e-02	1.06e+01	3.69e-01
chr11	62201016	62323707	ENSG00000124942	0.5005	-	AHNAK	protein_coding	2.18e-08	2.02e-05	2.51873	5.70e+00	7.06e-03	8.22e+00	4.11e-02
chr1	192778169	192781403	ENSG00000116741	0.4083	+	RGS2	protein_coding	4.76e-08	4.08e-05	-2.83519	7.29e+00	3.55e-02	4.46e+00	4.77e-03
chr16	68118654	68263162	ENSG00000072736	0.4153	+	NFATC3	protein_coding	2.82e-07	2.26e-04	2.03121	8.72e+00	2.35e-02	1.08e+01	9.63e-02
chr5	40825364	40835437	ENSG00000145592	0.4283	-	RPL37	protein_coding	3.19e-07	2.39e-04	2.03049	1.03e+01	1.27e-01	1.23e+01	5.21e-01
chr14	35870717	35873955	ENSG00000100906	0.5653	-	NFKBIA	protein_coding	1.97e-06	1.39e-03	-1.94278	9.02e+00	7.42e-02	7.08e+00	1.92e-02
chr5	150400124	150408554	ENSG00000211445	0.5277	+	GPX3	protein_coding	2.18e-06	1.41e-03	-4.11548	5.70e+00	6.97e-03	1.58e+00	2.73e-04
chr11	102165791	102166537	ENSG00000243777	0.4284	+	RP11-864G5.1	pseudogene	2.24e-06	1.41e-03	-3.83289	5.83e+00	4.68e-02	2.00e+00	2.51e-03
chr3	123328896	123603178	ENSG00000065534	0.4534	-	MYLK	protein_coding	2.69e-06	1.61e-03	-2.47644	7.00e+00	2.73e-03	4.52e+00	4.73e-04
chr2	157180944	157198860	ENSG00000153234	0.4515	-	NR4A2	protein_coding	4.22e-06	2.41e-03	-3.56071	5.88e+00	7.83e-03	2.32e+00	5.40e-04
chr1	203274619	203278730	ENSG00000159388	0.5333	+	BTG2	protein_coding	4.71e-06	2.54e-03	-2.89812	6.36e+00	2.41e-02	3.46e+00	2.98e-03
chr12	102789645	102874423	ENSG00000017427	0.3966	-	IGF1	protein_coding	4.86e-06	2.54e-03	2.77529	3.70e+00	1.28e-03	6.48e+00	9.37e-03
chr14	34985135	35008916	ENSG00000129518	0.4409	-	EAPP	protein_coding	5.47e-06	2.73e-03	2.05683	5.81e+00	7.44e-03	7.86e+00	3.14e-02
chr2	238232646	238323018	ENSG00000163359	0.4457	-	COL6A3	protein_coding	8.45e-06	4.06e-03	-3.96963	5.55e+00	4.92e-03	1.58e+00	2.14e-04
chr2	234526291	234681956	ENSG00000241635	0.4109	+	UGT1A8	protein_coding	1.34e-05	4.86e-03	1.85914	6.43e+00	1.64e-02	8.29e+00	5.98e-02
chr2	234545100	234681951	ENSG00000242515	0.4160	+	UGT1A10	protein_coding	1.34e-05	4.86e-03	1.85914	6.43e+00	2.20e-02	8.29e+00	8.07e-02
chr2	234580499	234681946	ENSG00000241119	0.4219	+	UGT1A9	protein_coding	1.34e-05	4.86e-03	1.85914	6.43e+00	4.39e-02	8.29e+00	1.61e-01

Download the DEG result list for Cplus_vs_vectorhuh75.
Download the whole result list for Cplus_vs_vectorhuh75.

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.
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.