metaseqR report

Summary

Analysis summary

Summary: The gene counts table was normalized for inherent systematic or experimental biases (e.g. sequencing depth, gene length, GC content bias etc.) using the Bioconductor package DESeq after removing genes that had zero counts over all the RNA-Seq samples (774 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 (0 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 0.0281 average reads per 100 bp), iii) genes with read counts below the median read counts of the total normalized count distribution (1904 genes with cutoff value 14 normalized read counts). The total number of genes excluded due to the application of gene filters was 523. The total (unified) number of genes excluded due to the application of all filters was 2678. The resulting gene counts table was subjected to differential expression analysis for the contrasts C versus K, C versus CL4, K versus KL4, CL4 versus KL4 using the Bioconductor package DESeq. The final numbers of differentially expressed genes were (per contrast): for the contrast C versus K, 55 (1) statistically significant genes were found with a p-value (FDR or adjusted p-value) threshold of 0.05 and of these, 29 (1) were up-regulated, 26 (0) 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 C versus CL4, 508 (216) statistically significant genes were found with a p-value (FDR or adjusted p-value) threshold of 0.05 and of these, 323 (162) were up-regulated, 185 (54) 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 K versus KL4, 694 (375) statistically significant genes were found with a p-value (FDR or adjusted p-value) threshold of 0.05 and of these, 398 (258) were up-regulated, 284 (117) were down-regulated and 12 (0) were not differentially expressed according to an absolute fold change cutoff value of 1 in log₂ scale, for the contrast CL4 versus KL4, 15 (0) statistically significant genes were found with a p-value (FDR or adjusted p-value) threshold of 0.05 and of these, 6 were up-regulated, 9 were down-regulated and 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 custom data frame
Conditions: C, K, CL4, KL4
Samples included: DK3R1r-C1, DK3R2r-C2, DK3R4b-K23, DK3R5-K2, DK3R6-K3, DK3R7-C1L4, DK3R8-C2L4, DK3R10b-K23L4, DK3R11-K2L4, DK3R12-K3L4
Samples excluded: none
Requested contrasts: C_vs_K, C_vs_CL4, K_vs_KL4, CL4_vs_KL4
Library sizes: not available
Annotation: embedded
Organism:
Annotation source: Ensembl genomes
Count type: utr
Exon filters: none applied
Gene filters: length, avg.reads, expression, biotype

length
- length: 500

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

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

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

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

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

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

method: blind
sharingMode: fit-only
fitType: local

Meta-analysis method: no meta-analysis, reported p-values from the first supplied statistical algorithm
Multiple testing correction: Benjamini-Hochberg FDR
p-value threshold: 0.05
Logarithmic tranformation offset: 1
Analysis preset: not available
Quality control plots: multidimensional scaling, biotype detection, biotype counts, sample and biotype saturation, filtered biotypes, correlation heatmap and correlogram, pairwise scatterplots between samples, boxplots, GC-content bias, transcript length bias, DEG biotype detection, volcano plot, DEG heatmap
Figure format: png, pdf
Output directory: /data/images/proton/run343/www/metaseqr_quantseq_run343c
Output data: Annotation, p-value, Adjusted p-value (FDR), Fold change, Statistics, Read counts
Output scale(s): log2 scale, Reads per Gene Model
Output values: Normalized values
Output statistics: Mean
Total run time: 01 minutes 43 seconds

Filtered genes

Number of filtered genes: 2678 which is the union of

Filtered because of zero reads: 774
Filtered because of exon filters: 0
Filtered because of gene filters: 1904 which is the union of
- length: 0 genes with filter cutoff value 500
- avg.reads: 522 genes with filter cutoff value 0.02809586
- expression: 1904 genes further decomposed to (filter name, filtered genes, filter cutoff):
- - median: 1904 genes with filter cutoff value 14
- biotype: 0 genes with filter cutoff value rRNA, IG_V_pseudogene, TR_V_pseudogene

Differentially expressed genes

Number of differentially expressed genes per contrast:

C_vs_K: 55 (1) statistically significant genes of which 29 (1) up regulated, 26 (0) down regulated and 0 (0) not differentially expressed according to a p-value (FDR or adjusted p-value) threshold of 0.05 and an absolute fold change cutoff value of 1 in log₂ scale.
C_vs_CL4: 508 (216) statistically significant genes of which 323 (162) up regulated, 185 (54) 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.
K_vs_KL4: 694 (375) statistically significant genes of which 398 (258) up regulated, 284 (117) down regulated and 12 (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.
CL4_vs_KL4: 15 (0) statistically significant genes of which 6 up regulated, 9 down regulated and 0 not differentially expressed according to a p-value (FDR or adjusted p-value) threshold of 0.05 and an absolute fold change cutoff value of 1 in log₂ scale.

Run log

[ 2017-05-02 16:23:12 ] FATAL Condition names in sample list and contrast list do not match! Check if the contrasts follow the appropriate format (e.g. "_vs_" separating contrasting conditions...
[ 2017-05-02 16:24:10 ] FATAL When org="custom" and count.type is not "gene", an additional gene file must be provided with the gene.file argument!
[ 2017-05-02 16:26:56 ] INFO 2017-05-02 16:26:56: Data processing started...
[ 2017-05-02 16:26:56 ] INFO Read counts file: imported custom data frame
[ 2017-05-02 16:26:56 ] INFO Conditions: C, K, CL4, KL4
[ 2017-05-02 16:26:56 ] INFO Samples to include: DK3R1r-C1, DK3R2r-C2, DK3R4b-K23, DK3R5-K2, DK3R6-K3, DK3R7-C1L4, DK3R8-C2L4, DK3R10b-K23L4, DK3R11-K2L4, DK3R12-K3L4
[ 2017-05-02 16:26:56 ] INFO Samples to exclude: none
[ 2017-05-02 16:26:56 ] INFO Requested contrasts: C_vs_K, C_vs_CL4, K_vs_KL4, CL4_vs_KL4
[ 2017-05-02 16:26:56 ] INFO Annotation: embedded
[ 2017-05-02 16:26:56 ] INFO Organism: custom
[ 2017-05-02 16:26:56 ] INFO Reference source: ensembl
[ 2017-05-02 16:26:56 ] INFO Count type: utr
[ 2017-05-02 16:26:56 ] INFO Exon filters: none applied
[ 2017-05-02 16:26:56 ] INFO Gene filters: length, avg.reads, expression, biotype
[ 2017-05-02 16:26:56 ] INFO length:
[ 2017-05-02 16:26:56 ] INFO length: 500
[ 2017-05-02 16:26:56 ] INFO avg.reads:
[ 2017-05-02 16:26:56 ] INFO average.per.bp: 100
[ 2017-05-02 16:26:56 ] INFO quantile: 0.25
[ 2017-05-02 16:26:56 ] INFO expression:
[ 2017-05-02 16:26:56 ] INFO median: TRUE
[ 2017-05-02 16:26:56 ] INFO mean: FALSE
[ 2017-05-02 16:26:56 ] INFO quantile: NA
[ 2017-05-02 16:26:56 ] INFO known: NA
[ 2017-05-02 16:26:56 ] INFO custom: NA
[ 2017-05-02 16:26:56 ] INFO biotype:
[ 2017-05-02 16:26:56 ] INFO pseudogene: FALSE
[ 2017-05-02 16:26:56 ] INFO snRNA: FALSE
[ 2017-05-02 16:26:56 ] INFO protein_coding: FALSE
[ 2017-05-02 16:26:56 ] INFO antisense: FALSE
[ 2017-05-02 16:26:56 ] INFO miRNA: FALSE
[ 2017-05-02 16:26:56 ] INFO snoRNA: FALSE
[ 2017-05-02 16:26:56 ] INFO lincRNA: FALSE
[ 2017-05-02 16:26:56 ] INFO processed_transcript: FALSE
[ 2017-05-02 16:26:56 ] INFO misc_RNA: FALSE
[ 2017-05-02 16:26:56 ] INFO rRNA: TRUE
[ 2017-05-02 16:26:56 ] INFO sense_intronic: FALSE
[ 2017-05-02 16:26:56 ] INFO sense_overlapping: FALSE
[ 2017-05-02 16:26:56 ] INFO polymorphic_pseudogene: FALSE
[ 2017-05-02 16:26:56 ] INFO IG_C_gene: FALSE
[ 2017-05-02 16:26:56 ] INFO IG_J_gene: FALSE
[ 2017-05-02 16:26:56 ] INFO IG_D_gene: FALSE
[ 2017-05-02 16:26:56 ] INFO IG_LV_gene: FALSE
[ 2017-05-02 16:26:56 ] INFO IG_V_gene: FALSE
[ 2017-05-02 16:26:56 ] INFO IG_V_pseudogene: TRUE
[ 2017-05-02 16:26:56 ] INFO TR_V_gene: FALSE
[ 2017-05-02 16:26:56 ] INFO TR_V_pseudogene: TRUE
[ 2017-05-02 16:26:56 ] INFO three_prime_overlapping_ncrna: FALSE
[ 2017-05-02 16:26:56 ] INFO Filter application: postnorm
[ 2017-05-02 16:26:56 ] INFO Normalization algorithm: deseq
[ 2017-05-02 16:26:56 ] INFO Normalization arguments:
[ 2017-05-02 16:26:56 ] INFO locfunc:
[ 2017-05-02 16:26:56 ] INFO [[
[ 2017-05-02 16:26:56 ] INFO list(locfunc = function (x, na.rm = FALSE) standardGeneric("median"))
[ 2017-05-02 16:26:56 ] INFO locfunc
[ 2017-05-02 16:26:56 ] INFO Statistical algorithm: deseq
[ 2017-05-02 16:26:56 ] INFO Statistical arguments:
[ 2017-05-02 16:26:56 ] INFO deseq: blind, fit-only, local
[ 2017-05-02 16:26:56 ] INFO Meta-analysis method: none
[ 2017-05-02 16:26:56 ] INFO Multiple testing correction: BH
[ 2017-05-02 16:26:56 ] INFO p-value threshold: 0.05
[ 2017-05-02 16:26:56 ] INFO Logarithmic transformation offset: 1
[ 2017-05-02 16:26:56 ] INFO Quality control plots: mds, biodetection, countsbio, saturation, readnoise, filtered, correl, pairwise, boxplot, gcbias, lengthbias, biodist, volcano, deheatmap
[ 2017-05-02 16:26:56 ] INFO Figure format: png, pdf
[ 2017-05-02 16:26:56 ] INFO Output directory: /data/images/proton/run343/www/metaseqr_quantseq_run343c
[ 2017-05-02 16:26:56 ] INFO Output data: annotation, p.value, adj.p.value, fold.change, stats, counts, flags
[ 2017-05-02 16:26:56 ] INFO Output scale(s): log2, rpgm
[ 2017-05-02 16:26:56 ] INFO Output values: normalized
[ 2017-05-02 16:26:56 ] INFO Output statistics: mean
[ 2017-05-02 16:26:56 ] INFO Reading custom external gene annotation for from /data/results/tools/rnaseq/metaseqr/mm10_more_than_2AREs/gene_data_mm10.txt.gz...
[ 2017-05-02 16:26:57 ] INFO Checking chromosomes in transcript counts and gene annotation...
[ 2017-05-02 16:26:57 ] INFO Processing transcripts...
[ 2017-05-02 16:26:57 ] INFO Separating transcripts (UTR regions) per gene for DK3R1r-C1...
[ 2017-05-02 16:27:00 ] INFO Separating transcripts (UTR regions) per gene for DK3R2r-C2...
[ 2017-05-02 16:27:03 ] INFO Separating transcripts (UTR regions) per gene for DK3R4b-K23...
[ 2017-05-02 16:27:07 ] INFO Separating transcripts (UTR regions) per gene for DK3R5-K2...
[ 2017-05-02 16:27:10 ] INFO Separating transcripts (UTR regions) per gene for DK3R6-K3...
[ 2017-05-02 16:27:13 ] INFO Separating transcripts (UTR regions) per gene for DK3R7-C1L4...
[ 2017-05-02 16:27:16 ] INFO Separating transcripts (UTR regions) per gene for DK3R8-C2L4...
[ 2017-05-02 16:27:19 ] INFO Separating transcripts (UTR regions) per gene for DK3R10b-K23L4...
[ 2017-05-02 16:27:23 ] INFO Separating transcripts (UTR regions) per gene for DK3R11-K2L4...
[ 2017-05-02 16:27:27 ] INFO Separating transcripts (UTR regions) per gene for DK3R12-K3L4...
[ 2017-05-02 16:27:30 ] INFO Saving gene model to /data/images/proton/run343/www/metaseqr_quantseq_run343c/data/gene_model.RData
[ 2017-05-02 16:27:31 ] INFO Summarizing count data...
[ 2017-05-02 16:27:33 ] INFO Removing genes with zero counts in all samples...
[ 2017-05-02 16:27:33 ] INFO Normalizing with: deseq
[ 2017-05-02 16:27:33 ] INFO Applying gene filter length...
[ 2017-05-02 16:27:33 ] INFO Threshold below which ignored: 500
[ 2017-05-02 16:27:33 ] INFO Applying gene filter avg.reads...
[ 2017-05-02 16:27:33 ] INFO Threshold below which ignored: 0.0280958570953987
[ 2017-05-02 16:27:33 ] INFO Applying gene filter expression...
[ 2017-05-02 16:27:33 ] INFO Threshold below which ignored: 14
[ 2017-05-02 16:27:33 ] INFO Applying gene filter biotype...
[ 2017-05-02 16:27:33 ] INFO Biotypes ignored: rRNA, IG_V_pseudogene, TR_V_pseudogene
[ 2017-05-02 16:27:33 ] INFO 2678 genes filtered out
[ 2017-05-02 16:27:33 ] INFO 3174 genes remain after filtering
[ 2017-05-02 16:27:33 ] INFO Running statistical tests with: deseq
[ 2017-05-02 16:27:33 ] INFO Contrast: C_vs_K
[ 2017-05-02 16:27:36 ] INFO Contrast: C_vs_CL4
[ 2017-05-02 16:27:37 ] INFO Contrast: K_vs_KL4
[ 2017-05-02 16:27:39 ] INFO Contrast: CL4_vs_KL4
[ 2017-05-02 16:27:41 ] INFO Contrast C_vs_K: found 55 genes
[ 2017-05-02 16:27:41 ] INFO Contrast C_vs_CL4: found 508 genes
[ 2017-05-02 16:27:41 ] INFO Contrast K_vs_KL4: found 694 genes
[ 2017-05-02 16:27:41 ] INFO Contrast CL4_vs_KL4: found 15 genes
[ 2017-05-02 16:27:41 ] INFO Building output files...
[ 2017-05-02 16:27:41 ] INFO Contrast: C_vs_K
[ 2017-05-02 16:27:41 ] INFO Adding non-filtered data...
[ 2017-05-02 16:27:41 ] INFO binding annotation...
[ 2017-05-02 16:27:41 ] INFO binding p-values...
[ 2017-05-02 16:27:41 ] INFO binding FDRs...
[ 2017-05-02 16:27:41 ] INFO binding log2 normalized fold changes...
[ 2017-05-02 16:27:41 ] INFO binding normalized mean counts...
[ 2017-05-02 16:27:41 ] INFO binding normalized mean counts...
[ 2017-05-02 16:27:41 ] INFO binding all normalized counts for C...
[ 2017-05-02 16:27:41 ] INFO binding all normalized counts for K...
[ 2017-05-02 16:27:41 ] INFO binding filtering flags...
[ 2017-05-02 16:27:42 ] INFO Writing output...
[ 2017-05-02 16:27:42 ] INFO Adding filtered data...
[ 2017-05-02 16:27:42 ] INFO binding annotation...
[ 2017-05-02 16:27:42 ] INFO binding p-values...
[ 2017-05-02 16:27:42 ] INFO binding FDRs...
[ 2017-05-02 16:27:42 ] INFO binding log2 normalized fold changes...
[ 2017-05-02 16:27:42 ] INFO binding normalized mean counts...
[ 2017-05-02 16:27:42 ] INFO binding normalized mean counts...
[ 2017-05-02 16:27:42 ] INFO binding all normalized counts for C...
[ 2017-05-02 16:27:42 ] INFO binding all normalized counts for K...
[ 2017-05-02 16:27:42 ] INFO binding filtering flags...
[ 2017-05-02 16:27:42 ] INFO Writing output...
[ 2017-05-02 16:27:42 ] INFO Contrast: C_vs_CL4
[ 2017-05-02 16:27:42 ] INFO Adding non-filtered data...
[ 2017-05-02 16:27:42 ] INFO binding annotation...
[ 2017-05-02 16:27:42 ] INFO binding p-values...
[ 2017-05-02 16:27:42 ] INFO binding FDRs...
[ 2017-05-02 16:27:42 ] INFO binding log2 normalized fold changes...
[ 2017-05-02 16:27:42 ] INFO binding normalized mean counts...
[ 2017-05-02 16:27:42 ] INFO binding normalized mean counts...
[ 2017-05-02 16:27:42 ] INFO binding all normalized counts for C...
[ 2017-05-02 16:27:43 ] INFO binding all normalized counts for CL4...
[ 2017-05-02 16:27:43 ] INFO binding filtering flags...
[ 2017-05-02 16:27:43 ] INFO Writing output...
[ 2017-05-02 16:27:43 ] INFO Adding filtered data...
[ 2017-05-02 16:27:43 ] INFO binding annotation...
[ 2017-05-02 16:27:43 ] INFO binding p-values...
[ 2017-05-02 16:27:43 ] INFO binding FDRs...
[ 2017-05-02 16:27:43 ] INFO binding log2 normalized fold changes...
[ 2017-05-02 16:27:43 ] INFO binding normalized mean counts...
[ 2017-05-02 16:27:43 ] INFO binding normalized mean counts...
[ 2017-05-02 16:27:43 ] INFO binding all normalized counts for C...
[ 2017-05-02 16:27:43 ] INFO binding all normalized counts for CL4...
[ 2017-05-02 16:27:43 ] INFO binding filtering flags...
[ 2017-05-02 16:27:43 ] INFO Writing output...
[ 2017-05-02 16:27:43 ] INFO Contrast: K_vs_KL4
[ 2017-05-02 16:27:43 ] INFO Adding non-filtered data...
[ 2017-05-02 16:27:43 ] INFO binding annotation...
[ 2017-05-02 16:27:43 ] INFO binding p-values...
[ 2017-05-02 16:27:43 ] INFO binding FDRs...
[ 2017-05-02 16:27:43 ] INFO binding log2 normalized fold changes...
[ 2017-05-02 16:27:43 ] INFO binding normalized mean counts...
[ 2017-05-02 16:27:43 ] INFO binding normalized mean counts...
[ 2017-05-02 16:27:44 ] INFO binding all normalized counts for K...
[ 2017-05-02 16:27:44 ] INFO binding all normalized counts for KL4...
[ 2017-05-02 16:27:44 ] INFO binding filtering flags...
[ 2017-05-02 16:27:44 ] INFO Writing output...
[ 2017-05-02 16:27:44 ] INFO Adding filtered data...
[ 2017-05-02 16:27:44 ] INFO binding annotation...
[ 2017-05-02 16:27:44 ] INFO binding p-values...
[ 2017-05-02 16:27:44 ] INFO binding FDRs...
[ 2017-05-02 16:27:44 ] INFO binding log2 normalized fold changes...
[ 2017-05-02 16:27:44 ] INFO binding normalized mean counts...
[ 2017-05-02 16:27:44 ] INFO binding normalized mean counts...
[ 2017-05-02 16:27:44 ] INFO binding all normalized counts for K...
[ 2017-05-02 16:27:44 ] INFO binding all normalized counts for KL4...
[ 2017-05-02 16:27:44 ] INFO binding filtering flags...
[ 2017-05-02 16:27:44 ] INFO Writing output...
[ 2017-05-02 16:27:44 ] INFO Contrast: CL4_vs_KL4
[ 2017-05-02 16:27:44 ] INFO Adding non-filtered data...
[ 2017-05-02 16:27:44 ] INFO binding annotation...
[ 2017-05-02 16:27:45 ] INFO binding p-values...
[ 2017-05-02 16:27:45 ] INFO binding FDRs...
[ 2017-05-02 16:27:45 ] INFO binding log2 normalized fold changes...
[ 2017-05-02 16:27:45 ] INFO binding normalized mean counts...
[ 2017-05-02 16:27:45 ] INFO binding normalized mean counts...
[ 2017-05-02 16:27:45 ] INFO binding all normalized counts for CL4...
[ 2017-05-02 16:27:45 ] INFO binding all normalized counts for KL4...
[ 2017-05-02 16:27:45 ] INFO binding filtering flags...
[ 2017-05-02 16:27:45 ] INFO Writing output...
[ 2017-05-02 16:27:45 ] INFO Adding filtered data...
[ 2017-05-02 16:27:45 ] INFO binding annotation...
[ 2017-05-02 16:27:45 ] INFO binding p-values...
[ 2017-05-02 16:27:45 ] INFO binding FDRs...
[ 2017-05-02 16:27:45 ] INFO binding log2 normalized fold changes...
[ 2017-05-02 16:27:45 ] INFO binding normalized mean counts...
[ 2017-05-02 16:27:45 ] INFO binding normalized mean counts...
[ 2017-05-02 16:27:45 ] INFO binding all normalized counts for CL4...
[ 2017-05-02 16:27:45 ] INFO binding all normalized counts for KL4...
[ 2017-05-02 16:27:45 ] INFO binding filtering flags...
[ 2017-05-02 16:27:45 ] INFO Writing output...
[ 2017-05-02 16:27:46 ] INFO Creating quality control graphs...
[ 2017-05-02 16:27:46 ] INFO Plotting in png format...
[ 2017-05-02 16:27:46 ] INFO Plotting mds...
[ 2017-05-02 16:27:46 ] INFO Plotting biodetection...
[ 2017-05-02 16:27:47 ] INFO Plotting countsbio...
[ 2017-05-02 16:27:47 ] INFO Plotting saturation...
[ 2017-05-02 16:27:50 ] INFO Plotting readnoise...
[ 2017-05-02 16:27:50 ] INFO Plotting correl...
[ 2017-05-02 16:27:50 ] INFO Plotting pairwise...
[ 2017-05-02 16:27:57 ] INFO Plotting boxplot...
[ 2017-05-02 16:27:57 ] INFO Plotting gcbias...
[ 2017-05-02 16:27:58 ] INFO Plotting lengthbias...
[ 2017-05-02 16:27:58 ] INFO Plotting boxplot...
[ 2017-05-02 16:27:58 ] INFO Plotting gcbias...
[ 2017-05-02 16:27:58 ] INFO Plotting lengthbias...
[ 2017-05-02 16:27:58 ] INFO Plotting biodist...
[ 2017-05-02 16:27:58 ] INFO Contrast: C_vs_K
[ 2017-05-02 16:27:58 ] INFO Contrast: C_vs_CL4
[ 2017-05-02 16:27:59 ] INFO Contrast: K_vs_KL4
[ 2017-05-02 16:27:59 ] INFO Contrast: CL4_vs_KL4
[ 2017-05-02 16:27:59 ] INFO Plotting volcano...
[ 2017-05-02 16:27:59 ] INFO Contrast: C_vs_K
[ 2017-05-02 16:27:59 ] INFO Contrast: C_vs_CL4
[ 2017-05-02 16:27:59 ] INFO Contrast: K_vs_KL4
[ 2017-05-02 16:27:59 ] INFO Contrast: CL4_vs_KL4
[ 2017-05-02 16:27:59 ] INFO Plotting deheatmap...
[ 2017-05-02 16:27:59 ] INFO Contrast: C_vs_K
[ 2017-05-02 16:28:04 ] INFO Contrast: C_vs_CL4
[ 2017-05-02 16:28:08 ] INFO Contrast: K_vs_KL4
[ 2017-05-02 16:28:12 ] INFO Contrast: CL4_vs_KL4
[ 2017-05-02 16:28:16 ] INFO Plotting filtered...
[ 2017-05-02 16:28:16 ] INFO Plotting in pdf format...
[ 2017-05-02 16:28:16 ] INFO Plotting mds...
[ 2017-05-02 16:28:16 ] INFO Plotting biodetection...
[ 2017-05-02 16:28:17 ] INFO Plotting countsbio...
[ 2017-05-02 16:28:17 ] INFO Plotting saturation...
[ 2017-05-02 16:28:19 ] INFO Plotting readnoise...
[ 2017-05-02 16:28:19 ] INFO Plotting correl...
[ 2017-05-02 16:28:19 ] INFO Plotting pairwise...
[ 2017-05-02 16:28:23 ] INFO Plotting boxplot...
[ 2017-05-02 16:28:23 ] INFO Plotting gcbias...
[ 2017-05-02 16:28:23 ] INFO Plotting lengthbias...
[ 2017-05-02 16:28:23 ] INFO Plotting boxplot...
[ 2017-05-02 16:28:23 ] INFO Plotting gcbias...
[ 2017-05-02 16:28:24 ] INFO Plotting lengthbias...
[ 2017-05-02 16:28:24 ] INFO Plotting biodist...
[ 2017-05-02 16:28:24 ] INFO Contrast: C_vs_K
[ 2017-05-02 16:28:24 ] INFO Contrast: C_vs_CL4
[ 2017-05-02 16:28:24 ] INFO Contrast: K_vs_KL4
[ 2017-05-02 16:28:24 ] INFO Contrast: CL4_vs_KL4
[ 2017-05-02 16:28:24 ] INFO Plotting volcano...
[ 2017-05-02 16:28:24 ] INFO Contrast: C_vs_K
[ 2017-05-02 16:28:24 ] INFO Contrast: C_vs_CL4
[ 2017-05-02 16:28:24 ] INFO Contrast: K_vs_KL4
[ 2017-05-02 16:28:25 ] INFO Contrast: CL4_vs_KL4
[ 2017-05-02 16:28:25 ] INFO Plotting deheatmap...
[ 2017-05-02 16:28:25 ] INFO Contrast: C_vs_K
[ 2017-05-02 16:28:28 ] INFO Contrast: C_vs_CL4
[ 2017-05-02 16:28:31 ] INFO Contrast: K_vs_KL4
[ 2017-05-02 16:28:36 ] INFO Contrast: CL4_vs_KL4
[ 2017-05-02 16:28:39 ] INFO Plotting filtered...
[ 2017-05-02 16:28:39 ] INFO Creating HTML report...
[ 2017-05-02 16:28:39 ] 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

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 C_vs_K

Volcano plot for the contrast C_vs_CL4

Volcano plot for the contrast K_vs_KL4

Volcano plot for the contrast CL4_vs_KL4

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 C_vs_K

DEG heatmap for the contrast C_vs_CL4

DEG heatmap for the contrast K_vs_KL4

DEG heatmap for the contrast CL4_vs_KL4

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 C_vs_K

Biotype distribution in DEG for the contrast C_vs_CL4

Biotype distribution in DEG for the contrast K_vs_KL4

Biotype distribution in DEG for the contrast CL4_vs_KL4

Get all the figures in png, pdf format.

Results

DEG table for the contrast C_vs_K

The following table presents the top 10% statistically significant genes (use the download links below the table to retrieve the whole list) for the contrast C_vs_K. 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_C_vs_K	log2_normalized_mean_counts_C	rpgm_normalized_mean_counts_C	log2_normalized_mean_counts_K	rpgm_normalized_mean_counts_K	log2_normalized_counts_DK3R1r-C1	log2_normalized_counts_DK3R2r-C2	rpgm_normalized_counts_DK3R1r-C1	rpgm_normalized_counts_DK3R2r-C2	log2_normalized_counts_DK3R4b-K23	log2_normalized_counts_DK3R5-K2	log2_normalized_counts_DK3R6-K3	rpgm_normalized_counts_DK3R4b-K23	rpgm_normalized_counts_DK3R5-K2	rpgm_normalized_counts_DK3R6-K3
chrY	1260771	1286629	ENSMUSG00000069045	0.3678	-	Ddx3y	protein_coding	1.41e-07	0.000448	4.89077	0.00e+00	0.00e+00	4.87e+00	1.53e-03	0.00e+00	0.00e+00	0.00e+00	0.00e+00	5.13e+00	4.91e+00	4.58e+00	1.81e-03	1.55e-03	1.23e-03
chr5	30013114	30019981	ENSMUSG00000025746	0.4078	+	Il6	protein_coding	1.96e-04	0.215288	3.36923	0.00e+00	0.00e+00	2.88e+00	3.19e-03	0.00e+00	0.00e+00	0.00e+00	0.00e+00	1.58e+00	2.58e+00	4.46e+00	6.84e-04	1.71e-03	7.19e-03
chr11	44400063	44414033	ENSMUSG00000004296	0.4385	+	Il12b	protein_coding	2.28e-04	0.215288	-2.90689	1.90e+00	3.17e-03	0.00e+00	0.00e+00	3.81e+00	0.00e+00	6.34e-03	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr6	54982580	54992950	ENSMUSG00000002797	0.4386	-	Ggct	protein_coding	2.71e-04	0.215288	-2.31288	4.43e+00	3.08e-03	2.33e+00	5.23e-04	3.46e+00	5.39e+00	1.21e-03	4.95e-03	2.81e+00	1.58e+00	2.58e+00	7.24e-04	2.41e-04	6.04e-04
chr5	99955935	99978938	ENSMUSG00000000568	0.3855	-	Hnrnpd	protein_coding	3.70e-04	0.234514	-2.07306	6.72e+00	7.41e-03	4.49e+00	1.71e-03	6.43e+00	7.02e+00	5.89e-03	8.94e-03	5.46e+00	4.32e+00	3.70e+00	2.98e-03	1.32e-03	8.31e-04
chrY	1096861	1245759	ENSMUSG00000068457	0.3693	-	Uty	protein_coding	4.43e-04	0.234514	3.87447	0.00e+00	0.00e+00	3.83e+00	3.48e-04	0.00e+00	0.00e+00	0.00e+00	0.00e+00	3.91e+00	3.32e+00	4.25e+00	3.57e-04	2.29e-04	4.59e-04

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

DEG table for the contrast C_vs_CL4

The following table presents the top 10% statistically significant genes (use the download links below the table to retrieve the whole list) for the contrast C_vs_CL4. 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_C_vs_CL4	log2_normalized_mean_counts_C	rpgm_normalized_mean_counts_C	log2_normalized_mean_counts_CL4	rpgm_normalized_mean_counts_CL4	log2_normalized_counts_DK3R1r-C1	log2_normalized_counts_DK3R2r-C2	rpgm_normalized_counts_DK3R1r-C1	rpgm_normalized_counts_DK3R2r-C2	log2_normalized_counts_DK3R7-C1L4	log2_normalized_counts_DK3R8-C2L4	rpgm_normalized_counts_DK3R7-C1L4	rpgm_normalized_counts_DK3R8-C2L4
chr18	74939322	74961263	ENSMUSG00000053846	0.4758	-	Lipg	protein_coding	9.97e-21	3.17e-17	7.97585	7.92e-01	3.08e-04	8.95e+00	1.55e-01	0.00e+00	1.58e+00	0.00e+00	6.16e-04	9.21e+00	8.70e+00	1.82e-01	1.28e-01
chr12	26469204	26479835	ENSMUSG00000020638	0.4656	+	Cmpk2	protein_coding	4.57e-15	7.25e-12	6.42000	4.67e+00	8.28e-03	1.11e+01	7.37e-01	4.81e+00	4.52e+00	9.12e-03	7.43e-03	1.10e+01	1.12e+01	6.89e-01	7.86e-01
chr5	30013114	30019981	ENSMUSG00000025746	0.4078	+	Il6	protein_coding	3.17e-14	3.35e-11	6.68650	0.00e+00	0.00e+00	6.68e+00	3.49e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00	6.79e+00	6.57e+00	3.76e-02	3.22e-02
chr1	144242296	144249242	ENSMUSG00000026358	0.3610	-	Rgs1	protein_coding	5.53e-13	4.05e-10	5.42206	1.95e+00	3.51e-04	7.38e+00	1.99e-02	1.58e+00	2.32e+00	2.34e-04	4.67e-04	7.74e+00	7.01e+00	2.49e-02	1.50e-02
chr18	60376029	60392627	ENSMUSG00000054072	0.4020	+	Iigp1	protein_coding	6.38e-13	4.05e-10	5.47032	2.16e+00	1.24e-03	7.62e+00	7.02e-02	2.32e+00	2.00e+00	1.41e-03	1.06e-03	7.85e+00	7.39e+00	8.13e-02	5.91e-02
chr2	34941750	34961772	ENSMUSG00000026875	0.4977	-	Traf1	protein_coding	9.78e-13	4.92e-10	5.28701	3.51e+00	1.23e-03	8.73e+00	5.26e-02	3.32e+00	3.70e+00	1.06e-03	1.41e-03	8.24e+00	9.22e+00	3.55e-02	6.98e-02
chr2	165055627	165072948	ENSMUSG00000017652	0.4963	+	Cd40	protein_coding	1.08e-12	4.92e-10	5.48298	5.43e+00	5.15e-03	1.08e+01	2.36e-01	5.21e+00	5.64e+00	4.36e-03	5.94e-03	1.03e+01	1.14e+01	1.53e-01	3.18e-01
chr9	7272514	7283331	ENSMUSG00000050578	0.3772	+	Mmp13	protein_coding	1.97e-12	7.82e-10	5.15754	2.66e+00	2.23e-03	7.83e+00	9.36e-02	3.00e+00	2.32e+00	2.84e-03	1.62e-03	8.10e+00	7.56e+00	1.11e-01	7.62e-02
chr17	67842713	68004120	ENSMUSG00000024043	0.4441	-	Arhgap28	protein_coding	5.46e-12	1.93e-09	5.37752	1.29e+00	3.13e-04	7.15e+00	1.81e-02	0.00e+00	2.58e+00	0.00e+00	6.27e-04	6.86e+00	7.45e+00	1.44e-02	2.18e-02
chr6	123281789	123289870	ENSMUSG00000030142	0.4134	-	Clec4e	protein_coding	1.71e-11	5.32e-09	4.90319	2.70e+00	9.99e-04	7.58e+00	3.51e-02	2.58e+00	2.81e+00	9.08e-04	1.09e-03	7.83e+00	7.34e+00	4.11e-02	2.92e-02
chr19	34550694	34576419	ENSMUSG00000045932	0.4241	+	Ifit2	protein_coding	1.84e-11	5.32e-09	4.80278	5.27e+00	8.65e-03	1.01e+01	2.47e-01	5.58e+00	4.95e+00	1.06e-02	6.74e-03	1.04e+01	9.77e+00	2.99e-01	1.96e-01
chr5	92321347	92328079	ENSMUSG00000029417	0.4151	-	Cxcl9	protein_coding	1.76e-10	4.65e-08	5.61471	0.00e+00	0.00e+00	5.60e+00	9.68e-03	0.00e+00	0.00e+00	0.00e+00	0.00e+00	5.78e+00	5.43e+00	1.09e-02	8.47e-03
chr5	105214907	105293695	ENSMUSG00000079362	0.4146	-	Gm43302	protein_coding	1.70e-09	4.15e-07	4.48727	1.79e+00	6.85e-04	6.18e+00	2.12e-02	2.00e+00	1.58e+00	8.21e-04	5.48e-04	6.77e+00	5.58e+00	2.96e-02	1.29e-02
chr13	42301270	42307989	ENSMUSG00000021367	0.4580	+	Edn1	protein_coding	1.46e-08	3.31e-06	5.16993	0.00e+00	0.00e+00	5.11e+00	1.48e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00	5.52e+00	4.70e+00	1.90e-02	1.06e-02
chr2	11705290	11734317	ENSMUSG00000023206	0.4426	+	Il15ra	protein_coding	2.05e-08	3.90e-06	3.92765	3.51e+00	1.68e-03	7.45e+00	2.79e-02	3.70e+00	3.32e+00	1.92e-03	1.44e-03	7.50e+00	7.40e+00	2.88e-02	2.69e-02
chr11	78920787	78960254	ENSMUSG00000020826	0.5008	+	Nos2	protein_coding	2.08e-08	3.90e-06	5.35755	0.00e+00	0.00e+00	5.31e+00	1.33e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00	4.95e+00	5.67e+00	9.97e-03	1.66e-02
chr11	117966079	117970047	ENSMUSG00000053113	0.6102	-	Socs3	protein_coding	2.22e-08	3.90e-06	3.85677	5.34e+00	9.90e-03	9.14e+00	1.47e-01	5.13e+00	5.55e+00	8.41e-03	1.14e-02	8.68e+00	9.60e+00	1.01e-01	1.92e-01
chr3	107741048	107760469	ENSMUSG00000014599	0.5122	-	Csf1	protein_coding	2.38e-08	3.90e-06	4.19162	2.45e+00	4.67e-04	6.62e+00	1.03e-02	2.32e+00	2.58e+00	4.16e-04	5.19e-04	6.30e+00	6.93e+00	8.10e-03	1.26e-02
chr11	21091291	21150323	ENSMUSG00000020134	0.3866	+	Peli1	protein_coding	2.39e-08	3.90e-06	3.83706	6.49e+00	2.09e-02	1.03e+01	3.02e-01	6.73e+00	6.25e+00	2.44e-02	1.74e-02	1.07e+01	9.95e+00	3.74e-01	2.30e-01
chr17	26505590	26508519	ENSMUSG00000024190	0.5334	-	Dusp1	protein_coding	2.46e-08	3.90e-06	3.79236	5.25e+00	1.70e-02	9.03e+00	2.41e-01	5.21e+00	5.29e+00	1.65e-02	1.74e-02	8.82e+00	9.23e+00	2.07e-01	2.75e-01
chr17	29490812	29496111	ENSMUSG00000024014	0.5274	+	Pim1	protein_coding	3.64e-08	5.50e-06	4.60920	7.85e+00	9.17e-02	1.25e+01	2.25e+00	7.43e+00	8.28e+00	6.53e-02	1.18e-01	1.21e+01	1.29e+01	1.64e+00	2.85e+00
chr5	105115767	105139586	ENSMUSG00000079363	0.4090	-	Gbp4	protein_coding	5.06e-08	7.30e-06	3.84327	2.95e+00	5.38e-04	6.91e+00	8.67e-03	3.58e+00	2.32e+00	7.88e-04	2.87e-04	7.14e+00	6.69e+00	1.00e-02	7.31e-03
chr3	142530342	142550149	ENSMUSG00000040253	0.4126	+	Gbp7	protein_coding	5.41e-08	7.47e-06	3.51525	4.71e+00	5.28e-03	8.11e+00	6.25e-02	4.46e+00	4.95e+00	4.35e-03	6.21e-03	8.73e+00	7.49e+00	8.80e-02	3.71e-02
chr3	127869058	127896324	ENSMUSG00000050549	0.4029	-	5730508B09Rik	protein_coding	7.81e-08	1.03e-05	3.73471	2.99e+00	2.20e-03	6.72e+00	3.31e-02	3.17e+00	2.81e+00	2.51e-03	1.88e-03	6.89e+00	6.55e+00	3.70e-02	2.92e-02
chr7	141429744	141437892	ENSMUSG00000019082	0.5701	-	Slc25a22	protein_coding	9.17e-08	1.16e-05	3.78338	3.36e+00	1.58e-03	7.42e+00	2.34e-02	2.32e+00	4.39e+00	5.26e-04	2.63e-03	6.99e+00	7.85e+00	1.66e-02	3.03e-02
chr2	129364570	129371139	ENSMUSG00000027398	0.4534	-	Il1b	protein_coding	1.02e-07	1.21e-05	3.51121	5.60e+00	1.63e-02	9.18e+00	1.90e-01	5.13e+00	6.07e+00	1.11e-02	2.16e-02	9.23e+00	9.13e+00	1.96e-01	1.83e-01
chr11	114922781	114934386	ENSMUSG00000063193	0.4817	-	Cd300lb	protein_coding	1.03e-07	1.21e-05	-4.32193	5.78e+00	1.28e-02	1.50e+00	4.33e-04	6.39e+00	5.17e+00	1.80e-02	7.58e-03	2.00e+00	1.00e+00	6.49e-04	2.16e-04
chr4	58435255	58553898	ENSMUSG00000038668	0.4240	-	Lpar1	protein_coding	1.21e-07	1.38e-05	3.74416	2.35e+00	1.39e-03	6.64e+00	2.13e-02	1.00e+00	3.70e+00	2.14e-04	2.57e-03	6.78e+00	6.51e+00	2.33e-02	1.93e-02
chr11	82101845	82103400	ENSMUSG00000035352	0.4389	+	Ccl12	protein_coding	1.80e-07	1.95e-05	3.47947	5.02e+00	2.22e-02	8.50e+00	2.54e-01	5.13e+00	4.91e+00	2.39e-02	2.04e-02	8.41e+00	8.59e+00	2.39e-01	2.70e-01
chr5	90903871	90905938	ENSMUSG00000058427	0.4507	+	Cxcl2	protein_coding	1.84e-07	1.95e-05	3.39506	4.64e+00	8.48e-03	7.97e+00	9.25e-02	4.64e+00	4.64e+00	8.48e-03	8.48e-03	8.41e+00	7.53e+00	1.20e-01	6.50e-02
chr8	88647315	88688474	ENSMUSG00000055994	0.4864	+	Nod2	protein_coding	1.96e-07	2.01e-05	4.08746	1.66e+00	4.99e-04	5.88e+00	1.17e-02	1.00e+00	2.32e+00	1.99e-04	7.98e-04	5.64e+00	6.11e+00	9.77e-03	1.36e-02
chr11	83647844	83649355	ENSMUSG00000000982	0.4630	-	Ccl3	protein_coding	2.06e-07	2.04e-05	3.55616	6.75e+00	6.40e-02	1.03e+01	7.60e-01	6.74e+00	6.75e+00	6.37e-02	6.43e-02	1.02e+01	1.04e+01	6.84e-01	8.36e-01
chr1	171632403	171653035	ENSMUSG00000038179	0.4492	-	Slamf7	protein_coding	2.24e-07	2.16e-05	3.42917	6.77e+00	1.93e-02	1.02e+01	2.10e-01	6.85e+00	6.70e+00	2.03e-02	1.83e-02	1.04e+01	1.00e+01	2.37e-01	1.83e-01
chr19	29367455	29388095	ENSMUSG00000016496	0.4295	+	Cd274	protein_coding	2.37e-07	2.21e-05	3.64106	7.02e+00	3.39e-02	1.07e+01	4.26e-01	6.61e+00	7.42e+00	2.46e-02	4.32e-02	1.07e+01	1.07e+01	4.15e-01	4.37e-01
chr11	83002158	83020810	ENSMUSG00000035208	0.4404	-	Slfn8	protein_coding	4.07e-07	3.69e-05	3.30046	5.38e+00	1.36e-02	8.76e+00	1.37e-01	4.91e+00	5.86e+00	9.20e-03	1.81e-02	8.87e+00	8.64e+00	1.48e-01	1.27e-01
chr17	35199381	35202007	ENSMUSG00000024401	0.5230	-	Tnf	protein_coding	5.07e-07	4.47e-05	4.51780	8.33e+00	1.75e-01	1.30e+01	4.02e+00	7.68e+00	8.98e+00	1.01e-01	2.49e-01	1.27e+01	1.32e+01	3.27e+00	4.77e+00
chr14	47153895	47189413	ENSMUSG00000037580	0.4495	-	Gch1	protein_coding	6.03e-07	5.17e-05	3.36189	5.38e+00	1.38e-02	8.81e+00	1.45e-01	5.91e+00	4.86e+00	1.87e-02	8.87e-03	8.55e+00	9.08e+00	1.18e-01	1.71e-01
chr2	105386291	105399319	ENSMUSG00000005973	0.4793	-	Rcn1	protein_coding	7.89e-07	6.59e-05	3.25673	4.36e+00	7.11e-03	7.68e+00	7.10e-02	3.91e+00	4.81e+00	4.86e-03	9.37e-03	7.73e+00	7.64e+00	7.32e-02	6.87e-02
chr2	145784734	145887616	ENSMUSG00000001768	0.4527	+	Rin2	protein_coding	1.15e-06	9.40e-05	3.15754	5.70e+00	2.88e-03	8.85e+00	2.62e-02	5.78e+00	5.61e+00	3.05e-03	2.71e-03	9.00e+00	8.70e+00	2.88e-02	2.35e-02
chr9	5308828	5336783	ENSMUSG00000033538	0.3695	+	Casp4	protein_coding	1.38e-06	1.09e-04	3.66297	1.50e+00	1.94e-04	5.09e+00	3.60e-03	1.00e+00	2.00e+00	9.72e-05	2.92e-04	5.78e+00	4.39e+00	5.25e-03	1.94e-03
chr8	27138773	27174646	ENSMUSG00000031488	0.4754	-	Rab11fip1	protein_coding	1.80e-06	1.40e-04	3.33498	3.36e+00	1.04e-03	6.79e+00	1.15e-02	2.81e+00	3.91e+00	6.25e-04	1.46e-03	6.64e+00	6.93e+00	1.03e-02	1.26e-02
chr17	44096308	44105809	ENSMUSG00000023961	0.4252	-	Enpp4	protein_coding	2.45e-06	1.85e-04	3.33151	2.88e+00	1.53e-03	6.24e+00	1.75e-02	3.17e+00	2.58e+00	1.88e-03	1.17e-03	6.27e+00	6.21e+00	1.78e-02	1.71e-02
chr5	67607882	67623552	ENSMUSG00000050010	0.4424	+	Shisa3	protein_coding	2.73e-06	2.01e-04	3.06413	4.01e+00	3.00e-03	7.52e+00	2.62e-02	2.81e+00	5.21e+00	8.58e-04	5.15e-03	7.59e+00	7.45e+00	2.75e-02	2.49e-02
chr1	173630917	173647928	ENSMUSG00000043263	0.3789	+	Ifi209	protein_coding	2.86e-06	2.06e-04	3.02066	4.66e+00	3.62e-03	7.70e+00	3.04e-02	4.32e+00	5.00e+00	2.75e-03	4.49e-03	7.96e+00	7.43e+00	3.60e-02	2.49e-02
chr6	86647042	86669161	ENSMUSG00000001156	0.4843	-	Mxd1	protein_coding	2.96e-06	2.09e-04	3.06380	5.50e+00	4.67e-03	8.61e+00	3.98e-02	5.09e+00	5.91e+00	3.35e-03	5.99e-03	8.42e+00	8.79e+00	3.47e-02	4.49e-02
chr10	19000910	19015657	ENSMUSG00000019850	0.4554	-	Tnfaip3	protein_coding	3.39e-06	2.34e-04	3.12594	3.86e+00	2.56e-03	7.34e+00	2.35e-02	4.91e+00	2.81e+00	4.24e-03	8.78e-04	7.32e+00	7.35e+00	2.33e-02	2.37e-02
chr10	13966075	14151374	ENSMUSG00000015501	0.4215	+	Hivep2	protein_coding	3.66e-06	2.47e-04	3.03653	4.26e+00	2.78e-03	7.31e+00	2.39e-02	4.52e+00	4.00e+00	3.31e-03	2.25e-03	7.51e+00	7.11e+00	2.72e-02	2.06e-02
chr11	53770014	53778374	ENSMUSG00000018899	0.5246	+	Irf1	protein_coding	4.40e-06	2.91e-04	3.44778	8.40e+00	4.03e-02	1.18e+01	4.41e-01	8.24e+00	8.55e+00	3.60e-02	4.46e-02	1.14e+01	1.22e+01	3.14e-01	5.68e-01
chr19	7557459	7588545	ENSMUSG00000060675	0.4566	+	Pla2g16	protein_coding	4.78e-06	3.10e-04	3.02444	6.11e+00	8.58e-03	9.12e+00	7.07e-02	6.23e+00	6.00e+00	9.27e-03	7.89e-03	8.87e+00	9.38e+00	5.84e-02	8.31e-02
chr9	43221235	43239911	ENSMUSG00000032014	0.5232	-	Oaf	protein_coding	6.37e-06	4.03e-04	3.17880	3.01e+00	2.08e-03	6.35e+00	2.09e-02	2.32e+00	3.70e+00	1.04e-03	3.12e-03	6.34e+00	6.36e+00	2.08e-02	2.11e-02
chr8	45395665	45411080	ENSMUSG00000031639	0.3944	-	Tlr3	protein_coding	6.48e-06	4.03e-04	2.86817	5.17e+00	6.18e-03	8.01e+00	4.62e-02	5.39e+00	4.95e+00	7.14e-03	5.22e-03	8.39e+00	7.62e+00	5.83e-02	3.41e-02

Download the DEG result list for C_vs_CL4.
Download the whole result list for C_vs_CL4.

DEG table for the contrast K_vs_KL4

The following table presents the top 10% statistically significant genes (use the download links below the table to retrieve the whole list) for the contrast K_vs_KL4. 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_K_vs_KL4	log2_normalized_mean_counts_K	rpgm_normalized_mean_counts_K	log2_normalized_mean_counts_KL4	rpgm_normalized_mean_counts_KL4	log2_normalized_counts_DK3R4b-K23	log2_normalized_counts_DK3R5-K2	log2_normalized_counts_DK3R6-K3	rpgm_normalized_counts_DK3R4b-K23	rpgm_normalized_counts_DK3R5-K2	rpgm_normalized_counts_DK3R6-K3	log2_normalized_counts_DK3R10b-K23L4	log2_normalized_counts_DK3R11-K2L4	log2_normalized_counts_DK3R12-K3L4	rpgm_normalized_counts_DK3R10b-K23L4	rpgm_normalized_counts_DK3R11-K2L4	rpgm_normalized_counts_DK3R12-K3L4
chr18	74939322	74961263	ENSMUSG00000053846	0.4758	-	Lipg	protein_coding	5.07e-29	1.61e-25	8.01458	6.67e-01	2.05e-04	8.69e+00	1.32e-01	1.00e+00	0.00e+00	1.00e+00	3.08e-04	0.00e+00	3.08e-04	9.11e+00	8.09e+00	8.87e+00	1.70e-01	8.34e-02	1.43e-01
chr12	26469204	26479835	ENSMUSG00000020638	0.4656	+	Cmpk2	protein_coding	2.24e-22	3.55e-19	6.64965	4.65e+00	8.33e-03	1.13e+01	8.70e-01	4.75e+00	4.95e+00	4.25e+00	8.78e-03	1.01e-02	6.08e-03	1.14e+01	1.14e+01	1.11e+01	9.43e-01	9.40e-01	7.27e-01
chr2	34941750	34961772	ENSMUSG00000026875	0.4977	-	Traf1	protein_coding	5.38e-21	5.70e-18	5.99398	2.30e+00	4.70e-04	8.30e+00	3.73e-02	2.32e+00	2.00e+00	2.58e+00	4.70e-04	3.52e-04	5.87e-04	8.55e+00	8.28e+00	8.08e+00	4.40e-02	3.63e-02	3.16e-02
chr2	165055627	165072948	ENSMUSG00000017652	0.4963	+	Cd40	protein_coding	1.45e-19	1.15e-16	5.68975	4.67e+00	3.35e-03	1.05e+01	1.79e-01	5.49e+00	3.70e+00	4.81e+00	5.33e-03	1.45e-03	3.27e-03	1.03e+01	1.08e+01	1.05e+01	1.50e-01	2.11e-01	1.78e-01
chr9	7272514	7283331	ENSMUSG00000050578	0.3772	+	Mmp13	protein_coding	2.15e-19	1.37e-16	5.71195	1.39e+00	1.22e-03	7.70e+00	8.46e-02	3.17e+00	0.00e+00	1.00e+00	3.24e-03	0.00e+00	4.05e-04	7.64e+00	7.52e+00	7.95e+00	8.03e-02	7.38e-02	9.97e-02
chr17	67842713	68004120	ENSMUSG00000024043	0.4441	-	Arhgap28	protein_coding	3.35e-19	1.77e-16	6.19377	6.67e-01	8.36e-05	6.92e+00	1.52e-02	1.00e+00	1.00e+00	0.00e+00	1.25e-04	1.25e-04	0.00e+00	7.13e+00	6.70e+00	6.93e+00	1.74e-02	1.29e-02	1.52e-02
chr19	34550694	34576419	ENSMUSG00000045932	0.4241	+	Ifit2	protein_coding	8.41e-18	3.81e-15	5.14568	5.03e+00	7.26e-03	1.02e+01	2.65e-01	5.39e+00	5.04e+00	4.64e+00	9.21e-03	7.19e-03	5.39e-03	1.03e+01	9.70e+00	1.05e+01	2.78e-01	1.87e-01	3.29e-01
chr2	145784734	145887616	ENSMUSG00000001768	0.4527	+	Rin2	protein_coding	5.12e-16	2.03e-13	4.82291	3.89e+00	9.98e-04	9.04e+00	2.98e-02	4.58e+00	4.75e+00	2.32e+00	1.30e-03	1.47e-03	2.26e-04	8.99e+00	9.17e+00	8.97e+00	2.86e-02	3.26e-02	2.82e-02
chr5	105115767	105139586	ENSMUSG00000079363	0.4090	-	Gbp4	protein_coding	3.59e-14	1.27e-11	4.60486	2.24e+00	2.87e-04	6.90e+00	8.65e-03	1.58e+00	2.81e+00	2.32e+00	1.43e-04	4.30e-04	2.87e-04	6.86e+00	6.61e+00	7.24e+00	8.24e-03	6.95e-03	1.08e-02
chr2	11705290	11734317	ENSMUSG00000023206	0.4426	+	Il15ra	protein_coding	2.09e-13	6.63e-11	4.36394	2.82e+00	1.01e-03	7.20e+00	2.40e-02	3.32e+00	2.81e+00	2.32e+00	1.44e-03	9.60e-04	6.40e-04	7.64e+00	6.98e+00	6.99e+00	3.19e-02	2.00e-02	2.02e-02
chr1	171632403	171653035	ENSMUSG00000038179	0.4492	-	Slamf7	protein_coding	1.19e-12	3.20e-10	3.94526	5.48e+00	8.19e-03	9.49e+00	1.29e-01	5.83e+00	4.81e+00	5.81e+00	9.97e-03	4.81e-03	9.79e-03	9.40e+00	9.41e+00	9.67e+00	1.20e-01	1.21e-01	1.45e-01
chr5	92321347	92328079	ENSMUSG00000029417	0.4151	-	Cxcl9	protein_coding	1.21e-12	3.20e-10	4.53605	1.64e+00	4.70e-04	6.23e+00	1.54e-02	1.00e+00	1.58e+00	2.32e+00	2.02e-04	4.03e-04	8.06e-04	6.55e+00	5.75e+00	6.39e+00	1.87e-02	1.07e-02	1.67e-02
chr1	144242296	144249242	ENSMUSG00000026358	0.3610	-	Rgs1	protein_coding	1.43e-12	3.48e-10	4.09686	3.01e+00	9.35e-04	7.19e+00	1.79e-02	2.00e+00	3.70e+00	3.32e+00	3.51e-04	1.40e-03	1.05e-03	7.75e+00	6.57e+00	7.25e+00	2.50e-02	1.10e-02	1.76e-02
chr6	123281789	123289870	ENSMUSG00000030142	0.4134	-	Clec4e	protein_coding	1.69e-12	3.83e-10	4.06178	3.29e+00	1.63e-03	7.37e+00	3.02e-02	3.17e+00	3.00e+00	3.70e+00	1.45e-03	1.27e-03	2.18e-03	7.59e+00	7.18e+00	7.35e+00	3.49e-02	2.62e-02	2.94e-02
chr19	7557459	7588545	ENSMUSG00000060675	0.4566	+	Pla2g16	protein_coding	3.47e-12	7.35e-10	3.87267	4.89e+00	3.67e-03	8.79e+00	5.55e-02	5.32e+00	4.70e+00	4.64e+00	4.89e-03	3.13e-03	3.01e-03	8.70e+00	8.75e+00	8.92e+00	5.21e-02	5.40e-02	6.05e-02
chr17	35199381	35202007	ENSMUSG00000024401	0.5230	-	Tnf	protein_coding	4.26e-12	8.44e-10	5.25960	7.87e+00	1.34e-01	1.33e+01	5.16e+00	8.82e+00	6.81e+00	7.98e+00	2.23e-01	5.50e-02	1.24e-01	1.29e+01	1.39e+01	1.31e+01	3.74e+00	7.37e+00	4.38e+00
chr3	107741048	107760469	ENSMUSG00000014599	0.5122	-	Csf1	protein_coding	4.76e-12	8.89e-10	4.13955	2.19e+00	4.50e-04	6.55e+00	9.66e-03	3.00e+00	1.00e+00	2.58e+00	7.27e-04	1.04e-04	5.19e-04	6.58e+00	6.60e+00	6.48e+00	9.87e-03	9.97e-03	9.14e-03
chr11	83647844	83649355	ENSMUSG00000000982	0.4630	-	Ccl3	protein_coding	5.36e-12	9.14e-10	3.84490	6.21e+00	4.61e-02	1.01e+01	6.70e-01	6.41e+00	5.58e+00	6.64e+00	5.05e-02	2.83e-02	5.95e-02	1.04e+01	9.83e+00	1.01e+01	7.85e-01	5.48e-01	6.78e-01
chr3	27317028	27342427	ENSMUSG00000039304	0.4494	+	Tnfsf10	protein_coding	5.52e-12	9.14e-10	3.89838	3.49e+00	1.54e-03	7.38e+00	2.50e-02	3.32e+00	3.70e+00	3.46e+00	1.34e-03	1.79e-03	1.49e-03	7.48e+00	7.06e+00	7.61e+00	2.63e-02	1.96e-02	2.90e-02
chr11	21091291	21150323	ENSMUSG00000020134	0.3866	+	Peli1	protein_coding	5.76e-12	9.14e-10	4.00683	6.58e+00	2.32e-02	1.06e+01	3.77e-01	6.32e+00	7.22e+00	6.21e+00	1.83e-02	3.44e-02	1.69e-02	1.09e+01	1.03e+01	1.07e+01	4.40e-01	3.01e-01	3.89e-01
chr13	69497959	69534617	ENSMUSG00000034575	0.4928	-	Papd7	protein_coding	8.96e-12	1.35e-09	3.93029	4.11e+00	5.19e-03	8.01e+00	8.36e-02	4.46e+00	4.17e+00	3.70e+00	6.54e-03	5.29e-03	3.74e-03	8.50e+00	8.08e+00	7.45e+00	1.12e-01	8.41e-02	5.42e-02
chr11	82101845	82103400	ENSMUSG00000035352	0.4389	+	Ccl12	protein_coding	1.02e-11	1.48e-09	3.81412	4.63e+00	1.71e-02	8.48e+00	2.50e-01	4.95e+00	4.70e+00	4.25e+00	2.11e-02	1.76e-02	1.27e-02	8.54e+00	8.43e+00	8.46e+00	2.62e-01	2.41e-01	2.47e-01
chr11	114922781	114934386	ENSMUSG00000063193	0.4817	-	Cd300lb	protein_coding	1.15e-11	1.58e-09	-4.49185	5.88e+00	1.28e-02	1.19e+00	3.61e-04	5.52e+00	5.95e+00	6.17e+00	9.74e-03	1.32e-02	1.54e-02	2.00e+00	0.00e+00	1.58e+00	6.49e-04	0.00e+00	4.33e-04
chr8	27138773	27174646	ENSMUSG00000031488	0.4754	-	Rab11fip1	protein_coding	2.04e-11	2.70e-09	3.98935	2.46e+00	4.86e-04	6.47e+00	9.28e-03	2.58e+00	2.00e+00	2.81e+00	5.21e-04	3.13e-04	6.25e-04	6.17e+00	6.43e+00	6.81e+00	7.40e-03	8.86e-03	1.16e-02
chr17	29490812	29496111	ENSMUSG00000024014	0.5274	+	Pim1	protein_coding	2.39e-11	3.03e-09	4.44028	7.89e+00	9.31e-02	1.24e+01	2.03e+00	7.97e+00	7.41e+00	8.29e+00	9.51e-02	6.46e-02	1.20e-01	1.22e+01	1.25e+01	1.24e+01	1.83e+00	2.14e+00	2.11e+00
chr13	42301270	42307989	ENSMUSG00000021367	0.4580	+	Edn1	protein_coding	5.69e-11	6.95e-09	4.89482	3.33e-01	1.41e-04	5.29e+00	1.64e-02	0.00e+00	1.00e+00	0.00e+00	0.00e+00	4.23e-04	0.00e+00	5.39e+00	5.52e+00	4.95e+00	1.73e-02	1.90e-02	1.27e-02
chr5	30013114	30019981	ENSMUSG00000025746	0.4078	+	Il6	protein_coding	9.10e-11	1.07e-08	3.58884	2.88e+00	3.19e-03	6.95e+00	4.22e-02	1.58e+00	2.58e+00	4.46e+00	6.84e-04	1.71e-03	7.19e-03	7.10e+00	6.79e+00	6.97e+00	4.65e-02	3.76e-02	4.24e-02
chr18	60376029	60392627	ENSMUSG00000054072	0.4020	+	Iigp1	protein_coding	1.22e-10	1.38e-08	3.51488	4.21e+00	6.48e-03	7.75e+00	7.78e-02	3.58e+00	4.52e+00	4.52e+00	3.89e-03	7.78e-03	7.78e-03	7.90e+00	7.30e+00	8.06e+00	8.42e-02	5.55e-02	9.37e-02
chr11	44400063	44414033	ENSMUSG00000004296	0.4385	+	Il12b	protein_coding	1.43e-10	1.56e-08	4.89077	0.00e+00	0.00e+00	4.84e+00	1.40e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	4.39e+00	4.81e+00	5.32e+00	9.75e-03	1.32e-02	1.90e-02
chr11	59541568	59566956	ENSMUSG00000032691	0.4597	+	Nlrp3	protein_coding	1.62e-10	1.71e-08	3.58260	4.06e+00	5.00e-03	7.61e+00	6.33e-02	4.39e+00	4.09e+00	3.70e+00	6.25e-03	5.00e-03	3.75e-03	8.16e+00	7.13e+00	7.52e+00	8.93e-02	4.34e-02	5.72e-02
chr5	90903871	90905938	ENSMUSG00000058427	0.4507	+	Cxcl2	protein_coding	2.34e-10	2.39e-08	3.40453	4.17e+00	6.24e-03	7.53e+00	6.95e-02	4.70e+00	3.81e+00	4.00e+00	8.83e-03	4.59e-03	5.30e-03	7.50e+00	6.91e+00	8.19e+00	6.36e-02	4.20e-02	1.03e-01
chr10	62493833	62527451	ENSMUSG00000020077	0.4550	-	Srgn	protein_coding	3.44e-10	3.41e-08	3.48922	4.33e+00	3.98e-03	7.81e+00	4.68e-02	4.00e+00	4.58e+00	4.39e+00	3.09e-03	4.73e-03	4.11e-03	8.20e+00	7.58e+00	7.64e+00	6.03e-02	3.91e-02	4.09e-02
chr2	127336159	127338376	ENSMUSG00000027368	0.5762	+	Dusp2	protein_coding	3.94e-10	3.79e-08	5.48909	8.72e+00	1.39e-01	1.42e+01	6.25e+00	8.70e+00	8.40e+00	9.07e+00	1.34e-01	1.09e-01	1.74e-01	1.41e+01	1.44e+01	1.42e+01	5.82e+00	6.98e+00	5.95e+00
chr10	19000910	19015657	ENSMUSG00000019850	0.4554	-	Tnfaip3	protein_coding	4.33e-10	4.04e-08	3.38042	4.18e+00	2.68e-03	7.64e+00	2.93e-02	4.09e+00	3.58e+00	4.86e+00	2.34e-03	1.61e-03	4.10e-03	7.92e+00	7.44e+00	7.55e+00	3.52e-02	2.53e-02	2.74e-02
chr11	78920787	78960254	ENSMUSG00000020826	0.5008	+	Nos2	protein_coding	5.23e-10	4.74e-08	4.58496	3.33e-01	1.11e-04	4.96e+00	1.03e-02	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	3.32e-04	4.58e+00	4.91e+00	5.39e+00	7.64e-03	9.63e-03	1.36e-02
chr4	135213969	135272814	ENSMUSG00000037242	0.4548	-	Clic4	protein_coding	7.45e-10	6.57e-08	3.63902	7.73e+00	3.87e-02	1.14e+01	4.84e-01	8.07e+00	7.20e+00	7.92e+00	4.73e-02	2.59e-02	4.27e-02	1.15e+01	1.12e+01	1.15e+01	5.05e-01	4.29e-01	5.17e-01
chr3	142530342	142550149	ENSMUSG00000040253	0.4126	+	Gbp7	protein_coding	7.71e-10	6.62e-08	3.30919	4.88e+00	6.07e-03	8.18e+00	6.21e-02	5.21e+00	5.04e+00	4.39e+00	7.45e-03	6.63e-03	4.14e-03	8.16e+00	7.71e+00	8.67e+00	5.92e-02	4.31e-02	8.39e-02
chr2	105386291	105399319	ENSMUSG00000005973	0.4793	-	Rcn1	protein_coding	1.21e-09	1.01e-07	3.37156	4.15e+00	6.59e-03	7.69e+00	7.15e-02	5.04e+00	4.09e+00	3.32e+00	1.11e-02	5.55e-03	3.12e-03	7.85e+00	7.52e+00	7.69e+00	7.98e-02	6.35e-02	7.11e-02
chr10	42181841	42276755	ENSMUSG00000048756	0.4750	-	Foxo3	protein_coding	1.75e-09	1.40e-07	-3.72935	6.39e+00	8.35e-03	2.38e+00	5.36e-04	6.29e+00	6.49e+00	6.39e+00	7.74e-03	8.95e-03	8.35e-03	2.81e+00	1.00e+00	3.32e+00	6.03e-04	1.01e-04	9.05e-04
chr6	53287270	53700376	ENSMUSG00000053007	0.4349	+	9430076C15Rik	protein_coding	1.77e-09	1.40e-07	3.20163	4.64e+00	1.12e-03	7.76e+00	1.07e-02	4.64e+00	4.52e+00	4.75e+00	1.12e-03	1.03e-03	1.22e-03	7.13e+00	7.84e+00	8.33e+00	6.51e-03	1.07e-02	1.50e-02
chr4	58435255	58553898	ENSMUSG00000038668	0.4240	-	Lpar1	protein_coding	2.26e-09	1.75e-07	3.41709	2.69e+00	1.35e-03	6.28e+00	1.65e-02	3.17e+00	1.58e+00	3.32e+00	1.71e-03	4.28e-04	1.93e-03	5.98e+00	6.38e+00	6.48e+00	1.33e-02	1.75e-02	1.88e-02
chr11	83002158	83020810	ENSMUSG00000035208	0.4404	-	Slfn8	protein_coding	2.93e-09	2.21e-07	3.21690	4.96e+00	9.62e-03	8.15e+00	9.21e-02	5.17e+00	4.86e+00	4.86e+00	1.11e-02	8.88e-03	8.88e-03	7.71e+00	8.46e+00	8.29e+00	6.60e-02	1.11e-01	9.90e-02
chr19	29367455	29388095	ENSMUSG00000016496	0.4295	+	Cd274	protein_coding	7.36e-09	5.43e-07	3.21619	7.75e+00	5.55e-02	1.10e+01	5.17e-01	7.86e+00	7.38e+00	8.01e+00	5.89e-02	4.22e-02	6.53e-02	1.09e+01	1.08e+01	1.12e+01	4.84e-01	4.64e-01	6.05e-01
chr11	117966079	117970047	ENSMUSG00000053113	0.6102	-	Socs3	protein_coding	1.00e-08	7.23e-07	3.03097	5.54e+00	1.16e-02	8.61e+00	9.68e-02	5.86e+00	5.04e+00	5.73e+00	1.41e-02	7.92e-03	1.29e-02	8.78e+00	8.38e+00	8.66e+00	1.08e-01	8.24e-02	9.97e-02
chr7	45522916	45526268	ENSMUSG00000040435	0.5452	-	Ppp1r15a	protein_coding	1.10e-08	7.77e-07	3.13927	3.46e+00	8.51e-03	6.73e+00	8.09e-02	3.00e+00	3.00e+00	4.39e+00	5.26e-03	5.26e-03	1.50e-02	7.10e+00	6.78e+00	6.30e+00	1.02e-01	8.19e-02	5.86e-02
chr8	88647315	88688474	ENSMUSG00000055994	0.4864	+	Nod2	protein_coding	1.66e-08	1.14e-06	3.80735	1.44e+00	3.99e-04	5.36e+00	8.18e-03	1.00e+00	2.32e+00	1.00e+00	1.99e-04	7.98e-04	1.99e-04	5.75e+00	5.21e+00	5.13e+00	1.06e-02	7.18e-03	6.78e-03
chr8	45395665	45411080	ENSMUSG00000031639	0.3944	-	Tlr3	protein_coding	1.90e-08	1.28e-06	3.02937	4.69e+00	4.41e-03	7.73e+00	3.73e-02	5.09e+00	4.46e+00	4.52e+00	5.75e-03	3.66e-03	3.83e-03	8.03e+00	7.47e+00	7.69e+00	4.53e-02	3.06e-02	3.59e-02
chr2	57237635	57370719	ENSMUSG00000026827	0.4212	+	Gpd2	protein_coding	1.94e-08	1.28e-06	2.97764	6.71e+00	1.49e-02	9.69e+00	1.19e-01	6.81e+00	6.46e+00	6.87e+00	1.58e-02	1.24e-02	1.66e-02	9.86e+00	9.44e+00	9.77e+00	1.32e-01	9.89e-02	1.25e-01
chr10	56377300	56390419	ENSMUSG00000050953	0.4398	+	Gja1	protein_coding	2.12e-08	1.37e-06	3.04350	3.97e+00	5.15e-03	7.01e+00	4.49e-02	4.52e+00	3.70e+00	3.70e+00	7.39e-03	4.03e-03	4.03e-03	7.39e+00	6.41e+00	7.24e+00	5.61e-02	2.82e-02	5.04e-02
chr17	44096308	44105809	ENSMUSG00000023961	0.4252	-	Enpp4	protein_coding	2.22e-08	1.40e-06	3.26589	2.77e+00	1.41e-03	6.02e+00	1.56e-02	2.32e+00	3.00e+00	3.00e+00	9.39e-04	1.64e-03	1.64e-03	5.67e+00	5.83e+00	6.55e+00	1.17e-02	1.31e-02	2.18e-02
chr3	127869058	127896324	ENSMUSG00000050549	0.4029	-	5730508B09Rik	protein_coding	2.26e-08	1.40e-06	3.07912	4.08e+00	5.02e-03	7.16e+00	4.47e-02	4.25e+00	3.81e+00	4.17e+00	5.64e-03	4.08e-03	5.33e-03	7.24e+00	7.06e+00	7.20e+00	4.70e-02	4.14e-02	4.58e-02
chr15	86139128	86186141	ENSMUSG00000035891	0.5089	-	Cerk	protein_coding	3.09e-08	1.88e-06	-3.14764	6.38e+00	2.30e-02	3.23e+00	2.35e-03	6.85e+00	5.98e+00	6.30e+00	3.09e-02	1.68e-02	2.12e-02	3.17e+00	3.70e+00	2.81e+00	2.17e-03	3.26e-03	1.63e-03
chr11	97362551	97398542	ENSMUSG00000038485	0.4674	+	Socs7	protein_coding	3.46e-08	2.07e-06	2.85443	6.87e+00	1.44e-02	9.77e+00	1.05e-01	7.28e+00	6.02e+00	7.31e+00	1.77e-02	7.36e-03	1.82e-02	9.17e+00	9.89e+00	1.03e+01	6.60e-02	1.09e-01	1.40e-01
chr14	74754674	74947877	ENSMUSG00000068015	0.4449	-	Lrch1	protein_coding	4.54e-08	2.58e-06	2.91396	4.95e+00	8.49e-03	7.85e+00	6.58e-02	4.91e+00	4.58e+00	5.36e+00	8.03e-03	6.37e-03	1.11e-02	8.38e+00	7.56e+00	7.60e+00	9.19e-02	5.20e-02	5.34e-02
chr6	134715468	134792625	ENSMUSG00000030203	0.4174	-	Dusp16	protein_coding	4.54e-08	2.58e-06	2.84455	5.86e+00	4.69e-03	8.74e+00	3.42e-02	6.54e+00	5.25e+00	5.78e+00	7.08e-03	2.85e-03	4.16e-03	8.19e+00	8.87e+00	9.16e+00	2.25e-02	3.60e-02	4.41e-02
chr2	68861441	69114282	ENSMUSG00000027035	0.4189	+	Cers6	protein_coding	4.55e-08	2.58e-06	2.90578	5.81e+00	4.31e-03	8.74e+00	3.28e-02	6.25e+00	5.83e+00	5.36e+00	5.68e-03	4.24e-03	3.03e-03	9.02e+00	8.35e+00	8.84e+00	3.92e-02	2.47e-02	3.46e-02
chr7	141429744	141437892	ENSMUSG00000019082	0.5701	-	Slc25a22	protein_coding	4.78e-08	2.66e-06	2.90545	4.46e+00	2.81e-03	7.37e+00	2.19e-02	4.39e+00	4.17e+00	4.81e+00	2.63e-03	2.24e-03	3.55e-03	7.63e+00	7.15e+00	7.34e+00	2.59e-02	1.86e-02	2.12e-02
chr14	50944302	50965237	ENSMUSG00000021871	0.4510	+	Pnp	protein_coding	5.05e-08	2.76e-06	2.88100	7.02e+00	2.44e-02	9.92e+00	1.81e-01	6.71e+00	6.87e+00	7.48e+00	1.91e-02	2.13e-02	3.27e-02	1.02e+01	9.61e+00	9.95e+00	2.17e-01	1.44e-01	1.82e-01
chr2	132052496	132145108	ENSMUSG00000027340	0.4544	-	Slc23a2	protein_coding	5.21e-08	2.80e-06	2.84503	6.45e+00	7.61e-03	9.33e+00	5.52e-02	6.66e+00	5.98e+00	6.73e+00	8.55e-03	5.30e-03	8.98e-03	9.43e+00	9.24e+00	9.34e+00	5.89e-02	5.15e-02	5.52e-02
chr2	32414487	32451445	ENSMUSG00000026819	0.4721	-	Slc25a25	protein_coding	5.63e-08	2.98e-06	3.10852	2.88e+00	9.80e-04	6.10e+00	9.52e-03	3.32e+00	2.00e+00	3.32e+00	1.26e-03	4.20e-04	1.26e-03	6.00e+00	6.27e+00	6.04e+00	8.82e-03	1.06e-02	9.10e-03
chr15	58941244	59082026	ENSMUSG00000022353	0.4852	-	Mtss1	protein_coding	6.10e-08	3.18e-06	-2.88264	7.28e+00	4.54e-02	4.27e+00	5.91e-03	7.03e+00	7.25e+00	7.56e+00	3.78e-02	4.39e-02	5.47e-02	4.64e+00	4.86e+00	3.32e+00	6.98e-03	8.14e-03	2.62e-03
chr4	88522025	88522774	ENSMUSG00000048806	0.4373	-	Ifnb1	protein_coding	6.53e-08	3.34e-06	4.62449	0.00e+00	0.00e+00	4.62e+00	1.97e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	4.46e+00	4.81e+00	4.58e+00	1.75e-02	2.25e-02	1.92e-02
chr19	40831279	40848572	ENSMUSG00000025010	0.4570	+	Ccnj	protein_coding	7.32e-08	3.69e-06	3.18982	2.30e+00	1.16e-03	5.55e+00	1.28e-02	1.58e+00	2.32e+00	3.00e+00	5.36e-04	1.07e-03	1.88e-03	5.29e+00	5.29e+00	6.09e+00	1.02e-02	1.02e-02	1.80e-02
chr14	47153895	47189413	ENSMUSG00000037580	0.4495	-	Gch1	protein_coding	9.05e-08	4.49e-06	2.88128	5.55e+00	1.49e-02	8.46e+00	1.12e-01	5.39e+00	6.00e+00	5.25e+00	1.30e-02	2.00e-02	1.17e-02	8.64e+00	8.38e+00	8.36e+00	1.26e-01	1.05e-01	1.04e-01
chr1	173630917	173647928	ENSMUSG00000043263	0.3789	+	Ifi209	protein_coding	1.35e-07	6.62e-06	2.78828	4.78e+00	4.06e-03	7.60e+00	2.89e-02	4.17e+00	5.32e+00	4.86e+00	2.46e-03	5.65e-03	4.06e-03	7.79e+00	7.06e+00	7.95e+00	3.19e-02	1.91e-02	3.57e-02
chr2	129364570	129371139	ENSMUSG00000027398	0.4534	-	Il1b	protein_coding	1.50e-07	7.20e-06	2.81522	5.87e+00	1.96e-02	8.74e+00	1.40e-01	5.75e+00	6.41e+00	5.46e+00	1.73e-02	2.75e-02	1.41e-02	8.67e+00	8.71e+00	8.85e+00	1.33e-01	1.37e-01	1.51e-01
chr1	78658038	78671512	ENSMUSG00000079470	0.4361	+	Utp14b	protein_coding	1.52e-07	7.21e-06	-3.57154	5.08e+00	5.74e-03	1.58e+00	3.31e-04	5.17e+00	5.61e+00	4.46e+00	5.80e-03	7.95e-03	3.48e-03	1.58e+00	1.58e+00	1.58e+00	3.31e-04	3.31e-04	3.31e-04
chr4	10988662	11007927	ENSMUSG00000049969	0.4376	-	Plekhf2	protein_coding	2.10e-07	9.80e-06	2.76587	5.39e+00	1.23e-02	8.15e+00	8.52e-02	5.36e+00	5.49e+00	5.32e+00	1.20e-02	1.32e-02	1.17e-02	7.98e+00	8.36e+00	8.10e+00	7.54e-02	9.82e-02	8.20e-02
chr5	105214907	105293695	ENSMUSG00000079362	0.4146	-	Gm43302	protein_coding	2.16e-07	9.80e-06	3.01560	2.76e+00	1.83e-03	5.93e+00	1.67e-02	2.00e+00	3.70e+00	2.58e+00	8.21e-04	3.29e-03	1.37e-03	5.78e+00	5.75e+00	6.27e+00	1.48e-02	1.45e-02	2.08e-02
chr11	53770014	53778374	ENSMUSG00000018899	0.5246	+	Irf1	protein_coding	2.16e-07	9.80e-06	3.05638	8.32e+00	3.83e-02	1.14e+01	3.19e-01	8.39e+00	8.03e+00	8.52e+00	3.99e-02	3.11e-02	4.38e-02	1.12e+01	1.16e+01	1.13e+01	2.90e-01	3.73e-01	2.95e-01

Download the DEG result list for K_vs_KL4.
Download the whole result list for K_vs_KL4.

DEG table for the contrast CL4_vs_KL4

The following table presents the top 10% statistically significant genes (use the download links below the table to retrieve the whole list) for the contrast CL4_vs_KL4. 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_CL4_vs_KL4	log2_normalized_mean_counts_CL4	rpgm_normalized_mean_counts_CL4	log2_normalized_mean_counts_KL4	rpgm_normalized_mean_counts_KL4	log2_normalized_counts_DK3R7-C1L4	log2_normalized_counts_DK3R8-C2L4	rpgm_normalized_counts_DK3R7-C1L4	rpgm_normalized_counts_DK3R8-C2L4	log2_normalized_counts_DK3R10b-K23L4	log2_normalized_counts_DK3R11-K2L4	log2_normalized_counts_DK3R12-K3L4	rpgm_normalized_counts_DK3R10b-K23L4	rpgm_normalized_counts_DK3R11-K2L4	rpgm_normalized_counts_DK3R12-K3L4	LN	AR	MD	MN	QN	KN	CM	BT
chr6	54982580	54992950	ENSMUSG00000002797	0.4386	-	Ggct	protein_coding	0.000180	0.572	-2.207400	5.85e+00	6.94e-03	3.61e+00	1.41e-03	6.09e+00	5.61e+00	8.09e-03	5.79e-03	3.58e+00	4.09e+00	3.17e+00	1.33e-03	1.93e-03	9.66e-04	0	0	0	0	0	0	0	0
chr5	99955935	99978938	ENSMUSG00000000568	0.3855	-	Hnrnpd	protein_coding	0.000434	0.688	-1.948510	6.70e+00	7.24e-03	4.71e+00	1.82e-03	6.94e+00	6.46e+00	8.45e-03	6.03e-03	5.25e+00	4.25e+00	4.64e+00	2.56e-03	1.25e-03	1.66e-03	0	0	0	0	0	0	0	0

Download the DEG result list for CL4_vs_KL4.
Download the whole result list for CL4_vs_KL4.

References

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