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 (20007 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 (2051 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 0.02248 average reads per 100 bp), iii) genes with read counts below the median read counts of the total normalized count distribution (11358 genes with cutoff value 16 normalized read counts). The total number of genes excluded due to the application of gene filters was 5298. The total (unified) number of genes excluded due to the application of all filters was 32595. The resulting gene counts table was subjected to differential expression analysis for the contrasts WT versus KPM using the Bioconductor package DESeq. The final numbers of differentially expressed genes were (per contrast): for the contrast WT versus KPM, 2344 (735) statistically significant genes were found with a p-value (FDR or adjusted p-value) threshold of 0.05 and of these, 650 (132) were up-regulated, 1694 (603) 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: WT, KPM
Samples included: wt1, KPM1, KPM2, KPM3
Samples excluded: none
Requested contrasts: WT_vs_KPM
Library sizes:

wt1: 50343382
KPM1: 28487937
KPM2: 40280648
KPM3: 46204543

Annotation: download
Organism: mouse (Mus musculus), genome version alias mm10
Annotation source: Ensembl genomes
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
- 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, mean-difference plot, mean-variance plot, DEG biotype detection, volcano plot, DEG heatmap
Figure format: png, pdf
Output directory: /data/images/proton/run326/www/metaseqr_WT_vs_KPM
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: 08 minutes 51 seconds

Filtered genes

Number of filtered genes: 32595 which is the union of

Filtered because of zero reads: 20007
Filtered because of exon filters: 0
Filtered because of gene filters: 12588 which is the union of
- length: 2051 genes with filter cutoff value 500
- avg.reads: 3180 genes with filter cutoff value 0.02247696
- expression: 11358 genes further decomposed to (filter name, filtered genes, filter cutoff):
- - median: 11358 genes with filter cutoff value 16
- biotype: 66 genes with filter cutoff value rRNA, IG_V_pseudogene, TR_V_pseudogene

Differentially expressed genes

Number of differentially expressed genes per contrast:

WT_vs_KPM: 2344 (735) statistically significant genes of which 650 (132) up regulated, 1694 (603) 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

[ 2017-01-09 19:04:39 ] 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...
[ 2017-01-09 19:07:40 ] INFO 2017-01-09 19:07:40: Data processing started...
[ 2017-01-09 19:07:40 ] INFO Read counts file: imported sam/bam/bed files
[ 2017-01-09 19:07:40 ] INFO Conditions: WT, KPM
[ 2017-01-09 19:07:40 ] INFO Samples to include: wt1, KPM1, KPM2, KPM3
[ 2017-01-09 19:07:40 ] INFO Samples to exclude: none
[ 2017-01-09 19:07:40 ] INFO Requested contrasts: WT_vs_KPM
[ 2017-01-09 19:07:40 ] INFO Annotation: download
[ 2017-01-09 19:07:40 ] INFO Organism: mm10
[ 2017-01-09 19:07:40 ] INFO Reference source: ensembl
[ 2017-01-09 19:07:40 ] INFO Count type: utr
[ 2017-01-09 19:07:40 ] INFO Exon filters: none applied
[ 2017-01-09 19:07:40 ] INFO Gene filters: length, avg.reads, expression, biotype
[ 2017-01-09 19:07:40 ] INFO length:
[ 2017-01-09 19:07:40 ] INFO length: 500
[ 2017-01-09 19:07:40 ] INFO avg.reads:
[ 2017-01-09 19:07:40 ] INFO average.per.bp: 100
[ 2017-01-09 19:07:40 ] INFO quantile: 0.25
[ 2017-01-09 19:07:40 ] INFO expression:
[ 2017-01-09 19:07:40 ] INFO median: TRUE
[ 2017-01-09 19:07:40 ] INFO mean: FALSE
[ 2017-01-09 19:07:40 ] INFO quantile: NA
[ 2017-01-09 19:07:40 ] INFO known: NA
[ 2017-01-09 19:07:40 ] INFO custom: NA
[ 2017-01-09 19:07:40 ] INFO biotype:
[ 2017-01-09 19:07:40 ] INFO pseudogene: FALSE
[ 2017-01-09 19:07:40 ] INFO snRNA: FALSE
[ 2017-01-09 19:07:40 ] INFO protein_coding: FALSE
[ 2017-01-09 19:07:40 ] INFO antisense: FALSE
[ 2017-01-09 19:07:40 ] INFO miRNA: FALSE
[ 2017-01-09 19:07:40 ] INFO snoRNA: FALSE
[ 2017-01-09 19:07:40 ] INFO lincRNA: FALSE
[ 2017-01-09 19:07:40 ] INFO processed_transcript: FALSE
[ 2017-01-09 19:07:40 ] INFO misc_RNA: FALSE
[ 2017-01-09 19:07:40 ] INFO rRNA: TRUE
[ 2017-01-09 19:07:40 ] INFO sense_intronic: FALSE
[ 2017-01-09 19:07:40 ] INFO sense_overlapping: FALSE
[ 2017-01-09 19:07:40 ] INFO polymorphic_pseudogene: FALSE
[ 2017-01-09 19:07:40 ] INFO IG_C_gene: FALSE
[ 2017-01-09 19:07:40 ] INFO IG_J_gene: FALSE
[ 2017-01-09 19:07:40 ] INFO IG_D_gene: FALSE
[ 2017-01-09 19:07:40 ] INFO IG_LV_gene: FALSE
[ 2017-01-09 19:07:40 ] INFO IG_V_gene: FALSE
[ 2017-01-09 19:07:40 ] INFO IG_V_pseudogene: TRUE
[ 2017-01-09 19:07:40 ] INFO TR_V_gene: FALSE
[ 2017-01-09 19:07:40 ] INFO TR_V_pseudogene: TRUE
[ 2017-01-09 19:07:40 ] INFO three_prime_overlapping_ncrna: FALSE
[ 2017-01-09 19:07:40 ] INFO Filter application: postnorm
[ 2017-01-09 19:07:40 ] INFO Normalization algorithm: deseq
[ 2017-01-09 19:07:40 ] INFO Normalization arguments:
[ 2017-01-09 19:07:40 ] INFO locfunc:
[ 2017-01-09 19:07:40 ] INFO [[
[ 2017-01-09 19:07:40 ] INFO list(locfunc = function (x, na.rm = FALSE) standardGeneric("median"))
[ 2017-01-09 19:07:40 ] INFO locfunc
[ 2017-01-09 19:07:40 ] INFO Statistical algorithm: deseq
[ 2017-01-09 19:07:40 ] INFO Statistical arguments:
[ 2017-01-09 19:07:40 ] INFO deseq: blind, fit-only, local
[ 2017-01-09 19:07:40 ] INFO Meta-analysis method: none
[ 2017-01-09 19:07:40 ] INFO Multiple testing correction: BH
[ 2017-01-09 19:07:40 ] INFO p-value threshold: 0.05
[ 2017-01-09 19:07:40 ] INFO Logarithmic transformation offset: 1
[ 2017-01-09 19:07:40 ] INFO Quality control plots: mds, biodetection, countsbio, saturation, readnoise, filtered, correl, pairwise, boxplot, gcbias, lengthbias, meandiff, meanvar, biodist, volcano, deheatmap
[ 2017-01-09 19:07:40 ] INFO Figure format: png, pdf
[ 2017-01-09 19:07:40 ] INFO Output directory: /data/images/proton/run326/www/metaseqr_WT_vs_KPM
[ 2017-01-09 19:07:40 ] INFO Output data: annotation, p.value, adj.p.value, fold.change, counts, flags
[ 2017-01-09 19:07:40 ] INFO Output scale(s): log2, rpgm
[ 2017-01-09 19:07:40 ] INFO Output values: normalized
[ 2017-01-09 19:07:40 ] INFO Downloading gene annotation for mm10...
[ 2017-01-09 19:07:48 ] INFO Downloading transcript annotation for mm10...
[ 2017-01-09 19:09:53 ] INFO Converting annotation to GenomicRanges object...
[ 2017-01-09 19:09:53 ] INFO Merging transcript 3' UTRs to create unique gene/transcript models...
[ 2017-01-09 19:11:28 ] INFO 2017-01-09 19:11:28: Data processing started...
[ 2017-01-09 19:11:28 ] INFO Read counts file: imported sam/bam/bed files
[ 2017-01-09 19:11:28 ] INFO Conditions: WT, KPM
[ 2017-01-09 19:11:28 ] INFO Samples to include: wt1, KPM1, KPM2, KPM3
[ 2017-01-09 19:11:28 ] INFO Samples to exclude: none
[ 2017-01-09 19:11:28 ] INFO Requested contrasts: WT_vs_KPM
[ 2017-01-09 19:11:28 ] INFO Annotation: download
[ 2017-01-09 19:11:28 ] INFO Organism: mm10
[ 2017-01-09 19:11:28 ] INFO Reference source: ensembl
[ 2017-01-09 19:11:28 ] INFO Count type: gene
[ 2017-01-09 19:11:28 ] INFO Exon filters: none applied
[ 2017-01-09 19:11:28 ] INFO Gene filters: length, avg.reads, expression, biotype
[ 2017-01-09 19:11:28 ] INFO length:
[ 2017-01-09 19:11:28 ] INFO length: 500
[ 2017-01-09 19:11:28 ] INFO avg.reads:
[ 2017-01-09 19:11:28 ] INFO average.per.bp: 100
[ 2017-01-09 19:11:28 ] INFO quantile: 0.25
[ 2017-01-09 19:11:28 ] INFO expression:
[ 2017-01-09 19:11:28 ] INFO median: TRUE
[ 2017-01-09 19:11:28 ] INFO mean: FALSE
[ 2017-01-09 19:11:28 ] INFO quantile: NA
[ 2017-01-09 19:11:28 ] INFO known: NA
[ 2017-01-09 19:11:28 ] INFO custom: NA
[ 2017-01-09 19:11:28 ] INFO biotype:
[ 2017-01-09 19:11:28 ] INFO pseudogene: FALSE
[ 2017-01-09 19:11:28 ] INFO snRNA: FALSE
[ 2017-01-09 19:11:28 ] INFO protein_coding: FALSE
[ 2017-01-09 19:11:28 ] INFO antisense: FALSE
[ 2017-01-09 19:11:28 ] INFO miRNA: FALSE
[ 2017-01-09 19:11:28 ] INFO snoRNA: FALSE
[ 2017-01-09 19:11:28 ] INFO lincRNA: FALSE
[ 2017-01-09 19:11:28 ] INFO processed_transcript: FALSE
[ 2017-01-09 19:11:28 ] INFO misc_RNA: FALSE
[ 2017-01-09 19:11:28 ] INFO rRNA: TRUE
[ 2017-01-09 19:11:28 ] INFO sense_intronic: FALSE
[ 2017-01-09 19:11:28 ] INFO sense_overlapping: FALSE
[ 2017-01-09 19:11:28 ] INFO polymorphic_pseudogene: FALSE
[ 2017-01-09 19:11:28 ] INFO IG_C_gene: FALSE
[ 2017-01-09 19:11:28 ] INFO IG_J_gene: FALSE
[ 2017-01-09 19:11:28 ] INFO IG_D_gene: FALSE
[ 2017-01-09 19:11:28 ] INFO IG_LV_gene: FALSE
[ 2017-01-09 19:11:28 ] INFO IG_V_gene: FALSE
[ 2017-01-09 19:11:28 ] INFO IG_V_pseudogene: TRUE
[ 2017-01-09 19:11:28 ] INFO TR_V_gene: FALSE
[ 2017-01-09 19:11:28 ] INFO TR_V_pseudogene: TRUE
[ 2017-01-09 19:11:28 ] INFO three_prime_overlapping_ncrna: FALSE
[ 2017-01-09 19:11:28 ] INFO Filter application: postnorm
[ 2017-01-09 19:11:28 ] INFO Normalization algorithm: deseq
[ 2017-01-09 19:11:28 ] INFO Normalization arguments:
[ 2017-01-09 19:11:28 ] INFO locfunc:
[ 2017-01-09 19:11:28 ] INFO [[
[ 2017-01-09 19:11:28 ] INFO list(locfunc = function (x, na.rm = FALSE) standardGeneric("median"))
[ 2017-01-09 19:11:28 ] INFO locfunc
[ 2017-01-09 19:11:28 ] INFO Statistical algorithm: deseq
[ 2017-01-09 19:11:28 ] INFO Statistical arguments:
[ 2017-01-09 19:11:28 ] INFO deseq: blind, fit-only, local
[ 2017-01-09 19:11:28 ] INFO Meta-analysis method: none
[ 2017-01-09 19:11:28 ] INFO Multiple testing correction: BH
[ 2017-01-09 19:11:28 ] INFO p-value threshold: 0.05
[ 2017-01-09 19:11:28 ] INFO Logarithmic transformation offset: 1
[ 2017-01-09 19:11:28 ] INFO Quality control plots: mds, biodetection, countsbio, saturation, readnoise, filtered, correl, pairwise, boxplot, gcbias, lengthbias, meandiff, meanvar, biodist, volcano, deheatmap
[ 2017-01-09 19:11:28 ] INFO Figure format: png, pdf
[ 2017-01-09 19:11:28 ] INFO Output directory: /data/images/proton/run326/www/metaseqr_WT_vs_KPM
[ 2017-01-09 19:11:28 ] INFO Output data: annotation, p.value, adj.p.value, fold.change, counts, flags
[ 2017-01-09 19:11:28 ] INFO Output scale(s): log2, rpgm
[ 2017-01-09 19:11:28 ] INFO Output values: normalized
[ 2017-01-09 19:11:28 ] INFO Downloading gene annotation for mm10...
[ 2017-01-09 19:11:35 ] INFO Converting annotation to GenomicRanges object...
[ 2017-01-09 19:11:35 ] INFO Reading bam file GSR11_wt1.bam for sample with name wt1. This might take some time...
[ 2017-01-09 19:11:35 ] WARN Information regarding strandedness of the reads is not correctly provided! Assuming unstranded...
[ 2017-01-09 19:11:35 ] INFO Reading bam file GSR12_KPM1.bam for sample with name KPM1. This might take some time...
[ 2017-01-09 19:11:35 ] WARN Information regarding strandedness of the reads is not correctly provided! Assuming unstranded...
[ 2017-01-09 19:11:35 ] INFO Reading bam file GSR13_KPM3.bam for sample with name KPM2. This might take some time...
[ 2017-01-09 19:11:35 ] WARN Information regarding strandedness of the reads is not correctly provided! Assuming unstranded...
[ 2017-01-09 19:11:35 ] INFO Reading bam file GSR14_KPM4.bam for sample with name KPM3. This might take some time...
[ 2017-01-09 19:11:35 ] WARN Information regarding strandedness of the reads is not correctly provided! Assuming unstranded...
[ 2017-01-09 19:12:12 ] INFO Counting reads overlapping with given annotation...
[ 2017-01-09 19:12:12 ] INFO ...for single-end reads...
[ 2017-01-09 19:12:12 ] INFO ...ignoring strandedness...
[ 2017-01-09 19:12:34 ] INFO Counting reads overlapping with given annotation...
[ 2017-01-09 19:12:34 ] INFO ...for single-end reads...
[ 2017-01-09 19:12:34 ] INFO ...ignoring strandedness...
[ 2017-01-09 19:12:35 ] INFO Counting reads overlapping with given annotation...
[ 2017-01-09 19:12:35 ] INFO ...for single-end reads...
[ 2017-01-09 19:12:35 ] INFO ...ignoring strandedness...
[ 2017-01-09 19:12:46 ] INFO Counting reads overlapping with given annotation...
[ 2017-01-09 19:12:46 ] INFO ...for single-end reads...
[ 2017-01-09 19:12:46 ] INFO ...ignoring strandedness...
[ 2017-01-09 19:16:45 ] INFO Exporting raw read counts table to /data/images/proton/run326/www/metaseqr_WT_vs_KPM/lists/raw_counts_table.txt.gz
[ 2017-01-09 19:16:46 ] INFO Saving gene model to /data/images/proton/run326/www/metaseqr_WT_vs_KPM/data/gene_model.RData
[ 2017-01-09 19:16:47 ] INFO Removing genes with zero counts in all samples...
[ 2017-01-09 19:16:47 ] INFO Normalizing with: deseq
[ 2017-01-09 19:16:47 ] INFO Applying gene filter length...
[ 2017-01-09 19:16:47 ] INFO Threshold below which ignored: 500
[ 2017-01-09 19:16:47 ] INFO Applying gene filter avg.reads...
[ 2017-01-09 19:16:47 ] INFO Threshold below which ignored: 0.0224769611148573
[ 2017-01-09 19:16:47 ] INFO Applying gene filter expression...
[ 2017-01-09 19:16:47 ] INFO Threshold below which ignored: 16
[ 2017-01-09 19:16:47 ] INFO Applying gene filter biotype...
[ 2017-01-09 19:16:47 ] INFO Biotypes ignored: rRNA, IG_V_pseudogene, TR_V_pseudogene
[ 2017-01-09 19:16:47 ] INFO 32595 genes filtered out
[ 2017-01-09 19:16:47 ] INFO 16953 genes remain after filtering
[ 2017-01-09 19:16:47 ] INFO Running statistical tests with: deseq
[ 2017-01-09 19:16:48 ] INFO Contrast: WT_vs_KPM
[ 2017-01-09 19:18:17 ] INFO Contrast WT_vs_KPM: found 2344 genes
[ 2017-01-09 19:18:17 ] INFO Exporting and compressing normalized read counts table to /data/images/proton/run326/www/metaseqr_WT_vs_KPM/lists/normalized_counts_table.txt
[ 2017-01-09 19:18:18 ] INFO Building output files...
[ 2017-01-09 19:18:20 ] INFO Contrast: WT_vs_KPM
[ 2017-01-09 19:18:20 ] INFO Adding non-filtered data...
[ 2017-01-09 19:18:20 ] INFO binding annotation...
[ 2017-01-09 19:18:20 ] INFO binding p-values...
[ 2017-01-09 19:18:20 ] INFO binding FDRs...
[ 2017-01-09 19:18:21 ] INFO binding log2 normalized fold changes...
[ 2017-01-09 19:18:21 ] INFO binding all normalized counts for WT...
[ 2017-01-09 19:18:21 ] INFO binding all normalized counts for KPM...
[ 2017-01-09 19:18:21 ] INFO binding filtering flags...
[ 2017-01-09 19:18:22 ] INFO Writing output...
[ 2017-01-09 19:18:22 ] INFO Adding filtered data...
[ 2017-01-09 19:18:22 ] INFO binding annotation...
[ 2017-01-09 19:18:22 ] INFO binding p-values...
[ 2017-01-09 19:18:22 ] INFO binding FDRs...
[ 2017-01-09 19:18:23 ] INFO binding log2 normalized fold changes...
[ 2017-01-09 19:18:23 ] INFO binding all normalized counts for WT...
[ 2017-01-09 19:18:23 ] INFO binding all normalized counts for KPM...
[ 2017-01-09 19:18:23 ] INFO binding filtering flags...
[ 2017-01-09 19:18:24 ] INFO Writing output...
[ 2017-01-09 19:18:25 ] INFO Creating quality control graphs...
[ 2017-01-09 19:18:25 ] INFO Plotting in png format...
[ 2017-01-09 19:18:25 ] INFO Plotting mds...
[ 2017-01-09 19:18:25 ] INFO Plotting biodetection...
[ 2017-01-09 19:18:26 ] INFO Plotting countsbio...
[ 2017-01-09 19:18:27 ] INFO Plotting saturation...
[ 2017-01-09 19:18:32 ] INFO Plotting readnoise...
[ 2017-01-09 19:18:32 ] INFO Plotting correl...
[ 2017-01-09 19:18:33 ] INFO Plotting pairwise...
[ 2017-01-09 19:18:37 ] INFO Plotting boxplot...
[ 2017-01-09 19:18:37 ] INFO Plotting gcbias...
[ 2017-01-09 19:18:37 ] INFO Plotting lengthbias...
[ 2017-01-09 19:18:38 ] INFO Plotting meandiff...
[ 2017-01-09 19:18:38 ] WARN Cannot create a mean-difference plot with one sample per condition! Skipping...
[ 2017-01-09 19:18:38 ] INFO Plotting meanvar...
[ 2017-01-09 19:18:38 ] WARN Cannot create a mean-variance plot with one sample per condition! Skipping...
[ 2017-01-09 19:18:42 ] INFO Plotting boxplot...
[ 2017-01-09 19:18:42 ] INFO Plotting gcbias...
[ 2017-01-09 19:18:42 ] INFO Plotting lengthbias...
[ 2017-01-09 19:18:43 ] INFO Plotting meandiff...
[ 2017-01-09 19:18:43 ] WARN Cannot create a mean-difference plot with one sample per condition! Skipping...
[ 2017-01-09 19:18:43 ] INFO Plotting meanvar...
[ 2017-01-09 19:18:44 ] WARN Cannot create a mean-variance plot with one sample per condition! Skipping...
[ 2017-01-09 19:18:47 ] INFO Plotting biodist...
[ 2017-01-09 19:18:47 ] INFO Contrast: WT_vs_KPM
[ 2017-01-09 19:18:47 ] INFO Plotting volcano...
[ 2017-01-09 19:18:47 ] INFO Contrast: WT_vs_KPM
[ 2017-01-09 19:18:47 ] INFO Plotting deheatmap...
[ 2017-01-09 19:18:47 ] INFO Contrast: WT_vs_KPM
[ 2017-01-09 19:19:24 ] INFO Plotting filtered...
[ 2017-01-09 19:19:24 ] INFO Plotting in pdf format...
[ 2017-01-09 19:19:24 ] INFO Plotting mds...
[ 2017-01-09 19:19:24 ] INFO Plotting biodetection...
[ 2017-01-09 19:19:25 ] INFO Plotting countsbio...
[ 2017-01-09 19:19:26 ] INFO Plotting saturation...
[ 2017-01-09 19:19:29 ] INFO Plotting readnoise...
[ 2017-01-09 19:19:30 ] INFO Plotting correl...
[ 2017-01-09 19:19:30 ] INFO Plotting pairwise...
[ 2017-01-09 19:19:32 ] INFO Plotting boxplot...
[ 2017-01-09 19:19:32 ] INFO Plotting gcbias...
[ 2017-01-09 19:19:33 ] INFO Plotting lengthbias...
[ 2017-01-09 19:19:33 ] INFO Plotting meandiff...
[ 2017-01-09 19:19:33 ] WARN Cannot create a mean-difference plot with one sample per condition! Skipping...
[ 2017-01-09 19:19:34 ] INFO Plotting meanvar...
[ 2017-01-09 19:19:34 ] WARN Cannot create a mean-variance plot with one sample per condition! Skipping...
[ 2017-01-09 19:19:38 ] INFO Plotting boxplot...
[ 2017-01-09 19:19:38 ] INFO Plotting gcbias...
[ 2017-01-09 19:19:38 ] INFO Plotting lengthbias...
[ 2017-01-09 19:19:39 ] INFO Plotting meandiff...
[ 2017-01-09 19:19:39 ] WARN Cannot create a mean-difference plot with one sample per condition! Skipping...
[ 2017-01-09 19:19:40 ] INFO Plotting meanvar...
[ 2017-01-09 19:19:40 ] WARN Cannot create a mean-variance plot with one sample per condition! Skipping...
[ 2017-01-09 19:19:43 ] INFO Plotting biodist...
[ 2017-01-09 19:19:43 ] INFO Contrast: WT_vs_KPM
[ 2017-01-09 19:19:44 ] INFO Plotting volcano...
[ 2017-01-09 19:19:44 ] INFO Contrast: WT_vs_KPM
[ 2017-01-09 19:19:44 ] INFO Plotting deheatmap...
[ 2017-01-09 19:19:44 ] INFO Contrast: WT_vs_KPM
[ 2017-01-09 19:20:19 ] INFO Plotting filtered...
[ 2017-01-09 19:20:19 ] INFO Creating HTML report...
[ 2017-01-09 19:20:19 ] 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 KPM

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 KPM

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 WT_vs_KPM

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 WT_vs_KPM

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 WT_vs_KPM

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Results

DEG table for the contrast WT_vs_KPM

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 WT_vs_KPM. 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_WT_vs_KPM	log2_normalized_counts_wt1	rpgm_normalized_counts_wt1	log2_normalized_counts_KPM1	log2_normalized_counts_KPM2	log2_normalized_counts_KPM3	rpgm_normalized_counts_KPM1	rpgm_normalized_counts_KPM2	rpgm_normalized_counts_KPM3
chr4	136880129	136886187	ENSMUSG00000036905	0.4925	-	C1qb	protein_coding	1.23e-18	1.33e-14	-1.53e+01	1.68e+01	1.82e+01	1.58e+00	0.00e+00	2.00e+00	3.30e-04	0.00e+00	4.95e-04
chr4	136895917	136898803	ENSMUSG00000036887	0.5535	-	C1qa	protein_coding	1.57e-18	1.33e-14	-1.33e+01	1.49e+01	1.06e+01	2.32e+00	1.58e+00	0.00e+00	1.39e-03	6.93e-04	0.00e+00
chr10	117287796	117292868	ENSMUSG00000069515	0.4609	-	Lyz1	protein_coding	2.81e-18	1.59e-14	-1.47e+01	1.67e+01	2.03e+01	2.58e+00	1.00e+00	2.00e+00	9.86e-04	1.97e-04	5.91e-04
chr4	136889804	136893065	ENSMUSG00000036896	0.5512	-	C1qc	protein_coding	6.77e-18	2.87e-14	-1.28e+01	1.52e+01	1.16e+01	3.70e+00	1.00e+00	0.00e+00	3.68e-03	3.07e-04	0.00e+00
chr5	90772435	90773383	ENSMUSG00000029373	0.5195	+	Pf4	protein_coding	1.54e-17	5.23e-14	-1.26e+01	1.49e+01	3.32e+01	3.32e+00	0.00e+00	2.00e+00	9.49e-03	0.00e+00	3.16e-03
chr7	30413760	30417585	ENSMUSG00000030579	0.4851	+	Tyrobp	protein_coding	2.12e-17	6.00e-14	-1.34e+01	1.38e+01	3.75e+00	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	2.61e-04
chr11	106311341	106314762	ENSMUSG00000040592	0.5406	-	Cd79b	protein_coding	2.66e-17	6.45e-14	-1.35e+01	1.35e+01	3.44e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr17	34282744	34287827	ENSMUSG00000036594	0.4636	-	H2-Aa	protein_coding	6.26e-17	1.33e-13	-1.21e+01	1.51e+01	7.00e+00	4.25e+00	2.00e+00	0.00e+00	3.54e-03	5.90e-04	0.00e+00
chr3	95526786	95556403	ENSMUSG00000038642	0.4437	+	Ctss	protein_coding	1.39e-16	2.62e-13	-1.22e+01	1.43e+01	6.90e-01	3.00e+00	1.00e+00	1.58e+00	2.36e-04	3.38e-05	6.75e-05
chr3	87357881	87371073	ENSMUSG00000015854	0.4301	+	Cd5l	protein_coding	1.99e-16	3.38e-13	-1.24e+01	1.46e+01	1.90e+00	1.58e+00	2.81e+00	2.00e+00	1.52e-04	4.55e-04	2.27e-04
chr12	113418558	113422730	ENSMUSG00000076617	0.4992	-	Ighm	IG_C_gene	6.30e-16	9.71e-13	-1.26e+01	1.61e+01	1.68e+01	2.58e+00	4.17e+00	3.32e+00	1.20e-03	4.07e-03	2.16e-03
chr17	34263209	34269418	ENSMUSG00000073421	0.5225	+	H2-Ab1	protein_coding	1.08e-15	1.52e-12	-1.15e+01	1.52e+01	6.12e+00	4.86e+00	2.58e+00	2.32e+00	4.51e-03	8.05e-04	6.44e-04
chr11	70344152	70352031	ENSMUSG00000018924	0.5194	-	Alox15	protein_coding	1.63e-15	2.12e-12	-1.20e+01	1.63e+01	1.05e+01	5.61e+00	2.81e+00	2.32e+00	6.09e-03	7.62e-04	5.08e-04
chr4	130913125	130936141	ENSMUSG00000028581	0.5309	+	Laptm5	protein_coding	2.80e-15	3.39e-12	-1.19e+01	1.57e+01	2.36e+00	3.58e+00	3.00e+00	4.58e+00	4.78e-04	3.04e-04	9.99e-04
chr16	48842552	48844461	ENSMUSG00000061100	0.4288	+	Retnla	protein_coding	3.69e-15	4.17e-12	-1.24e+01	1.31e+01	4.60e+00	1.58e+00	0.00e+00	0.00e+00	1.05e-03	0.00e+00	0.00e+00
chr7	45233632	45239115	ENSMUSG00000030798	0.5450	-	Cd37	protein_coding	2.16e-14	2.17e-11	-1.12e+01	1.41e+01	3.20e+00	3.00e+00	2.58e+00	3.17e+00	1.28e-03	9.12e-04	1.46e-03
chr5	113818536	113832644	ENSMUSG00000048163	0.5032	-	Selplg	protein_coding	2.17e-14	2.17e-11	-1.12e+01	1.42e+01	1.32e+00	2.58e+00	2.81e+00	3.32e+00	3.54e-04	4.25e-04	6.38e-04
chr11	106377656	106388075	ENSMUSG00000001029	0.4884	-	Icam2	protein_coding	6.81e-14	6.41e-11	-1.12e+01	1.38e+01	1.34e+00	2.32e+00	1.58e+00	3.32e+00	3.84e-04	1.92e-04	8.64e-04
chr7	126408450	126414889	ENSMUSG00000030724	0.5179	-	Cd19	protein_coding	9.61e-14	8.57e-11	-1.10e+01	1.36e+01	1.96e+00	3.00e+00	2.58e+00	2.00e+00	1.09e-03	7.77e-04	4.66e-04
chr10	62493833	62527451	ENSMUSG00000020077	0.4550	-	Srgn	protein_coding	1.33e-13	1.13e-10	-1.09e+01	1.37e+01	4.09e-01	3.00e+00	2.58e+00	3.00e+00	2.08e-04	1.49e-04	2.08e-04
chr11	6506548	6520965	ENSMUSG00000020437	0.5216	-	Myo1g	protein_coding	2.64e-13	2.13e-10	-1.12e+01	1.34e+01	7.37e-01	1.58e+00	2.32e+00	2.58e+00	1.39e-04	2.77e-04	3.47e-04
chr4	140745865	140774236	ENSMUSG00000025330	0.5231	-	Padi4	protein_coding	6.57e-13	5.06e-10	-9.71e+00	1.42e+01	6.49e-01	5.75e+00	3.00e+00	2.00e+00	1.87e-03	2.47e-04	1.06e-04
chr17	56119680	56121946	ENSMUSG00000037095	0.5880	-	Lrg1	protein_coding	8.04e-13	5.92e-10	-9.73e+00	1.38e+01	6.11e+00	5.55e+00	0.00e+00	0.00e+00	2.03e-02	0.00e+00	0.00e+00
chr1	150449412	150466165	ENSMUSG00000006014	0.4048	-	Prg4	protein_coding	1.03e-12	7.31e-10	-1.08e+01	1.66e+01	6.06e+00	5.88e+00	6.43e+00	4.91e+00	3.46e-03	5.07e-03	1.73e-03
chr17	57358686	57483529	ENSMUSG00000004730	0.4116	+	Adgre1	protein_coding	1.77e-12	1.20e-09	-9.93e+00	1.41e+01	1.43e-01	4.25e+00	3.32e+00	4.70e+00	1.44e-04	7.21e-05	2.00e-04
chr11	46810800	46844332	ENSMUSG00000055546	0.4251	+	Timd4	protein_coding	1.97e-12	1.28e-09	-1.06e+01	1.32e+01	2.84e-01	2.58e+00	2.32e+00	2.81e+00	1.49e-04	1.19e-04	1.79e-04
chr8	3665754	3669259	ENSMUSG00000013974	0.4932	+	Mcemp1	protein_coding	3.57e-12	2.24e-09	-1.11e+01	1.26e+01	1.73e+00	1.58e+00	1.58e+00	1.00e+00	5.71e-04	5.71e-04	2.85e-04
chr7	3711409	3720391	ENSMUSG00000058818	0.4884	-	Pirb	protein_coding	5.62e-12	3.41e-09	-1.07e+01	1.30e+01	9.00e-01	1.58e+00	1.58e+00	3.17e+00	2.23e-04	2.23e-04	8.91e-04
chr1	164151838	164220277	ENSMUSG00000026579	0.4194	+	F5	protein_coding	6.24e-12	3.65e-09	-9.48e+00	1.41e+01	2.57e-01	5.13e+00	3.17e+00	4.91e+00	4.97e-04	1.17e-04	4.24e-04
chr11	83703991	83706268	ENSMUSG00000069792	0.4451	+	Wfdc17	protein_coding	7.86e-12	4.44e-09	-1.02e+01	1.27e+01	2.99e+00	3.91e+00	0.00e+00	0.00e+00	6.15e-03	0.00e+00	0.00e+00
chr17	34809092	34823464	ENSMUSG00000015451	0.5544	-	C4a	processed_transcript	1.16e-11	6.28e-09	-9.31e+00	1.64e+01	5.93e+00	8.46e+00	3.00e+00	5.43e+00	2.44e-02	4.87e-04	2.92e-03
chr15	78559169	78572783	ENSMUSG00000033220	0.5340	-	Rac2	protein_coding	1.19e-11	6.28e-09	-9.83e+00	1.34e+01	7.82e-01	3.70e+00	3.70e+00	3.17e+00	8.81e-04	8.81e-04	5.88e-04
chr19	11067471	11081102	ENSMUSG00000024730	0.4271	-	Ms4a8a	protein_coding	1.32e-11	6.79e-09	-1.12e+01	1.22e+01	3.43e-01	1.58e+00	0.00e+00	1.00e+00	1.47e-04	0.00e+00	7.34e-05
chr1	170958617	170976547	ENSMUSG00000026656	0.4545	-	Fcgr2b	protein_coding	1.50e-11	7.46e-09	-1.08e+01	1.26e+01	3.43e-01	0.00e+00	2.81e+00	1.00e+00	0.00e+00	3.35e-04	5.58e-05
chr7	24897381	24902197	ENSMUSG00000003379	0.5227	+	Cd79a	protein_coding	2.09e-11	1.01e-08	-9.43e+00	1.37e+01	2.82e+00	4.25e+00	4.58e+00	4.00e+00	3.74e-03	4.78e-03	3.11e-03
chr18	60803848	60812646	ENSMUSG00000024610	0.5080	+	Cd74	protein_coding	2.46e-11	1.16e-08	-9.11e+00	1.64e+01	1.01e+01	8.75e+00	2.81e+00	5.43e+00	4.89e-02	6.82e-04	4.77e-03
chr11	32187541	32200279	ENSMUSG00000020279	0.5178	-	Il9r	protein_coding	3.06e-11	1.40e-08	-1.07e+01	1.23e+01	3.89e-01	2.32e+00	1.00e+00	1.00e+00	3.14e-04	7.85e-05	7.85e-05
chr8	88626563	88636128	ENSMUSG00000031662	0.5140	-	Snx20	protein_coding	3.31e-11	1.48e-08	-1.05e+01	1.24e+01	5.69e-01	2.32e+00	1.58e+00	1.58e+00	4.18e-04	2.09e-04	2.09e-04
chr8	39581685	39642673	ENSMUSG00000025044	0.3639	-	Msr1	protein_coding	3.48e-11	1.51e-08	-9.90e+00	1.28e+01	1.20e-01	3.91e+00	1.58e+00	2.32e+00	2.30e-04	3.28e-05	6.56e-05
chr10	117277334	117282274	ENSMUSG00000069516	0.4710	-	Lyz2	protein_coding	3.75e-11	1.59e-08	-1.27e+01	1.85e+01	7.51e+01	7.30e+00	2.32e+00	1.00e+00	3.18e-02	8.10e-04	2.02e-04
chr10	51480679	51496613	ENSMUSG00000062593	0.4042	+	Lilrb4a	protein_coding	4.90e-11	2.02e-08	-9.08e+00	1.33e+01	6.43e-01	5.49e+00	3.00e+00	2.00e+00	2.76e-03	4.39e-04	1.88e-04
chr17	34728380	34743882	ENSMUSG00000073418	0.5498	-	C4b	protein_coding	5.10e-11	2.03e-08	-9.41e+00	1.70e+01	8.59e+00	9.16e+00	2.81e+00	3.00e+00	3.70e-02	3.87e-04	4.52e-04
chrX	20925454	20931555	ENSMUSG00000001128	0.4782	-	Cfp	protein_coding	5.15e-11	2.03e-08	-8.96e+00	1.57e+01	8.52e+00	6.83e+00	6.02e+00	7.07e+00	1.85e-02	1.05e-02	2.18e-02
chr6	48764464	48770851	ENSMUSG00000039264	0.4732	-	Gimap3	protein_coding	7.31e-11	2.82e-08	-1.06e+01	1.24e+01	8.26e-01	1.58e+00	1.58e+00	2.00e+00	3.13e-04	3.13e-04	4.70e-04
chr15	82997721	83033306	ENSMUSG00000058099	0.5134	-	Nfam1	protein_coding	8.01e-11	3.02e-08	-9.22e+00	1.31e+01	2.51e-01	4.95e+00	2.81e+00	2.81e+00	8.43e-04	1.69e-04	1.69e-04
chr11	16971206	17052381	ENSMUSG00000020120	0.4175	-	Plek	protein_coding	8.33e-11	3.07e-08	-9.24e+00	1.35e+01	1.42e-01	3.58e+00	5.09e+00	3.46e+00	1.36e-04	4.07e-04	1.23e-04
chr3	101275899	101287939	ENSMUSG00000027863	0.4928	-	Cd2	protein_coding	8.66e-11	3.12e-08	-1.03e+01	1.23e+01	4.31e-01	2.32e+00	2.00e+00	1.58e+00	3.32e-04	2.49e-04	1.66e-04
chr5	149264767	149288153	ENSMUSG00000060063	0.4839	+	Alox5ap	protein_coding	1.07e-10	3.78e-08	-9.88e+00	1.25e+01	2.42e-01	3.70e+00	1.58e+00	1.00e+00	5.13e-04	8.55e-05	4.28e-05
chr17	33555719	33607764	ENSMUSG00000024300	0.4636	+	Myo1f	protein_coding	1.32e-10	4.57e-08	-9.69e+00	1.29e+01	1.42e-01	3.32e+00	2.58e+00	3.46e+00	1.73e-04	9.61e-05	1.92e-04
chr10	57794335	57811830	ENSMUSG00000019872	0.3910	+	Smpdl3a	protein_coding	1.46e-10	4.94e-08	-9.47e+00	1.32e+01	5.27e-01	0.00e+00	5.09e+00	2.00e+00	0.00e+00	1.89e-03	1.71e-04
chr14	103814625	103844173	ENSMUSG00000022122	0.4093	-	Ednrb	protein_coding	1.64e-10	5.45e-08	-8.65e+00	1.38e+01	4.76e-01	5.67e+00	5.00e+00	4.46e+00	1.69e-03	1.05e-03	7.11e-04
chrX	9435252	9487771	ENSMUSG00000015340	0.3972	-	Cybb	protein_coding	1.74e-10	5.67e-08	-8.42e+00	1.46e+01	4.59e-01	6.27e+00	6.54e+00	5.36e+00	1.45e-03	1.75e-03	7.62e-04
chr1	171388954	171410298	ENSMUSG00000048865	0.4665	+	Arhgap30	protein_coding	2.02e-10	6.47e-08	-9.12e+00	1.32e+01	4.52e-01	4.32e+00	3.70e+00	4.25e+00	8.90e-04	5.62e-04	8.43e-04
chr11	34226815	34783892	ENSMUSG00000020143	0.4449	-	Dock2	protein_coding	2.35e-10	7.36e-08	-8.84e+00	1.35e+01	2.09e-02	4.70e+00	5.09e+00	4.00e+00	4.49e-05	5.92e-05	2.69e-05
chr17	34305877	34316199	ENSMUSG00000060586	0.4846	+	H2-Eb1	protein_coding	2.49e-10	7.67e-08	-7.69e+00	1.48e+01	2.77e+00	8.67e+00	2.32e+00	1.00e+00	3.94e-02	3.88e-04	9.69e-05
chr2	91082390	91115756	ENSMUSG00000002111	0.4989	+	Spi1	protein_coding	2.55e-10	7.73e-08	-9.36e+00	1.29e+01	2.29e-01	3.91e+00	3.32e+00	3.32e+00	4.20e-04	2.70e-04	2.70e-04
chr7	30865402	30880342	ENSMUSG00000030577	0.5054	-	Cd22	protein_coding	2.83e-10	8.42e-08	-9.60e+00	1.28e+01	4.67e-01	3.00e+00	2.32e+00	3.81e+00	4.69e-04	2.68e-04	8.70e-04
chr15	103453825	103498800	ENSMUSG00000022488	0.4234	+	Nckap1l	protein_coding	3.17e-10	9.26e-08	-9.09e+00	1.31e+01	1.90e-01	4.32e+00	4.17e+00	3.17e+00	4.22e-04	3.78e-04	1.78e-04
chr16	36934983	36963212	ENSMUSG00000022831	0.4295	+	Hcls1	protein_coding	3.65e-10	1.05e-07	-9.68e+00	1.26e+01	2.14e-01	3.32e+00	1.58e+00	3.17e+00	3.19e-04	7.08e-05	2.83e-04
chr5	113612354	113650426	ENSMUSG00000042190	0.5263	-	Cmklr1	protein_coding	3.93e-10	1.09e-07	-9.91e+00	1.24e+01	1.43e-01	2.32e+00	2.32e+00	2.81e+00	1.05e-04	1.05e-04	1.58e-04
chr14	32959547	33185508	ENSMUSG00000051506	0.4573	-	Wdfy4	protein_coding	3.93e-10	1.09e-07	-8.69e+00	1.35e+01	4.99e-02	5.04e+00	4.70e+00	4.52e+00	1.42e-04	1.11e-04	9.74e-05
chr1	171229572	171234365	ENSMUSG00000058715	0.4864	-	Fcer1g	protein_coding	4.09e-10	1.12e-07	-9.89e+00	1.25e+01	1.19e+00	1.58e+00	2.81e+00	3.00e+00	4.17e-04	1.25e-03	1.46e-03
chr9	51213708	51240080	ENSMUSG00000032053	0.4270	+	Pou2af1	protein_coding	5.54e-10	1.48e-07	-9.46e+00	1.25e+01	2.14e-01	3.91e+00	2.00e+00	2.32e+00	5.31e-04	1.14e-04	1.52e-04
chr14	63372837	63417187	ENSMUSG00000014453	0.4749	-	Blk	protein_coding	5.58e-10	1.48e-07	-9.96e+00	1.23e+01	1.12e-01	1.58e+00	3.00e+00	2.00e+00	4.51e-05	1.58e-04	6.76e-05
chr6	116410077	116461178	ENSMUSG00000025701	0.4476	-	Alox5	protein_coding	7.78e-10	2.03e-07	-1.00e+01	1.21e+01	8.87e-02	2.32e+00	0.00e+00	2.81e+00	7.83e-05	0.00e+00	1.17e-04
chr1	152800194	152836991	ENSMUSG00000026480	0.4698	+	Ncf2	protein_coding	8.65e-10	2.22e-07	-8.23e+00	1.37e+01	3.66e-01	5.70e+00	6.09e+00	3.91e+00	1.39e-03	1.82e-03	3.80e-04
chr9	45253837	45269149	ENSMUSG00000032089	0.5000	-	Il10ra	protein_coding	1.01e-09	2.55e-07	-9.78e+00	1.22e+01	3.06e-01	2.58e+00	2.58e+00	2.00e+00	3.27e-04	3.27e-04	1.96e-04
chr19	6998958	7019469	ENSMUSG00000024965	0.4977	-	Fermt3	protein_coding	1.44e-09	3.59e-07	-8.08e+00	1.38e+01	7.15e-01	6.02e+00	5.32e+00	5.86e+00	3.12e-03	1.90e-03	2.78e-03
chr17	29360914	29379660	ENSMUSG00000024013	0.5348	+	Fgd2	protein_coding	1.89e-09	4.65e-07	-8.49e+00	1.28e+01	3.83e-01	5.39e+00	3.17e+00	3.17e+00	2.19e-03	4.27e-04	4.27e-04
chr5	17781690	17888801	ENSMUSG00000002944	0.3716	-	Cd36	protein_coding	1.95e-09	4.71e-07	-9.17e+00	1.24e+01	5.20e-02	3.58e+00	3.17e+00	3.00e+00	1.03e-04	7.47e-05	6.54e-05
chr6	116633008	116673952	ENSMUSG00000042129	0.4884	-	Rassf4	protein_coding	2.51e-09	5.99e-07	-8.58e+00	1.30e+01	1.96e-01	3.91e+00	5.43e+00	2.32e+00	3.42e-04	1.03e-03	9.77e-05
chr11	95837216	95845734	ENSMUSG00000038811	0.5113	+	Gngt2	protein_coding	2.56e-09	5.99e-07	-8.45e+00	1.27e+01	7.68e-01	5.49e+00	2.58e+00	2.32e+00	5.17e-03	5.87e-04	4.70e-04
chr12	112760655	112768986	ENSMUSG00000052160	0.5368	+	Pld4	protein_coding	2.58e-09	5.99e-07	-8.37e+00	1.29e+01	9.41e-01	5.39e+00	4.25e+00	3.32e+00	4.92e-03	2.16e-03	1.08e-03
chr17	57066056	57078510	ENSMUSG00000002668	0.5057	-	Dennd1c	protein_coding	2.90e-09	6.64e-07	-9.92e+00	1.19e+01	3.11e-01	2.58e+00	0.00e+00	2.32e+00	4.01e-04	0.00e+00	3.21e-04
chr2	160363703	160367065	ENSMUSG00000074622	0.5555	-	Mafb	protein_coding	3.80e-09	8.58e-07	-9.90e+00	1.20e+01	1.23e+00	0.00e+00	3.00e+00	2.00e+00	0.00e+00	2.08e-03	8.92e-04
chr9	21526176	21545333	ENSMUSG00000057191	0.4736	+	AB124611	protein_coding	4.18e-09	9.33e-07	-9.10e+00	1.25e+01	2.95e-01	2.58e+00	3.81e+00	3.46e+00	2.61e-04	6.79e-04	5.22e-04
chr1	120474538	120505024	ENSMUSG00000026390	0.4694	-	Marco	protein_coding	4.55e-09	1.00e-06	-9.95e+00	1.17e+01	1.08e-01	2.32e+00	1.00e+00	1.58e+00	1.31e-04	3.28e-05	6.56e-05
chr6	48701582	48708225	ENSMUSG00000047867	0.4356	-	Gimap6	protein_coding	4.82e-09	1.05e-06	-1.01e+01	1.19e+01	5.56e-01	0.00e+00	2.32e+00	2.00e+00	0.00e+00	6.02e-04	4.52e-04
chr9	85320439	85327348	ENSMUSG00000032265	0.4436	-	Fam46a	protein_coding	6.74e-09	1.45e-06	-7.38e+00	1.38e+01	2.07e+00	7.36e+00	5.98e+00	4.91e+00	2.36e-02	8.97e-03	4.20e-03
chr7	106504239	106511671	ENSMUSG00000063286	0.4090	-	Gm8995	transcribed_unprocessed_pseudogene	7.04e-09	1.49e-06	-1.08e+01	1.12e+01	3.26e-01	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	1.35e-04
chr17	34238903	34245907	ENSMUSG00000041538	0.4778	+	H2-Ob	protein_coding	9.28e-09	1.94e-06	-1.00e+01	1.16e+01	4.40e-01	1.58e+00	1.00e+00	2.00e+00	2.86e-04	1.43e-04	4.28e-04
chr13	52583173	52648792	ENSMUSG00000021457	0.4700	+	Syk	protein_coding	1.06e-08	2.20e-06	-7.36e+00	1.47e+01	4.20e-01	7.43e+00	6.09e+00	8.03e+00	2.62e-03	1.02e-03	3.96e-03
chr16	19198396	19200351	ENSMUSG00000076937	0.4422	-	Iglc2	IG_C_gene	1.17e-08	2.38e-06	-1.06e+01	1.13e+01	1.29e+00	0.00e+00	1.00e+00	1.00e+00	0.00e+00	5.12e-04	5.12e-04
chr13	55523235	55528538	ENSMUSG00000035711	0.5396	-	Dok3	protein_coding	1.30e-08	2.62e-06	-8.00e+00	1.27e+01	1.27e+00	5.55e+00	4.81e+00	2.00e+00	8.67e-03	5.09e-03	5.66e-04
chr15	74762056	74763620	ENSMUSG00000034634	0.5208	-	Ly6d	protein_coding	1.40e-08	2.79e-06	-9.33e+00	1.20e+01	2.61e+00	0.00e+00	3.81e+00	2.00e+00	0.00e+00	8.31e-03	1.92e-03
chr1	138062861	138175708	ENSMUSG00000026395	0.3796	-	Ptprc	protein_coding	1.58e-08	3.11e-06	-8.73e+00	1.24e+01	4.63e-02	3.00e+00	4.00e+00	3.70e+00	6.20e-05	1.33e-04	1.06e-04
chr6	70719074	70740554	ENSMUSG00000105646	0.3970	+	Gm43291	lincRNA	2.16e-08	4.20e-06	-1.09e+01	1.09e+01	8.65e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr14	55941453	55944661	ENSMUSG00000022225	0.5039	-	Cma1	protein_coding	2.33e-08	4.50e-06	-7.81e+00	1.25e+01	1.75e+00	6.04e+00	2.58e+00	1.58e+00	2.03e-02	1.56e-03	6.23e-04
chr15	78244801	78262580	ENSMUSG00000071715	0.5247	+	Ncf4	protein_coding	2.36e-08	4.50e-06	-9.39e+00	1.17e+01	1.88e-01	2.58e+00	2.32e+00	2.00e+00	2.81e-04	2.25e-04	1.69e-04
chr9	44511787	44561877	ENSMUSG00000047880	0.4686	-	Cxcr5	protein_coding	2.64e-08	4.95e-06	-8.04e+00	1.28e+01	1.42e-01	4.70e+00	4.00e+00	5.29e+00	4.99e-04	2.99e-04	7.59e-04
chr7	126699773	126707787	ENSMUSG00000030707	0.5471	-	Coro1a	protein_coding	2.66e-08	4.95e-06	-7.09e+00	1.46e+01	3.19e+00	7.65e+00	6.69e+00	8.02e+00	2.50e-02	1.27e-02	3.23e-02
chr11	72961169	72993044	ENSMUSG00000020788	0.5179	+	Atp2a3	protein_coding	2.81e-08	5.18e-06	-7.02e+00	1.35e+01	3.66e-01	7.62e+00	5.70e+00	4.39e+00	6.15e-03	1.60e-03	6.27e-04
chr19	12460779	12465284	ENSMUSG00000046805	0.4567	+	Mpeg1	protein_coding	3.87e-08	7.05e-06	-7.98e+00	1.28e+01	1.53e+00	4.46e+00	4.25e+00	5.36e+00	4.66e-03	4.00e-03	8.88e-03
chrX	37874778	37878944	ENSMUSG00000064137	0.4298	-	Rhox8	protein_coding	4.57e-08	8.24e-06	-9.01e+00	1.18e+01	8.66e-01	3.00e+00	3.00e+00	2.32e+00	1.68e-03	1.68e-03	9.60e-04
chr10	77530252	77565708	ENSMUSG00000000290	0.5026	+	Itgb2	protein_coding	4.85e-08	8.65e-06	-6.94e+00	1.57e+01	1.51e+00	9.30e+00	7.08e+00	9.09e+00	1.77e-02	3.78e-03	1.53e-02
chr1	140084708	140183764	ENSMUSG00000026365	0.3624	-	Cfh	protein_coding	5.69e-08	1.00e-05	-7.35e+00	1.30e+01	8.44e-02	6.39e+00	4.95e+00	5.29e+00	8.38e-04	3.03e-04	3.84e-04
chrX	48146436	48161565	ENSMUSG00000031101	0.4816	+	Sash3	protein_coding	6.46e-08	1.13e-05	-9.25e+00	1.15e+01	1.87e-01	3.00e+00	1.00e+00	2.00e+00	4.63e-04	6.61e-05	1.98e-04
chr1	171051174	171064935	ENSMUSG00000059498	0.4641	-	Fcgr3	protein_coding	6.69e-08	1.16e-05	-7.19e+00	1.30e+01	6.16e-01	6.61e+00	6.00e+00	3.58e+00	7.05e-03	4.58e-03	7.99e-04
chr1	171839697	171890718	ENSMUSG00000038147	0.4093	+	Cd84	protein_coding	7.52e-08	1.29e-05	-8.63e+00	1.20e+01	7.79e-02	3.32e+00	2.58e+00	3.81e+00	1.76e-04	9.80e-05	2.55e-04
chr13	30749226	30766927	ENSMUSG00000021356	0.4666	+	Irf4	protein_coding	9.04e-08	1.53e-05	-8.95e+00	1.17e+01	1.86e-01	3.00e+00	2.00e+00	3.00e+00	3.95e-04	1.69e-04	3.95e-04
chr9	85332352	85348065	ENSMUSG00000111840	0.4051	-	RP24-216P6.3	lincRNA	9.59e-08	1.61e-05	-1.06e+01	1.06e+01	9.74e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr6	48684549	48692060	ENSMUSG00000054435	0.4348	+	Gimap4	protein_coding	9.98e-08	1.66e-05	-9.71e+00	1.13e+01	3.35e-01	0.00e+00	2.81e+00	0.00e+00	0.00e+00	7.99e-04	0.00e+00
chr16	20308387	20323065	ENSMUSG00000022818	0.4688	-	Cyp2ab1	protein_coding	1.04e-07	1.71e-05	-9.40e+00	1.14e+01	1.84e-01	1.58e+00	2.58e+00	1.58e+00	1.36e-04	3.41e-04	1.36e-04
chr4	134082448	134095082	ENSMUSG00000000682	0.4953	-	Cd52	protein_coding	1.05e-07	1.71e-05	-1.06e+01	1.06e+01	1.20e-01	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr6	123262111	123275265	ENSMUSG00000030144	0.3845	+	Clec4d	protein_coding	1.09e-07	1.76e-05	-8.46e+00	1.21e+01	3.39e-01	0.00e+00	4.46e+00	3.91e+00	0.00e+00	1.60e-03	1.06e-03
chr4	132974095	133001910	ENSMUSG00000028874	0.5003	+	Fgr	protein_coding	1.12e-07	1.79e-05	-8.41e+00	1.18e+01	1.31e-01	4.25e+00	3.17e+00	2.00e+00	6.47e-04	2.88e-04	1.08e-04
chr7	29134851	29153961	ENSMUSG00000030589	0.5072	+	Rasgrp4	protein_coding	1.17e-07	1.85e-05	-8.94e+00	1.17e+01	1.71e-01	2.32e+00	3.32e+00	2.32e+00	2.09e-04	4.71e-04	2.09e-04
chr7	126672865	126676432	ENSMUSG00000030711	0.4938	-	Sult1a1	protein_coding	1.18e-07	1.86e-05	-9.62e+00	1.12e+01	6.63e-01	1.58e+00	2.00e+00	1.00e+00	5.61e-04	8.41e-04	2.80e-04
chr7	19696109	19699188	ENSMUSG00000002985	0.5464	-	Apoe	protein_coding	1.26e-07	1.96e-05	-6.54e+00	1.66e+01	3.28e+01	1.16e+01	6.43e+00	6.02e+00	1.01e+00	2.76e-02	2.08e-02
chr7	106156556	106215326	ENSMUSG00000045868	0.3966	-	Gvin1	protein_coding	1.34e-07	2.06e-05	-8.01e+00	1.20e+01	7.17e-02	4.86e+00	3.58e+00	3.00e+00	4.76e-04	1.87e-04	1.19e-04
chr8	13037308	13056676	ENSMUSG00000031444	0.4783	+	F10	protein_coding	1.43e-07	2.18e-05	-7.51e+00	1.23e+01	2.57e-01	5.98e+00	3.17e+00	3.32e+00	3.20e-03	4.13e-04	4.65e-04
chr12	113406604	113416324	ENSMUSG00000104213	0.4510	-	Ighd	IG_C_gene	1.54e-07	2.33e-05	-1.05e+01	1.05e+01	1.47e-01	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00
chr7	103812524	103813996	ENSMUSG00000073940	0.4569	-	Hbb-bt	protein_coding	1.55e-07	2.33e-05	-1.85e+01	2.05e+01	1.03e+03	3.32e+00	0.00e+00	0.00e+00	6.11e-03	0.00e+00	0.00e+00
chr15	78325990	78351000	ENSMUSG00000071713	0.5008	+	Csf2rb	protein_coding	1.56e-07	2.33e-05	-8.05e+00	1.22e+01	1.83e-01	3.91e+00	4.58e+00	3.70e+00	5.60e-04	9.20e-04	4.80e-04
chr2	153108468	153151441	ENSMUSG00000003283	0.4781	+	Hck	protein_coding	1.63e-07	2.40e-05	-8.00e+00	1.23e+01	1.17e-01	3.70e+00	3.17e+00	5.21e+00	2.79e-04	1.86e-04	8.38e-04
chr8	70616155	70620890	ENSMUSG00000049988	0.5329	+	Lrrc25	protein_coding	1.65e-07	2.42e-05	-9.80e+00	1.12e+01	5.03e-01	0.00e+00	0.00e+00	2.58e+00	0.00e+00	0.00e+00	1.06e-03
chr3	142594847	142619179	ENSMUSG00000040264	0.3951	+	Gbp2b	protein_coding	1.70e-07	2.46e-05	-8.95e+00	1.14e+01	1.15e-01	3.00e+00	2.32e+00	2.00e+00	2.88e-04	1.64e-04	1.23e-04
chr1	164115264	164150026	ENSMUSG00000026580	0.4372	+	Selp	protein_coding	2.22e-07	3.18e-05	-7.47e+00	1.26e+01	1.83e-01	4.58e+00	5.46e+00	5.32e+00	6.62e-04	1.24e-03	1.12e-03

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

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.