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 (2613 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 (202 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 0.07336 average reads per 100 bp), iii) genes with read counts below the median read counts of the total normalized count distribution (7079 genes with cutoff value 191 normalized read counts). The total number of genes excluded due to the application of gene filters was 2805. The total (unified) number of genes excluded due to the application of all filters was 9765. The resulting gene counts table was subjected to differential expression analysis for the contrasts Benign versus FULA, Benign versus BULA, Benign versus BELA, Benign versus CULA using the Bioconductor package DESeq. The final numbers of differentially expressed genes were (per contrast): for the contrast Benign versus FULA, 1605 (647) statistically significant genes were found with a p-value (FDR or adjusted p-value) threshold of 0.05 and of these, 526 (178) were up-regulated, 1079 (469) were down-regulated and 0 (0) were not differentially expressed according to an absolute fold change cutoff value of 1 in log₂ scale, for the contrast Benign versus BULA, 1360 (426) statistically significant genes were found with a p-value (FDR or adjusted p-value) threshold of 0.05 and of these, 506 (120) were up-regulated, 854 (306) were down-regulated and 0 (0) were not differentially expressed according to an absolute fold change cutoff value of 1 in log₂ scale, for the contrast Benign versus BELA, 1343 (414) statistically significant genes were found with a p-value (FDR or adjusted p-value) threshold of 0.05 and of these, 452 (76) were up-regulated, 891 (338) were down-regulated and 0 (0) were not differentially expressed according to an absolute fold change cutoff value of 1 in log₂ scale, for the contrast Benign versus CULA, 862 (193) statistically significant genes were found with a p-value (FDR or adjusted p-value) threshold of 0.05 and of these, 428 (62) were up-regulated, 434 (131) 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: FULA, BULA, BELA, Benign, CULA
Samples included: GSR1_FULA1, GSR2_FULA1, GSR3_FULA3, GSR4_BULA1, GSR5_BULA2, GSR6_BELA1, GSR7_BELA2, GSR8_FUBtrachea, GSR9_mTECsBalbc, GSR10_P53FllFl
Samples excluded: none
Requested contrasts: Benign_vs_FULA, Benign_vs_BULA, Benign_vs_BELA, Benign_vs_CULA
Library sizes:

GSR1_FULA1: 31459332
GSR2_FULA1: 29920358
GSR3_FULA3: 27954783
GSR4_BULA1: 34466262
GSR5_BULA2: 23162538
GSR6_BELA1: 23813854
GSR7_BELA2: 35590427
GSR8_FUBtrachea: 45754801
GSR9_mTECsBalbc: 44526283
GSR10_P53FllFl: 22945589

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: 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, mean-difference plot, mean-variance plot, DEG biotype detection, volcano plot, DEG heatmap
Figure format: png, pdf
Output directory: /data/images/proton/StathopoulosLab/lung/www/metaseqr_StathopoulosLab_lung
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: 18 minutes 25 seconds

Filtered genes

Number of filtered genes: 9765 which is the union of

Filtered because of zero reads: 2613
Filtered because of exon filters: 0
Filtered because of gene filters: 7152 which is the union of
- length: 202 genes with filter cutoff value 500
- avg.reads: 2598 genes with filter cutoff value 0.0733551
- expression: 7079 genes further decomposed to (filter name, filtered genes, filter cutoff):
- - median: 7079 genes with filter cutoff value 191
- biotype: 4 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:

Benign_vs_FULA: 1605 (647) statistically significant genes of which 526 (178) up regulated, 1079 (469) 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.
Benign_vs_BULA: 1360 (426) statistically significant genes of which 506 (120) up regulated, 854 (306) 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.
Benign_vs_BELA: 1343 (414) statistically significant genes of which 452 (76) up regulated, 891 (338) 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.
Benign_vs_CULA: 862 (193) statistically significant genes of which 428 (62) up regulated, 434 (131) 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

[ 2017-03-29 14:55:13 ] INFO 2017-03-29 14:55:13: Data processing started...
[ 2017-03-29 14:55:13 ] INFO Read counts file: imported sam/bam/bed files
[ 2017-03-29 14:55:13 ] INFO Conditions: FULA, BULA, BELA, Benign, CULA
[ 2017-03-29 14:55:13 ] INFO Samples to include: GSR1_FULA1, GSR2_FULA1, GSR3_FULA3, GSR4_BULA1, GSR5_BULA2, GSR6_BELA1, GSR7_BELA2, GSR8_FUBtrachea, GSR9_mTECsBalbc, GSR10_P53FllFl
[ 2017-03-29 14:55:13 ] INFO Samples to exclude: none
[ 2017-03-29 14:55:13 ] INFO Requested contrasts: Benign_vs_FULA, Benign_vs_BULA, Benign_vs_BELA, Benign_vs_CULA
[ 2017-03-29 14:55:13 ] INFO Annotation: download
[ 2017-03-29 14:55:13 ] INFO Organism: mm10
[ 2017-03-29 14:55:13 ] INFO Reference source: ensembl
[ 2017-03-29 14:55:13 ] INFO Count type: gene
[ 2017-03-29 14:55:13 ] INFO Exon filters: none applied
[ 2017-03-29 14:55:13 ] INFO Gene filters: length, avg.reads, expression, biotype
[ 2017-03-29 14:55:13 ] INFO length:
[ 2017-03-29 14:55:13 ] INFO length: 500
[ 2017-03-29 14:55:13 ] INFO avg.reads:
[ 2017-03-29 14:55:13 ] INFO average.per.bp: 100
[ 2017-03-29 14:55:13 ] INFO quantile: 0.25
[ 2017-03-29 14:55:13 ] INFO expression:
[ 2017-03-29 14:55:13 ] INFO median: TRUE
[ 2017-03-29 14:55:13 ] INFO mean: FALSE
[ 2017-03-29 14:55:13 ] INFO quantile: NA
[ 2017-03-29 14:55:13 ] INFO known: NA
[ 2017-03-29 14:55:13 ] INFO custom: NA
[ 2017-03-29 14:55:13 ] INFO biotype:
[ 2017-03-29 14:55:13 ] INFO pseudogene: FALSE
[ 2017-03-29 14:55:13 ] INFO lincRNA: FALSE
[ 2017-03-29 14:55:13 ] INFO protein_coding: FALSE
[ 2017-03-29 14:55:13 ] INFO antisense: FALSE
[ 2017-03-29 14:55:13 ] INFO processed_transcript: FALSE
[ 2017-03-29 14:55:13 ] INFO snRNA: FALSE
[ 2017-03-29 14:55:13 ] INFO sense_intronic: FALSE
[ 2017-03-29 14:55:13 ] INFO miRNA: FALSE
[ 2017-03-29 14:55:13 ] INFO misc_RNA: FALSE
[ 2017-03-29 14:55:13 ] INFO snoRNA: FALSE
[ 2017-03-29 14:55:13 ] INFO rRNA: TRUE
[ 2017-03-29 14:55:13 ] INFO polymorphic_pseudogene: FALSE
[ 2017-03-29 14:55:13 ] INFO sense_overlapping: FALSE
[ 2017-03-29 14:55:13 ] INFO three_prime_overlapping_ncrna: FALSE
[ 2017-03-29 14:55:13 ] INFO TR_V_gene: FALSE
[ 2017-03-29 14:55:13 ] INFO TR_V_pseudogene: TRUE
[ 2017-03-29 14:55:13 ] INFO TR_D_gene: FALSE
[ 2017-03-29 14:55:13 ] INFO TR_J_gene: FALSE
[ 2017-03-29 14:55:13 ] INFO TR_C_gene: FALSE
[ 2017-03-29 14:55:13 ] INFO TR_J_pseudogene: TRUE
[ 2017-03-29 14:55:13 ] INFO IG_C_gene: FALSE
[ 2017-03-29 14:55:13 ] INFO IG_C_pseudogene: TRUE
[ 2017-03-29 14:55:13 ] INFO IG_J_gene: FALSE
[ 2017-03-29 14:55:13 ] INFO IG_J_pseudogene: TRUE
[ 2017-03-29 14:55:13 ] INFO IG_D_gene: FALSE
[ 2017-03-29 14:55:13 ] INFO IG_V_gene: FALSE
[ 2017-03-29 14:55:13 ] INFO IG_V_pseudogene: TRUE
[ 2017-03-29 14:55:13 ] INFO Filter application: postnorm
[ 2017-03-29 14:55:13 ] INFO Normalization algorithm: deseq
[ 2017-03-29 14:55:13 ] INFO Normalization arguments:
[ 2017-03-29 14:55:13 ] INFO locfunc:
[ 2017-03-29 14:55:13 ] INFO [[
[ 2017-03-29 14:55:13 ] INFO list(locfunc = function (x, na.rm = FALSE) standardGeneric("median"))
[ 2017-03-29 14:55:13 ] INFO locfunc
[ 2017-03-29 14:55:13 ] INFO Statistical algorithm: deseq
[ 2017-03-29 14:55:13 ] INFO Statistical arguments:
[ 2017-03-29 14:55:13 ] INFO deseq: blind, fit-only, local
[ 2017-03-29 14:55:13 ] INFO Meta-analysis method: none
[ 2017-03-29 14:55:13 ] INFO Multiple testing correction: BH
[ 2017-03-29 14:55:13 ] INFO p-value threshold: 0.05
[ 2017-03-29 14:55:13 ] INFO Logarithmic transformation offset: 1
[ 2017-03-29 14:55:13 ] INFO Quality control plots: mds, biodetection, countsbio, saturation, readnoise, filtered, correl, pairwise, boxplot, gcbias, lengthbias, meandiff, meanvar, biodist, volcano, deheatmap
[ 2017-03-29 14:55:13 ] INFO Figure format: png, pdf
[ 2017-03-29 14:55:13 ] INFO Output directory: /data/images/proton/StathopoulosLab/lung/www/metaseqr_StathopoulosLab_lung
[ 2017-03-29 14:55:13 ] INFO Output data: annotation, p.value, adj.p.value, fold.change, counts, flags
[ 2017-03-29 14:55:13 ] INFO Output scale(s): log2, rpgm
[ 2017-03-29 14:55:13 ] INFO Output values: normalized
[ 2017-03-29 14:55:13 ] INFO Downloading gene annotation for mm10...
[ 2017-03-29 14:59:52 ] INFO 2017-03-29 14:59:52: Data processing started...
[ 2017-03-29 14:59:52 ] INFO Read counts file: imported sam/bam/bed files
[ 2017-03-29 14:59:52 ] INFO Conditions: FULA, BULA, BELA, Benign, CULA
[ 2017-03-29 14:59:52 ] INFO Samples to include: GSR1_FULA1, GSR2_FULA1, GSR3_FULA3, GSR4_BULA1, GSR5_BULA2, GSR6_BELA1, GSR7_BELA2, GSR8_FUBtrachea, GSR9_mTECsBalbc, GSR10_P53FllFl
[ 2017-03-29 14:59:52 ] INFO Samples to exclude: none
[ 2017-03-29 14:59:52 ] INFO Requested contrasts: Benign_vs_FULA, Benign_vs_BULA, Benign_vs_BELA, Benign_vs_CULA
[ 2017-03-29 14:59:52 ] INFO Annotation: download
[ 2017-03-29 14:59:52 ] INFO Organism: mm10
[ 2017-03-29 14:59:52 ] INFO Reference source: refseq
[ 2017-03-29 14:59:52 ] INFO Count type: gene
[ 2017-03-29 14:59:52 ] INFO Exon filters: none applied
[ 2017-03-29 14:59:52 ] INFO Gene filters: length, avg.reads, expression, biotype
[ 2017-03-29 14:59:52 ] INFO length:
[ 2017-03-29 14:59:52 ] INFO length: 500
[ 2017-03-29 14:59:52 ] INFO avg.reads:
[ 2017-03-29 14:59:52 ] INFO average.per.bp: 100
[ 2017-03-29 14:59:52 ] INFO quantile: 0.25
[ 2017-03-29 14:59:52 ] INFO expression:
[ 2017-03-29 14:59:52 ] INFO median: TRUE
[ 2017-03-29 14:59:52 ] INFO mean: FALSE
[ 2017-03-29 14:59:52 ] INFO quantile: NA
[ 2017-03-29 14:59:52 ] INFO known: NA
[ 2017-03-29 14:59:52 ] INFO custom: NA
[ 2017-03-29 14:59:52 ] INFO biotype:
[ 2017-03-29 14:59:52 ] INFO pseudogene: FALSE
[ 2017-03-29 14:59:52 ] INFO lincRNA: FALSE
[ 2017-03-29 14:59:52 ] INFO protein_coding: FALSE
[ 2017-03-29 14:59:52 ] INFO antisense: FALSE
[ 2017-03-29 14:59:52 ] INFO processed_transcript: FALSE
[ 2017-03-29 14:59:52 ] INFO snRNA: FALSE
[ 2017-03-29 14:59:52 ] INFO sense_intronic: FALSE
[ 2017-03-29 14:59:52 ] INFO miRNA: FALSE
[ 2017-03-29 14:59:52 ] INFO misc_RNA: FALSE
[ 2017-03-29 14:59:52 ] INFO snoRNA: FALSE
[ 2017-03-29 14:59:52 ] INFO rRNA: TRUE
[ 2017-03-29 14:59:52 ] INFO polymorphic_pseudogene: FALSE
[ 2017-03-29 14:59:52 ] INFO sense_overlapping: FALSE
[ 2017-03-29 14:59:52 ] INFO three_prime_overlapping_ncrna: FALSE
[ 2017-03-29 14:59:52 ] INFO TR_V_gene: FALSE
[ 2017-03-29 14:59:52 ] INFO TR_V_pseudogene: TRUE
[ 2017-03-29 14:59:52 ] INFO TR_D_gene: FALSE
[ 2017-03-29 14:59:52 ] INFO TR_J_gene: FALSE
[ 2017-03-29 14:59:52 ] INFO TR_C_gene: FALSE
[ 2017-03-29 14:59:52 ] INFO TR_J_pseudogene: TRUE
[ 2017-03-29 14:59:52 ] INFO IG_C_gene: FALSE
[ 2017-03-29 14:59:52 ] INFO IG_C_pseudogene: TRUE
[ 2017-03-29 14:59:52 ] INFO IG_J_gene: FALSE
[ 2017-03-29 14:59:52 ] INFO IG_J_pseudogene: TRUE
[ 2017-03-29 14:59:52 ] INFO IG_D_gene: FALSE
[ 2017-03-29 14:59:52 ] INFO IG_V_gene: FALSE
[ 2017-03-29 14:59:52 ] INFO IG_V_pseudogene: TRUE
[ 2017-03-29 14:59:52 ] INFO Filter application: postnorm
[ 2017-03-29 14:59:52 ] INFO Normalization algorithm: deseq
[ 2017-03-29 14:59:52 ] INFO Normalization arguments:
[ 2017-03-29 14:59:52 ] INFO locfunc:
[ 2017-03-29 14:59:52 ] INFO [[
[ 2017-03-29 14:59:52 ] INFO list(locfunc = function (x, na.rm = FALSE) standardGeneric("median"))
[ 2017-03-29 14:59:52 ] INFO locfunc
[ 2017-03-29 14:59:52 ] INFO Statistical algorithm: deseq
[ 2017-03-29 14:59:52 ] INFO Statistical arguments:
[ 2017-03-29 14:59:52 ] INFO deseq: blind, fit-only, local
[ 2017-03-29 14:59:52 ] INFO Meta-analysis method: none
[ 2017-03-29 14:59:52 ] INFO Multiple testing correction: BH
[ 2017-03-29 14:59:52 ] INFO p-value threshold: 0.05
[ 2017-03-29 14:59:52 ] INFO Logarithmic transformation offset: 1
[ 2017-03-29 14:59:52 ] INFO Quality control plots: mds, biodetection, countsbio, saturation, readnoise, filtered, correl, pairwise, boxplot, gcbias, lengthbias, meandiff, meanvar, biodist, volcano, deheatmap
[ 2017-03-29 14:59:52 ] INFO Figure format: png, pdf
[ 2017-03-29 14:59:52 ] INFO Output directory: /data/images/proton/StathopoulosLab/lung/www/metaseqr_StathopoulosLab_lung
[ 2017-03-29 14:59:52 ] INFO Output data: annotation, p.value, adj.p.value, fold.change, counts, flags
[ 2017-03-29 14:59:52 ] INFO Output scale(s): log2, rpgm
[ 2017-03-29 14:59:52 ] INFO Output values: normalized
[ 2017-03-29 14:59:52 ] INFO Downloading gene annotation for mm10...
[ 2017-03-29 15:00:14 ] INFO Converting annotation to GenomicRanges object...
[ 2017-03-29 15:00:16 ] INFO Getting DNA sequences...
[ 2017-03-29 15:00:27 ] INFO Getting GC content...
[ 2017-03-29 15:00:29 ] INFO Converting annotation to GenomicRanges object...
[ 2017-03-29 15:00:29 ] INFO Reading bam file GSR1-FULA1.bam for sample with name GSR1_FULA1. This might take some time...
[ 2017-03-29 15:00:29 ] WARN Information regarding strandedness of the reads is not correctly provided! Assuming unstranded...
[ 2017-03-29 15:00:29 ] INFO Reading bam file GSR2-FULA1.bam for sample with name GSR2_FULA1. This might take some time...
[ 2017-03-29 15:00:29 ] WARN Information regarding strandedness of the reads is not correctly provided! Assuming unstranded...
[ 2017-03-29 15:00:29 ] INFO Reading bam file GSR3-FULA3.bam for sample with name GSR3_FULA3. This might take some time...
[ 2017-03-29 15:00:29 ] WARN Information regarding strandedness of the reads is not correctly provided! Assuming unstranded...
[ 2017-03-29 15:00:29 ] INFO Reading bam file GSR4-BULA1.bam for sample with name GSR4_BULA1. This might take some time...
[ 2017-03-29 15:00:29 ] WARN Information regarding strandedness of the reads is not correctly provided! Assuming unstranded...
[ 2017-03-29 15:00:29 ] INFO Reading bam file GSR5-BULA2.bam for sample with name GSR5_BULA2. This might take some time...
[ 2017-03-29 15:00:29 ] WARN Information regarding strandedness of the reads is not correctly provided! Assuming unstranded...
[ 2017-03-29 15:00:29 ] INFO Reading bam file GSR6-BELA1.bam for sample with name GSR6_BELA1. This might take some time...
[ 2017-03-29 15:00:29 ] WARN Information regarding strandedness of the reads is not correctly provided! Assuming unstranded...
[ 2017-03-29 15:00:29 ] INFO Reading bam file GSR7-BELA2.bam for sample with name GSR7_BELA2. This might take some time...
[ 2017-03-29 15:00:29 ] WARN Information regarding strandedness of the reads is not correctly provided! Assuming unstranded...
[ 2017-03-29 15:00:29 ] INFO Reading bam file GSR8-FUBtrachea.bam for sample with name GSR8_FUBtrachea. This might take some time...
[ 2017-03-29 15:00:29 ] WARN Information regarding strandedness of the reads is not correctly provided! Assuming unstranded...
[ 2017-03-29 15:00:29 ] INFO Reading bam file GSR9-mTECsBalbc.bam for sample with name GSR9_mTECsBalbc. This might take some time...
[ 2017-03-29 15:00:29 ] WARN Information regarding strandedness of the reads is not correctly provided! Assuming unstranded...
[ 2017-03-29 15:00:29 ] INFO Reading bam file GSR10-P53FllFl.bam for sample with name GSR10_P53FllFl. This might take some time...
[ 2017-03-29 15:00:29 ] WARN Information regarding strandedness of the reads is not correctly provided! Assuming unstranded...
[ 2017-03-29 15:01:03 ] INFO Counting reads overlapping with given annotation...
[ 2017-03-29 15:01:03 ] INFO ...for single-end reads...
[ 2017-03-29 15:01:03 ] INFO ...ignoring strandedness...
[ 2017-03-29 15:01:10 ] INFO Counting reads overlapping with given annotation...
[ 2017-03-29 15:01:10 ] INFO ...for single-end reads...
[ 2017-03-29 15:01:10 ] INFO ...ignoring strandedness...
[ 2017-03-29 15:01:14 ] INFO Counting reads overlapping with given annotation...
[ 2017-03-29 15:01:14 ] INFO ...for single-end reads...
[ 2017-03-29 15:01:14 ] INFO ...ignoring strandedness...
[ 2017-03-29 15:01:14 ] INFO Counting reads overlapping with given annotation...
[ 2017-03-29 15:01:14 ] INFO ...for single-end reads...
[ 2017-03-29 15:01:14 ] INFO ...ignoring strandedness...
[ 2017-03-29 15:01:16 ] INFO Counting reads overlapping with given annotation...
[ 2017-03-29 15:01:16 ] INFO ...for single-end reads...
[ 2017-03-29 15:01:16 ] INFO ...ignoring strandedness...
[ 2017-03-29 15:01:18 ] INFO Counting reads overlapping with given annotation...
[ 2017-03-29 15:01:18 ] INFO ...for single-end reads...
[ 2017-03-29 15:01:18 ] INFO ...ignoring strandedness...
[ 2017-03-29 15:01:29 ] INFO Counting reads overlapping with given annotation...
[ 2017-03-29 15:01:29 ] INFO ...for single-end reads...
[ 2017-03-29 15:01:29 ] INFO ...ignoring strandedness...
[ 2017-03-29 15:01:32 ] INFO Counting reads overlapping with given annotation...
[ 2017-03-29 15:01:32 ] INFO ...for single-end reads...
[ 2017-03-29 15:01:32 ] INFO ...ignoring strandedness...
[ 2017-03-29 15:01:38 ] INFO Counting reads overlapping with given annotation...
[ 2017-03-29 15:01:38 ] INFO ...for single-end reads...
[ 2017-03-29 15:01:38 ] INFO ...ignoring strandedness...
[ 2017-03-29 15:01:50 ] INFO Counting reads overlapping with given annotation...
[ 2017-03-29 15:01:50 ] INFO ...for single-end reads...
[ 2017-03-29 15:01:50 ] INFO ...ignoring strandedness...
[ 2017-03-29 15:07:29 ] INFO Exporting raw read counts table to /data/images/proton/StathopoulosLab/lung/www/metaseqr_StathopoulosLab_lung/lists/raw_counts_table.txt.gz
[ 2017-03-29 15:07:30 ] INFO Saving gene model to /data/images/proton/StathopoulosLab/lung/www/metaseqr_StathopoulosLab_lung/data/gene_model.RData
[ 2017-03-29 15:07:30 ] INFO Removing genes with zero counts in all samples...
[ 2017-03-29 15:07:30 ] INFO Normalizing with: deseq
[ 2017-03-29 15:07:30 ] INFO Applying gene filter length...
[ 2017-03-29 15:07:30 ] INFO Threshold below which ignored: 500
[ 2017-03-29 15:07:30 ] INFO Applying gene filter avg.reads...
[ 2017-03-29 15:07:31 ] INFO Threshold below which ignored: 0.0733550986952037
[ 2017-03-29 15:07:31 ] INFO Applying gene filter expression...
[ 2017-03-29 15:07:31 ] INFO Threshold below which ignored: 191
[ 2017-03-29 15:07:31 ] INFO Applying gene filter biotype...
[ 2017-03-29 15:07:31 ] INFO Biotypes ignored: rRNA, TR_V_pseudogene, TR_J_pseudogene, IG_C_pseudogene, IG_J_pseudogene, IG_V_pseudogene
[ 2017-03-29 15:07:31 ] INFO 9765 genes filtered out
[ 2017-03-29 15:07:31 ] INFO 12542 genes remain after filtering
[ 2017-03-29 15:07:31 ] INFO Running statistical tests with: deseq
[ 2017-03-29 15:07:31 ] INFO Contrast: Benign_vs_FULA
[ 2017-03-29 15:09:20 ] INFO Contrast: Benign_vs_BULA
[ 2017-03-29 15:10:46 ] INFO Contrast: Benign_vs_BELA
[ 2017-03-29 15:12:13 ] INFO Contrast: Benign_vs_CULA
[ 2017-03-29 15:13:19 ] INFO Contrast Benign_vs_FULA: found 1605 genes
[ 2017-03-29 15:13:19 ] INFO Contrast Benign_vs_BULA: found 1360 genes
[ 2017-03-29 15:13:19 ] INFO Contrast Benign_vs_BELA: found 1343 genes
[ 2017-03-29 15:13:19 ] INFO Contrast Benign_vs_CULA: found 862 genes
[ 2017-03-29 15:13:19 ] INFO Exporting and compressing normalized read counts table to /data/images/proton/StathopoulosLab/lung/www/metaseqr_StathopoulosLab_lung/lists/normalized_counts_table.txt
[ 2017-03-29 15:13:20 ] INFO Building output files...
[ 2017-03-29 15:13:20 ] INFO Contrast: Benign_vs_FULA
[ 2017-03-29 15:13:20 ] INFO Adding non-filtered data...
[ 2017-03-29 15:13:20 ] INFO binding annotation...
[ 2017-03-29 15:13:21 ] INFO binding p-values...
[ 2017-03-29 15:13:21 ] INFO binding FDRs...
[ 2017-03-29 15:13:21 ] INFO binding log2 normalized fold changes...
[ 2017-03-29 15:13:21 ] INFO binding all normalized counts for Benign...
[ 2017-03-29 15:13:21 ] INFO binding all normalized counts for FULA...
[ 2017-03-29 15:13:22 ] INFO binding filtering flags...
[ 2017-03-29 15:13:22 ] INFO Writing output...
[ 2017-03-29 15:13:22 ] INFO Adding filtered data...
[ 2017-03-29 15:13:22 ] INFO binding annotation...
[ 2017-03-29 15:13:22 ] INFO binding p-values...
[ 2017-03-29 15:13:22 ] INFO binding FDRs...
[ 2017-03-29 15:13:22 ] INFO binding log2 normalized fold changes...
[ 2017-03-29 15:13:22 ] INFO binding all normalized counts for Benign...
[ 2017-03-29 15:13:22 ] INFO binding all normalized counts for FULA...
[ 2017-03-29 15:13:22 ] INFO binding filtering flags...
[ 2017-03-29 15:13:23 ] INFO Writing output...
[ 2017-03-29 15:13:24 ] INFO Contrast: Benign_vs_BULA
[ 2017-03-29 15:13:24 ] INFO Adding non-filtered data...
[ 2017-03-29 15:13:24 ] INFO binding annotation...
[ 2017-03-29 15:13:24 ] INFO binding p-values...
[ 2017-03-29 15:13:24 ] INFO binding FDRs...
[ 2017-03-29 15:13:24 ] INFO binding log2 normalized fold changes...
[ 2017-03-29 15:13:25 ] INFO binding all normalized counts for Benign...
[ 2017-03-29 15:13:25 ] INFO binding all normalized counts for BULA...
[ 2017-03-29 15:13:25 ] INFO binding filtering flags...
[ 2017-03-29 15:13:26 ] INFO Writing output...
[ 2017-03-29 15:13:26 ] INFO Adding filtered data...
[ 2017-03-29 15:13:26 ] INFO binding annotation...
[ 2017-03-29 15:13:26 ] INFO binding p-values...
[ 2017-03-29 15:13:26 ] INFO binding FDRs...
[ 2017-03-29 15:13:26 ] INFO binding log2 normalized fold changes...
[ 2017-03-29 15:13:26 ] INFO binding all normalized counts for Benign...
[ 2017-03-29 15:13:26 ] INFO binding all normalized counts for BULA...
[ 2017-03-29 15:13:26 ] INFO binding filtering flags...
[ 2017-03-29 15:13:26 ] INFO Writing output...
[ 2017-03-29 15:13:27 ] INFO Contrast: Benign_vs_BELA
[ 2017-03-29 15:13:27 ] INFO Adding non-filtered data...
[ 2017-03-29 15:13:27 ] INFO binding annotation...
[ 2017-03-29 15:13:27 ] INFO binding p-values...
[ 2017-03-29 15:13:27 ] INFO binding FDRs...
[ 2017-03-29 15:13:28 ] INFO binding log2 normalized fold changes...
[ 2017-03-29 15:13:28 ] INFO binding all normalized counts for Benign...
[ 2017-03-29 15:13:28 ] INFO binding all normalized counts for BELA...
[ 2017-03-29 15:13:28 ] INFO binding filtering flags...
[ 2017-03-29 15:13:29 ] INFO Writing output...
[ 2017-03-29 15:13:29 ] INFO Adding filtered data...
[ 2017-03-29 15:13:29 ] INFO binding annotation...
[ 2017-03-29 15:13:29 ] INFO binding p-values...
[ 2017-03-29 15:13:29 ] INFO binding FDRs...
[ 2017-03-29 15:13:29 ] INFO binding log2 normalized fold changes...
[ 2017-03-29 15:13:29 ] INFO binding all normalized counts for Benign...
[ 2017-03-29 15:13:29 ] INFO binding all normalized counts for BELA...
[ 2017-03-29 15:13:29 ] INFO binding filtering flags...
[ 2017-03-29 15:13:29 ] INFO Writing output...
[ 2017-03-29 15:13:30 ] INFO Contrast: Benign_vs_CULA
[ 2017-03-29 15:13:30 ] INFO Adding non-filtered data...
[ 2017-03-29 15:13:30 ] INFO binding annotation...
[ 2017-03-29 15:13:31 ] INFO binding p-values...
[ 2017-03-29 15:13:31 ] INFO binding FDRs...
[ 2017-03-29 15:13:31 ] INFO binding log2 normalized fold changes...
[ 2017-03-29 15:13:31 ] INFO binding all normalized counts for Benign...
[ 2017-03-29 15:13:31 ] INFO binding all normalized counts for CULA...
[ 2017-03-29 15:13:31 ] INFO binding filtering flags...
[ 2017-03-29 15:13:32 ] INFO Writing output...
[ 2017-03-29 15:13:32 ] INFO Adding filtered data...
[ 2017-03-29 15:13:32 ] INFO binding annotation...
[ 2017-03-29 15:13:32 ] INFO binding p-values...
[ 2017-03-29 15:13:32 ] INFO binding FDRs...
[ 2017-03-29 15:13:32 ] INFO binding log2 normalized fold changes...
[ 2017-03-29 15:13:32 ] INFO binding all normalized counts for Benign...
[ 2017-03-29 15:13:32 ] INFO binding all normalized counts for CULA...
[ 2017-03-29 15:13:32 ] INFO binding filtering flags...
[ 2017-03-29 15:13:32 ] INFO Writing output...
[ 2017-03-29 15:13:33 ] INFO Creating quality control graphs...
[ 2017-03-29 15:13:33 ] INFO Plotting in png format...
[ 2017-03-29 15:13:33 ] INFO Plotting mds...
[ 2017-03-29 15:13:33 ] INFO Plotting biodetection...
[ 2017-03-29 15:13:35 ] INFO Plotting countsbio...
[ 2017-03-29 15:13:36 ] INFO Plotting saturation...
[ 2017-03-29 15:13:42 ] INFO Plotting readnoise...
[ 2017-03-29 15:13:43 ] INFO Plotting correl...
[ 2017-03-29 15:13:43 ] INFO Plotting pairwise...
[ 2017-03-29 15:14:10 ] INFO Plotting boxplot...
[ 2017-03-29 15:14:10 ] INFO Plotting gcbias...
[ 2017-03-29 15:14:11 ] INFO Plotting lengthbias...
[ 2017-03-29 15:14:12 ] INFO Plotting meandiff...
[ 2017-03-29 15:14:12 ] WARN Cannot create a mean-difference plot with one sample per condition! Skipping...
[ 2017-03-29 15:14:12 ] INFO Plotting meanvar...
[ 2017-03-29 15:14:18 ] WARN Cannot create a mean-variance plot with one sample per condition! Skipping...
[ 2017-03-29 15:14:18 ] INFO Plotting boxplot...
[ 2017-03-29 15:14:18 ] INFO Plotting gcbias...
[ 2017-03-29 15:14:19 ] INFO Plotting lengthbias...
[ 2017-03-29 15:14:20 ] INFO Plotting meandiff...
[ 2017-03-29 15:14:21 ] WARN Cannot create a mean-difference plot with one sample per condition! Skipping...
[ 2017-03-29 15:14:21 ] INFO Plotting meanvar...
[ 2017-03-29 15:14:26 ] WARN Cannot create a mean-variance plot with one sample per condition! Skipping...
[ 2017-03-29 15:14:26 ] INFO Plotting biodist...
[ 2017-03-29 15:14:26 ] INFO Contrast: Benign_vs_FULA
[ 2017-03-29 15:14:26 ] INFO Contrast: Benign_vs_BULA
[ 2017-03-29 15:14:26 ] INFO Contrast: Benign_vs_BELA
[ 2017-03-29 15:14:26 ] INFO Contrast: Benign_vs_CULA
[ 2017-03-29 15:14:27 ] INFO Plotting volcano...
[ 2017-03-29 15:14:27 ] INFO Contrast: Benign_vs_FULA
[ 2017-03-29 15:14:27 ] INFO Contrast: Benign_vs_BULA
[ 2017-03-29 15:14:28 ] INFO Contrast: Benign_vs_BELA
[ 2017-03-29 15:14:28 ] INFO Contrast: Benign_vs_CULA
[ 2017-03-29 15:14:28 ] INFO Plotting deheatmap...
[ 2017-03-29 15:14:28 ] INFO Contrast: Benign_vs_FULA
[ 2017-03-29 15:14:52 ] INFO Contrast: Benign_vs_BULA
[ 2017-03-29 15:15:15 ] INFO Contrast: Benign_vs_BELA
[ 2017-03-29 15:15:38 ] INFO Contrast: Benign_vs_CULA
[ 2017-03-29 15:16:01 ] INFO Plotting filtered...
[ 2017-03-29 15:16:01 ] INFO Plotting in pdf format...
[ 2017-03-29 15:16:01 ] INFO Plotting mds...
[ 2017-03-29 15:16:01 ] INFO Plotting biodetection...
[ 2017-03-29 15:16:02 ] INFO Plotting countsbio...
[ 2017-03-29 15:16:03 ] INFO Plotting saturation...
[ 2017-03-29 15:16:07 ] INFO Plotting readnoise...
[ 2017-03-29 15:16:09 ] INFO Plotting correl...
[ 2017-03-29 15:16:09 ] INFO Plotting pairwise...
[ 2017-03-29 15:16:23 ] INFO Plotting boxplot...
[ 2017-03-29 15:16:23 ] INFO Plotting gcbias...
[ 2017-03-29 15:16:24 ] INFO Plotting lengthbias...
[ 2017-03-29 15:16:25 ] INFO Plotting meandiff...
[ 2017-03-29 15:16:26 ] WARN Cannot create a mean-difference plot with one sample per condition! Skipping...
[ 2017-03-29 15:16:26 ] INFO Plotting meanvar...
[ 2017-03-29 15:16:32 ] WARN Cannot create a mean-variance plot with one sample per condition! Skipping...
[ 2017-03-29 15:16:32 ] INFO Plotting boxplot...
[ 2017-03-29 15:16:32 ] INFO Plotting gcbias...
[ 2017-03-29 15:16:33 ] INFO Plotting lengthbias...
[ 2017-03-29 15:16:34 ] INFO Plotting meandiff...
[ 2017-03-29 15:16:35 ] WARN Cannot create a mean-difference plot with one sample per condition! Skipping...
[ 2017-03-29 15:16:35 ] INFO Plotting meanvar...
[ 2017-03-29 15:16:40 ] WARN Cannot create a mean-variance plot with one sample per condition! Skipping...
[ 2017-03-29 15:16:40 ] INFO Plotting biodist...
[ 2017-03-29 15:16:40 ] INFO Contrast: Benign_vs_FULA
[ 2017-03-29 15:16:40 ] INFO Contrast: Benign_vs_BULA
[ 2017-03-29 15:16:41 ] INFO Contrast: Benign_vs_BELA
[ 2017-03-29 15:16:41 ] INFO Contrast: Benign_vs_CULA
[ 2017-03-29 15:16:41 ] INFO Plotting volcano...
[ 2017-03-29 15:16:41 ] INFO Contrast: Benign_vs_FULA
[ 2017-03-29 15:16:41 ] INFO Contrast: Benign_vs_BULA
[ 2017-03-29 15:16:41 ] INFO Contrast: Benign_vs_BELA
[ 2017-03-29 15:16:42 ] INFO Contrast: Benign_vs_CULA
[ 2017-03-29 15:16:42 ] INFO Plotting deheatmap...
[ 2017-03-29 15:16:42 ] INFO Contrast: Benign_vs_FULA
[ 2017-03-29 15:17:06 ] INFO Contrast: Benign_vs_BULA
[ 2017-03-29 15:17:31 ] INFO Contrast: Benign_vs_BELA
[ 2017-03-29 15:17:54 ] INFO Contrast: Benign_vs_CULA
[ 2017-03-29 15:18:16 ] INFO Plotting filtered...
[ 2017-03-29 15:18:16 ] INFO Creating HTML report...
[ 2017-03-29 15:18:16 ] INFO Compressing figures...

Figures

Multidimensional scaling

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

Biotype detection

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

Biotype detection counts

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

Read and biotype saturation

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

Read saturation per biotype for all samples

Read saturation per sample for all biotypes

RNA-Seq reads noise

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

Correlation plots

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

Correlation heatmap

Data correlogram

Pairwise scatterplots

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

Boxplots

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

Boxplot of un-normalized data

Boxplot of normalized data

GC content bias

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

GC content bias un-normalized

GC content bias normalized

Gene/transcript length bias

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

Gene/transcript length bias un-normalized

Gene/transcript length bias normalized

Mean-difference

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

Mean-difference plots for the replicates of FULA

Mean-difference plots for the replicates of BULA

Mean-difference plots for the replicates of BELA

Mean-difference plots for the replicates of Benign

Mean-variance

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

Mean-variance plots for the replicates of FULA

Mean-variance plots for the replicates of BULA

Mean-variance plots for the replicates of BELA

Mean-variance plots for the replicates of Benign

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 Benign_vs_FULA

Volcano plot for the contrast Benign_vs_BULA

Volcano plot for the contrast Benign_vs_BELA

Volcano plot for the contrast Benign_vs_CULA

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 Benign_vs_FULA

DEG heatmap for the contrast Benign_vs_BULA

DEG heatmap for the contrast Benign_vs_BELA

DEG heatmap for the contrast Benign_vs_CULA

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 Benign_vs_FULA

Biotype distribution in DEG for the contrast Benign_vs_BULA

Biotype distribution in DEG for the contrast Benign_vs_BELA

Biotype distribution in DEG for the contrast Benign_vs_CULA

Get all the figures in png, pdf format.

Results

DEG table for the contrast Benign_vs_FULA

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 Benign_vs_FULA. 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_Benign_vs_FULA	log2_normalized_counts_GSR8_FUBtrachea	log2_normalized_counts_GSR9_mTECsBalbc	rpgm_normalized_counts_GSR8_FUBtrachea	rpgm_normalized_counts_GSR9_mTECsBalbc	log2_normalized_counts_GSR1_FULA1	log2_normalized_counts_GSR2_FULA1	log2_normalized_counts_GSR3_FULA3	rpgm_normalized_counts_GSR1_FULA1	rpgm_normalized_counts_GSR2_FULA1	rpgm_normalized_counts_GSR3_FULA3
chr6	67534058	67535822	NM_020047	0.5422	-	Tacstd2	protein_coding	1.49e-23	1.87e-19	-1.44e+01	1.49e+01	1.34e+01	1.79e+01	6.32e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr10	117287794	117292868	NM_013590	0.4609	-	Lyz1	protein_coding	2.37e-21	1.02e-17	-1.27e+01	1.45e+01	4.00e+00	4.43e+00	2.96e-03	0.00e+00	1.58e+00	0.00e+00	0.00e+00	3.94e-04	0.00e+00
chr14	41131787	41136373	NM_023134	0.4559	+	Sftpa1	protein_coding	2.44e-21	1.02e-17	-1.27e+01	1.45e+01	2.32e+00	4.90e+00	8.72e-04	0.00e+00	1.58e+00	0.00e+00	0.00e+00	4.36e-04	0.00e+00
chr6	78466268	78468874	NM_011260	0.4212	-	Reg3g	protein_coding	1.15e-20	3.62e-17	-1.56e+01	1.66e+01	0.00e+00	3.87e+01	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr12	76792334	76795554	NM_030677	0.4806	-	Gpx2	protein_coding	2.17e-20	5.43e-17	-1.27e+01	1.30e+01	1.24e+01	2.48e+00	1.68e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr1	171518123	171535294	NM_010584	0.4250	-	Itln1	protein_coding	1.96e-19	4.10e-16	-1.19e+01	1.50e+01	3.81e+00	1.95e+00	7.57e-04	2.00e+00	0.00e+00	3.00e+00	1.75e-04	0.00e+00	4.08e-04
chr5	87554649	87569006	NM_016771	0.3344	-	Sult1d1	protein_coding	3.25e-19	5.73e-16	-1.16e+01	1.44e+01	3.70e+00	1.46e+00	8.36e-04	1.58e+00	0.00e+00	2.58e+00	1.39e-04	0.00e+00	3.48e-04
chr11	4064852	4078990	NM_001029937	0.4932	+	Sec14l3	protein_coding	3.66e-19	5.73e-16	-1.33e+01	1.61e+01	5.36e+00	4.89e+00	2.83e-03	0.00e+00	1.00e+00	2.81e+00	0.00e+00	7.07e-05	4.24e-04
chr11	70344146	70352031	NM_009660	0.5191	-	Alox15	protein_coding	5.20e-19	7.00e-16	-1.25e+01	1.35e+01	1.58e+00	1.45e+00	2.54e-04	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr2	119237359	119249513	NM_016907	0.5107	+	Spint1	protein_coding	5.58e-19	7.00e-16	-1.16e+01	1.33e+01	1.27e+01	8.03e-01	5.55e-01	1.58e+00	0.00e+00	2.00e+00	1.65e-04	0.00e+00	2.47e-04
chr7	131032075	131121628	NM_007769	0.4550	+	Dmbt1	protein_coding	1.09e-18	1.24e-15	-1.13e+01	1.44e+01	2.32e+00	2.46e-01	4.47e-05	2.58e+00	2.00e+00	1.58e+00	5.58e-05	3.35e-05	2.23e-05
chr8	125995101	126030688	NM_008430	0.4745	+	Kcnk1	protein_coding	1.21e-18	1.27e-15	-1.11e+01	1.40e+01	1.29e+01	4.67e-01	2.10e-01	2.58e+00	2.00e+00	2.58e+00	1.41e-04	8.43e-05	1.41e-04
chr15	54571365	54602846	NM_178920	0.4201	+	Mal2	protein_coding	1.71e-18	1.65e-15	-1.10e+01	1.32e+01	1.42e+01	3.06e-01	6.16e-01	2.00e+00	2.32e+00	3.58e+00	9.53e-05	1.27e-04	3.49e-04
chr2	154190817	154220343	NM_001012392	0.5061	+	Bpifb1	protein_coding	4.95e-18	4.44e-15	-1.13e+01	1.40e+01	1.58e+00	5.56e-01	6.77e-05	1.00e+00	1.00e+00	2.58e+00	3.39e-05	3.39e-05	1.69e-04
chr15	101689927	101694305	NM_008476	0.5099	-	Krt6a	protein_coding	7.32e-18	5.89e-15	-1.11e+01	1.39e+01	8.68e+00	3.48e+00	9.37e-02	2.81e+00	1.58e+00	0.00e+00	1.37e-03	4.57e-04	0.00e+00
chr7	25897657	25926624	NM_009999	0.4534	+	Cyp2b10	protein_coding	7.51e-18	5.89e-15	-1.08e+01	1.43e+01	1.18e+01	6.96e-01	1.21e-01	2.81e+00	2.32e+00	3.00e+00	2.07e-04	1.38e-04	2.42e-04
chr16	32735885	32782390	NM_080457	0.4847	+	Muc4	protein_coding	2.03e-17	1.49e-14	-1.07e+01	1.41e+01	1.03e+01	3.82e-01	2.63e-02	2.58e+00	2.32e+00	2.58e+00	1.08e-04	8.60e-05	1.08e-04
chr11	49663594	49665118	NM_054037	0.5200	+	Scgb3a1	protein_coding	5.60e-17	3.90e-14	-1.62e+01	1.77e+01	6.34e+00	1.36e+02	5.25e-02	1.00e+00	0.00e+00	0.00e+00	6.56e-04	0.00e+00	0.00e+00
chr6	128539821	128581606	NM_001001179	0.4176	-	A2ml1	protein_coding	7.94e-17	5.24e-14	-1.11e+01	1.56e+01	5.32e+00	1.16e+00	9.33e-04	4.46e+00	1.00e+00	3.32e+00	5.03e-04	2.39e-05	2.15e-04
chr10	117277540	117282272	NM_017372	0.4731	-	Lyz2	protein_coding	1.93e-16	1.21e-13	-1.31e+01	1.65e+01	5.17e+00	1.98e+01	7.40e-03	3.58e+00	1.58e+00	0.00e+00	2.32e-03	4.23e-04	0.00e+00
chr15	101707069	101712891	NM_027011	0.5216	-	Krt5	protein_coding	2.64e-16	1.58e-13	-1.12e+01	1.58e+01	1.23e+01	9.71e+00	8.40e-01	4.00e+00	3.00e+00	3.81e+00	2.58e-03	1.20e-03	2.23e-03
chr4	115411623	115439034	NM_172306	0.4002	+	Cyp4a12b	protein_coding	5.02e-16	2.86e-13	-1.01e+01	1.40e+01	8.37e+00	6.08e-01	1.20e-02	3.81e+00	0.00e+00	3.00e+00	4.74e-04	0.00e+00	2.55e-04
chr4	116161880	116167598	NM_133681	0.4803	-	Tspan1	protein_coding	8.04e-16	4.39e-13	-1.02e+01	1.35e+01	1.13e+01	2.10e+00	4.49e-01	2.00e+00	3.32e+00	2.32e+00	5.25e-04	1.57e-03	7.00e-04
chr1	93015454	93082480	NM_001294150	0.5250	-	Kif1a	protein_coding	8.62e-16	4.51e-13	-9.60e+00	7.59e+00	1.41e+01	2.86e-03	2.59e-01	1.58e+00	3.58e+00	4.25e+00	2.98e-05	1.64e-04	2.69e-04
chr11	100131758	100135949	NM_008469	0.5253	-	Krt15	protein_coding	9.17e-16	4.60e-13	-1.11e+01	1.60e+01	9.69e+00	1.57e+01	1.97e-01	1.00e+00	4.70e+00	4.00e+00	2.39e-04	5.97e-03	3.58e-03
chr11	69965420	69967886	NM_016887	0.5849	+	Cldn7	protein_coding	1.67e-15	8.05e-13	-9.83e+00	1.31e+01	1.27e+01	3.45e+00	2.68e+00	4.25e+00	1.00e+00	2.00e+00	7.30e-03	4.06e-04	1.22e-03
chr4	133600555	133602168	NM_018754	0.5973	-	Sfn	protein_coding	1.86e-15	8.62e-13	-1.20e+01	1.56e+01	1.56e+01	3.10e+01	3.13e+01	2.58e+00	3.17e+00	4.39e+00	3.10e-03	4.96e-03	1.24e-02
chr1	162875046	162898712	NM_018881	0.4155	-	Fmo2	protein_coding	2.57e-15	1.15e-12	-1.11e+01	1.31e+01	8.14e+00	3.59e-01	1.19e-02	1.58e+00	1.00e+00	0.00e+00	8.45e-05	4.23e-05	0.00e+00
chr11	100256214	100261029	NM_010663	0.5438	-	Krt17	protein_coding	4.16e-15	1.80e-12	-1.19e+01	1.47e+01	1.63e+01	5.52e+00	1.64e+01	2.58e+00	2.81e+00	4.86e+00	1.04e-03	1.25e-03	5.82e-03
chr7	26835338	26843264	NM_007812	0.4656	+	Cyp2a5	protein_coding	4.78e-15	2.00e-12	-1.43e+01	1.75e+01	6.02e+00	2.35e+01	8.07e-03	1.58e+00	3.17e+00	1.00e+00	2.52e-04	1.01e-03	1.26e-04
chr18	20510273	20541310	NM_030596	0.4080	+	Dsg3	protein_coding	6.14e-15	2.48e-12	-9.84e+00	8.37e+00	1.35e+01	1.06e-02	3.63e-01	2.81e+00	2.32e+00	2.81e+00	1.93e-04	1.29e-04	1.93e-04
chr19	9083641	9087956	NM_011681	0.4627	-	Scgb1a1	protein_coding	8.97e-15	3.52e-12	-1.51e+01	1.79e+01	7.12e+00	5.50e+01	3.20e-02	1.00e+00	2.00e+00	2.00e+00	2.32e-04	6.95e-04	6.95e-04
chr9	31088589	31131799	NM_011176	0.4919	-	St14	protein_coding	1.51e-14	5.68e-12	-9.04e+00	1.34e+01	1.30e+01	2.48e-01	1.91e-01	3.46e+00	4.52e+00	4.32e+00	2.31e-04	5.09e-04	4.40e-04
chr15	35296111	35303305	NM_054049	0.5062	+	Osr2	protein_coding	1.54e-14	5.68e-12	-1.12e+01	1.28e+01	9.06e+00	1.00e+00	7.42e-02	0.00e+00	0.00e+00	1.58e+00	0.00e+00	0.00e+00	2.78e-04
chr5	123863569	123865516	NM_030701	0.5077	-	Hcar2	protein_coding	2.54e-14	9.10e-12	-1.05e+01	1.31e+01	6.48e+00	4.52e+00	4.52e-02	0.00e+00	0.00e+00	2.81e+00	0.00e+00	0.00e+00	3.08e-03
chr4	11331932	11386783	NM_194055	0.4323	-	Esrp1	protein_coding	4.39e-14	1.53e-11	-9.65e+00	1.25e+01	1.23e+01	1.03e-01	9.29e-02	2.32e+00	2.32e+00	3.32e+00	7.29e-05	7.29e-05	1.64e-04
chr5	134986213	134987476	NM_009902	0.6274	+	Cldn3	protein_coding	5.19e-14	1.76e-11	-8.76e+00	1.32e+01	1.30e+01	7.58e+00	6.36e+00	5.88e+00	0.00e+00	0.00e+00	4.59e-02	0.00e+00	0.00e+00
chr15	101918534	101924735	NM_008475	0.5123	-	Krt4	protein_coding	7.46e-14	2.46e-11	-1.06e+01	1.29e+01	8.22e+00	1.26e+00	4.79e-02	1.58e+00	1.58e+00	1.00e+00	3.23e-04	3.23e-04	1.61e-04
chr11	100203161	100207510	NM_016958	0.5262	-	Krt14	protein_coding	1.14e-13	3.68e-11	-8.51e+00	1.36e+01	1.38e+01	2.84e+00	3.27e+00	5.17e+00	4.64e+00	5.58e+00	8.05e-03	5.52e-03	1.08e-02
chrX	21714899	21742357	NM_020049	0.3494	+	Slc6a14	protein_coding	2.57e-13	8.05e-11	-1.02e+01	1.27e+01	1.05e+01	2.38e-01	5.32e-02	1.00e+00	0.00e+00	2.81e+00	3.64e-05	0.00e+00	2.19e-04
chr4	117926159	117929763	NM_009711	0.5825	-	Artn	protein_coding	3.27e-13	1.00e-10	-1.02e+01	1.26e+01	1.06e+01	1.74e+00	4.44e-01	1.58e+00	2.00e+00	1.58e+00	5.55e-04	8.32e-04	5.55e-04
chr7	27119954	27133660	NM_007817	0.4772	+	Cyp2f2	protein_coding	3.51e-13	1.03e-10	-1.44e+01	1.81e+01	7.30e+00	2.03e+01	1.15e-02	1.58e+00	2.81e+00	3.17e+00	1.46e-04	4.38e-04	5.84e-04
chr17	87635978	87651127	NM_008532	0.4631	+	Epcam	protein_coding	3.53e-13	1.03e-10	-8.51e+00	1.32e+01	1.26e+01	6.37e-01	4.06e-01	2.58e+00	3.70e+00	5.52e+00	3.30e-04	7.92e-04	2.97e-03
chr8	93166071	93197804	NM_053200	0.4247	-	Ces1d	protein_coding	5.45e-13	1.55e-10	-8.63e+00	1.52e+01	6.11e+00	1.23e+00	2.14e-03	2.32e+00	3.46e+00	7.04e+00	1.26e-04	3.15e-04	4.13e-03
chr13	71958231	71963723	NM_010573	0.5376	-	Irx1	protein_coding	6.64e-13	1.85e-10	-1.12e+01	1.21e+01	1.07e+01	7.98e-01	3.06e-01	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	1.82e-04
chr19	34240335	34255373	NM_007392	0.4424	-	Acta2	protein_coding	7.80e-13	2.13e-10	-7.94e+00	8.43e+00	1.40e+01	2.29e-02	1.12e+00	4.32e+00	4.17e+00	6.07e+00	1.26e-03	1.13e-03	4.39e-03
chr11	120729484	120732021	NM_007621	0.5623	-	Cbr2	protein_coding	1.15e-12	3.08e-10	-1.30e+01	1.78e+01	1.08e+01	8.89e+01	6.81e-01	1.00e+00	3.00e+00	4.95e+00	3.94e-04	2.76e-03	1.18e-02
chr12	85473900	85477270	NM_010234	0.5307	+	Fos	protein_coding	1.22e-12	3.20e-10	-8.31e+00	1.50e+01	7.72e+00	9.61e+00	6.23e-02	4.70e+00	6.11e+00	5.88e+00	7.42e-03	2.02e-02	1.72e-02
chr4	115624727	115647705	NM_007823	0.4336	-	Cyp4b1	protein_coding	1.48e-12	3.78e-10	-8.12e+00	1.45e+01	6.55e+00	1.00e+00	4.05e-03	6.04e+00	4.09e+00	5.39e+00	2.83e-03	6.96e-04	1.78e-03
chr9	111019291	111042766	NM_008522	0.4661	+	Ltf	protein_coding	1.79e-12	4.49e-10	-1.31e+01	1.79e+01	9.98e+00	1.02e+01	4.31e-02	4.70e+00	2.00e+00	3.58e+00	1.06e-03	1.28e-04	4.69e-04
chr7	25461701	25477625	NM_001039185	0.4612	-	Ceacam1	protein_coding	2.96e-12	7.29e-10	-8.26e+00	1.51e+01	1.05e+01	2.21e+00	9.05e-02	4.00e+00	6.83e+00	5.64e+00	9.42e-04	7.10e-03	3.08e-03
chr6	125321339	125344942	NM_011324	0.5021	+	Scnn1a	protein_coding	3.41e-12	8.21e-10	-7.84e+00	1.32e+01	1.30e+01	4.06e-01	3.52e-01	4.95e+00	5.21e+00	5.61e+00	1.27e-03	1.53e-03	2.03e-03
chr7	141839071	141873085	NM_028801	0.5298	+	Muc5b	protein_coding	4.04e-12	9.57e-10	-8.35e+00	1.35e+01	2.32e+00	3.39e-01	1.18e-04	2.32e+00	5.21e+00	3.46e+00	1.18e-04	1.06e-03	2.94e-04
chr1	58113135	58200452	NM_023617	0.4373	+	Aox3	protein_coding	4.33e-12	1.00e-09	-8.80e+00	1.33e+01	3.81e+00	1.16e-01	1.49e-04	3.46e+00	3.46e+00	3.58e+00	1.15e-04	1.15e-04	1.26e-04
chr15	11839896	11905674	NM_008728	0.4269	-	Npr3	protein_coding	5.06e-12	1.15e-09	-8.47e+00	4.52e+00	1.30e+01	3.34e-04	1.29e-01	3.32e+00	3.91e+00	3.46e+00	1.37e-04	2.13e-04	1.52e-04
chr16	56477845	56690135	NM_001014424	0.3922	+	Abi3bp	protein_coding	5.27e-12	1.18e-09	-8.06e+00	1.39e+01	4.64e+00	7.29e-02	1.13e-04	4.32e+00	3.46e+00	5.81e+00	8.95e-05	4.71e-05	2.59e-04
chr7	25516041	25540004	NM_001113368	0.4422	-	Ceacam2	protein_coding	5.51e-12	1.19e-09	-7.81e+00	1.45e+01	9.93e+00	9.48e-01	4.07e-02	5.09e+00	6.25e+00	5.58e+00	1.38e-03	3.13e-03	1.96e-03
chr14	41172211	41185198	NM_009160	0.4393	-	Sftpd	protein_coding	5.51e-12	1.19e-09	-7.86e+00	1.40e+01	1.11e+01	1.24e+00	1.65e-01	4.75e+00	5.88e+00	5.00e+00	2.00e-03	4.47e-03	2.39e-03
chr1	193153111	193172035	NM_016851	0.4656	+	Irf6	protein_coding	7.64e-12	1.62e-09	-7.77e+00	1.35e+01	1.24e+01	6.03e-01	2.91e-01	6.71e+00	2.32e+00	2.58e+00	5.50e-03	2.11e-04	2.64e-04
chr16	38773183	38800203	NM_178924	0.4395	-	Upk1b	protein_coding	8.68e-12	1.81e-09	-1.03e+01	1.17e+01	1.14e+01	1.19e-01	1.02e-01	1.00e+00	1.00e+00	1.58e+00	3.70e-05	3.70e-05	7.40e-05
chr8	40990911	41133726	NM_001005863	0.4323	-	Mtus1	protein_coding	1.39e-11	2.86e-09	-7.57e+00	1.34e+01	1.22e+01	7.58e-02	3.38e-02	5.32e+00	4.91e+00	5.75e+00	2.73e-04	2.03e-04	3.71e-04
chr19	47646340	47692042	NM_001290825	0.5136	-	Col17a1	protein_coding	1.74e-11	3.52e-09	-8.46e+00	1.29e+01	1.09e+01	1.70e-01	4.10e-02	3.58e+00	3.70e+00	4.00e+00	2.41e-04	2.63e-04	3.28e-04
chr7	28071235	28120864	NM_001122603	0.4946	+	Fcgbp	protein_coding	2.03e-11	4.04e-09	-8.47e+00	1.32e+01	7.21e+00	1.93e-01	2.96e-03	2.81e+00	2.00e+00	4.91e+00	1.21e-04	6.04e-05	5.84e-04
chr18	20030632	20059554	NM_001317365	0.3992	-	Dsc2	protein_coding	2.13e-11	4.18e-09	-8.46e+00	1.18e+01	1.21e+01	1.20e-01	1.49e-01	2.81e+00	3.17e+00	4.09e+00	2.07e-04	2.77e-04	5.53e-04
chrX	141710997	141725217	NM_010572	0.4235	-	Irs4	protein_coding	2.44e-11	4.70e-09	9.60e+00	4.00e+00	1.58e+00	1.05e-03	1.41e-04	1.58e+00	1.35e+01	1.33e+01	1.41e-04	8.26e-01	7.24e-01
chr11	70315612	70322518	NM_145684	0.5167	-	Alox12e	protein_coding	2.85e-11	5.42e-09	-1.12e+01	1.22e+01	6.11e+00	6.74e-01	9.85e-03	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr1	130826683	130852249	NM_011082	0.4499	+	Pigr	protein_coding	4.35e-11	8.15e-09	-7.35e+00	1.46e+01	9.61e+00	9.85e-01	3.04e-02	4.46e+00	6.66e+00	6.86e+00	8.21e-04	3.91e-03	4.50e-03
chr9	45929627	45936058	NM_011526	0.5016	-	Tagln	protein_coding	5.91e-11	1.09e-08	-7.15e+00	7.43e+00	1.39e+01	2.67e-02	2.33e+00	5.32e+00	5.83e+00	5.98e+00	6.06e-03	8.71e-03	9.64e-03
chr6	130043744	130067258	NM_053153	0.3671	-	Klra18	protein_coding	6.61e-11	1.20e-08	1.05e+01	1.58e+00	0.00e+00	8.51e-05	0.00e+00	1.12e+01	1.21e+01	1.09e+01	9.93e-02	1.80e-01	8.17e-02
chr5	134945123	134946934	NM_009903	0.5684	-	Cldn4	protein_coding	7.46e-11	1.34e-08	-1.27e+01	1.78e+01	1.65e+01	1.27e+02	5.15e+01	5.91e+00	3.00e+00	2.58e+00	3.26e-02	3.87e-03	2.76e-03
chr14	118233233	118237030	NM_177753	0.5835	-	Sox21	protein_coding	7.79e-11	1.38e-08	-1.08e+01	1.17e+01	1.04e+01	8.87e-01	3.64e-01	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	2.63e-04
chr3	90526848	90528835	NM_025393	0.5312	+	S100a14	protein_coding	8.78e-11	1.53e-08	-7.02e+00	1.28e+01	1.45e+01	3.68e+00	1.15e+01	7.87e+00	5.83e+00	5.83e+00	1.17e-01	2.82e-02	2.82e-02
chr13	36725621	36734477	NM_153529	0.4832	-	Nrn1	protein_coding	9.46e-11	1.63e-08	9.92e+00	4.25e+00	2.32e+00	2.03e-03	4.52e-04	1.27e+01	1.40e+01	1.36e+01	7.35e-01	1.79e+00	1.41e+00
chr5	101765129	101823852	NM_173370	0.4685	+	Cds1	protein_coding	9.74e-11	1.64e-08	-9.88e+00	1.20e+01	1.06e+01	7.07e-02	2.59e-02	0.00e+00	0.00e+00	2.81e+00	0.00e+00	0.00e+00	1.02e-04
chr19	5549136	5560575	NM_019935	0.5393	-	Ovol1	protein_coding	9.81e-11	1.64e-08	-8.94e+00	1.28e+01	6.89e+00	6.19e-01	1.03e-02	1.00e+00	3.81e+00	2.58e+00	8.74e-05	1.14e-03	4.37e-04
chr18	60803848	60812652	NM_001042605	0.5079	+	Cd74	protein_coding	1.53e-10	2.52e-08	-8.34e+00	1.26e+01	9.40e+00	7.08e-01	7.67e-02	1.00e+00	3.00e+00	4.46e+00	1.14e-04	7.95e-04	2.39e-03
chr8	105467491	105471422	NM_026481	0.5610	-	Tppp3	protein_coding	1.68e-10	2.74e-08	-7.39e+00	1.37e+01	6.82e+00	3.41e+00	2.85e-02	6.04e+00	4.17e+00	5.21e+00	1.65e-02	4.32e-03	9.16e-03
chr6	5168089	5193946	NM_011134	0.4331	-	Pon1	protein_coding	2.04e-10	3.28e-08	-9.46e+00	1.26e+01	1.58e+00	2.35e-01	7.73e-05	1.00e+00	2.81e+00	2.00e+00	3.87e-05	2.32e-04	1.16e-04
chr1	40466005	40500854	NM_008365	0.4170	+	Il18r1	protein_coding	2.22e-10	3.52e-08	-7.52e+00	1.19e+01	1.27e+01	1.07e-01	1.85e-01	5.17e+00	3.17e+00	5.25e+00	1.00e-03	2.30e-04	1.06e-03
chr15	74732251	74734313	NM_026671	0.5521	-	Lypd2	protein_coding	2.51e-10	3.94e-08	-1.05e+01	1.22e+01	2.00e+00	2.32e+00	1.45e-03	0.00e+00	1.58e+00	0.00e+00	0.00e+00	9.70e-04	0.00e+00
chr7	19302695	19310045	NM_008036	0.5481	-	Fosb	protein_coding	2.68e-10	4.15e-08	-7.11e+00	1.50e+01	7.89e+00	4.47e+00	3.22e-02	6.57e+00	7.24e+00	6.82e+00	1.28e-02	2.04e-02	1.52e-02

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

DEG table for the contrast Benign_vs_BULA

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 Benign_vs_BULA. 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_Benign_vs_BULA	log2_normalized_counts_GSR8_FUBtrachea	log2_normalized_counts_GSR9_mTECsBalbc	rpgm_normalized_counts_GSR8_FUBtrachea	rpgm_normalized_counts_GSR9_mTECsBalbc	log2_normalized_counts_GSR4_BULA1	log2_normalized_counts_GSR5_BULA2	rpgm_normalized_counts_GSR4_BULA1	rpgm_normalized_counts_GSR5_BULA2
chr6	67534058	67535822	NM_020047	0.5422	-	Tacstd2	protein_coding	1.77e-15	2.21e-11	-1.44e+01	1.49e+01	1.34e+01	1.79e+01	6.32e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr18	52516059	52529823	NM_010728	0.4154	-	Lox	protein_coding	1.36e-14	8.53e-11	-1.32e+01	9.05e+00	1.52e+01	3.85e-02	2.78e+00	1.00e+00	1.00e+00	7.27e-05	7.27e-05
chr6	78466268	78468874	NM_011260	0.4212	-	Reg3g	protein_coding	1.40e-13	4.51e-10	-1.56e+01	1.66e+01	0.00e+00	3.87e+01	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr11	4064852	4078990	NM_001029937	0.4932	+	Sec14l3	protein_coding	1.66e-13	4.51e-10	-1.41e+01	1.61e+01	5.36e+00	4.89e+00	2.83e-03	1.58e+00	0.00e+00	1.41e-04	0.00e+00
chr10	117287794	117292868	NM_013590	0.4609	-	Lyz1	protein_coding	1.80e-13	4.51e-10	-1.21e+01	1.45e+01	4.00e+00	4.43e+00	2.96e-03	1.58e+00	1.00e+00	3.94e-04	1.97e-04
chr5	87554649	87569006	NM_016771	0.3344	-	Sult1d1	protein_coding	2.62e-12	5.49e-09	-1.14e+01	1.44e+01	3.70e+00	1.46e+00	8.36e-04	2.81e+00	0.00e+00	4.18e-04	0.00e+00
chr11	99477972	99493110	NM_033373	0.4545	-	Krt23	protein_coding	4.03e-12	7.23e-09	-1.18e+01	1.51e+01	1.06e+01	2.27e+00	1.05e-01	3.17e+00	0.00e+00	5.28e-04	0.00e+00
chr8	125995101	126030688	NM_008430	0.4745	+	Kcnk1	protein_coding	5.46e-12	7.86e-09	-1.07e+01	1.40e+01	1.29e+01	4.67e-01	2.10e-01	2.58e+00	3.17e+00	1.41e-04	2.25e-04
chr2	154190817	154220343	NM_001012392	0.5061	+	Bpifb1	protein_coding	5.64e-12	7.86e-09	-1.10e+01	1.40e+01	1.58e+00	5.56e-01	6.77e-05	1.00e+00	2.58e+00	3.39e-05	1.69e-04
chr1	93015454	93082480	NM_001294150	0.5250	-	Kif1a	protein_coding	7.08e-12	8.70e-09	-1.04e+01	7.59e+00	1.41e+01	2.86e-03	2.59e-01	2.00e+00	3.17e+00	4.48e-05	1.19e-04
chr1	193153111	193172035	NM_016851	0.4656	+	Irf6	protein_coding	7.63e-12	8.70e-09	-1.09e+01	1.35e+01	1.24e+01	6.03e-01	2.91e-01	2.32e+00	2.00e+00	2.11e-04	1.59e-04
chr8	93166071	93197804	NM_053200	0.4247	-	Ces1d	protein_coding	1.20e-11	1.25e-08	-1.11e+01	1.52e+01	6.11e+00	1.23e+00	2.14e-03	3.58e+00	2.58e+00	3.47e-04	1.58e-04
chr11	69965420	69967886	NM_016887	0.5849	+	Cldn7	protein_coding	1.79e-11	1.73e-08	-1.04e+01	1.31e+01	1.27e+01	3.45e+00	2.68e+00	1.00e+00	3.17e+00	4.06e-04	3.24e-03
chr11	70344146	70352031	NM_009660	0.5191	-	Alox15	protein_coding	1.96e-11	1.75e-08	-1.12e+01	1.35e+01	1.58e+00	1.45e+00	2.54e-04	0.00e+00	2.00e+00	0.00e+00	3.80e-04
chr13	12790820	12800079	NM_011118	0.3564	-	Prl2c3	protein_coding	2.37e-11	1.91e-08	1.03e+01	3.91e+00	0.00e+00	1.51e-03	0.00e+00	1.26e+01	1.38e+01	6.50e-01	1.53e+00
chr7	25897657	25926624	NM_009999	0.4534	+	Cyp2b10	protein_coding	2.44e-11	1.91e-08	-1.01e+01	1.43e+01	1.18e+01	6.96e-01	1.21e-01	3.00e+00	3.70e+00	2.42e-04	4.14e-04
chr6	128539821	128581606	NM_001001179	0.4176	-	A2ml1	protein_coding	3.39e-11	2.50e-08	-1.10e+01	1.56e+01	5.32e+00	1.16e+00	9.33e-04	3.91e+00	3.17e+00	3.35e-04	1.91e-04
chr10	117277540	117282272	NM_017372	0.4731	-	Lyz2	protein_coding	6.07e-11	4.23e-08	-1.31e+01	1.65e+01	5.17e+00	1.98e+01	7.40e-03	2.58e+00	2.32e+00	1.06e-03	8.45e-04
chr7	26835338	26843264	NM_007812	0.4656	+	Cyp2a5	protein_coding	9.43e-11	6.22e-08	-1.52e+01	1.75e+01	6.02e+00	2.35e+01	8.07e-03	2.00e+00	0.00e+00	3.79e-04	0.00e+00
chr2	119237359	119249513	NM_016907	0.5107	+	Spint1	protein_coding	1.05e-10	6.60e-08	-9.84e+00	1.33e+01	1.27e+01	8.03e-01	5.55e-01	3.00e+00	3.32e+00	5.76e-04	7.40e-04
chr9	44134657	44142726	NM_023061	0.5385	+	Mcam	protein_coding	1.32e-10	7.61e-08	-9.55e+00	6.70e+00	1.52e+01	1.28e-02	4.65e+00	4.09e+00	5.04e+00	1.98e-03	3.97e-03
chr18	20510273	20541310	NM_030596	0.4080	+	Dsg3	protein_coding	1.33e-10	7.61e-08	-1.02e+01	8.37e+00	1.35e+01	1.06e-02	3.63e-01	2.00e+00	2.58e+00	9.67e-05	1.61e-04
chr11	100131758	100135949	NM_008469	0.5253	-	Krt15	protein_coding	1.85e-10	1.01e-07	-1.09e+01	1.60e+01	9.69e+00	1.57e+01	1.97e-01	4.09e+00	4.09e+00	3.82e-03	3.82e-03
chr13	12996123	13005330	NM_031191	0.3544	-	Prl2c2	protein_coding	1.99e-10	1.04e-07	1.10e+01	4.52e+00	0.00e+00	2.39e-03	0.00e+00	1.44e+01	1.47e+01	2.31e+00	2.87e+00
chr4	133600555	133602168	NM_018754	0.5973	-	Sfn	protein_coding	3.31e-10	1.66e-07	-1.20e+01	1.56e+01	1.56e+01	3.10e+01	3.13e+01	4.25e+00	2.58e+00	1.12e-02	3.10e-03
chr9	123172284	123216038	NM_133974	0.4617	-	Cdcp1	protein_coding	3.49e-10	1.69e-07	-9.34e+00	1.30e+01	1.27e+01	1.94e-01	1.48e-01	2.00e+00	4.25e+00	6.86e-05	4.11e-04
chr4	117926159	117929763	NM_009711	0.5825	-	Artn	protein_coding	4.77e-10	2.19e-07	-1.14e+01	1.26e+01	1.06e+01	1.74e+00	4.44e-01	1.00e+00	0.00e+00	2.77e-04	0.00e+00
chr14	41131787	41136373	NM_023134	0.4559	+	Sftpa1	protein_coding	4.98e-10	2.19e-07	-9.46e+00	1.45e+01	2.32e+00	4.90e+00	8.72e-04	4.95e+00	0.00e+00	6.54e-03	0.00e+00
chr11	116330703	116335354	NM_008240	0.5957	-	Foxj1	protein_coding	5.07e-10	2.19e-07	-1.08e+01	1.18e+01	1.23e+01	7.69e-01	1.09e+00	2.00e+00	0.00e+00	6.45e-04	0.00e+00
chr7	27119954	27133660	NM_007817	0.4772	+	Cyp2f2	protein_coding	5.67e-10	2.37e-07	-1.61e+01	1.81e+01	7.30e+00	2.03e+01	1.15e-02	1.58e+00	0.00e+00	1.46e-04	0.00e+00
chr9	99690796	99710063	NM_019815	0.4572	-	Cldn18	protein_coding	7.82e-10	3.16e-07	8.91e+00	2.00e+00	4.09e+00	1.56e-04	8.30e-04	1.27e+01	1.17e+01	3.52e-01	1.71e-01
chr1	40466005	40500854	NM_008365	0.4170	+	Il18r1	protein_coding	8.79e-10	3.44e-07	-9.73e+00	1.19e+01	1.27e+01	1.07e-01	1.85e-01	2.00e+00	3.00e+00	8.61e-05	2.01e-04
chr18	20558073	20604526	NM_007883	0.4243	+	Dsg2	protein_coding	1.37e-09	5.19e-07	-8.74e+00	1.18e+01	1.34e+01	7.92e-02	2.25e-01	3.58e+00	4.39e+00	2.37e-04	4.31e-04
chr4	11331932	11386783	NM_194055	0.4323	-	Esrp1	protein_coding	1.49e-09	5.48e-07	-9.69e+00	1.25e+01	1.23e+01	1.03e-01	9.29e-02	2.81e+00	2.58e+00	1.09e-04	9.12e-05
chr11	100256214	100261029	NM_010663	0.5438	-	Krt17	protein_coding	1.97e-09	7.05e-07	-1.11e+01	1.47e+01	1.63e+01	5.52e+00	1.64e+01	4.81e+00	4.39e+00	5.61e-03	4.15e-03
chr14	102976580	102982637	NM_177715	0.4518	-	Kctd12	protein_coding	3.74e-09	1.30e-06	-8.92e+00	1.35e+01	7.44e+00	1.89e+00	2.86e-02	0.00e+00	4.52e+00	0.00e+00	3.63e-03
chrX	21714899	21742357	NM_020049	0.3494	+	Slc6a14	protein_coding	3.90e-09	1.32e-06	-1.02e+01	1.27e+01	1.05e+01	2.38e-01	5.32e-02	1.58e+00	2.00e+00	7.28e-05	1.09e-04
chr5	147300899	147307249	NM_007673	0.5431	-	Cdx2	protein_coding	4.89e-09	1.61e-06	9.31e+00	3.00e+00	0.00e+00	1.10e-03	0.00e+00	9.81e+00	1.22e+01	1.41e-01	7.55e-01
chr13	27057569	27065203	NM_011164	0.3563	+	Prl	protein_coding	5.17e-09	1.61e-06	8.96e+00	2.32e+00	1.00e+00	5.24e-04	1.31e-04	1.17e+01	7.83e+00	4.27e-01	2.97e-02
chr4	115411623	115439034	NM_172306	0.4002	+	Cyp4a12b	protein_coding	5.19e-09	1.61e-06	-8.50e+00	1.40e+01	8.37e+00	6.08e-01	1.20e-02	4.09e+00	4.91e+00	5.84e-04	1.06e-03
chr15	11839896	11905674	NM_008728	0.4269	-	Npr3	protein_coding	5.27e-09	1.61e-06	-9.24e+00	4.52e+00	1.30e+01	3.34e-04	1.29e-01	3.32e+00	2.00e+00	1.37e-04	4.56e-05
chr1	171518123	171535294	NM_010584	0.4250	-	Itln1	protein_coding	5.58e-09	1.67e-06	-8.49e+00	1.50e+01	3.81e+00	1.95e+00	7.57e-04	5.39e+00	5.67e+00	2.39e-03	2.91e-03
chr15	35296111	35303305	NM_054049	0.5062	+	Osr2	protein_coding	6.51e-09	1.90e-06	-1.03e+01	1.28e+01	9.06e+00	1.00e+00	7.42e-02	2.00e+00	1.00e+00	4.17e-04	1.39e-04
chr16	32735885	32782390	NM_080457	0.4847	+	Muc4	protein_coding	6.84e-09	1.95e-06	-8.38e+00	1.41e+01	1.03e+01	3.82e-01	2.63e-02	4.95e+00	4.70e+00	6.45e-04	5.38e-04
chr5	77086985	77095267	NM_001159901	0.4712	-	Hopx	protein_coding	7.34e-09	2.05e-06	-1.10e+01	1.21e+01	1.08e+01	5.28e-01	2.16e-01	0.00e+00	1.00e+00	0.00e+00	1.21e-04
chr4	116161880	116167598	NM_133681	0.4803	-	Tspan1	protein_coding	8.28e-09	2.26e-06	-8.54e+00	1.35e+01	1.13e+01	2.10e+00	4.49e-01	4.39e+00	4.17e+00	3.50e-03	2.97e-03
chr2	163547154	163572907	NM_008261	0.4997	+	Hnf4a	protein_coding	9.46e-09	2.53e-06	7.77e+00	4.52e+00	2.00e+00	8.54e-04	1.16e-04	1.18e+01	1.13e+01	1.34e-01	9.49e-02
chr9	31088589	31131799	NM_011176	0.4919	-	St14	protein_coding	1.19e-08	3.11e-06	-7.89e+00	1.34e+01	1.30e+01	2.48e-01	1.91e-01	4.64e+00	5.78e+00	5.55e-04	1.25e-03
chr5	134945123	134946934	NM_009903	0.5684	-	Cldn4	protein_coding	1.37e-08	3.50e-06	-1.47e+01	1.78e+01	1.65e+01	1.27e+02	5.15e+01	2.58e+00	2.58e+00	2.76e-03	2.76e-03
chr16	56477845	56690135	NM_001014424	0.3922	+	Abi3bp	protein_coding	1.54e-08	3.86e-06	-8.30e+00	1.39e+01	4.64e+00	7.29e-02	1.13e-04	4.75e+00	4.46e+00	1.22e-04	9.89e-05
chr12	85473900	85477270	NM_010234	0.5307	+	Fos	protein_coding	1.87e-08	4.51e-06	-8.03e+00	1.50e+01	7.72e+00	9.61e+00	6.23e-02	5.64e+00	6.23e+00	1.45e-02	2.20e-02
chr14	56082163	56085197	NM_008572	0.4560	-	Mcpt8	protein_coding	1.87e-08	4.51e-06	8.63e+00	2.32e+00	3.17e+00	1.32e-03	2.64e-03	1.19e+01	1.08e+01	1.26e+00	5.71e-01
chr15	101918534	101924735	NM_008475	0.5123	-	Krt4	protein_coding	1.92e-08	4.51e-06	-9.66e+00	1.29e+01	8.22e+00	1.26e+00	4.79e-02	2.32e+00	2.32e+00	6.45e-04	6.45e-04
chr1	130826683	130852249	NM_011082	0.4499	+	Pigr	protein_coding	1.94e-08	4.51e-06	-8.13e+00	1.46e+01	9.61e+00	9.85e-01	3.04e-02	6.29e+00	3.91e+00	3.01e-03	5.48e-04
chr15	101707069	101712891	NM_027011	0.5216	-	Krt5	protein_coding	2.12e-08	4.83e-06	-8.78e+00	1.58e+01	1.23e+01	9.71e+00	8.40e-01	5.98e+00	6.27e+00	1.06e-02	1.31e-02
chr4	11758156	11817905	NM_019753	0.4190	+	Cdh17	protein_coding	2.40e-08	5.38e-06	7.72e+00	5.88e+00	4.00e+00	9.71e-04	2.51e-04	1.13e+01	1.37e+01	4.30e-02	2.21e-01
chr1	58113135	58200452	NM_023617	0.4373	+	Aox3	protein_coding	2.75e-08	6.05e-06	-8.72e+00	1.33e+01	3.81e+00	1.16e-01	1.49e-04	3.17e+00	3.91e+00	9.16e-05	1.60e-04
chr19	9083641	9087956	NM_011681	0.4627	-	Scgb1a1	protein_coding	3.17e-08	6.85e-06	-1.30e+01	1.79e+01	7.12e+00	5.50e+01	3.20e-02	4.81e+00	1.00e+00	6.26e-03	2.32e-04
chr5	123863569	123865516	NM_030701	0.5077	-	Hcar2	protein_coding	4.00e-08	8.51e-06	-8.95e+00	1.31e+01	6.48e+00	4.52e+00	4.52e-02	3.32e+00	3.00e+00	4.62e-03	3.60e-03
chr13	71958231	71963723	NM_010573	0.5376	-	Irx1	protein_coding	4.11e-08	8.59e-06	-1.06e+01	1.21e+01	1.07e+01	7.98e-01	3.06e-01	1.00e+00	1.00e+00	1.82e-04	1.82e-04
chr15	74669080	74672570	NM_001276684	0.5878	-	Arc	protein_coding	4.27e-08	8.78e-06	-8.48e+00	1.32e+01	6.07e+00	2.75e+00	1.89e-02	2.58e+00	4.39e+00	1.43e-03	5.73e-03
chr1	162875046	162898712	NM_018881	0.4155	-	Fmo2	protein_coding	5.57e-08	1.13e-05	-8.51e+00	1.31e+01	8.14e+00	3.59e-01	1.19e-02	1.58e+00	4.39e+00	8.45e-05	8.45e-04
chr7	99345374	99353239	NM_009825	0.5374	-	Serpinh1	protein_coding	5.76e-08	1.15e-05	-7.85e+00	1.09e+01	1.31e+01	2.42e-01	1.14e+00	5.21e+00	3.32e+00	4.58e-03	1.14e-03
chr17	87635978	87651127	NM_008532	0.4631	+	Epcam	protein_coding	6.18e-08	1.21e-05	-7.36e+00	1.32e+01	1.26e+01	6.37e-01	4.06e-01	4.09e+00	6.30e+00	1.06e-03	5.15e-03
chr18	60803848	60812652	NM_001042605	0.5079	+	Cd74	protein_coding	6.31e-08	1.22e-05	-9.17e+00	1.26e+01	9.40e+00	7.08e-01	7.67e-02	3.46e+00	0.00e+00	1.14e-03	0.00e+00
chr2	58129138	58160122	NM_139200	0.3998	-	Cytip	protein_coding	6.52e-08	1.24e-05	-8.96e+00	9.68e+00	1.27e+01	2.65e-02	2.14e-01	2.58e+00	3.17e+00	1.61e-04	2.58e-04
chr11	49663594	49665118	NM_054037	0.5200	+	Scgb3a1	protein_coding	6.68e-08	1.25e-05	-1.19e+01	1.77e+01	6.34e+00	1.36e+02	5.25e-02	5.61e+00	2.00e+00	3.15e-02	1.97e-03
chr7	19302695	19310045	NM_008036	0.5481	-	Fosb	protein_coding	7.10e-08	1.29e-05	-7.74e+00	1.50e+01	7.89e+00	4.47e+00	3.22e-02	6.58e+00	5.88e+00	1.29e-02	7.89e-03

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

DEG table for the contrast Benign_vs_BELA

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 Benign_vs_BELA. 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_Benign_vs_BELA	log2_normalized_counts_GSR8_FUBtrachea	log2_normalized_counts_GSR9_mTECsBalbc	rpgm_normalized_counts_GSR8_FUBtrachea	rpgm_normalized_counts_GSR9_mTECsBalbc	log2_normalized_counts_GSR6_BELA1	log2_normalized_counts_GSR7_BELA2	rpgm_normalized_counts_GSR6_BELA1	rpgm_normalized_counts_GSR7_BELA2
chr6	67534058	67535822	NM_020047	0.5422	-	Tacstd2	protein_coding	8.52e-16	1.07e-11	-1.44e+01	1.49e+01	1.34e+01	1.79e+01	6.32e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr8	93166071	93197804	NM_053200	0.4247	-	Ces1d	protein_coding	1.56e-14	5.70e-11	-1.33e+01	1.52e+01	6.11e+00	1.23e+00	2.14e-03	1.00e+00	1.00e+00	3.15e-05	3.15e-05
chr11	99477972	99493110	NM_033373	0.4545	-	Krt23	protein_coding	1.62e-14	5.70e-11	-1.35e+01	1.51e+01	1.06e+01	2.27e+00	1.05e-01	0.00e+00	1.00e+00	0.00e+00	6.61e-05
chr5	87554649	87569006	NM_016771	0.3344	-	Sult1d1	protein_coding	1.82e-14	5.70e-11	-1.28e+01	1.44e+01	3.70e+00	1.46e+00	8.36e-04	1.00e+00	0.00e+00	6.97e-05	0.00e+00
chr11	4064852	4078990	NM_001029937	0.4932	+	Sec14l3	protein_coding	3.06e-14	7.67e-11	-1.45e+01	1.61e+01	5.36e+00	4.89e+00	2.83e-03	1.00e+00	0.00e+00	7.07e-05	0.00e+00
chr6	78466268	78468874	NM_011260	0.4212	-	Reg3g	protein_coding	7.22e-14	1.51e-10	-1.56e+01	1.66e+01	0.00e+00	3.87e+01	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr6	128539821	128581606	NM_001001179	0.4176	-	A2ml1	protein_coding	1.83e-13	3.27e-10	-1.28e+01	1.56e+01	5.32e+00	1.16e+00	9.33e-04	1.58e+00	2.00e+00	4.79e-05	7.18e-05
chr4	115411623	115439034	NM_172306	0.4002	+	Cyp4a12b	protein_coding	2.57e-13	4.03e-10	-1.21e+01	1.40e+01	8.37e+00	6.08e-01	1.20e-02	0.00e+00	1.58e+00	0.00e+00	7.30e-05
chr8	125995101	126030688	NM_008430	0.4745	+	Kcnk1	protein_coding	5.08e-13	6.40e-10	-1.12e+01	1.40e+01	1.29e+01	4.67e-01	2.10e-01	2.58e+00	2.00e+00	1.41e-04	8.43e-05
chr12	76792334	76795554	NM_030677	0.4806	-	Gpx2	protein_coding	5.10e-13	6.40e-10	-1.21e+01	1.30e+01	1.24e+01	2.48e+00	1.68e+00	0.00e+00	1.00e+00	0.00e+00	3.11e-04
chr18	52516059	52529823	NM_010728	0.4154	-	Lox	protein_coding	6.60e-13	7.53e-10	-1.14e+01	9.05e+00	1.52e+01	3.85e-02	2.78e+00	3.00e+00	2.58e+00	5.09e-04	3.63e-04
chr2	154190817	154220343	NM_001012392	0.5061	+	Bpifb1	protein_coding	8.56e-13	8.94e-10	-1.17e+01	1.40e+01	1.58e+00	5.56e-01	6.77e-05	1.58e+00	1.00e+00	6.77e-05	3.39e-05
chr2	119237359	119249513	NM_016907	0.5107	+	Spint1	protein_coding	1.90e-12	1.83e-09	-1.12e+01	1.33e+01	1.27e+01	8.03e-01	5.55e-01	1.58e+00	2.00e+00	1.65e-04	2.47e-04
chr1	171518123	171535294	NM_010584	0.4250	-	Itln1	protein_coding	2.09e-12	1.87e-09	-1.11e+01	1.50e+01	3.81e+00	1.95e+00	7.57e-04	3.00e+00	2.81e+00	4.08e-04	3.49e-04
chr1	93015454	93082480	NM_001294150	0.5250	-	Kif1a	protein_coding	2.84e-12	2.37e-09	-1.06e+01	7.59e+00	1.41e+01	2.86e-03	2.59e-01	2.81e+00	2.00e+00	8.95e-05	4.48e-05
chr11	100256214	100261029	NM_010663	0.5438	-	Krt17	protein_coding	3.37e-12	2.64e-09	-1.37e+01	1.47e+01	1.63e+01	5.52e+00	1.64e+01	2.58e+00	1.00e+00	1.04e-03	2.08e-04
chr11	69965420	69967886	NM_016887	0.5849	+	Cldn7	protein_coding	3.96e-12	2.92e-09	-1.11e+01	1.31e+01	1.27e+01	3.45e+00	2.68e+00	1.00e+00	2.32e+00	4.06e-04	1.62e-03
chr4	43513725	43523554	NM_001277876	0.5208	-	Tpm2	protein_coding	6.43e-12	4.45e-09	-1.02e+01	8.67e+00	1.40e+01	4.14e-02	1.67e+00	3.46e+00	1.58e+00	1.02e-03	2.03e-04
chr15	101707069	101712891	NM_027011	0.5216	-	Krt5	protein_coding	6.74e-12	4.45e-09	-1.18e+01	1.58e+01	1.23e+01	9.71e+00	8.40e-01	3.00e+00	3.17e+00	1.20e-03	1.37e-03
chr11	100131758	100135949	NM_008469	0.5253	-	Krt15	protein_coding	7.49e-12	4.70e-09	-1.21e+01	1.60e+01	9.69e+00	1.57e+01	1.97e-01	2.58e+00	3.17e+00	1.19e-03	1.91e-03
chr7	25897657	25926624	NM_009999	0.4534	+	Cyp2b10	protein_coding	1.42e-11	8.46e-09	-1.03e+01	1.43e+01	1.18e+01	6.96e-01	1.21e-01	3.17e+00	3.32e+00	2.76e-04	3.11e-04
chr11	49663594	49665118	NM_054037	0.5200	+	Scgb3a1	protein_coding	2.53e-11	1.44e-08	-1.61e+01	1.77e+01	6.34e+00	1.36e+02	5.25e-02	1.00e+00	0.00e+00	6.56e-04	0.00e+00
chr4	116161880	116167598	NM_133681	0.4803	-	Tspan1	protein_coding	2.91e-11	1.58e-08	-1.04e+01	1.35e+01	1.13e+01	2.10e+00	4.49e-01	3.17e+00	1.00e+00	1.40e-03	1.75e-04
chr11	70344146	70352031	NM_009660	0.5191	-	Alox15	protein_coding	5.05e-11	2.61e-08	-1.07e+01	1.35e+01	1.58e+00	1.45e+00	2.54e-04	2.58e+00	0.00e+00	6.34e-04	0.00e+00
chr5	134986213	134987476	NM_009902	0.6274	+	Cldn3	protein_coding	5.39e-11	2.61e-08	-9.86e+00	1.32e+01	1.30e+01	7.58e+00	6.36e+00	0.00e+00	4.17e+00	0.00e+00	1.35e-02
chr18	20510273	20541310	NM_030596	0.4080	+	Dsg3	protein_coding	5.42e-11	2.61e-08	-1.03e+01	8.37e+00	1.35e+01	1.06e-02	3.63e-01	2.81e+00	1.00e+00	1.93e-04	3.22e-05
chr16	32735885	32782390	NM_080457	0.4847	+	Muc4	protein_coding	7.85e-11	3.65e-08	-9.76e+00	1.41e+01	1.03e+01	3.82e-01	2.63e-02	3.46e+00	3.46e+00	2.15e-04	2.15e-04
chr7	26835338	26843264	NM_007812	0.4656	+	Cyp2a5	protein_coding	8.95e-11	4.01e-08	-1.49e+01	1.75e+01	6.02e+00	2.35e+01	8.07e-03	1.00e+00	2.00e+00	1.26e-04	3.79e-04
chr14	41131787	41136373	NM_023134	0.4559	+	Sftpa1	protein_coding	1.65e-10	7.15e-08	-9.25e+00	1.45e+01	2.32e+00	4.90e+00	8.72e-04	5.17e+00	0.00e+00	7.63e-03	0.00e+00
chr9	123172284	123216038	NM_133974	0.4617	-	Cdcp1	protein_coding	1.79e-10	7.47e-08	-9.62e+00	1.30e+01	1.27e+01	1.94e-01	1.48e-01	2.81e+00	3.58e+00	1.37e-04	2.51e-04
chr4	130253496	130275586	NM_172702	0.5132	-	Serinc2	protein_coding	2.28e-10	9.24e-08	-9.52e+00	1.18e+01	1.31e+01	1.60e-01	4.03e-01	3.46e+00	2.58e+00	4.53e-04	2.26e-04
chr19	47646340	47692042	NM_001290825	0.5136	-	Col17a1	protein_coding	2.51e-10	9.86e-08	-1.07e+01	1.29e+01	1.09e+01	1.70e-01	4.10e-02	1.58e+00	1.58e+00	4.38e-05	4.38e-05
chr19	34240335	34255373	NM_007392	0.4424	-	Acta2	protein_coding	5.16e-10	1.96e-07	-8.78e+00	8.43e+00	1.40e+01	2.29e-02	1.12e+00	3.91e+00	4.58e+00	9.31e-04	1.53e-03
chr11	120729484	120732021	NM_007621	0.5623	-	Cbr2	protein_coding	5.49e-10	2.02e-07	-1.46e+01	1.78e+01	1.08e+01	8.89e+01	6.81e-01	2.81e+00	1.00e+00	2.36e-03	3.94e-04
chrX	21714899	21742357	NM_020049	0.3494	+	Slc6a14	protein_coding	6.72e-10	2.35e-07	-1.10e+01	1.27e+01	1.05e+01	2.38e-01	5.32e-02	1.58e+00	0.00e+00	7.28e-05	0.00e+00
chr9	31088589	31131799	NM_011176	0.4919	-	St14	protein_coding	6.75e-10	2.35e-07	-8.82e+00	1.34e+01	1.30e+01	2.48e-01	1.91e-01	4.70e+00	4.00e+00	5.79e-04	3.47e-04
chr7	131032075	131121628	NM_007769	0.4550	+	Dmbt1	protein_coding	7.04e-10	2.39e-07	-8.75e+00	1.44e+01	2.32e+00	2.46e-01	4.47e-05	5.55e+00	2.00e+00	5.14e-04	3.35e-05
chr5	135887918	135889563	NM_013560	0.5960	+	Hspb1	protein_coding	7.59e-10	2.44e-07	-9.85e+00	1.15e+01	1.26e+01	1.77e+00	3.83e+00	0.00e+00	3.17e+00	0.00e+00	4.86e-03
chr1	130826683	130852249	NM_011082	0.4499	+	Pigr	protein_coding	7.76e-10	2.44e-07	-8.99e+00	1.46e+01	9.61e+00	9.85e-01	3.04e-02	3.58e+00	5.29e+00	4.30e-04	1.49e-03
chr4	117926159	117929763	NM_009711	0.5825	-	Artn	protein_coding	7.98e-10	2.44e-07	-1.09e+01	1.26e+01	1.06e+01	1.74e+00	4.44e-01	1.58e+00	0.00e+00	5.55e-04	0.00e+00
chr1	40466005	40500854	NM_008365	0.4170	+	Il18r1	protein_coding	7.99e-10	2.44e-07	-9.73e+00	1.19e+01	1.27e+01	1.07e-01	1.85e-01	3.00e+00	2.00e+00	2.01e-04	8.61e-05
chr12	85473900	85477270	NM_010234	0.5307	+	Fos	protein_coding	8.43e-10	2.49e-07	-9.11e+00	1.50e+01	7.72e+00	9.61e+00	6.23e-02	4.09e+00	5.39e+00	4.75e-03	1.22e-02
chr4	133600555	133602168	NM_018754	0.5973	-	Sfn	protein_coding	8.69e-10	2.49e-07	-1.12e+01	1.56e+01	1.56e+01	3.10e+01	3.13e+01	3.91e+00	4.81e+00	8.68e-03	1.67e-02
chr7	25461701	25477625	NM_001039185	0.4612	-	Ceacam1	protein_coding	8.73e-10	2.49e-07	-9.14e+00	1.51e+01	1.05e+01	2.21e+00	9.05e-02	5.43e+00	4.46e+00	2.64e-03	1.32e-03
chr7	27119954	27133660	NM_007817	0.4772	+	Cyp2f2	protein_coding	9.08e-10	2.53e-07	-1.55e+01	1.81e+01	7.30e+00	2.03e+01	1.15e-02	1.00e+00	2.00e+00	7.30e-05	2.19e-04
chr19	9083641	9087956	NM_011681	0.4627	-	Scgb1a1	protein_coding	9.32e-10	2.54e-07	-1.47e+01	1.79e+01	7.12e+00	5.50e+01	3.20e-02	1.58e+00	2.58e+00	4.63e-04	1.16e-03
chr17	87635978	87651127	NM_008532	0.4631	+	Epcam	protein_coding	1.00e-09	2.67e-07	-8.90e+00	1.32e+01	1.26e+01	6.37e-01	4.06e-01	4.09e+00	4.00e+00	1.06e-03	9.90e-04
chr11	28853204	28926743	NM_146015	0.3874	+	Efemp1	protein_coding	1.12e-09	2.92e-07	-8.61e+00	1.18e+01	1.16e+01	4.84e-02	4.17e-02	3.91e+00	1.00e+00	1.90e-04	1.36e-05
chr15	54571365	54602846	NM_178920	0.4201	+	Mal2	protein_coding	1.20e-09	3.07e-07	-8.70e+00	1.32e+01	1.42e+01	3.06e-01	6.16e-01	2.81e+00	5.98e+00	1.91e-04	1.97e-03
chr15	101918534	101924735	NM_008475	0.5123	-	Krt4	protein_coding	1.31e-09	3.30e-07	-1.04e+01	1.29e+01	8.22e+00	1.26e+00	4.79e-02	2.32e+00	0.00e+00	6.45e-04	0.00e+00
chr15	101412402	101427806	NM_033073	0.5105	+	Krt7	protein_coding	1.35e-09	3.32e-07	-1.21e+01	1.51e+01	1.63e+01	2.28e+00	5.25e+00	3.58e+00	3.91e+00	7.14e-04	9.09e-04
chr11	16752202	16913907	NM_207655	0.4202	+	Egfr	protein_coding	1.64e-09	3.96e-07	-8.38e+00	1.24e+01	1.33e+01	3.30e-02	6.18e-02	4.81e+00	4.17e+00	1.67e-04	1.05e-04
chr1	45374330	45503282	NM_007737	0.3619	-	Col5a2	protein_coding	2.07e-09	4.90e-07	-8.24e+00	8.96e+00	1.36e+01	3.85e-03	9.91e-02	4.64e+00	4.25e+00	1.86e-04	1.40e-04
chr4	154278469	154299895	NM_001112744	0.5439	-	Arhgef16	protein_coding	2.50e-09	5.80e-07	-1.09e+01	1.04e+01	1.21e+01	6.31e-02	1.99e-01	1.00e+00	0.00e+00	4.67e-05	0.00e+00
chr13	71958231	71963723	NM_010573	0.5376	-	Irx1	protein_coding	3.17e-09	7.24e-07	-1.16e+01	1.21e+01	1.07e+01	7.98e-01	3.06e-01	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr1	193153111	193172035	NM_016851	0.4656	+	Irf6	protein_coding	3.49e-09	7.82e-07	-8.46e+00	1.35e+01	1.24e+01	6.03e-01	2.91e-01	4.09e+00	4.95e+00	8.45e-04	1.59e-03
chr7	19302695	19310045	NM_008036	0.5481	-	Fosb	protein_coding	3.61e-09	7.94e-07	-8.60e+00	1.50e+01	7.89e+00	4.47e+00	3.22e-02	5.13e+00	5.64e+00	4.63e-03	6.67e-03
chr9	111019291	111042766	NM_008522	0.4661	+	Ltf	protein_coding	3.80e-09	8.22e-07	-1.37e+01	1.79e+01	9.98e+00	1.02e+01	4.31e-02	3.58e+00	2.58e+00	4.69e-04	2.13e-04
chr15	35296111	35303305	NM_054049	0.5062	+	Osr2	protein_coding	4.32e-09	9.18e-07	-1.01e+01	1.28e+01	9.06e+00	1.00e+00	7.42e-02	2.32e+00	1.00e+00	5.56e-04	1.39e-04
chr1	58113135	58200452	NM_023617	0.4373	+	Aox3	protein_coding	4.74e-09	9.90e-07	-9.22e+00	1.33e+01	3.81e+00	1.16e-01	1.49e-04	3.46e+00	2.58e+00	1.15e-04	5.73e-05
chr5	134945123	134946934	NM_009903	0.5684	-	Cldn4	protein_coding	4.86e-09	1.00e-06	-1.51e+01	1.78e+01	1.65e+01	1.27e+02	5.15e+01	0.00e+00	3.00e+00	0.00e+00	3.87e-03
chr11	100203161	100207510	NM_016958	0.5262	-	Krt14	protein_coding	8.89e-09	1.80e-06	-7.70e+00	1.36e+01	1.38e+01	2.84e+00	3.27e+00	6.93e+00	2.58e+00	2.78e-02	1.15e-03
chr2	103263432	103303196	NM_007914	0.4435	-	Ehf	protein_coding	9.27e-09	1.85e-06	-8.58e+00	1.31e+01	9.83e+00	2.28e-01	2.28e-02	4.58e+00	1.00e+00	5.78e-04	2.51e-05
chr16	38773183	38800203	NM_178924	0.4395	-	Upk1b	protein_coding	1.08e-08	2.09e-06	-1.10e+01	1.17e+01	1.14e+01	1.19e-01	1.02e-01	1.00e+00	0.00e+00	3.70e-05	0.00e+00
chr1	135818597	135833341	NM_133664	0.5322	+	Lad1	protein_coding	1.09e-08	2.09e-06	-7.72e+00	1.23e+01	1.31e+01	3.41e-01	6.01e-01	5.32e+00	4.70e+00	2.65e-03	1.70e-03
chr13	27057569	27065203	NM_011164	0.3563	+	Prl	protein_coding	1.13e-08	2.15e-06	8.97e+00	2.32e+00	1.00e+00	5.24e-04	1.31e-04	1.09e+01	1.06e+01	2.56e-01	2.02e-01
chr16	56477845	56690135	NM_001014424	0.3922	+	Abi3bp	protein_coding	1.23e-08	2.26e-06	-8.14e+00	1.39e+01	4.64e+00	7.29e-02	1.13e-04	5.25e+00	4.09e+00	1.74e-04	7.54e-05
chr15	74669080	74672570	NM_001276684	0.5878	-	Arc	protein_coding	1.23e-08	2.26e-06	-8.84e+00	1.32e+01	6.07e+00	2.75e+00	1.89e-02	4.09e+00	2.00e+00	4.58e-03	8.60e-04

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

DEG table for the contrast Benign_vs_CULA

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 Benign_vs_CULA. 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_Benign_vs_CULA	log2_normalized_counts_GSR8_FUBtrachea	log2_normalized_counts_GSR9_mTECsBalbc	rpgm_normalized_counts_GSR8_FUBtrachea	rpgm_normalized_counts_GSR9_mTECsBalbc	log2_normalized_counts_GSR10_P53FllFl	rpgm_normalized_counts_GSR10_P53FllFl
chr13	12790820	12800079	NM_011118	0.3564	-	Prl2c3	protein_coding	1.50e-11	1.89e-07	1.07e+01	3.91e+00	0.00e+00	1.51e-03	0.00e+00	1.37e+01	1.45e+00
chr13	36725621	36734477	NM_153529	0.4832	-	Nrn1	protein_coding	7.11e-10	4.46e-06	9.71e+00	4.25e+00	2.32e+00	2.03e-03	4.52e-04	1.33e+01	1.14e+00
chrX	139800807	139811388	NM_016675	0.4605	+	Cldn2	protein_coding	1.11e-09	4.66e-06	9.28e+00	5.75e+00	1.00e+00	5.01e-03	9.45e-05	1.41e+01	1.65e+00
chr2	163547154	163572907	NM_008261	0.4997	+	Hnf4a	protein_coding	1.63e-09	5.11e-06	8.87e+00	4.52e+00	2.00e+00	8.54e-04	1.16e-04	1.26e+01	2.44e-01
chr6	130043744	130067258	NM_053153	0.3671	-	Klra18	protein_coding	2.20e-09	5.51e-06	9.60e+00	1.58e+00	0.00e+00	8.51e-05	0.00e+00	1.06e+01	6.62e-02
chr13	12996123	13005330	NM_031191	0.3544	-	Prl2c2	protein_coding	4.00e-09	7.53e-06	1.00e+01	4.52e+00	0.00e+00	2.39e-03	0.00e+00	1.36e+01	1.33e+00
chr12	7977676	8016839	NM_009693	0.4253	+	Apob	protein_coding	4.20e-09	7.53e-06	8.99e+00	7.43e+00	2.81e+00	4.37e-03	1.53e-04	1.55e+01	1.16e+00
chr5	147300899	147307249	NM_007673	0.5431	-	Cdx2	protein_coding	1.38e-08	2.17e-05	9.00e+00	3.00e+00	0.00e+00	1.10e-03	0.00e+00	1.12e+01	3.63e-01
chr6	86078047	86124409	NM_001271859	0.4909	+	Add2	protein_coding	2.38e-08	3.31e-05	8.15e+00	3.17e+00	4.39e+00	1.73e-04	4.31e-04	1.21e+01	9.18e-02
chr13	27057569	27065203	NM_011164	0.3563	+	Prl	protein_coding	3.42e-08	4.29e-05	8.86e+00	2.32e+00	1.00e+00	5.24e-04	1.31e-04	1.07e+01	2.13e-01
chr1	171518123	171535294	NM_010584	0.4250	-	Itln1	protein_coding	3.81e-08	4.34e-05	-1.40e+01	1.50e+01	3.81e+00	1.95e+00	7.57e-04	0.00e+00	0.00e+00
chr10	117287794	117292868	NM_013590	0.4609	-	Lyz1	protein_coding	6.12e-08	5.97e-05	-1.35e+01	1.45e+01	4.00e+00	4.43e+00	2.96e-03	0.00e+00	0.00e+00
chr14	41131787	41136373	NM_023134	0.4559	+	Sftpa1	protein_coding	6.19e-08	5.97e-05	-1.35e+01	1.45e+01	2.32e+00	4.90e+00	8.72e-04	0.00e+00	0.00e+00
chr18	64340363	64398488	NM_194268	0.4448	+	Onecut2	protein_coding	6.96e-08	6.21e-05	7.78e+00	5.49e+00	4.81e+00	7.57e-04	4.65e-04	1.30e+01	1.38e-01
chr4	11758156	11817905	NM_019753	0.4190	+	Cdh17	protein_coding	7.42e-08	6.21e-05	7.46e+00	5.88e+00	4.00e+00	9.71e-04	2.51e-04	1.27e+01	1.10e-01
chr16	32735885	32782390	NM_080457	0.4847	+	Muc4	protein_coding	1.01e-07	7.18e-05	-1.32e+01	1.41e+01	1.03e+01	3.82e-01	2.63e-02	0.00e+00	0.00e+00
chr11	4064852	4078990	NM_001029937	0.4932	+	Sec14l3	protein_coding	1.02e-07	7.18e-05	-1.51e+01	1.61e+01	5.36e+00	4.89e+00	2.83e-03	0.00e+00	0.00e+00
chr5	116408490	116422864	NM_030704	0.4917	-	Hspb8	protein_coding	1.08e-07	7.18e-05	-1.33e+01	1.39e+01	1.25e+01	1.04e+00	3.92e-01	0.00e+00	0.00e+00
chr1	173962568	173982844	NM_008327	0.3711	-	Ifi202b	protein_coding	1.11e-07	7.18e-05	-1.32e+01	1.37e+01	1.27e+01	6.34e-01	3.25e-01	0.00e+00	0.00e+00
chr6	67534058	67535822	NM_020047	0.5422	-	Tacstd2	protein_coding	1.15e-07	7.18e-05	-1.34e+01	1.49e+01	1.34e+01	1.79e+01	6.32e+00	1.00e+00	5.67e-04
chr7	25897657	25926624	NM_009999	0.4534	+	Cyp2b10	protein_coding	1.62e-07	9.67e-05	-1.25e+01	1.43e+01	1.18e+01	6.96e-01	1.21e-01	1.00e+00	3.45e-05
chr2	154190817	154220343	NM_001012392	0.5061	+	Bpifb1	protein_coding	1.94e-07	1.11e-04	-1.30e+01	1.40e+01	1.58e+00	5.56e-01	6.77e-05	0.00e+00	0.00e+00
chr15	101707069	101712891	NM_027011	0.5216	-	Krt5	protein_coding	2.13e-07	1.16e-04	-1.39e+01	1.58e+01	1.23e+01	9.71e+00	8.40e-01	1.00e+00	1.72e-04
chr6	106710378	106749037	NM_008370	0.4041	-	Il5ra	protein_coding	2.69e-07	1.41e-04	8.48e+00	3.17e+00	5.25e+00	2.07e-04	9.57e-04	1.30e+01	2.17e-01
chr1	130826683	130852249	NM_011082	0.4499	+	Pigr	protein_coding	3.01e-07	1.51e-04	-1.17e+01	1.46e+01	9.61e+00	9.85e-01	3.04e-02	2.00e+00	1.17e-04
chr6	78466268	78468874	NM_011260	0.4212	-	Reg3g	protein_coding	3.34e-07	1.61e-04	-1.56e+01	1.66e+01	0.00e+00	3.87e+01	0.00e+00	0.00e+00	0.00e+00
chr12	76792334	76795554	NM_030677	0.4806	-	Gpx2	protein_coding	4.38e-07	1.99e-04	-1.27e+01	1.30e+01	1.24e+01	2.48e+00	1.68e+00	0.00e+00	0.00e+00
chr18	52516059	52529823	NM_010728	0.4154	-	Lox	protein_coding	4.78e-07	1.99e-04	-1.17e+01	9.05e+00	1.52e+01	3.85e-02	2.78e+00	2.58e+00	3.63e-04
chr13	31625815	31631406	NM_010225	0.5594	+	Foxf2	protein_coding	4.84e-07	1.99e-04	8.40e+00	1.58e+00	2.32e+00	3.58e-04	7.15e-04	1.04e+01	2.42e-01
chr6	128539821	128581606	NM_001001179	0.4176	-	A2ml1	protein_coding	4.84e-07	1.99e-04	-1.22e+01	1.56e+01	5.32e+00	1.16e+00	9.33e-04	2.32e+00	9.57e-05
chr5	87554649	87569006	NM_016771	0.3344	-	Sult1d1	protein_coding	4.91e-07	1.99e-04	-1.14e+01	1.44e+01	3.70e+00	1.46e+00	8.36e-04	2.00e+00	2.09e-04
chr15	101689927	101694305	NM_008476	0.5099	-	Krt6a	protein_coding	5.60e-07	2.17e-04	-1.19e+01	1.39e+01	8.68e+00	3.48e+00	9.37e-02	1.00e+00	2.28e-04
chr5	98929772	99037079	NM_008926	0.4154	-	Prkg2	protein_coding	5.71e-07	2.17e-04	7.31e+00	6.04e+00	5.36e+00	6.06e-04	3.73e-04	1.31e+01	7.91e-02
chr1	93015454	93082480	NM_001294150	0.5250	-	Kif1a	protein_coding	6.49e-07	2.40e-04	-1.11e+01	7.59e+00	1.41e+01	2.86e-03	2.59e-01	2.00e+00	4.48e-05
chr7	141839071	141873085	NM_028801	0.5298	+	Muc5b	protein_coding	7.33e-07	2.63e-04	-1.25e+01	1.35e+01	2.32e+00	3.39e-01	1.18e-04	0.00e+00	0.00e+00
chr8	125995101	126030688	NM_008430	0.4745	+	Kcnk1	protein_coding	8.04e-07	2.75e-04	-1.10e+01	1.40e+01	1.29e+01	4.67e-01	2.10e-01	2.58e+00	1.41e-04
chr5	43978857	43981799	NM_008009	0.4642	-	Fgfbp1	protein_coding	8.12e-07	2.75e-04	-1.24e+01	1.29e+01	1.18e+01	2.52e+00	1.21e+00	0.00e+00	0.00e+00
chr4	115411623	115439034	NM_172306	0.4002	+	Cyp4a12b	protein_coding	1.04e-06	3.45e-04	-1.11e+01	1.40e+01	8.37e+00	6.08e-01	1.20e-02	2.00e+00	1.09e-04
chr11	70344146	70352031	NM_009660	0.5191	-	Alox15	protein_coding	1.18e-06	3.69e-04	-1.25e+01	1.35e+01	1.58e+00	1.45e+00	2.54e-04	0.00e+00	0.00e+00
chr8	93166071	93197804	NM_053200	0.4247	-	Ces1d	protein_coding	1.21e-06	3.69e-04	-1.11e+01	1.52e+01	6.11e+00	1.23e+00	2.14e-03	3.17e+00	2.52e-04
chr15	84232042	84315609	NM_133167	0.5107	+	Parvb	protein_coding	1.21e-06	3.69e-04	6.54e+00	6.87e+00	5.49e+00	1.39e-03	5.27e-04	1.29e+01	9.00e-02
chr5	90759297	90761625	NM_009141	0.4208	+	Cxcl5	protein_coding	1.75e-06	5.23e-04	7.42e+00	8.09e+00	3.32e+00	1.17e-01	3.87e-03	1.46e+01	1.04e+01
chr5	17781689	17849792	NM_001159556	0.3675	-	Cd36	protein_coding	2.11e-06	6.16e-04	-1.10e+01	1.30e+01	5.39e+00	1.21e-01	6.02e-04	1.00e+00	1.47e-05
chr10	117277540	117282272	NM_017372	0.4731	-	Lyz2	protein_coding	2.17e-06	6.18e-04	-1.35e+01	1.65e+01	5.17e+00	1.98e+01	7.40e-03	2.00e+00	6.34e-04

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

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.