Instructions

Welcome to the metaseqR2 report! If you are familiar with the metaseqR report, then you will find that there are not many differences with respect to the presented information. Some diagnostic and exploration plots were added. The most notable difference is that all plots are interactive. This helps a lot with exploration and interpretation but also adds a lot of computational burden. However, relatively modern systems with recent browser versions should be capable of rendering all the graphics. The metaseqR2 report has been tested with Google Chrome, Mozilla Firefox and Microsoft Edge. It has not been tested with Internet Explorer, Opera and Safari and most probably will not be. Other Chromium browsers (e.g. Brave) should also be fine.

One particular characteristic of the metaseqR2 report is that all plots are interactive. This is achieved by using the standard graphics underlying data with libraries including Highcharts, Plotly and jvenn to create more user-friendly and directly explorable plots. Instructions on the usage of these plots follow:

  • All plots are interactively explorable. This means that if you move your mouse inside the plot area (a move called mouse-over), you can retrieve information on each single data point. This applies to all plots. More specifically:
    • In scatterplots, if you mouse-over each point, information about this point is presented, depending on the type of the plot. The data series from which the point comes is also presented. For example, in a Volcano plot, fold change and significance, as well as the name of the gene and the data category (e.g. up-regulated) will be presented.
    • In barplots, if you mouse-over each bar, information about this bar is presented, such as the value it represents and the data series from which it comes. If the barplot contains groups of bars, then information about each group is displayed. For example, in a Biodetection plot, each bar group presents the percentage of a biotype in the examined genome, the percentage in the sample and the detected percentage according to read counts.
    • In boxplots, if you mouse-over the boxes, the information about the underlying distribution is displayed (maximum, upper quartile, median, lower quartile and minimum) as well as the data series. If you mouse-over an outlier, then information on this single point is presented (e.g. value).
    • Some barplots have a double y-axis system corresponding to different measurements or scales. For example in Biodetection barplots, the left y-axis presents abundant features while the right y-axis presents non-abundant features. In the Filtered barplot, y-axes present different values (numbers and fractions).
    • Line plots can be moused-over too. Depending on the plot type, exact values may or may not be shown, depending on how important it is to display them, and to avoid over-crowding the plots. For example in Reads noise plot, we are interested in the trend and not so much in exact values.
    • Heatmaps can be moused-over too. Information on each heatmap cell will be displayed.
  • All scatterplots and heatmaps are zoomable. You need to press the left mouse button inside a plot area and draw a square area to zoom-in. If you wish to reset the zoom, there is a button appearing for this when zooming-in.
  • Data series in scatterplots, barplots and boxplots can be toggled on or off by clicking on the legend name of each data series which is placed below each plot. For example, in Volcano plots, if you click on the name “Unregulated”, then the respective data series will stop appearing in the plot. You can bring it back by clicking the legend again.
  • All plots are exportable. On the top right corner of each scatterplot, barplot and boxplot, there is a menu button with several functionalities, including exporting in various formats and presenting the plot in full-screen mode. For heatmaps, this functionality is offered by a set of small buttons that appear if you mouse-over at the top of the heatmap.
  • In Venn diagrams, if you click on the number for each category, the respective gene/transcript names will appear in the box on the right of the diagram.
  • All plots can be downloaded in static formats (in formats according to metaseqr2 call) from the Results section.

The metaseqR2 report contains the sections described below, depending on which diagnostic and exploration plots have been asked for from the run command. As plots are categorized, if no plot from a specific category is asked for, then this category will not appear. Below, are the categories:

Summary

The Summary section is further categorized in several subsections. Specifically:

  • Analysis summary: This section contains an auto-generated text that analytically describes the computational process followed and summarized the results of each step. This text can be used as is or with slight modifications in the Methods section of an article.
  • Input options: This section provides a list of the input arguments to the pipeline in a more human-readable format.
  • Filtering: This section reports in detail the number of filtered genes decomposed according to the number of genes removed by each applied filter.
  • Differential expression: This section reports in detail the number of differentially expressed genes for each contrast, both when using only a p-value cutoff as well as an FDR cutoff (numbers in parentheses), that is, genes passing the multiple testing correction procedure selected. These numbers are also calculated based on a simple fold change cutoff in log2 scale. Finally, when multiple algorithms are used with p-value combination, this section reports all the findings analytically per algorithm.
  • Command: This section contains the command used to run the metaseqr2 pipeline for users that want to experiment as well as a critical messages displayed within the R session running metaseqr2 displayed as a log. Finally, if a targets file has been used to perform the analysis, a table depicting the parameters in the targets files is created and a link to download the actual targets file, but any relative paths to BAM files are stripped and the user is responsible to prepend them if the targets file has to be reused in another location, e.g. locally.
  • Tracks: This section contains a link which opens a new window to the UCSC Genome Browser where normalized tracks based on the input BAM files are displayed. If stranded tracks have been requested (according to the sequencing protocol or technology), the a track hub is created to display the stranded tracks. From this tab, you can also download bigWig files as well as copy track lines for manual input to the UCSC Genome Browser.

Quality control

The Quality control section contains several interactive plots concerning the overall quality control of each sample provided as well as overall assessments. The quality control plots are the Multidimensional Scaling (MDS) plot, the Biotypes detection (Biodetection) plot, the Biotype abundance (Countsbio) plot, the Read saturation (Saturation) plot, the Read noise (ReadNoise) plot, the Correlation heatmap (Correlation), the Pairwise sample scatterplots (Pairwise) and the Filtered entities (Filtered) plot. Each plot is accompanied by a detailed description of what it depicts. Where multiple plot are available (e.g. one for each sample), a selection list on the top of the respective section allows the selection of the sample to be displayed.

Normalization

The Normalization section contains several interactive plots that can be used to inspect and assess the normalization procedure. Therefore, normalization plots are usually paired, showing the same data instance normalized and not normalized. The normalization plots are the Expression boxplots (Boxplots) plots, the GC content bias (GC bias) plots, the Gene length bias (Length bias) plots, the Within condition mean-difference (Mean-Difference) plots, the Mean-variance relationship (Mean-Variance) plot and the RNA composition (Rna composition) plot. Each plot is accompanied by a detailed description of what it depicts. Where multiple plot are available (e.g. one for each sample), a selection list on the top of the respective section allows the selection of the sample to be displayed.

Statistics

The Statistics section contains several interactive plots that can be used to inspect and explore the outcome of statistical testing procedures. The statistics plots are the Volcano plot (Volcano), the MA or Mean-Difference across conditions (MA) plot, the Expression heatmap (Heatmap) plot, the Chromosome and biotype distributions (Biodist) plot, the Venn diagram across statistical tests (StatVenn), the Venn diagram across contrasts (FoldVenn) and the Deregulogram. Each plot is accompanied by a detailed description of what it depicts. Please note that the heatmap plots only show the top percentage of differentially expressed genes as this is controlled by the reportTop parameter of the metaseqr2 pipeline. When multiple plots are available (e.g. one for each contrast), a selection list on the top of the respective section allows the selection of the sample to be displayed.

Results

The Results section contains a snapshot of differentially expressed genes in table format with basic information about each gene and links to external resources. Certain columns of the table are colored according to significance. Larger bars and more intense colors indicate higher significance. For example, the bar in the p_value column is larger if the genes has higher statistical significance and the fold change cell background is bright red if the gene is highly up-regulated. From the Results section, full gene lists can be downloaded in text tab-delimited format and viewed with a spreadsheet application such as MS Excel. A selector on the top of the section above the table allows the display of different contrasts.

References

The References section contains bibliographical references regading the algorithms used by the metaseqr2 pipeline and is adjusted according to the algorithms selected.

Summary

Analysis summary

Analysis summary

The exon read counts were filtered for artifacts that could affect the subsequent normalization and statistical testing procedures as follows: if an annotated gene had up to 5 exons, read presence was required in at least 2 of the exons, else if an annotated gene had more than 5 exons, then read presence was required in at least 0.2x⌈E⌉ exons, where ⌈.⌉ is the ceiling mathematical function. The application of this filter resulted in the exclusion of 16530 genes from further analysis. The total number of genes excluded due to the application of exon filters was 16530. The final read counts for each gene model were calculated as the sums of their exon reads, creating a gene counts table where each row corresponded to an Ensembl gene model and each column corresponded to an RNA-Seq sample. 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 (12928 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 may affect the subsequent statistical testing procedures. Genes/transcripts presenting any of the following were excluded from further analysis: i) genes with length less than 100bp (714 genes), ii) genes with read counts below the 90th quantile of the counts of the following genes, known to not being expressed from the related literature: Dub1, Gdnf, Gria2, Kcna7, Kcna1, Klf4, Myod1, Myoz1, Myoz2, Nalcn, Nanos1, Nanos2, Nfatc2, Neurod1, Nkx2-1, Nov, Nova1, Nrcam, Phactr1, Phyhip, Ptprn, Ptpro, Rbmy1a1, Scn2a1, Myoc, Mypn, Rlbp1, Ntf5, Bai3, Ttn, Dnahc3, Magea1, Gpc2, Cdh17, Bcl2, Ckm, Slc22a2, Slc22a8, Ucp3, Cidea, Ifng, Tubb3, Olig2, Sox2(15254 genes with cutoff value28 normalized read counts), iii) genes whose biotype matched the following: rRNA (130 genes). The total number of genes excluded due to the application of gene filters was 845. The total (unified) number of genes excluded due to the application of all filters was 28425. The resulting gene counts table was subjected to differential expression analysis for the contrasts e18.5 versus e15.5, P0.5 versus e15.5, P22 versus e15.5, P60 versus e15.5, e18.5 versus P0.5 versus P4 versus P14 versus P22 versus P60 versus e15.5 using the Bioconductor packages DESeq2, edgeR, limma, ABSSeq, DSS. In order to combine the statistical significance from multiple algorithms and perform meta-analysis, the PANDORA weighted p-value across results method was applied. The final numbers of differentially expressed genes were (per contrast): for the contrast e18.5 versus e15.5, 562 (0) statistically significant genes were found with a p-value (FDR or adjusted p-value) threshold of 0.05 and of these, 359 were up-regulated, 187 were down-regulated and 16 were not differentially expressed according to an absolute fold change cutoff value of 1 in log2 scale, for the contrast P0.5 versus e15.5, 247 (0) statistically significant genes were found with a p-value (FDR or adjusted p-value) threshold of 0.05 and of these, 106 were up-regulated, 141 were down-regulated and 0 were not differentially expressed according to an absolute fold change cutoff value of 1 in log2 scale, for the contrast P22 versus e15.5, 916 (33) statistically significant genes were found with a p-value (FDR or adjusted p-value) threshold of 0.05 and of these, 127 (2) were up-regulated, 779 (31) were down-regulated and 10 (0) were not differentially expressed according to an absolute fold change cutoff value of 1 in log2 scale, for the contrast P60 versus e15.5, 3060 (1787) statistically significant genes were found with a p-value (FDR or adjusted p-value) threshold of 0.05 and of these, 1180 (686) were up-regulated, 1509 (1085) were down-regulated and 371 (16) were not differentially expressed according to an absolute fold change cutoff value of 1 in log2 scale, for the contrast e18.5 versus P0.5 versus P4 versus P14 versus P22 versus P60 versus e15.5, 1044 (398) differentially expressed genes were found with a p-value (FDR or adjusted p-value) threshold of 0.05 at least in one condition. Literature references for all the algorithms used can be found at the end of this report.

Input options

Input options

Read counts file: previously stored project

Conditions: e15.5, e18.5, P0.5, P4, P14, P22, P60

Samples included: e15.5_BR1, e15.5_BR2, e18.5_BR1, e18.5_BR2, P0.5_BR1, P0.5_BR2, P4_BR1, P4_BR2, P14_BR1, P14_BR2, P22_BR1, P22_BR2, P60_BR1, P60_BR2

Samples excluded: none

Requested contrasts: e18.5_vs_e15.5, P0.5_vs_e15.5, P22_vs_e15.5, P60_vs_e15.5, e18.5_vs_P0.5_vs_P4_vs_P14_vs_P22_vs_P60_vs_e15.5

Library sizes: not available

Organism: mouse (Mus musculus), genome version alias mm9

Annotation source: Ensembl genomes

Count type: exon

Exon filters: minActiveExons
  • minActiveExons
    • exonsPerGene: 5
    • minExons: 2
    • frac: 0.2
Gene filters: length, avgReads, expression, biotype
  • length
    • length: 100
  • avgReads
  • expression
    • median: FALSE
    • mean: FALSE
    • quantile: NA
    • known: Dub1, Gdnf, Gria2, Kcna7, Kcna1, Klf4, Myod1, Myoz1, Myoz2, Nalcn, Nanos1, Nanos2, Nfatc2, Neurod1, Nkx2-1, Nov, Nova1, Nrcam, Phactr1, Phyhip, Ptprn, Ptpro, Rbmy1a1, Scn2a1, Myoc, Mypn, Rlbp1, Ntf5, Bai3, Ttn, Dnahc3, Magea1, Gpc2, Cdh17, Bcl2, Ckm, Slc22a2, Slc22a8, Ucp3, Cidea, Ifng, Tubb3, Olig2, Sox2
    • custom: NA
  • biotype
    • pseudogene: FALSE
    • snRNA: FALSE
    • protein_coding: FALSE
    • antisense: FALSE
    • miRNA: FALSE
    • lincRNA: FALSE
    • snoRNA: FALSE
    • processed_transcript: FALSE
    • misc_RNA: FALSE
    • rRNA: TRUE
    • sense_overlapping: FALSE
    • sense_intronic: FALSE
    • polymorphic_pseudogene: FALSE
    • non_coding: FALSE
    • three_prime_overlapping_ncrna: FALSE
    • IG_C_gene: FALSE
    • IG_J_gene: FALSE
    • IG_D_gene: FALSE
    • IG_V_gene: FALSE
    • ncrna_host: FALSE

Filter application: after normalization

Normalization algorithm: DESeq

Normalization arguments: locfunc
  • [[list(new(“standardGeneric”, .Data = function (x, na.rm = FALSE, …) standardGeneric(“median”), generic = “median”, package = “stats”, group = list(), valueClass = character(0), signature = c(“x”, “na.rm”), default = new(“derivedDefaultMethod”, .Data = function (x, na.rm = FALSE, …) UseMethod(“median”), target = new(“signature”, .Data = “ANY”, names = “x”, package = “methods”), defined = new(“signature”, .Data = “ANY”, names = “x”, package = “methods”), generic = “median”), skeleton = (new(“derivedDefaultMethod”, .Data = function (x, na.rm = FALSE, …) UseMethod(“median”), target = new(“signature”, .Data = “ANY”, names = “x”, package = “methods”), defined = new(“signature”, .Data = “ANY”, names = “x”, package = “methods”), generic = “median”))(x, na.rm, …)))locfunc

Statistical algorithm(s): DESeq2, edgeR, limma, ABSSeq, DSS

Statistical arguments for DESeq2: tidy, fitType, maxit, quiet, modelMatrix, betaPrior, betaTol, useOptim, useT, useQR, lfcThreshold, altHypothesis, independentFiltering, alpha, pAdjustMethod, format, addMLE, parallel
  • tidy: FALSE
  • fitType: parametric
  • maxit: 100
  • quiet: FALSE
  • betaPrior: FALSE
  • betaTol: 1e-08
  • useOptim: TRUE
  • useT: FALSE
  • useQR: TRUE
  • lfcThreshold: 0
  • altHypothesis: greaterAbs
  • independentFiltering: TRUE
  • alpha: 0.1
  • pAdjustMethod: BH
  • format: DataFrame
  • addMLE: FALSE
  • parallel: FALSE
Statistical arguments for edgeR: main.method, rowsum.filter, prior.df, trend, span, tag.method, grid.length, grid.range, offset, glm.method, subset, AveLogCPM, trend.method, dispersion, offset, weights, lib.size, prior.count, start, method, test, abundance.trend, robust, winsor.tail.p
  • main.method: classic
  • rowsum.filter: 5
  • prior.df: 10
  • trend: movingave
  • tag.method: grid
  • grid.length: 11
  • grid.range: -6, 6
  • glm.method: CoxReid
  • subset: 10000
  • trend.method: auto
  • prior.count: 0.125
  • method: auto
  • test: chisq
  • abundance.trend: TRUE
  • robust: FALSE
  • winsor.tail.p: 0.05, 0.1
Statistical arguments for limma: normalize.method
  • normalize.method: none
Statistical arguments for ABSSeq: paired, minDispersion, minRates, maxRates, LevelstoNormFC, adjmethod, replaceOutliers, useaFold, quiet, lmodel, preval, qforkappa, scale
  • paired: FALSE
  • minRates: 0.1
  • maxRates: 0.3
  • LevelstoNormFC: 100
  • adjmethod: BH
  • replaceOutliers: TRUE
  • useaFold: FALSE
  • quiet: FALSE
  • lmodel: TRUE
  • preval: 0.05
  • qforkappa: 0
  • scale: FALSE
Statistical arguments for DSS: trend, equal.var
  • trend: FALSE
  • equal.var: FALSE

Meta-analysis method: PANDORA weighted p-value across results

Multiple testing correction: Benjamini-Hochberg FDR

p-value threshold: 0.05

Logarithmic tranformation offset: 1

Analysis preset: not available

Quality control plots:

Figure format: png

Output directory: /home/panos/public_html/metaseqR2_showcase/metaseqR2_LiverDevelopment_PANDORA

Output data: Annotation, p-value, Adjusted p-value (FDR), Combined p-value, Adjusted combined p-value (FDR), Fold change

Output scale(s): Natural scale

Output values: Normalized values

Output statistics: Mean

Total run time: 04 minutes 56 seconds

Filtering

Filtered genes

Number of filtered genes: 28425 which is the union of

  • Filtered because of zero reads: 12928
  • Filtered because of exon filters: 16530 which is the union of
    • minActiveExons : 16530
  • Filtered because of gene filters: 15311 which is the union of
    • length: 714 genes with filter cutoff value 100
    • biotype: 130 genes with filter cutoff value rRNA
    • expression: 15254 genes further decomposed to (filter name, filtered genes, filter cutoff):
      • known: 15254 genes with filter cutoff value 28

Differential expression

Differentially expressed genes

Number of differentially expressed genes per contrast:
  • e18.5_vs_e15.5: 562 (0) statistically significant genes of which 359 up regulated, 187 down regulated and 16 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. These numbers refer to the combined analysis performed by metaseqR2. Per statistical algorithm, the differentially expressed genes are:
    • DESeq2: 743 (91) statistically significant genes of which 419 (69) up regulated, 221(21) down regulated and 103 (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.
    • edgeR: 60 (0) statistically significant genes of which 54 (0) up regulated, 6(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.
    • limma: 2200 (446) statistically significant genes of which 838 (245) up regulated, 366(172) down regulated and 996 (29) 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.
    • ABSSeq: 504 (70) statistically significant genes of which 392 (68) up regulated, 112(2) 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.
    • DSS: 1881 (456) statistically significant genes of which 312 (98) up regulated, 92(15) down regulated and 1477 (343) 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.
  • P0.5_vs_e15.5: 247 (0) statistically significant genes of which 106 up regulated, 141 down regulated and 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. These numbers refer to the combined analysis performed by metaseqR2. Per statistical algorithm, the differentially expressed genes are:
    • DESeq2: 1003 (238) statistically significant genes of which 512 (137) up regulated, 377(94) down regulated and 114 (7) 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.
    • edgeR: 102 (1) statistically significant genes of which 59 (1) up regulated, 43(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.
    • limma: 501 (5) statistically significant genes of which 40 (4) up regulated, 438(1) down regulated and 23 (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.
    • ABSSeq: 394 (5) statistically significant genes of which 222 (5) up regulated, 172(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.
    • DSS: 2322 (923) statistically significant genes of which 350 (153) up regulated, 279(95) down regulated and 1693 (675) 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.
  • P22_vs_e15.5: 916 (33) statistically significant genes of which 127 (2) up regulated, 779 (31) down regulated and 10 (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. These numbers refer to the combined analysis performed by metaseqR2. Per statistical algorithm, the differentially expressed genes are:
    • DESeq2: 2579 (1743) statistically significant genes of which 1308 (890) up regulated, 1154(836) down regulated and 117 (17) 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.
    • edgeR: 1063 (141) statistically significant genes of which 227 (23) up regulated, 790(118) down regulated and 46 (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.
    • limma: 2547 (294) statistically significant genes of which 93 (4) up regulated, 1370(220) down regulated and 1084 (70) 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.
    • ABSSeq: 314 (0) statistically significant genes of which 68 (0) up regulated, 246(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.
    • DSS: 4257 (3401) statistically significant genes of which 1049 (822) up regulated, 947(771) down regulated and 2261 (1808) 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.
  • P60_vs_e15.5: 3060 (1787) statistically significant genes of which 1180 (686) up regulated, 1509 (1085) down regulated and 371 (16) 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. These numbers refer to the combined analysis performed by metaseqR2. Per statistical algorithm, the differentially expressed genes are:
    • DESeq2: 3224 (2342) statistically significant genes of which 1758 (1239) up regulated, 1280(1049) down regulated and 186 (54) 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.
    • edgeR: 1136 (216) statistically significant genes of which 315 (62) up regulated, 821(154) 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.
    • limma: 5119 (4673) statistically significant genes of which 1360 (1214) up regulated, 1667(1658) down regulated and 2092 (1801) 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.
    • ABSSeq: 2432 (1267) statistically significant genes of which 1576 (756) up regulated, 856(511) 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.
    • DSS: 5158 (4515) statistically significant genes of which 1851 (1654) up regulated, 948(807) down regulated and 2359 (2054) 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.
  • e18.5_vs_P0.5_vs_P4_vs_P14_vs_P22_vs_P60_vs_e15.5: 1044 (398) statistically significant, differentially expressed in at least one condition at a p-value (FDR or adjusted p-value) threshold of 0.05. These numbers refer to the combined analysis performed by metaseqR2. Per statistical algorithm, the differentially expressed genes are:
    • DESeq2: 3715 (2923) statistically significant, differentially expressed in at least one condition at a p-value (FDR or adjusted p-value) threshold of 0.05.
    • edgeR: 1133 (631) statistically significant, differentially expressed in at least one condition at a p-value (FDR or adjusted p-value) threshold of 0.05.
    • limma: 1046 (108) statistically significant, differentially expressed in at least one condition at a p-value (FDR or adjusted p-value) threshold of 0.05.
    • ABSSeq: 286 (27) statistically significant, differentially expressed in at least one condition at a p-value (FDR or adjusted p-value) threshold of 0.05.
    • DSS: 1814 (1141) statistically significant, differentially expressed in at least one condition at a p-value (FDR or adjusted p-value) threshold of 0.05.

Command

The differential expression analysis and this report were generated using the following command:

metaseqr2(counts = file.path(exportPath, "metaseqR2_LiverDevelopment_PANDORA", 
    "data", "gene_model.RData"), contrast = theContrasts, org = "mm9", 
    countType = "exon", normalization = "deseq", statistics = c("deseq2", 
        "edger", "limma", "absseq", "dss"), metaP = "pandora", 
    weight = weights, figFormat = "png", exportWhere = file.path(exportPath, 
        "metaseqR2_LiverDevelopment_PANDORA"), restrictCores = 0.5, 
    qcPlots = c("foldvenn"), exonFilters = list(minActiveExons = list(exonsPerGene = 5, 
        minExons = 2, frac = 1/5)), geneFilters = list(length = list(length = 100), 
        avgReads = NULL, expression = list(median = FALSE, mean = FALSE, 
            quantile = NA, known = spikeOut, custom = NA), biotype = getDefaults("biotypeFilter", 
            "mm9")), pcut = 0.05, exportWhat = c("annotation", 
        "p_value", "adj_p_value", "meta_p_value", "adj_meta_p_value", 
        "fold_change"), exportScale = c("natural"), exportValues = "normalized", 
    exportStats = "mean", exportCountsTable = TRUE, reportTop = 0.05, 
    createTracks = FALSE)

The above command generated the following log output:

INFO [2020-04-29 14:34:39] 2020-04-29 14:34:39: Data processing started…
INFO [2020-04-29 14:34:39] Read counts file: previously stored project
INFO [2020-04-29 14:34:39] Conditions: e15.5, e18.5, P0.5, P4, P14, P22, P60
INFO [2020-04-29 14:34:39] Samples to include: e15.5_BR1, e15.5_BR2, e18.5_BR1, e18.5_BR2, P0.5_BR1, P0.5_BR2, P4_BR1, P4_BR2, P14_BR1, P14_BR2, P22_BR1, P22_BR2, P60_BR1, P60_BR2
INFO [2020-04-29 14:34:39] Samples to exclude: none
INFO [2020-04-29 14:34:39] Requested contrasts: e18.5_vs_e15.5, P0.5_vs_e15.5, P22_vs_e15.5, P60_vs_e15.5, e18.5_vs_P0.5_vs_P4_vs_P14_vs_P22_vs_P60_vs_e15.5
INFO [2020-04-29 14:34:39] Organism: mm9
INFO [2020-04-29 14:34:39] Reference source: ensembl
INFO [2020-04-29 14:34:39] Count type: exon
INFO [2020-04-29 14:34:39] Transcriptional level: gene
INFO [2020-04-29 14:34:39] Exon filters: minActiveExons
INFO [2020-04-29 14:34:39] minActiveExons:
INFO [2020-04-29 14:34:39] exonsPerGene: 5
INFO [2020-04-29 14:34:39] minExons: 2
INFO [2020-04-29 14:34:39] frac: 0.2
INFO [2020-04-29 14:34:39] Gene filters: length, avgReads, expression, biotype
INFO [2020-04-29 14:34:39] length:
INFO [2020-04-29 14:34:39] length: 100
INFO [2020-04-29 14:34:39] avgReads:
INFO [2020-04-29 14:34:39] expression:
INFO [2020-04-29 14:34:39] median: FALSE
INFO [2020-04-29 14:34:39] mean: FALSE
INFO [2020-04-29 14:34:39] quantile: NA
INFO [2020-04-29 14:34:39] known: Dub1, Gdnf, Gria2, Kcna7, Kcna1, Klf4, Myod1, Myoz1, Myoz2, Nalcn, Nanos1, Nanos2, Nfatc2, Neurod1, Nkx2-1, Nov, Nova1, Nrcam, Phactr1, Phyhip, Ptprn, Ptpro, Rbmy1a1, Scn2a1, Myoc, Mypn, Rlbp1, Ntf5, Bai3, Ttn, Dnahc3, Magea1, Gpc2, Cdh17, Bcl2, Ckm, Slc22a2, Slc22a8, Ucp3, Cidea, Ifng, Tubb3, Olig2, Sox2
INFO [2020-04-29 14:34:39] custom: NA
INFO [2020-04-29 14:34:39] biotype:
INFO [2020-04-29 14:34:39] pseudogene: FALSE
INFO [2020-04-29 14:34:39] snRNA: FALSE
INFO [2020-04-29 14:34:39] protein_coding: FALSE
INFO [2020-04-29 14:34:39] antisense: FALSE
INFO [2020-04-29 14:34:39] miRNA: FALSE
INFO [2020-04-29 14:34:39] lincRNA: FALSE
INFO [2020-04-29 14:34:39] snoRNA: FALSE
INFO [2020-04-29 14:34:39] processed_transcript: FALSE
INFO [2020-04-29 14:34:39] misc_RNA: FALSE
INFO [2020-04-29 14:34:39] rRNA: TRUE
INFO [2020-04-29 14:34:39] sense_overlapping: FALSE
INFO [2020-04-29 14:34:39] sense_intronic: FALSE
INFO [2020-04-29 14:34:39] polymorphic_pseudogene: FALSE
INFO [2020-04-29 14:34:39] non_coding: FALSE
INFO [2020-04-29 14:34:39] three_prime_overlapping_ncrna: FALSE
INFO [2020-04-29 14:34:39] IG_C_gene: FALSE
INFO [2020-04-29 14:34:39] IG_J_gene: FALSE
INFO [2020-04-29 14:34:39] IG_D_gene: FALSE
INFO [2020-04-29 14:34:39] IG_V_gene: FALSE
INFO [2020-04-29 14:34:39] ncrna_host: FALSE
INFO [2020-04-29 14:34:39] Filter application: postnorm
INFO [2020-04-29 14:34:39] Normalization algorithm: deseq
INFO [2020-04-29 14:34:39] Normalization arguments:
INFO [2020-04-29 14:34:39] locfunc:
INFO [2020-04-29 14:34:39] [[list(new(“standardGeneric”, .Data = function (x, na.rm = FALSE, …) standardGeneric(“median”), generic = “median”, package = “stats”, group = list(), valueClass = character(0), signature = c(“x”, “na.rm”), default = new(“derivedDefaultMethod”, .Data = function (x, na.rm = FALSE, …) UseMethod(“median”), target = new(“signature”, .Data = “ANY”, names = “x”, package = “methods”), defined = new(“signature”, .Data = “ANY”, names = “x”, package = “methods”), generic = “median”), skeleton = (new(“derivedDefaultMethod”, .Data = function (x, na.rm = FALSE, …) UseMethod(“median”), target = new(“signature”, .Data = “ANY”, names = “x”, package = “methods”), defined = new(“signature”, .Data = “ANY”, names = “x”, package = “methods”), generic = “median”))(x, na.rm, …)))locfunc
INFO [2020-04-29 14:34:39] Statistical algorithm: deseq2, edger, limma, absseq, dss
INFO [2020-04-29 14:34:39] Statistical arguments:
INFO [2020-04-29 14:34:39] deseq2: FALSE, parametric, 100, FALSE, NULL, FALSE, 1e-08, TRUE, FALSE, TRUE, 0, greaterAbs, TRUE, 0.1, BH, DataFrame, FALSE, FALSE
INFO [2020-04-29 14:34:39] edger: classic, 5, 10, movingave, NULL, grid, 11, c(-6, 6), NULL, CoxReid, 10000, NULL, auto, NULL, NULL, NULL, NULL, 0.125, NULL, auto, chisq, TRUE, FALSE, c(0.05, 0.1)
INFO [2020-04-29 14:34:39] limma: none
INFO [2020-04-29 14:34:39] absseq: FALSE, NULL, 0.1, 0.3, 100, BH, TRUE, FALSE, FALSE, TRUE, 0.05, 0, FALSE
INFO [2020-04-29 14:34:39] dss: FALSE, FALSE
INFO [2020-04-29 14:34:39] Meta-analysis method: pandora
INFO [2020-04-29 14:34:39] Multiple testing correction: BH
INFO [2020-04-29 14:34:39] p-value threshold: 0.05
INFO [2020-04-29 14:34:39] Logarithmic transformation offset: 1
INFO [2020-04-29 14:34:39] Quality control plots: foldvenn
INFO [2020-04-29 14:34:39] Figure format: png
INFO [2020-04-29 14:34:39] Output directory: /home/panos/public_html/metaseqR2_showcase/metaseqR2_LiverDevelopment_PANDORA
INFO [2020-04-29 14:34:39] Output data: annotation, p_value, adj_p_value, meta_p_value, adj_meta_p_value, fold_change
INFO [2020-04-29 14:34:39] Output scale(s): natural
INFO [2020-04-29 14:34:39] Output values: normalized
INFO [2020-04-29 14:34:39] Loading gene annotation…
INFO [2020-04-29 14:34:40] Applying exon filter minActiveExons…
INFO [2020-04-29 14:34:40] Checking read presence in exons for e15.5_BR1…
INFO [2020-04-29 14:34:43] Checking read presence in exons for e15.5_BR2…
INFO [2020-04-29 14:34:46] Checking read presence in exons for e18.5_BR1…
INFO [2020-04-29 14:34:48] Checking read presence in exons for e18.5_BR2…
INFO [2020-04-29 14:34:50] Checking read presence in exons for P0.5_BR1…
INFO [2020-04-29 14:34:53] Checking read presence in exons for P0.5_BR2…
INFO [2020-04-29 14:34:55] Checking read presence in exons for P4_BR1…
INFO [2020-04-29 14:34:57] Checking read presence in exons for P4_BR2…
INFO [2020-04-29 14:34:59] Checking read presence in exons for P14_BR1…
INFO [2020-04-29 14:35:02] Checking read presence in exons for P14_BR2…
INFO [2020-04-29 14:35:04] Checking read presence in exons for P22_BR1…
INFO [2020-04-29 14:35:06] Checking read presence in exons for P22_BR2…
INFO [2020-04-29 14:35:08] Checking read presence in exons for P60_BR1…
INFO [2020-04-29 14:35:10] Checking read presence in exons for P60_BR2…
INFO [2020-04-29 14:35:13] Summarizing count data…
INFO [2020-04-29 14:35:14] Removing genes with zero counts in all samples…
INFO [2020-04-29 14:35:14] Normalizing with: deseq
INFO [2020-04-29 14:35:15] Applying gene filter length…
INFO [2020-04-29 14:35:15] Threshold below which ignored: 100
INFO [2020-04-29 14:35:15] Applying gene filter avgReads…
INFO [2020-04-29 14:35:15] Applying gene filter expression…
INFO [2020-04-29 14:35:15] Threshold below which ignored: 28
INFO [2020-04-29 14:35:15] Applying gene filter biotype…
INFO [2020-04-29 14:35:15] Biotypes ignored: rRNA
INFO [2020-04-29 14:35:16] 28425 genes filtered out
INFO [2020-04-29 14:35:16] 9158 genes remain after filtering
INFO [2020-04-29 14:35:16] Running statistical tests with: deseq2
INFO [2020-04-29 14:35:26] Contrast: e18.5_vs_e15.5
INFO [2020-04-29 14:35:33] Contrast: P0.5_vs_e15.5
INFO [2020-04-29 14:35:40] Contrast: P22_vs_e15.5
INFO [2020-04-29 14:35:47] Contrast: P60_vs_e15.5
INFO [2020-04-29 14:35:54] Contrast: e18.5_vs_P0.5_vs_P4_vs_P14_vs_P22_vs_P60_vs_e15.5
INFO [2020-04-29 14:36:07] Contrast e18.5_vs_e15.5: found 743 genes
INFO [2020-04-29 14:36:07] Contrast P0.5_vs_e15.5: found 1003 genes
INFO [2020-04-29 14:36:07] Contrast P22_vs_e15.5: found 2579 genes
INFO [2020-04-29 14:36:07] Contrast P60_vs_e15.5: found 3224 genes
INFO [2020-04-29 14:36:07] Contrast e18.5_vs_P0.5_vs_P4_vs_P14_vs_P22_vs_P60_vs_e15.5: found 3715 genes
INFO [2020-04-29 14:36:07] Running statistical tests with: edger
INFO [2020-04-29 14:36:12] Contrast: e18.5_vs_e15.5
INFO [2020-04-29 14:36:17] Contrast: P0.5_vs_e15.5
INFO [2020-04-29 14:36:19] Contrast: P22_vs_e15.5
INFO [2020-04-29 14:36:21] Contrast: P60_vs_e15.5
INFO [2020-04-29 14:36:23] Contrast: e18.5_vs_P0.5_vs_P4_vs_P14_vs_P22_vs_P60_vs_e15.5
INFO [2020-04-29 14:36:23] Contrast e18.5_vs_e15.5: found 60 genes
INFO [2020-04-29 14:36:23] Contrast P0.5_vs_e15.5: found 102 genes
INFO [2020-04-29 14:36:23] Contrast P22_vs_e15.5: found 1063 genes
INFO [2020-04-29 14:36:23] Contrast P60_vs_e15.5: found 1136 genes
INFO [2020-04-29 14:36:23] Contrast e18.5_vs_P0.5_vs_P4_vs_P14_vs_P22_vs_P60_vs_e15.5: found 1133 genes
INFO [2020-04-29 14:36:23] Running statistical tests with: limma
INFO [2020-04-29 14:36:23] Contrast: e18.5_vs_e15.5
INFO [2020-04-29 14:36:25] Contrast: P0.5_vs_e15.5
INFO [2020-04-29 14:36:27] Contrast: P22_vs_e15.5
INFO [2020-04-29 14:36:28] Contrast: P60_vs_e15.5
INFO [2020-04-29 14:36:30] Contrast: e18.5_vs_P0.5_vs_P4_vs_P14_vs_P22_vs_P60_vs_e15.5
INFO [2020-04-29 14:36:32] Contrast e18.5_vs_e15.5: found 2200 genes
INFO [2020-04-29 14:36:32] Contrast P0.5_vs_e15.5: found 501 genes
INFO [2020-04-29 14:36:32] Contrast P22_vs_e15.5: found 2547 genes
INFO [2020-04-29 14:36:32] Contrast P60_vs_e15.5: found 5119 genes
INFO [2020-04-29 14:36:32] Contrast e18.5_vs_P0.5_vs_P4_vs_P14_vs_P22_vs_P60_vs_e15.5: found 1046 genes
INFO [2020-04-29 14:36:32] Running statistical tests with: absseq
INFO [2020-04-29 14:36:32] Contrast: e18.5_vs_e15.5
INFO [2020-04-29 14:36:39] Contrast: P0.5_vs_e15.5
INFO [2020-04-29 14:36:45] Contrast: P22_vs_e15.5
INFO [2020-04-29 14:36:51] Contrast: P60_vs_e15.5
INFO [2020-04-29 14:36:58] Contrast: e18.5_vs_P0.5_vs_P4_vs_P14_vs_P22_vs_P60_vs_e15.5
INFO [2020-04-29 14:37:05] Contrast e18.5_vs_e15.5: found 504 genes
INFO [2020-04-29 14:37:05] Contrast P0.5_vs_e15.5: found 394 genes
INFO [2020-04-29 14:37:05] Contrast P22_vs_e15.5: found 314 genes
INFO [2020-04-29 14:37:05] Contrast P60_vs_e15.5: found 2432 genes
INFO [2020-04-29 14:37:05] Contrast e18.5_vs_P0.5_vs_P4_vs_P14_vs_P22_vs_P60_vs_e15.5: found 286 genes
INFO [2020-04-29 14:37:05] Running statistical tests with: dss
INFO [2020-04-29 14:37:09] Contrast: e18.5_vs_e15.5
INFO [2020-04-29 14:37:09] Contrast: P0.5_vs_e15.5
INFO [2020-04-29 14:37:10] Contrast: P22_vs_e15.5
INFO [2020-04-29 14:37:10] Contrast: P60_vs_e15.5
INFO [2020-04-29 14:37:10] Contrast: e18.5_vs_P0.5_vs_P4_vs_P14_vs_P22_vs_P60_vs_e15.5
WARN [2020-04-29 14:37:10] DSS differential expression algorithm does not support multi-level designs (with more than two levels in a factor to be compared)! Switching to DESeq. Comparison: e18.5_vs_P0.5_vs_P4_vs_P14_vs_P22_vs_P60_vs_e15.5
INFO [2020-04-29 14:38:35] Contrast e18.5_vs_e15.5: found 1881 genes
INFO [2020-04-29 14:38:35] Contrast P0.5_vs_e15.5: found 2322 genes
INFO [2020-04-29 14:38:35] Contrast P22_vs_e15.5: found 4257 genes
INFO [2020-04-29 14:38:35] Contrast P60_vs_e15.5: found 5158 genes
INFO [2020-04-29 14:38:35] Contrast e18.5_vs_P0.5_vs_P4_vs_P14_vs_P22_vs_P60_vs_e15.5: found 1814 genes
INFO [2020-04-29 14:38:36] Exporting and compressing normalized read counts table to /home/panos/public_html/metaseqR2_showcase/metaseqR2_LiverDevelopment_PANDORA/lists/normalized_counts_table.txt
INFO [2020-04-29 14:38:38] Performing meta-analysis with pandora
INFO [2020-04-29 14:38:42] Building output files…
INFO [2020-04-29 14:38:42] Contrast: e18.5_vs_e15.5
INFO [2020-04-29 14:38:42] Adding non-filtered data…
INFO [2020-04-29 14:38:43] binding annotation…
INFO [2020-04-29 14:38:43] binding p-values…
INFO [2020-04-29 14:38:43] binding FDRs…
INFO [2020-04-29 14:38:43] binding meta p-values…
INFO [2020-04-29 14:38:43] binding adjusted meta p-values…
INFO [2020-04-29 14:38:43] binding natural normalized fold changes…
INFO [2020-04-29 14:38:43] Writing output…
INFO [2020-04-29 14:38:43] Adding filtered data…
INFO [2020-04-29 14:38:43] binding annotation…
INFO [2020-04-29 14:38:43] binding p-values…
INFO [2020-04-29 14:38:44] binding FDRs…
INFO [2020-04-29 14:38:44] binding meta p-values…
INFO [2020-04-29 14:38:44] binding adjusted meta p-values…
INFO [2020-04-29 14:38:45] binding natural normalized fold changes…
INFO [2020-04-29 14:38:46] Writing output…
INFO [2020-04-29 14:38:47] Adding report data…
INFO [2020-04-29 14:38:48] binding annotation…
INFO [2020-04-29 14:38:48] binding meta p-values…
INFO [2020-04-29 14:38:48] binding adjusted meta p-values…
INFO [2020-04-29 14:38:48] binding log2 normalized fold changes…
INFO [2020-04-29 14:38:48] binding normalized mean counts…
INFO [2020-04-29 14:38:48] binding normalized mean counts…
INFO [2020-04-29 14:38:49] Contrast: P0.5_vs_e15.5
INFO [2020-04-29 14:38:49] Adding non-filtered data…
INFO [2020-04-29 14:38:49] binding annotation…
INFO [2020-04-29 14:38:49] binding p-values…
INFO [2020-04-29 14:38:49] binding FDRs…
INFO [2020-04-29 14:38:49] binding meta p-values…
INFO [2020-04-29 14:38:49] binding adjusted meta p-values…
INFO [2020-04-29 14:38:49] binding natural normalized fold changes…
INFO [2020-04-29 14:38:49] Writing output…
INFO [2020-04-29 14:38:49] Adding filtered data…
INFO [2020-04-29 14:38:49] binding annotation…
INFO [2020-04-29 14:38:49] binding p-values…
INFO [2020-04-29 14:38:49] binding FDRs…
INFO [2020-04-29 14:38:49] binding meta p-values…
INFO [2020-04-29 14:38:49] binding adjusted meta p-values…
INFO [2020-04-29 14:38:51] binding natural normalized fold changes…
INFO [2020-04-29 14:38:52] Writing output…
INFO [2020-04-29 14:38:53] Adding report data…
INFO [2020-04-29 14:38:53] binding annotation…
INFO [2020-04-29 14:38:53] binding meta p-values…
INFO [2020-04-29 14:38:53] binding adjusted meta p-values…
INFO [2020-04-29 14:38:54] binding log2 normalized fold changes…
INFO [2020-04-29 14:38:54] binding normalized mean counts…
INFO [2020-04-29 14:38:54] binding normalized mean counts…
INFO [2020-04-29 14:38:54] Contrast: P22_vs_e15.5
INFO [2020-04-29 14:38:54] Adding non-filtered data…
INFO [2020-04-29 14:38:54] binding annotation…
INFO [2020-04-29 14:38:54] binding p-values…
INFO [2020-04-29 14:38:54] binding FDRs…
INFO [2020-04-29 14:38:54] binding meta p-values…
INFO [2020-04-29 14:38:54] binding adjusted meta p-values…
INFO [2020-04-29 14:38:55] binding natural normalized fold changes…
INFO [2020-04-29 14:38:55] Writing output…
INFO [2020-04-29 14:38:55] Adding filtered data…
INFO [2020-04-29 14:38:55] binding annotation…
INFO [2020-04-29 14:38:55] binding p-values…
INFO [2020-04-29 14:38:55] binding FDRs…
INFO [2020-04-29 14:38:55] binding meta p-values…
INFO [2020-04-29 14:38:55] binding adjusted meta p-values…
INFO [2020-04-29 14:38:56] binding natural normalized fold changes…
INFO [2020-04-29 14:38:57] Writing output…
INFO [2020-04-29 14:38:59] Adding report data…
INFO [2020-04-29 14:38:59] binding annotation…
INFO [2020-04-29 14:38:59] binding meta p-values…
INFO [2020-04-29 14:38:59] binding adjusted meta p-values…
INFO [2020-04-29 14:38:59] binding log2 normalized fold changes…
INFO [2020-04-29 14:38:59] binding normalized mean counts…
INFO [2020-04-29 14:39:00] binding normalized mean counts…
INFO [2020-04-29 14:39:00] Contrast: P60_vs_e15.5
INFO [2020-04-29 14:39:00] Adding non-filtered data…
INFO [2020-04-29 14:39:00] binding annotation…
INFO [2020-04-29 14:39:00] binding p-values…
INFO [2020-04-29 14:39:00] binding FDRs…
INFO [2020-04-29 14:39:00] binding meta p-values…
INFO [2020-04-29 14:39:00] binding adjusted meta p-values…
INFO [2020-04-29 14:39:00] binding natural normalized fold changes…
INFO [2020-04-29 14:39:00] Writing output…
INFO [2020-04-29 14:39:01] Adding filtered data…
INFO [2020-04-29 14:39:01] binding annotation…
INFO [2020-04-29 14:39:01] binding p-values…
INFO [2020-04-29 14:39:01] binding FDRs…
INFO [2020-04-29 14:39:01] binding meta p-values…
INFO [2020-04-29 14:39:01] binding adjusted meta p-values…
INFO [2020-04-29 14:39:02] binding natural normalized fold changes…
INFO [2020-04-29 14:39:03] Writing output…
INFO [2020-04-29 14:39:05] Adding report data…
INFO [2020-04-29 14:39:05] binding annotation…
INFO [2020-04-29 14:39:05] binding meta p-values…
INFO [2020-04-29 14:39:05] binding adjusted meta p-values…
INFO [2020-04-29 14:39:05] binding log2 normalized fold changes…
INFO [2020-04-29 14:39:05] binding normalized mean counts…
INFO [2020-04-29 14:39:05] binding normalized mean counts…
INFO [2020-04-29 14:39:06] Contrast: e18.5_vs_P0.5_vs_P4_vs_P14_vs_P22_vs_P60_vs_e15.5
INFO [2020-04-29 14:39:06] Adding non-filtered data…
INFO [2020-04-29 14:39:06] binding annotation…
INFO [2020-04-29 14:39:06] binding p-values…
INFO [2020-04-29 14:39:06] binding FDRs…
INFO [2020-04-29 14:39:06] binding meta p-values…
INFO [2020-04-29 14:39:06] binding adjusted meta p-values…
INFO [2020-04-29 14:39:08] binding natural normalized fold changes…
INFO [2020-04-29 14:39:08] Writing output…
INFO [2020-04-29 14:39:09] Adding filtered data…
INFO [2020-04-29 14:39:09] binding annotation…
INFO [2020-04-29 14:39:09] binding p-values…
INFO [2020-04-29 14:39:09] binding FDRs…
INFO [2020-04-29 14:39:09] binding meta p-values…
INFO [2020-04-29 14:39:09] binding adjusted meta p-values…
INFO [2020-04-29 14:39:17] binding natural normalized fold changes…
INFO [2020-04-29 14:39:18] Writing output…
INFO [2020-04-29 14:39:20] Adding report data…
INFO [2020-04-29 14:39:20] binding annotation…
INFO [2020-04-29 14:39:20] binding meta p-values…
INFO [2020-04-29 14:39:20] binding adjusted meta p-values…
INFO [2020-04-29 14:39:23] binding log2 normalized fold changes…
INFO [2020-04-29 14:39:23] binding normalized mean counts…
INFO [2020-04-29 14:39:23] binding normalized mean counts…
INFO [2020-04-29 14:39:23] binding normalized mean counts…
INFO [2020-04-29 14:39:24] binding normalized mean counts…
INFO [2020-04-29 14:39:24] binding normalized mean counts…
INFO [2020-04-29 14:39:24] binding normalized mean counts…
INFO [2020-04-29 14:39:24] binding normalized mean counts…
INFO [2020-04-29 14:39:25] Creating quality control graphs…
INFO [2020-04-29 14:39:25] Plotting in png format…
INFO [2020-04-29 14:39:26] Importing foldvenn
INFO [2020-04-29 14:39:31] deseq2
INFO [2020-04-29 14:39:31] edger
INFO [2020-04-29 14:39:31] limma
INFO [2020-04-29 14:39:31] absseq
INFO [2020-04-29 14:39:31] dss
INFO [2020-04-29 14:39:31] pandora
INFO [2020-04-29 14:39:31] Writing plot database in /home/panos/public_html/metaseqR2_showcase/metaseqR2_LiverDevelopment_PANDORA/data/reportdb.js
INFO [2020-04-29 14:39:31] Creating HTML report…
INFO [2020-04-29 14:39:31] Compressing figures…
INFO [2020-04-29 14:39:32] Downloading required JavaScript libraries…


Statistics

Differential expression assessment figures

The following figures allow for the assessment of the statistical testing procedures performed by the metaseqr2 pipeline. Each figure category is accompanied by an explanatory text. All figures are interactive wih additional controls on the top right corner of the figure.

FoldVenn

Venn diagram of differentially expressed genes

Venn diagrams are an intuitive way of presenting overlaps between lists, based on the overlap of basic geometrical shapes. The numbers of overlapping genes per statistical contrast are shown in the different areas of the Venn diagrams, one for each contrast. Apart from a p-value cutoff, a fold change threshold of 0.5 in log2 scale is applied for each contrast. For multi-condition contrasts, the first condition is used to calculate the fold change against the reference.

Select an algorithm to display Venn diagram for
Select a direction to display Venn diagram for
Click on a number on Venn diagrams to display the respective genes

Results

Tables of differentially expressed genes

The following tables allow for a quick exploration of the results of the statistical analysis performed by the metaseqr2 pipeline.

Each table presents the top 5% statistically significant genesUse the download links below each table to retrieve the total list of differentially expressed genes or the whole gene list of the selected genome irrespective of differential expression.Furthermore each table can be searched using the search field on the top right and you can also find the following information:

  • The chromosome column is linked to the genomic location of the gene and opens a new tab/window to the UCSC Genome Browser
  • The gene_id column opens a link to the respective full annotation source (only for Ensembl and RefSeq)
  • The background of the p_value and FDR columns displays a bar with length proportional to the significance of each gene
  • The background color of the fold change (vs) column(s) displays shows the deregulation of each gene and is proportional to the deregulation strength (red for up- green for down-regulation)
  • The background of the rest columns (condition average expression) displays a bar with length proportional to the expression strength of each condition
Select a contrast to display DEG table for

DEG table for the contrast e18.5 vs e15.5

The following table presents the top 5% statistically significant genes for the contrast e18.5 vs e15.5.


Download the DEG result list for e18.5_vs_e15.5.
Download the whole result list for e18.5_vs_e15.5.


DEG table for the contrast P0.5 vs e15.5

The following table presents the top 5% statistically significant genes for the contrast P0.5 vs e15.5.


Download the DEG result list for P0.5_vs_e15.5.
Download the whole result list for P0.5_vs_e15.5.


DEG table for the contrast P22 vs e15.5

The following table presents the top 5% statistically significant genes for the contrast P22 vs e15.5.


Download the DEG result list for P22_vs_e15.5.
Download the whole result list for P22_vs_e15.5.


DEG table for the contrast P60 vs e15.5

The following table presents the top 5% statistically significant genes for the contrast P60 vs e15.5.


Download the DEG result list for P60_vs_e15.5.
Download the whole result list for P60_vs_e15.5.


DEG table for the contrast e18.5 vs P0.5 vs P4 vs P14 vs P22 vs P60 vs e15.5

The following table presents the top 5% statistically significant genes for the contrast e18.5 vs P0.5 vs P4 vs P14 vs P22 vs P60 vs e15.5.


Download the DEG result list for e18.5_vs_P0.5_vs_P4_vs_P14_vs_P22_vs_P60_vs_e15.5.
Download the whole result list for e18.5_vs_P0.5_vs_P4_vs_P14_vs_P22_vs_P60_vs_e15.5.



Download the raw read counts table for the experiment.
Download the normalized read counts table for the experiment.
Get all the figures in png format.

References

  1. Moulos, P., Hatzis, P. (2015). Systematic integration of RNA-Seq statistical algorithms for accurate detection of differential gene expression patterns. Nucleic Acids Research 43(4), e25.
  2. Anders, S., and Huber, W. (2010). Differential expression analysis for sequence count data. Genome Biol 11, R106.
  3. Love, M.I., Huber, W., Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 15(12):550 (2014)
  4. Robinson, M.D., McCarthy, D.J., and Smyth, G.K. (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139-140.
  5. Smyth, G. (2005). Limma: linear models for microarray data. In Bioinformatics and Computational Biology Solutions using R and Bioconductor, G. R., C. V., D. S., I. R., and H. W., eds. (New York, Springer), pp. 397-420.
  6. Wentao Yang, Philip Rosenstiel and Hinrich Schulenburg: ABSSeq: a new RNA-Seq analysis method based on modelling absolute expression differences BMC Genomics 2016; 17: 541
  7. Hao Wu, Chi Wang, Zhijin Wu (2013): A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data. Biostatistics, 14(2):232-43. doi:10.1093/biostatistics/kxs033
  8. Chen, H., and Boutros, P.C. (2011). VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinformatics 12, 35.
  9. 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.