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 gene s that had zero counts over all the RNA-Seq samples (21585 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/transcripts presenting any of the following were excluded from further analysis: i) genes with length less than 500 (4611 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 average reads per 100 bp), iii) genes with read counts below the median read counts of the total normalized count distribution (9307 genes with cutoff value 3 normalized read counts), iv) genes whose biotype matched the following: rRNA, IG_V_pseudogene, TR_V_pseudogene (138 genes). The total number of genes excluded due to the application of gene filters was 5117. The total (unified) number of genes excluded due to the application of all filters was 33039. The resulting gene counts table was subjected to differential expression analysis for the contrasts VehicleSL versus AmisulprideSL, VehicleLL versus AmisulprideLL using the Bioconductor package DESeq. The final numbers of differentially expressed gene s were (per contrast): for the contrast VehicleSL versus AmisulprideSL, 225 (5) statistically significant genes were found with a p-value (FDR or adjusted p-value) threshold of 0.05 and of these, 71 (1) were up-regulated, 120 (4) were down-regulated and 34 (0) were not differentially expressed according to an absolute fold change cutoff value of 1 in log₂ scale, for the contrast VehicleLL versus AmisulprideLL, 433 (2) statistically significant genes were found with a p-value (FDR or adjusted p-value) threshold of 0.05 and of these, 114 (0) were up-regulated, 184 (2) were down-regulated and 135 (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: AmisulprideSL, AmisulprideLL, VehicleSL, VehicleLL
Samples included: GK3R313_1, GK3R315_3, GK3R317_5, GK3R314_2, GK3R316_4, GK3R318_6, GK3R319_7, GK3R321_9, GK3R323_11, GK3R320_8, GK3R322_10, GK3R324_12
Samples excluded: none
Requested contrasts: VehicleSL_vs_AmisulprideSL, VehicleLL_vs_AmisulprideLL
Library sizes: not available
Annotation: embedded
Organism:
Annotation source: Ensembl genomes
Count type: utr
3' UTR flanking :: 500
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(new("standardGeneric", .Data = function (x, na.rm = FALSE, ...) standardGeneric("median"), generic = "median", package = "stats", group = list(), valueClass = character(0), signature = c("x", "na.rm"), default = new("derivedDefaultMethod", .Data = function (x, na.rm = FALSE, ...) UseMethod("median"), target = new("signature", .Data = "ANY", names = "x", package = "methods"), defined = new("signature", .Data = "ANY", names = "x", package = "methods"), generic = "median"), skeleton = (new("derivedDefaultMethod", .Data = function (x, na.rm = FALSE, ...) UseMethod("median"), target = new("signature", .Data = "ANY", names = "x", package = "methods"), defined = new("signature", .Data = "ANY", names = "x", package = "methods"), generic = "median"))(x, na.rm, ...)))locfunc

Statistical algorithm(s): 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: ~/www/metaseqr_GKlab_run463/metaseqr_quantseq_run463_SL_LL_comparisons
Output data: Annotation, p-value, Adjusted p-value (FDR), Fold change, Statistics, Read counts
Output scale(s): Natural scale, log2 scale, Reads per Gene Model
Output values: Normalized values, Raw values
Output statistics: Mean
Total run time: 10 minutes 52 seconds

Filtered genes

Number of filtered gene s: 33039 which is the union of

Filtered because of zero reads: 21585
Filtered because of exon filters: 0
Filtered because of gene filters: 11454 which is the union of
- length: 4611 genes with filter cutoff value 500
- avg.reads: 367 genes with filter cutoff value 0
- expression: 9307 genes further decomposed to (filter name, filtered genes, filter cutoff):
- - median: 9307 genes with filter cutoff value 3
- biotype: 138 genes with filter cutoff value rRNA, IG_V_pseudogene, TR_V_pseudogene

Differentially expressed genes

Number of differentially expressed genes per contrast:

VehicleSL_vs_AmisulprideSL: 225 (5) statistically significant genes of which 71 (1) up regulated, 120 (4) down regulated and 34 (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.
VehicleLL_vs_AmisulprideLL: 433 (2) statistically significant genes of which 114 (0) up regulated, 184 (2) down regulated and 135 (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

FATAL [2022-08-09 15:43:21] 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...
INFO [2022-08-09 15:45:58] 2022-08-09 15:45:58: Data processing started...
INFO [2022-08-09 15:45:58] Read counts file: imported custom data frame
INFO [2022-08-09 15:45:58] Conditions: AmisulprideSL, AmisulprideLL, VehicleSL, VehicleLL
INFO [2022-08-09 15:45:58] Samples to include: GK3R313_1, GK3R315_3, GK3R317_5, GK3R314_2, GK3R316_4, GK3R318_6, GK3R319_7, GK3R321_9, GK3R323_11, GK3R320_8, GK3R322_10, GK3R324_12
INFO [2022-08-09 15:45:58] Samples to exclude: none
INFO [2022-08-09 15:45:58] Requested contrasts: VehicleSL_vs_AmisulprideSL, VehicleLL_vs_AmisulprideLL
INFO [2022-08-09 15:45:58] Annotation: embedded
INFO [2022-08-09 15:45:58] Organism: custom
INFO [2022-08-09 15:45:58] Reference source: ensembl
INFO [2022-08-09 15:45:58] Count type: utr
INFO [2022-08-09 15:45:58] 3' UTR flanking: 500
INFO [2022-08-09 15:45:58] Transcriptional level: gene
INFO [2022-08-09 15:45:58] Exon filters: none applied
INFO [2022-08-09 15:45:58] Gene filters: length, avg.reads, expression, biotype
INFO [2022-08-09 15:45:58] length:
INFO [2022-08-09 15:45:58] length: 500
INFO [2022-08-09 15:45:58] avg.reads:
INFO [2022-08-09 15:45:58] average.per.bp: 100
INFO [2022-08-09 15:45:58] quantile: 0.25
INFO [2022-08-09 15:45:58] expression:
INFO [2022-08-09 15:45:58] median: TRUE
INFO [2022-08-09 15:45:58] mean: FALSE
INFO [2022-08-09 15:45:58] quantile: NA
INFO [2022-08-09 15:45:58] known: NA
INFO [2022-08-09 15:45:58] custom: NA
INFO [2022-08-09 15:45:58] biotype:
INFO [2022-08-09 15:45:58] pseudogene: FALSE
INFO [2022-08-09 15:45:58] snRNA: FALSE
INFO [2022-08-09 15:45:58] protein_coding: FALSE
INFO [2022-08-09 15:45:58] antisense: FALSE
INFO [2022-08-09 15:45:58] miRNA: FALSE
INFO [2022-08-09 15:45:58] snoRNA: FALSE
INFO [2022-08-09 15:45:58] lincRNA: FALSE
INFO [2022-08-09 15:45:58] processed_transcript: FALSE
INFO [2022-08-09 15:45:58] misc_RNA: FALSE
INFO [2022-08-09 15:45:58] rRNA: TRUE
INFO [2022-08-09 15:45:58] sense_intronic: FALSE
INFO [2022-08-09 15:45:58] sense_overlapping: FALSE
INFO [2022-08-09 15:45:58] polymorphic_pseudogene: FALSE
INFO [2022-08-09 15:45:58] IG_C_gene: FALSE
INFO [2022-08-09 15:45:58] IG_J_gene: FALSE
INFO [2022-08-09 15:45:58] IG_D_gene: FALSE
INFO [2022-08-09 15:45:58] IG_LV_gene: FALSE
INFO [2022-08-09 15:45:58] IG_V_gene: FALSE
INFO [2022-08-09 15:45:58] IG_V_pseudogene: TRUE
INFO [2022-08-09 15:45:58] TR_V_gene: FALSE
INFO [2022-08-09 15:45:58] TR_V_pseudogene: TRUE
INFO [2022-08-09 15:45:58] three_prime_overlapping_ncrna: FALSE
INFO [2022-08-09 15:45:58] Filter application: postnorm
INFO [2022-08-09 15:45:58] Normalization algorithm: deseq
INFO [2022-08-09 15:45:58] Normalization arguments:
INFO [2022-08-09 15:45:58] locfunc:
INFO [2022-08-09 15:45:58] [[list(new("standardGeneric", .Data = function (x, na.rm = FALSE, ...) standardGeneric("median"), generic = "median", package = "stats", group = list(), valueClass = character(0), signature = c("x", "na.rm"), default = new("derivedDefaultMethod", .Data = function (x, na.rm = FALSE, ...) UseMethod("median"), target = new("signature", .Data = "ANY", names = "x", package = "methods"), defined = new("signature", .Data = "ANY", names = "x", package = "methods"), generic = "median"), skeleton = (new("derivedDefaultMethod", .Data = function (x, na.rm = FALSE, ...) UseMethod("median"), target = new("signature", .Data = "ANY", names = "x", package = "methods"), defined = new("signature", .Data = "ANY", names = "x", package = "methods"), generic = "median"))(x, na.rm, ...)))locfunc
INFO [2022-08-09 15:45:58] Statistical algorithm: deseq
INFO [2022-08-09 15:45:58] Statistical arguments:
INFO [2022-08-09 15:45:58] deseq: blind, fit-only, local
INFO [2022-08-09 15:45:58] Meta-analysis method: none
INFO [2022-08-09 15:45:58] Multiple testing correction: BH
INFO [2022-08-09 15:45:58] p-value threshold: 0.05
INFO [2022-08-09 15:45:58] Logarithmic transformation offset: 1
INFO [2022-08-09 15:45:58] Quality control plots: mds, biodetection, countsbio, saturation, readnoise, filtered, correl, pairwise, boxplot, gcbias, lengthbias, meandiff, meanvar, biodist, volcano, deheatmap
INFO [2022-08-09 15:45:58] Figure format: png, pdf
INFO [2022-08-09 15:45:58] Output directory: ~/www/metaseqr_GKlab_run463/metaseqr_quantseq_run463_SL_LL_comparisons
INFO [2022-08-09 15:45:58] Output data: annotation, p.value, adj.p.value, fold.change, stats, counts, flags
INFO [2022-08-09 15:45:58] Output scale(s): natural, log2, rpgm
INFO [2022-08-09 15:45:58] Output values: normalized, raw
INFO [2022-08-09 15:45:58] Output statistics: mean
INFO [2022-08-09 15:45:58] Reading custom external gene annotation for from /data/results/tools/rnaseq/metaseqr/mm10/gene_data_mm10.txt.gz...
INFO [2022-08-09 15:45:59] Checking chromosomes in transcript counts and gene annotation...
INFO [2022-08-09 15:46:00] Processing transcripts...
INFO [2022-08-09 15:46:00] Separating transcripts (UTR regions) per for GK3R313_1...
INFO [2022-08-09 15:46:15] Separating transcripts (UTR regions) per for GK3R315_3...
INFO [2022-08-09 15:46:35] Separating transcripts (UTR regions) per for GK3R317_5...
INFO [2022-08-09 15:46:52] Separating transcripts (UTR regions) per for GK3R314_2...
INFO [2022-08-09 15:47:12] Separating transcripts (UTR regions) per for GK3R316_4...
INFO [2022-08-09 15:47:32] Separating transcripts (UTR regions) per for GK3R318_6...
INFO [2022-08-09 15:47:51] Separating transcripts (UTR regions) per for GK3R319_7...
INFO [2022-08-09 15:48:14] Separating transcripts (UTR regions) per for GK3R321_9...
INFO [2022-08-09 15:48:33] Separating transcripts (UTR regions) per for GK3R323_11...
INFO [2022-08-09 15:48:49] Separating transcripts (UTR regions) per for GK3R320_8...
INFO [2022-08-09 15:49:08] Separating transcripts (UTR regions) per for GK3R322_10...
INFO [2022-08-09 15:49:31] Separating transcripts (UTR regions) per for GK3R324_12...
INFO [2022-08-09 15:49:58] Saving gene model to ~/www/metaseqr_GKlab_run463/metaseqr_quantseq_run463_SL_LL_comparisons/data/gene_model.RData
INFO [2022-08-09 15:50:04] Summarizing count data...
INFO [2022-08-09 15:50:16] Removing genes with zero counts in all samples...
INFO [2022-08-09 15:50:16] Normalizing with: deseq
INFO [2022-08-09 15:50:16] Applying gene filter length...
INFO [2022-08-09 15:50:16] Threshold below which ignored: 500
INFO [2022-08-09 15:50:16] Applying gene filter avg.reads...
INFO [2022-08-09 15:50:16] Threshold below which ignored: 0
INFO [2022-08-09 15:50:16] Applying gene filter expression...
INFO [2022-08-09 15:50:16] Threshold below which ignored: 3
INFO [2022-08-09 15:50:16] Applying gene filter biotype...
INFO [2022-08-09 15:50:16] Biotypes ignored: rRNA, IG_V_pseudogene, TR_V_pseudogene
INFO [2022-08-09 15:50:18] 33039 genes filtered out
INFO [2022-08-09 15:50:18] 16509 genes remain after filtering
INFO [2022-08-09 15:50:18] Running statistical tests with: deseq
INFO [2022-08-09 15:50:18] Contrast: VehicleSL_vs_AmisulprideSL
INFO [2022-08-09 15:50:25] Contrast: VehicleLL_vs_AmisulprideLL
INFO [2022-08-09 15:50:30] Contrast VehicleSL_vs_AmisulprideSL: found 225 genes
INFO [2022-08-09 15:50:30] Contrast VehicleLL_vs_AmisulprideLL: found 433 genes
INFO [2022-08-09 15:50:31] Building output files...
INFO [2022-08-09 15:50:34] Contrast: VehicleSL_vs_AmisulprideSL
INFO [2022-08-09 15:50:34] Adding non-filtered data...
INFO [2022-08-09 15:50:34] binding annotation...
INFO [2022-08-09 15:50:35] binding p-values...
INFO [2022-08-09 15:50:35] binding FDRs...
INFO [2022-08-09 15:50:35] binding natural normalized fold changes...
INFO [2022-08-09 15:50:35] binding log2 normalized fold changes...
INFO [2022-08-09 15:50:35] binding natural raw fold changes...
INFO [2022-08-09 15:50:36] binding log2 raw fold changes...
INFO [2022-08-09 15:50:36] binding normalized mean counts...
INFO [2022-08-09 15:50:38] binding raw mean counts...
INFO [2022-08-09 15:50:38] binding normalized mean counts...
INFO [2022-08-09 15:50:39] binding raw mean counts...
INFO [2022-08-09 15:50:39] binding all normalized counts for VehicleSL...
INFO [2022-08-09 15:50:40] binding all raw counts for VehicleSL...
INFO [2022-08-09 15:50:40] binding all normalized counts for AmisulprideSL...
INFO [2022-08-09 15:50:41] binding all raw counts for AmisulprideSL...
INFO [2022-08-09 15:50:41] binding filtering flags...
INFO [2022-08-09 15:50:42] Writing output...
INFO [2022-08-09 15:50:42] Adding filtered data...
INFO [2022-08-09 15:50:42] binding annotation...
INFO [2022-08-09 15:50:42] binding p-values...
INFO [2022-08-09 15:50:42] binding FDRs...
INFO [2022-08-09 15:50:42] binding natural normalized fold changes...
INFO [2022-08-09 15:50:42] binding log2 normalized fold changes...
INFO [2022-08-09 15:50:42] binding natural raw fold changes...
INFO [2022-08-09 15:50:42] binding log2 raw fold changes...
INFO [2022-08-09 15:50:43] binding normalized mean counts...
INFO [2022-08-09 15:50:44] binding raw mean counts...
INFO [2022-08-09 15:50:45] binding normalized mean counts...
INFO [2022-08-09 15:50:46] binding raw mean counts...
INFO [2022-08-09 15:50:46] binding all normalized counts for VehicleSL...
INFO [2022-08-09 15:50:46] binding all raw counts for VehicleSL...
INFO [2022-08-09 15:50:47] binding all normalized counts for AmisulprideSL...
INFO [2022-08-09 15:50:47] binding all raw counts for AmisulprideSL...
INFO [2022-08-09 15:50:47] binding filtering flags...
INFO [2022-08-09 15:50:48] Writing output...
INFO [2022-08-09 15:50:52] Contrast: VehicleLL_vs_AmisulprideLL
INFO [2022-08-09 15:50:52] Adding non-filtered data...
INFO [2022-08-09 15:50:52] binding annotation...
INFO [2022-08-09 15:50:53] binding p-values...
INFO [2022-08-09 15:50:53] binding FDRs...
INFO [2022-08-09 15:50:53] binding natural normalized fold changes...
INFO [2022-08-09 15:50:53] binding log2 normalized fold changes...
INFO [2022-08-09 15:50:53] binding natural raw fold changes...
INFO [2022-08-09 15:50:53] binding log2 raw fold changes...
INFO [2022-08-09 15:50:53] binding normalized mean counts...
INFO [2022-08-09 15:50:54] binding raw mean counts...
INFO [2022-08-09 15:50:54] binding normalized mean counts...
INFO [2022-08-09 15:50:55] binding raw mean counts...
INFO [2022-08-09 15:50:56] binding all normalized counts for VehicleLL...
INFO [2022-08-09 15:50:56] binding all raw counts for VehicleLL...
INFO [2022-08-09 15:50:57] binding all normalized counts for AmisulprideLL...
INFO [2022-08-09 15:50:57] binding all raw counts for AmisulprideLL...
INFO [2022-08-09 15:50:58] binding filtering flags...
INFO [2022-08-09 15:50:58] Writing output...
INFO [2022-08-09 15:50:58] Adding filtered data...
INFO [2022-08-09 15:50:58] binding annotation...
INFO [2022-08-09 15:50:58] binding p-values...
INFO [2022-08-09 15:50:58] binding FDRs...
INFO [2022-08-09 15:50:59] binding natural normalized fold changes...
INFO [2022-08-09 15:50:59] binding log2 normalized fold changes...
INFO [2022-08-09 15:50:59] binding natural raw fold changes...
INFO [2022-08-09 15:50:59] binding log2 raw fold changes...
INFO [2022-08-09 15:50:59] binding normalized mean counts...
INFO [2022-08-09 15:51:00] binding raw mean counts...
INFO [2022-08-09 15:51:01] binding normalized mean counts...
INFO [2022-08-09 15:51:01] binding raw mean counts...
INFO [2022-08-09 15:51:02] binding all normalized counts for VehicleLL...
INFO [2022-08-09 15:51:02] binding all raw counts for VehicleLL...
INFO [2022-08-09 15:51:02] binding all normalized counts for AmisulprideLL...
INFO [2022-08-09 15:51:02] binding all raw counts for AmisulprideLL...
INFO [2022-08-09 15:51:02] binding filtering flags...
INFO [2022-08-09 15:51:03] Writing output...
INFO [2022-08-09 15:51:07] Creating quality control graphs...
INFO [2022-08-09 15:51:07] Plotting in png format...
INFO [2022-08-09 15:51:07] Plotting mds...
INFO [2022-08-09 15:51:07] Plotting biodetection...
INFO [2022-08-09 15:51:10] Plotting countsbio...
INFO [2022-08-09 15:51:11] Plotting saturation...
INFO [2022-08-09 15:51:21] Plotting readnoise...
INFO [2022-08-09 15:51:23] Plotting correl...
INFO [2022-08-09 15:51:23] Plotting pairwise...
INFO [2022-08-09 15:52:06] Plotting boxplot...
INFO [2022-08-09 15:52:06] Plotting gcbias...
INFO [2022-08-09 15:52:08] Plotting lengthbias...
INFO [2022-08-09 15:52:08] Plotting meandiff...
INFO [2022-08-09 15:52:10] Plotting meanvar...
INFO [2022-08-09 15:52:33] Plotting boxplot...
INFO [2022-08-09 15:52:33] Plotting gcbias...
INFO [2022-08-09 15:52:34] Plotting lengthbias...
INFO [2022-08-09 15:52:35] Plotting meandiff...
INFO [2022-08-09 15:52:37] Plotting meanvar...
INFO [2022-08-09 15:53:01] Plotting biodist...
INFO [2022-08-09 15:53:01] Contrast: VehicleSL_vs_AmisulprideSL
INFO [2022-08-09 15:53:01] Contrast: VehicleLL_vs_AmisulprideLL
INFO [2022-08-09 15:53:02] Plotting volcano...
INFO [2022-08-09 15:53:02] Contrast: VehicleSL_vs_AmisulprideSL
INFO [2022-08-09 15:53:02] Contrast: VehicleLL_vs_AmisulprideLL
INFO [2022-08-09 15:53:03] Plotting deheatmap...
INFO [2022-08-09 15:53:03] Contrast: VehicleSL_vs_AmisulprideSL
INFO [2022-08-09 15:53:38] Contrast: VehicleLL_vs_AmisulprideLL
INFO [2022-08-09 15:54:10] Plotting filtered...
INFO [2022-08-09 15:54:10] Plotting in pdf format...
INFO [2022-08-09 15:54:10] Plotting mds...
INFO [2022-08-09 15:54:10] Plotting biodetection...
INFO [2022-08-09 15:54:11] Plotting countsbio...
INFO [2022-08-09 15:54:12] Plotting saturation...
INFO [2022-08-09 15:54:18] Plotting readnoise...
INFO [2022-08-09 15:54:20] Plotting correl...
INFO [2022-08-09 15:54:20] Plotting pairwise...
INFO [2022-08-09 15:54:46] Plotting boxplot...
INFO [2022-08-09 15:54:46] Plotting gcbias...
INFO [2022-08-09 15:54:47] Plotting lengthbias...
INFO [2022-08-09 15:54:48] Plotting meandiff...
INFO [2022-08-09 15:54:51] Plotting meanvar...
INFO [2022-08-09 15:55:15] Plotting boxplot...
INFO [2022-08-09 15:55:15] Plotting gcbias...
INFO [2022-08-09 15:55:16] Plotting lengthbias...
INFO [2022-08-09 15:55:17] Plotting meandiff...
INFO [2022-08-09 15:55:20] Plotting meanvar...
INFO [2022-08-09 15:55:44] Plotting biodist...
INFO [2022-08-09 15:55:44] Contrast: VehicleSL_vs_AmisulprideSL
INFO [2022-08-09 15:55:44] Contrast: VehicleLL_vs_AmisulprideLL
INFO [2022-08-09 15:55:44] Plotting volcano...
INFO [2022-08-09 15:55:44] Contrast: VehicleSL_vs_AmisulprideSL
INFO [2022-08-09 15:55:44] Contrast: VehicleLL_vs_AmisulprideLL
INFO [2022-08-09 15:55:44] Plotting deheatmap...
INFO [2022-08-09 15:55:44] Contrast: VehicleSL_vs_AmisulprideSL
INFO [2022-08-09 15:56:17] Contrast: VehicleLL_vs_AmisulprideLL
INFO [2022-08-09 15:56:49] Plotting filtered...
INFO [2022-08-09 15:56:49] Creating HTML report...
INFO [2022-08-09 15:56:49] 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 AmisulprideSL

Mean-difference plots for the replicates of AmisulprideLL

Mean-difference plots for the replicates of VehicleSL

Mean-difference plots for the replicates of VehicleLL

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 AmisulprideSL

Mean-variance plots for the replicates of AmisulprideLL

Mean-variance plots for the replicates of VehicleSL

Mean-variance plots for the replicates of VehicleLL

Chromosome and biotype distribution of filtered gene s

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 VehicleSL_vs_AmisulprideSL

Volcano plot for the contrast VehicleLL_vs_AmisulprideLL

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 VehicleSL_vs_AmisulprideSL

DEG heatmap for the contrast VehicleLL_vs_AmisulprideLL

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 VehicleSL_vs_AmisulprideSL

Biotype distribution in DEG for the contrast VehicleLL_vs_AmisulprideLL

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Results

DEG table for the contrast VehicleSL_vs_AmisulprideSL

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 VehicleSL_vs_AmisulprideSL. 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	natural_normalized_fold_change_VehicleSL_vs_AmisulprideSL	log2_normalized_fold_change_VehicleSL_vs_AmisulprideSL	natural_raw_fold_change_VehicleSL_vs_AmisulprideSL	log2_raw_fold_change_VehicleSL_vs_AmisulprideSL	natural_normalized_mean_counts_VehicleSL	log2_normalized_mean_counts_VehicleSL	rpgm_normalized_mean_counts_VehicleSL	natural_raw_mean_counts_VehicleSL	log2_raw_mean_counts_VehicleSL	rpgm_raw_mean_counts_VehicleSL	natural_normalized_mean_counts_AmisulprideSL	log2_normalized_mean_counts_AmisulprideSL	rpgm_normalized_mean_counts_AmisulprideSL	natural_raw_mean_counts_AmisulprideSL	log2_raw_mean_counts_AmisulprideSL	rpgm_raw_mean_counts_AmisulprideSL	natural_normalized_counts_GK3R319_7	natural_normalized_counts_GK3R321_9	natural_normalized_counts_GK3R323_11	log2_normalized_counts_GK3R319_7	log2_normalized_counts_GK3R321_9	log2_normalized_counts_GK3R323_11	rpgm_normalized_counts_GK3R319_7	rpgm_normalized_counts_GK3R321_9	rpgm_normalized_counts_GK3R323_11	natural_raw_counts_GK3R319_7	natural_raw_counts_GK3R321_9	natural_raw_counts_GK3R323_11	log2_raw_counts_GK3R319_7	log2_raw_counts_GK3R321_9	log2_raw_counts_GK3R323_11	rpgm_raw_counts_GK3R319_7	rpgm_raw_counts_GK3R321_9	rpgm_raw_counts_GK3R323_11	natural_normalized_counts_GK3R313_1	natural_normalized_counts_GK3R315_3	natural_normalized_counts_GK3R317_5	log2_normalized_counts_GK3R313_1	log2_normalized_counts_GK3R315_3	log2_normalized_counts_GK3R317_5	rpgm_normalized_counts_GK3R313_1	rpgm_normalized_counts_GK3R315_3	rpgm_normalized_counts_GK3R317_5	natural_raw_counts_GK3R313_1	natural_raw_counts_GK3R315_3	natural_raw_counts_GK3R317_5	log2_raw_counts_GK3R313_1	log2_raw_counts_GK3R315_3	log2_raw_counts_GK3R317_5	rpgm_raw_counts_GK3R313_1	rpgm_raw_counts_GK3R315_3	rpgm_raw_counts_GK3R317_5
chr3	95526786	95556403	ENSMUSG00000038642	44.37	+	Ctss	protein_coding	4.60e-09	7.59e-05	0.2023	-2.305345	0.1355	-2.883304	5.70e+01	5.30e+00	3.85e-02	9.00e+01	5.43e+00	6.11e-02	1.10e+01	3.32e+00	7.25e-03	1.10e+01	3.44e+00	7.70e-03	1.16e+02	1.10e+01	4.30e+01	6.87e+00	3.58e+00	5.46e+00	7.88e-02	7.47e-03	2.92e-02	2.29e+02	1.00e+01	3.10e+01	7.85e+00	3.46e+00	5.00e+00	1.56e-01	6.79e-03	2.11e-02	1.90e+01	9.00e+00	4.00e+00	4.32e+00	3.32e+00	2.32e+00	1.29e-02	6.11e-03	2.72e-03	1.40e+01	1.60e+01	4.00e+00	3.91e+00	4.09e+00	2.32e+00	9.51e-03	1.09e-02	2.72e-03
chr6	124430759	124464794	ENSMUSG00000008153	49.31	-	Clstn3	protein_coding	7.78e-07	6.42e-03	14.1250	3.820179	16.7143	4.063010	2.00e+00	1.11e+00	2.35e-04	1.00e+00	1.00e+00	1.88e-04	3.70e+01	2.59e+00	5.16e-03	3.80e+01	2.80e+00	5.35e-03	1.00e+00	0.00e+00	4.00e+00	1.00e+00	0.00e+00	2.32e+00	1.41e-04	0.00e+00	5.63e-04	1.00e+00	0.00e+00	3.00e+00	1.00e+00	0.00e+00	2.00e+00	1.41e-04	0.00e+00	4.22e-04	0.00e+00	1.00e+00	1.09e+02	0.00e+00	1.00e+00	6.78e+00	0.00e+00	1.41e-04	1.53e-02	0.00e+00	2.00e+00	1.12e+02	0.00e+00	1.58e+00	6.82e+00	0.00e+00	2.82e-04	1.58e-02
chr19	11586604	11604849	ENSMUSG00000024679	41.28	-	Ms4a6d	protein_coding	5.57e-06	3.06e-02	0.1912	-2.387023	0.1165	-3.101538	2.20e+01	4.10e+00	7.25e-03	3.30e+01	4.28e+00	1.11e-02	3.00e+00	1.97e+00	1.11e-03	3.00e+00	1.86e+00	1.00e-03	4.10e+01	5.00e+00	1.90e+01	5.39e+00	2.58e+00	4.32e+00	1.37e-02	1.67e-03	6.35e-03	8.10e+01	5.00e+00	1.40e+01	6.36e+00	2.58e+00	3.91e+00	2.71e-02	1.67e-03	4.68e-03	4.00e+00	1.00e+00	5.00e+00	2.32e+00	1.00e+00	2.58e+00	1.34e-03	3.34e-04	1.67e-03	3.00e+00	1.00e+00	5.00e+00	2.00e+00	1.00e+00	2.58e+00	1.00e-03	3.34e-04	1.67e-03
chr10	79890853	79892656	ENSMUSG00000061780	56.49	+	Cfd	protein_coding	1.17e-05	4.16e-02	0.0727	-3.781360	0.0755	-3.727920	1.70e+01	2.42e+00	1.31e-02	1.70e+01	2.53e+00	1.26e-02	0.00e+00	3.33e-01	2.52e-04	0.00e+00	3.33e-01	2.52e-04	2.00e+00	5.00e+01	0.00e+00	1.58e+00	5.67e+00	0.00e+00	1.51e-03	3.78e-02	0.00e+00	3.00e+00	4.70e+01	0.00e+00	2.00e+00	5.58e+00	0.00e+00	2.27e-03	3.55e-02	0.00e+00	1.00e+00	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	7.55e-04	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	7.55e-04	0.00e+00	0.00e+00
chr19	25671134	25672253	ENSMUSG00000101089	45.45	-	2610016A17Rik	lincRNA	1.26e-05	4.16e-02	0.0977	-3.355481	0.1259	-2.989353	5.70e+01	4.47e+00	2.24e-02	4.40e+01	4.56e+00	1.73e-02	5.00e+00	1.86e+00	1.83e-03	5.00e+00	1.99e+00	1.83e-03	3.00e+00	1.70e+01	1.51e+02	2.00e+00	4.17e+00	7.25e+00	1.18e-03	6.68e-03	5.93e-02	6.00e+00	1.60e+01	1.10e+02	2.81e+00	4.09e+00	6.79e+00	2.36e-03	6.28e-03	4.32e-02	1.10e+01	3.00e+00	0.00e+00	3.58e+00	2.00e+00	0.00e+00	4.32e-03	1.18e-03	0.00e+00	8.00e+00	6.00e+00	0.00e+00	3.17e+00	2.81e+00	0.00e+00	3.14e-03	2.36e-03	0.00e+00
chr3	121250134	121250737	ENSMUSG00000106619	58.11	-	Gm9353	processed_pseudogene	5.03e-05	1.38e-01	4.3571	2.123382	5.8276	2.542898	8.00e+00	3.17e+00	6.65e-03	9.00e+00	3.26e+00	6.91e-03	4.00e+01	5.03e+00	3.16e-02	5.50e+01	5.17e+00	4.41e-02	5.00e+00	1.00e+01	1.00e+01	2.58e+00	3.46e+00	3.46e+00	3.99e-03	7.97e-03	7.97e-03	1.00e+01	9.00e+00	7.00e+00	3.46e+00	3.32e+00	3.00e+00	7.97e-03	7.18e-03	5.58e-03	1.10e+01	6.50e+01	4.30e+01	3.58e+00	6.04e+00	5.46e+00	8.77e-03	5.18e-02	3.43e-02	8.00e+00	1.14e+02	4.40e+01	3.17e+00	6.85e+00	5.49e+00	6.38e-03	9.09e-02	3.51e-02
chr8	70139499	70167488	ENSMUSG00000002345	52.10	+	Borcs8	protein_coding	6.10e-05	1.38e-01	0.3157	-1.663545	0.2871	-1.800422	1.37e+02	7.10e+00	2.00e-02	1.72e+02	7.25e+00	2.50e-02	4.30e+01	5.42e+00	6.21e-03	4.90e+01	5.55e+00	7.08e-03	1.55e+02	1.13e+02	1.44e+02	7.29e+00	6.83e+00	7.18e+00	2.26e-02	1.64e-02	2.10e-02	3.04e+02	1.07e+02	1.05e+02	8.25e+00	6.75e+00	6.73e+00	4.42e-02	1.56e-02	1.53e-02	3.70e+01	3.60e+01	5.50e+01	5.25e+00	5.21e+00	5.81e+00	5.38e-03	5.24e-03	8.00e-03	2.70e+01	6.20e+01	5.70e+01	4.81e+00	5.98e+00	5.86e+00	3.93e-03	9.02e-03	8.30e-03
chr10	117287796	117292868	ENSMUSG00000069515	46.09	-	Lyz1	protein_coding	6.88e-05	1.38e-01	0.3433	-1.542527	0.2130	-2.231326	2.10e+01	3.83e+00	1.05e-02	3.50e+01	4.00e+00	1.71e-02	7.00e+00	2.88e+00	3.27e-03	7.00e+00	2.92e+00	3.27e-03	4.70e+01	3.00e+00	1.40e+01	5.58e+00	2.00e+00	3.91e+00	2.30e-02	1.47e-03	6.86e-03	9.20e+01	3.00e+00	1.00e+01	6.54e+00	2.00e+00	3.46e+00	4.51e-02	1.47e-03	4.90e-03	1.00e+01	5.00e+00	5.00e+00	3.46e+00	2.58e+00	2.58e+00	4.90e-03	2.45e-03	2.45e-03	7.00e+00	8.00e+00	5.00e+00	3.00e+00	3.17e+00	2.58e+00	3.43e-03	3.92e-03	2.45e-03
chr7	30413760	30417585	ENSMUSG00000030579	48.51	+	Tyrobp	protein_coding	7.52e-05	1.38e-01	0.2549	-1.971986	0.1750	-2.514573	1.60e+01	3.60e+00	4.51e-03	2.60e+01	3.79e+00	7.23e-03	3.00e+00	2.08e+00	9.39e-04	4.00e+00	2.21e+00	1.03e-03	3.30e+01	3.00e+00	1.20e+01	5.09e+00	2.00e+00	3.70e+00	9.30e-03	8.45e-04	3.38e-03	6.50e+01	3.00e+00	9.00e+00	6.04e+00	2.00e+00	3.32e+00	1.83e-02	8.45e-04	2.54e-03	4.00e+00	2.00e+00	4.00e+00	2.32e+00	1.58e+00	2.32e+00	1.13e-03	5.64e-04	1.13e-03	3.00e+00	4.00e+00	4.00e+00	2.00e+00	2.32e+00	2.32e+00	8.45e-04	1.13e-03	1.13e-03
chr14	69029289	69041401	ENSMUSG00000014813	44.45	+	Stc1	protein_coding	9.86e-05	1.63e-01	4.7879	2.259387	4.1739	2.061401	1.00e+01	3.29e+00	2.41e-03	1.40e+01	3.46e+00	3.45e-03	5.20e+01	5.29e+00	1.24e-02	6.30e+01	5.40e+00	1.52e-02	1.60e+01	4.00e+00	1.00e+01	4.09e+00	2.32e+00	3.46e+00	3.85e-03	9.63e-04	2.41e-03	3.20e+01	4.00e+00	7.00e+00	5.04e+00	2.32e+00	3.00e+00	7.71e-03	9.63e-04	1.69e-03	7.30e+01	7.20e+01	1.00e+01	6.21e+00	6.19e+00	3.46e+00	1.76e-02	1.73e-02	2.41e-03	5.30e+01	1.26e+02	1.00e+01	5.75e+00	6.99e+00	3.46e+00	1.28e-02	3.03e-02	2.41e-03
chr4	134082448	134095082	ENSMUSG00000000682	49.53	-	Cd52	protein_coding	2.09e-04	2.91e-01	0.2683	-1.898120	0.2360	-2.083416	2.60e+01	4.63e+00	6.66e-03	2.90e+01	4.77e+00	7.25e-03	6.00e+00	2.66e+00	1.60e-03	6.00e+00	2.77e+00	1.52e-03	2.00e+01	1.50e+01	4.40e+01	4.39e+00	4.00e+00	5.49e+00	5.06e-03	3.79e-03	1.11e-02	4.00e+01	1.40e+01	3.20e+01	5.36e+00	3.91e+00	5.04e+00	1.01e-02	3.54e-03	8.10e-03	1.10e+01	2.00e+00	6.00e+00	3.58e+00	1.58e+00	2.81e+00	2.78e-03	5.06e-04	1.52e-03	8.00e+00	4.00e+00	6.00e+00	3.17e+00	2.32e+00	2.81e+00	2.02e-03	1.01e-03	1.52e-03
chr17	48232768	48246924	ENSMUSG00000042265	46.83	+	Trem1	protein_coding	2.11e-04	2.91e-01	0.2000	-2.321928	0.1333	-2.906891	9.00e+00	3.11e+00	2.55e-03	1.40e+01	3.33e+00	3.97e-03	1.00e+00	8.62e-01	2.83e-04	1.00e+00	8.62e-01	2.83e-04	1.70e+01	5.00e+00	5.00e+00	4.17e+00	2.58e+00	2.58e+00	4.82e-03	1.42e-03	1.42e-03	3.30e+01	5.00e+00	4.00e+00	5.09e+00	2.58e+00	2.32e+00	9.35e-03	1.42e-03	1.13e-03	0.00e+00	1.00e+00	2.00e+00	0.00e+00	1.00e+00	1.58e+00	0.00e+00	2.83e-04	5.67e-04	0.00e+00	1.00e+00	2.00e+00	0.00e+00	1.00e+00	1.58e+00	0.00e+00	2.83e-04	5.67e-04
chr4	136889804	136893065	ENSMUSG00000036896	55.12	-	C1qc	protein_coding	2.78e-04	3.53e-01	0.2632	-1.925999	0.1385	-2.852443	1.20e+01	2.94e+00	4.25e-03	2.10e+01	3.15e+00	7.52e-03	2.00e+00	1.53e+00	8.49e-04	2.00e+00	1.44e+00	7.28e-04	2.90e+01	2.00e+00	4.00e+00	4.91e+00	1.58e+00	2.32e+00	1.06e-02	7.28e-04	1.46e-03	5.70e+01	2.00e+00	3.00e+00	5.86e+00	1.58e+00	2.00e+00	2.07e-02	7.28e-04	1.09e-03	5.00e+00	1.00e+00	1.00e+00	2.58e+00	1.00e+00	1.00e+00	1.82e-03	3.64e-04	3.64e-04	4.00e+00	1.00e+00	1.00e+00	2.32e+00	1.00e+00	1.00e+00	1.46e-03	3.64e-04	3.64e-04
chr19	11375523	11392790	ENSMUSG00000101389	38.17	+	Ms4a4a	protein_coding	3.03e-04	3.58e-01	0.2500	-2.000000	0.2017	-2.309855	2.40e+01	4.23e+00	1.94e-02	3.90e+01	4.41e+00	3.08e-02	5.00e+00	2.60e+00	4.25e-03	7.00e+00	2.73e+00	5.58e-03	4.90e+01	6.00e+00	1.80e+01	5.64e+00	2.81e+00	4.25e+00	3.91e-02	4.78e-03	1.44e-02	9.70e+01	6.00e+00	1.30e+01	6.61e+00	2.81e+00	3.81e+00	7.74e-02	4.78e-03	1.04e-02	3.00e+00	7.00e+00	6.00e+00	2.00e+00	3.00e+00	2.81e+00	2.39e-03	5.58e-03	4.78e-03	2.00e+00	1.30e+01	6.00e+00	1.58e+00	3.81e+00	2.81e+00	1.59e-03	1.04e-02	4.78e-03
chr4	132564067	132582683	ENSMUSG00000056529	46.38	+	Ptafr	protein_coding	8.02e-04	8.83e-01	0.2963	-1.754888	0.1633	-2.614710	8.00e+00	1.86e+00	2.22e-03	1.50e+01	2.17e+00	4.25e-03	2.00e+00	1.33e+00	4.62e-04	2.00e+00	1.33e+00	4.62e-04	2.30e+01	0.00e+00	1.00e+00	4.58e+00	0.00e+00	1.00e+00	6.37e-03	0.00e+00	2.77e-04	4.50e+01	0.00e+00	1.00e+00	5.52e+00	0.00e+00	1.00e+00	1.25e-02	0.00e+00	2.77e-04	1.00e+00	1.00e+00	3.00e+00	1.00e+00	1.00e+00	2.00e+00	2.77e-04	2.77e-04	8.31e-04	1.00e+00	1.00e+00	3.00e+00	1.00e+00	1.00e+00	2.00e+00	2.77e-04	2.77e-04	8.31e-04
chr11	78343482	78349164	ENSMUSG00000002058	56.47	+	Unc119	protein_coding	9.68e-04	9.99e-01	0.1905	-2.392317	0.1333	-2.906891	6.00e+00	2.50e+00	1.40e-03	9.00e+00	2.81e+00	2.10e-03	0.00e+00	3.33e-01	7.78e-05	0.00e+00	3.33e-01	7.78e-05	9.00e+00	8.00e+00	1.00e+00	3.32e+00	3.17e+00	1.00e+00	2.10e-03	1.87e-03	2.33e-04	1.80e+01	8.00e+00	1.00e+00	4.25e+00	3.17e+00	1.00e+00	4.20e-03	1.87e-03	2.33e-04	0.00e+00	1.00e+00	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	2.33e-04	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	2.33e-04	0.00e+00
chr10	117277334	117282274	ENSMUSG00000069516	47.10	-	Lyz2	protein_coding	1.05e-03	1.00e+00	0.5432	-0.880510	0.3763	-1.410155	1.19e+02	6.43e+00	5.81e-02	1.96e+02	6.58e+00	9.59e-02	6.40e+01	5.96e+00	3.14e-02	7.30e+01	6.09e+00	3.58e-02	2.57e+02	4.00e+01	5.90e+01	8.01e+00	5.36e+00	5.91e+00	1.26e-01	1.96e-02	2.89e-02	5.06e+02	3.80e+01	4.30e+01	8.99e+00	5.29e+00	5.46e+00	2.48e-01	1.86e-02	2.11e-02	8.50e+01	6.60e+01	4.10e+01	6.43e+00	6.07e+00	5.39e+00	4.16e-02	3.23e-02	2.01e-02	6.20e+01	1.15e+02	4.20e+01	5.98e+00	6.86e+00	5.43e+00	3.04e-02	5.63e-02	2.06e-02
chr14	32028989	32062197	ENSMUSG00000021903	48.44	+	Galnt15	protein_coding	1.27e-03	1.00e+00	0.3210	-1.639537	0.4063	-1.299408	5.83e+02	8.40e+00	1.13e-01	4.86e+02	8.54e+00	9.43e-02	1.86e+02	7.48e+00	3.61e-02	1.97e+02	7.61e+00	3.82e-02	1.06e+02	2.61e+02	1.38e+03	6.74e+00	8.03e+00	1.04e+01	2.05e-02	5.06e-02	2.68e-01	2.08e+02	2.46e+02	1.00e+03	7.71e+00	7.95e+00	9.97e+00	4.03e-02	4.77e-02	1.95e-01	2.18e+02	1.12e+02	2.29e+02	7.77e+00	6.82e+00	7.85e+00	4.23e-02	2.17e-02	4.44e-02	1.59e+02	1.96e+02	2.36e+02	7.32e+00	7.62e+00	7.89e+00	3.08e-02	3.80e-02	4.57e-02
chr9	50775019	50843542	ENSMUSG00000032059	45.27	+	Alg9	protein_coding	1.30e-03	1.00e+00	4.7000	2.232661	5.7000	2.510962	2.00e+00	1.64e+00	1.57e-04	2.00e+00	1.72e+00	1.57e-04	1.50e+01	3.93e+00	9.89e-04	1.80e+01	4.07e+00	1.21e-03	1.00e+00	2.00e+00	4.00e+00	1.00e+00	1.58e+00	2.32e+00	6.74e-05	1.35e-04	2.70e-04	2.00e+00	2.00e+00	3.00e+00	1.58e+00	1.58e+00	2.00e+00	1.35e-04	1.35e-04	2.02e-04	1.60e+01	1.80e+01	1.00e+01	4.09e+00	4.25e+00	3.46e+00	1.08e-03	1.21e-03	6.74e-04	1.20e+01	3.20e+01	1.00e+01	3.70e+00	5.04e+00	3.46e+00	8.09e-04	2.16e-03	6.74e-04
chr4	136895917	136898803	ENSMUSG00000036887	55.35	-	C1qa	protein_coding	1.31e-03	1.00e+00	0.3769	-1.407658	0.3200	-1.643856	4.20e+01	5.19e+00	2.84e-02	5.70e+01	5.33e+00	3.85e-02	1.50e+01	4.02e+00	1.03e-02	1.80e+01	4.16e+00	1.18e-02	6.20e+01	1.40e+01	5.10e+01	5.98e+00	3.91e+00	5.70e+00	4.16e-02	9.39e-03	3.42e-02	1.22e+02	1.30e+01	3.70e+01	6.94e+00	3.81e+00	5.25e+00	8.18e-02	8.72e-03	2.48e-02	1.50e+01	1.30e+01	1.80e+01	4.00e+00	3.81e+00	4.25e+00	1.01e-02	8.72e-03	1.21e-02	1.10e+01	2.30e+01	1.90e+01	3.58e+00	4.58e+00	4.32e+00	7.38e-03	1.54e-02	1.27e-02
chr15	103453825	103498800	ENSMUSG00000022488	42.34	+	Nckap1l	protein_coding	1.35e-03	1.00e+00	0.2000	-2.321928	0.2055	-2.282934	2.20e+01	3.30e+00	8.89e-03	2.30e+01	3.55e+00	9.29e-03	4.00e+00	1.77e+00	1.46e-03	4.00e+00	1.86e+00	1.59e-03	7.00e+00	5.90e+01	1.00e+00	3.00e+00	5.91e+00	1.00e+00	2.79e-03	2.35e-02	3.98e-04	1.30e+01	5.60e+01	1.00e+00	3.81e+00	5.83e+00	1.00e+00	5.18e-03	2.23e-02	3.98e-04	7.00e+00	4.00e+00	0.00e+00	3.00e+00	2.32e+00	0.00e+00	2.79e-03	1.59e-03	0.00e+00	5.00e+00	7.00e+00	0.00e+00	2.58e+00	3.00e+00	0.00e+00	1.99e-03	2.79e-03	0.00e+00
chrX	140539528	140600659	ENSMUSG00000031431	43.57	-	Tsc22d3	protein_coding	1.54e-03	1.00e+00	0.4146	-1.270189	0.4019	-1.315060	2.82e+02	8.11e+00	7.33e-02	3.13e+02	8.25e+00	8.14e-02	1.16e+02	6.74e+00	3.02e-02	1.25e+02	6.87e+00	3.26e-02	2.16e+02	2.63e+02	3.67e+02	7.76e+00	8.04e+00	8.52e+00	5.61e-02	6.83e-02	9.54e-02	4.25e+02	2.48e+02	2.67e+02	8.73e+00	7.96e+00	8.07e+00	1.10e-01	6.44e-02	6.94e-02	9.90e+01	6.50e+01	1.85e+02	6.64e+00	6.04e+00	7.54e+00	2.57e-02	1.69e-02	4.81e-02	7.20e+01	1.14e+02	1.90e+02	6.19e+00	6.85e+00	7.58e+00	1.87e-02	2.96e-02	4.94e-02
chr1	128588199	128592293	ENSMUSG00000045382	48.64	-	Cxcr4	protein_coding	1.56e-03	1.00e+00	0.3750	-1.415037	0.1786	-2.485427	4.00e+00	1.27e+00	1.37e-03	8.00e+00	1.57e+00	2.64e-03	1.00e+00	6.67e-01	3.17e-04	1.00e+00	5.28e-01	2.12e-04	1.30e+01	0.00e+00	0.00e+00	3.81e+00	0.00e+00	0.00e+00	4.12e-03	0.00e+00	0.00e+00	2.50e+01	0.00e+00	0.00e+00	4.70e+00	0.00e+00	0.00e+00	7.93e-03	0.00e+00	0.00e+00	3.00e+00	0.00e+00	0.00e+00	2.00e+00	0.00e+00	0.00e+00	9.52e-04	0.00e+00	0.00e+00	2.00e+00	0.00e+00	0.00e+00	1.58e+00	0.00e+00	0.00e+00	6.35e-04	0.00e+00	0.00e+00

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

DEG table for the contrast VehicleLL_vs_AmisulprideLL

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 VehicleLL_vs_AmisulprideLL. 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	natural_normalized_fold_change_VehicleLL_vs_AmisulprideLL	log2_normalized_fold_change_VehicleLL_vs_AmisulprideLL	natural_raw_fold_change_VehicleLL_vs_AmisulprideLL	log2_raw_fold_change_VehicleLL_vs_AmisulprideLL	natural_normalized_mean_counts_VehicleLL	log2_normalized_mean_counts_VehicleLL	rpgm_normalized_mean_counts_VehicleLL	natural_raw_mean_counts_VehicleLL	log2_raw_mean_counts_VehicleLL	rpgm_raw_mean_counts_VehicleLL	natural_normalized_mean_counts_AmisulprideLL	log2_normalized_mean_counts_AmisulprideLL	rpgm_normalized_mean_counts_AmisulprideLL	natural_raw_mean_counts_AmisulprideLL	log2_raw_mean_counts_AmisulprideLL	rpgm_raw_mean_counts_AmisulprideLL	natural_normalized_counts_GK3R320_8	natural_normalized_counts_GK3R322_10	natural_normalized_counts_GK3R324_12	log2_normalized_counts_GK3R320_8	log2_normalized_counts_GK3R322_10	log2_normalized_counts_GK3R324_12	rpgm_normalized_counts_GK3R320_8	rpgm_normalized_counts_GK3R322_10	rpgm_normalized_counts_GK3R324_12	natural_raw_counts_GK3R320_8	natural_raw_counts_GK3R322_10	natural_raw_counts_GK3R324_12	log2_raw_counts_GK3R320_8	log2_raw_counts_GK3R322_10	log2_raw_counts_GK3R324_12	rpgm_raw_counts_GK3R320_8	rpgm_raw_counts_GK3R322_10	rpgm_raw_counts_GK3R324_12	natural_normalized_counts_GK3R314_2	natural_normalized_counts_GK3R316_4	natural_normalized_counts_GK3R318_6	log2_normalized_counts_GK3R314_2	log2_normalized_counts_GK3R316_4	log2_normalized_counts_GK3R318_6	rpgm_normalized_counts_GK3R314_2	rpgm_normalized_counts_GK3R316_4	rpgm_normalized_counts_GK3R318_6	natural_raw_counts_GK3R314_2	natural_raw_counts_GK3R316_4	natural_raw_counts_GK3R318_6	log2_raw_counts_GK3R314_2	log2_raw_counts_GK3R316_4	log2_raw_counts_GK3R318_6	rpgm_raw_counts_GK3R314_2	rpgm_raw_counts_GK3R316_4	rpgm_raw_counts_GK3R318_6
chr11	98383811	98384953	ENSMUSG00000007877	60.37	+	Tcap	protein_coding	2.23e-12	3.69e-08	0.0626	-3.998298	0.149	-2.74487	2.81e+02	5.80e+00	1.69e-01	2.02e+02	5.30e+00	1.22e-01	1.70e+01	3.59e+00	1.00e-02	2.90e+01	3.95e+00	1.76e-02	9.00e+00	2.00e+01	8.15e+02	3.32e+00	4.39e+00	9.67e+00	5.42e-03	1.20e-02	4.90e-01	1.40e+01	6.00e+00	5.87e+02	3.91e+00	2.81e+00	9.20e+00	8.42e-03	3.61e-03	3.53e-01	8.00e+00	4.00e+00	3.80e+01	3.17e+00	2.32e+00	5.29e+00	4.81e-03	2.41e-03	2.29e-02	7.00e+00	5.00e+00	7.60e+01	3.00e+00	2.58e+00	6.27e+00	4.21e-03	3.01e-03	4.57e-02
chr12	69583762	69681852	ENSMUSG00000034801	40.65	-	Sos2	protein_coding	2.12e-06	1.75e-02	0.1496	-2.740568	0.286	-1.80735	1.33e+02	5.58e+00	2.47e-02	9.90e+01	5.12e+00	1.85e-02	1.90e+01	4.30e+00	3.53e-03	2.80e+01	4.66e+00	5.15e-03	1.90e+01	1.40e+01	3.65e+02	4.32e+00	3.91e+00	8.52e+00	3.53e-03	2.60e-03	6.79e-02	3.10e+01	4.00e+00	2.63e+02	5.00e+00	2.32e+00	8.04e+00	5.77e-03	7.44e-04	4.89e-02	1.80e+01	1.50e+01	2.40e+01	4.25e+00	4.00e+00	4.64e+00	3.35e-03	2.79e-03	4.47e-03	1.50e+01	1.90e+01	4.90e+01	4.00e+00	4.32e+00	5.64e+00	2.79e-03	3.53e-03	9.12e-03
chr6	114860628	114969994	ENSMUSG00000030315	48.35	-	Vgll4	protein_coding	1.74e-05	7.39e-02	3.8438	1.942515	6.974	2.80192	3.10e+01	4.98e+00	5.24e-03	2.40e+01	4.48e+00	4.12e-03	1.22e+02	6.78e+00	2.06e-02	1.76e+02	7.16e+00	2.97e-02	2.40e+01	3.40e+01	3.50e+01	4.64e+00	5.13e+00	5.17e+00	4.06e-03	5.75e-03	5.92e-03	3.80e+01	1.00e+01	2.50e+01	5.29e+00	3.46e+00	4.70e+00	6.43e-03	1.69e-03	4.23e-03	5.90e+01	1.95e+02	1.12e+02	5.91e+00	7.61e+00	6.82e+00	9.98e-03	3.30e-02	1.89e-02	5.00e+01	2.52e+02	2.25e+02	5.67e+00	7.98e+00	7.82e+00	8.46e-03	4.26e-02	3.81e-02
chr10	57784881	57788450	ENSMUSG00000019874	38.38	+	Fabp7	protein_coding	2.13e-05	7.39e-02	0.2347	-2.091148	0.330	-1.60090	3.20e+01	4.81e+00	1.05e-02	2.90e+01	4.33e+00	9.74e-03	7.00e+00	2.90e+00	2.21e-03	9.00e+00	3.25e+00	2.99e-03	4.30e+01	4.10e+01	1.10e+01	5.46e+00	5.39e+00	3.58e+00	1.43e-02	1.36e-02	3.65e-03	6.80e+01	1.20e+01	8.00e+00	6.11e+00	3.70e+00	3.17e+00	2.26e-02	3.98e-03	2.66e-03	6.00e+00	9.00e+00	5.00e+00	2.81e+00	3.32e+00	2.58e+00	1.99e-03	2.99e-03	1.66e-03	5.00e+00	1.10e+01	1.10e+01	2.58e+00	3.58e+00	3.58e+00	1.66e-03	3.65e-03	3.65e-03
chr14	54970688	54994634	ENSMUSG00000053093	50.82	-	Myh7	protein_coding	2.24e-05	7.39e-02	0.0708	-3.820179	0.134	-2.89812	3.70e+01	2.26e+00	9.48e-03	2.60e+01	2.11e+00	6.81e-03	2.00e+00	1.19e+00	4.31e-04	3.00e+00	1.46e+00	6.90e-04	0.00e+00	0.00e+00	1.10e+02	0.00e+00	0.00e+00	6.79e+00	0.00e+00	0.00e+00	2.84e-02	0.00e+00	0.00e+00	7.90e+01	0.00e+00	0.00e+00	6.32e+00	0.00e+00	0.00e+00	2.04e-02	2.00e+00	0.00e+00	3.00e+00	1.58e+00	0.00e+00	2.00e+00	5.17e-04	0.00e+00	7.76e-04	2.00e+00	0.00e+00	6.00e+00	1.58e+00	0.00e+00	2.81e+00	5.17e-04	0.00e+00	1.55e-03
chr7	46740497	46742980	ENSMUSG00000074115	53.82	-	Saa1	protein_coding	3.29e-05	9.06e-02	0.1910	-2.388017	0.364	-1.45805	1.11e+02	6.35e+00	7.57e-02	8.60e+01	5.84e+00	5.89e-02	2.00e+01	4.38e+00	1.39e-02	3.10e+01	4.75e+00	2.10e-02	4.50e+01	4.80e+01	2.39e+02	5.52e+00	5.61e+00	7.91e+00	3.08e-02	3.29e-02	1.64e-01	7.20e+01	1.40e+01	1.72e+02	6.19e+00	3.91e+00	7.43e+00	4.93e-02	9.58e-03	1.18e-01	1.50e+01	1.90e+01	2.70e+01	4.00e+00	4.32e+00	4.81e+00	1.03e-02	1.30e-02	1.85e-02	1.30e+01	2.40e+01	5.50e+01	3.81e+00	4.64e+00	5.81e+00	8.90e-03	1.64e-02	3.76e-02
chr7	4504570	4516382	ENSMUSG00000064179	51.27	-	Tnnt1	protein_coding	4.16e-05	9.80e-02	0.1438	-2.797956	0.326	-1.61710	1.01e+02	5.06e+00	1.53e-02	7.50e+01	4.64e+00	1.13e-02	1.40e+01	3.37e+00	2.08e-03	2.40e+01	3.75e+00	3.59e-03	1.10e+01	1.00e+01	2.82e+02	3.58e+00	3.46e+00	8.14e+00	1.67e-03	1.52e-03	4.28e-02	1.80e+01	3.00e+00	2.03e+02	4.25e+00	2.00e+00	7.67e+00	2.73e-03	4.56e-04	3.08e-02	8.00e+00	3.00e+00	3.00e+01	3.17e+00	2.00e+00	4.95e+00	1.21e-03	4.56e-04	4.56e-03	7.00e+00	4.00e+00	6.00e+01	3.00e+00	2.32e+00	5.93e+00	1.06e-03	6.07e-04	9.11e-03
chr12	25093801	25096092	ENSMUSG00000020644	44.59	-	Id2	protein_coding	5.64e-05	1.16e-01	3.2098	1.682472	5.380	2.42771	2.79e+02	8.12e+00	9.18e-02	2.30e+02	7.60e+00	7.59e-02	8.97e+02	9.76e+00	2.95e-01	1.24e+03	1.01e+01	4.10e-01	2.58e+02	3.23e+02	2.55e+02	8.02e+00	8.34e+00	8.00e+00	8.50e-02	1.06e-01	8.40e-02	4.12e+02	9.50e+01	1.84e+02	8.69e+00	6.58e+00	7.53e+00	1.36e-01	3.13e-02	6.06e-02	6.76e+02	1.24e+03	7.72e+02	9.40e+00	1.03e+01	9.59e+00	2.23e-01	4.09e-01	2.54e-01	5.77e+02	1.60e+03	1.55e+03	9.17e+00	1.06e+01	1.06e+01	1.90e-01	5.27e-01	5.12e-01
chr9	44753104	44753851	ENSMUSG00000111345	38.90	-	RP23-444K20.4	TEC	1.23e-04	2.25e-01	3.7179	1.894507	9.045	3.17719	1.20e+01	3.29e+00	9.57e-03	6.00e+00	2.76e+00	5.05e-03	4.70e+01	5.53e+00	3.77e-02	6.50e+01	5.91e+00	5.21e-02	2.00e+00	2.00e+01	1.40e+01	1.58e+00	4.39e+00	3.91e+00	1.59e-03	1.59e-02	1.12e-02	3.00e+00	6.00e+00	1.00e+01	2.00e+00	2.81e+00	3.46e+00	2.39e-03	4.78e-03	7.97e-03	3.50e+01	6.90e+01	3.80e+01	5.17e+00	6.13e+00	5.29e+00	2.79e-02	5.50e-02	3.03e-02	3.00e+01	8.90e+01	7.70e+01	4.95e+00	6.49e+00	6.29e+00	2.39e-02	7.10e-02	6.14e-02
chr17	48232768	48246924	ENSMUSG00000042265	46.83	+	Trem1	protein_coding	1.57e-04	2.59e-01	0.1557	-2.682810	0.233	-2.10154	4.00e+01	5.07e+00	1.12e-02	3.30e+01	4.59e+00	9.44e-03	5.00e+00	2.64e+00	1.51e-03	7.00e+00	2.99e+00	1.98e-03	4.60e+01	6.10e+01	1.20e+01	5.55e+00	5.95e+00	3.70e+00	1.30e-02	1.73e-02	3.40e-03	7.30e+01	1.80e+01	9.00e+00	6.21e+00	4.25e+00	3.32e+00	2.07e-02	5.10e-03	2.55e-03	7.00e+00	5.00e+00	4.00e+00	3.00e+00	2.58e+00	2.32e+00	1.98e-03	1.42e-03	1.13e-03	6.00e+00	7.00e+00	8.00e+00	2.81e+00	3.00e+00	3.17e+00	1.70e-03	1.98e-03	2.27e-03
chrX	106027276	106124926	ENSMUSG00000033792	39.80	+	Atp7a	protein_coding	2.15e-04	3.05e-01	2.6170	1.387926	7.560	2.91839	1.50e+01	3.42e+00	3.60e-03	7.00e+00	2.93e+00	1.80e-03	4.00e+01	5.27e+00	9.82e-03	6.20e+01	5.65e+00	1.52e-02	6.00e+00	3.40e+01	4.00e+00	2.81e+00	5.13e+00	2.32e+00	1.47e-03	8.35e-03	9.82e-04	9.00e+00	1.00e+01	3.00e+00	3.32e+00	3.46e+00	2.00e+00	2.21e-03	2.46e-03	7.37e-04	3.00e+01	2.90e+01	6.10e+01	4.95e+00	4.91e+00	5.95e+00	7.37e-03	7.12e-03	1.50e-02	2.60e+01	3.70e+01	1.23e+02	4.75e+00	5.25e+00	6.95e+00	6.38e-03	9.08e-03	3.02e-02
chrX	155323918	155340754	ENSMUSG00000025289	38.51	-	Prdx4	protein_coding	2.22e-04	3.05e-01	3.3436	1.741384	5.615	2.48938	5.30e+01	5.69e+00	1.40e-02	5.50e+01	5.20e+00	1.45e-02	1.81e+02	7.01e+00	4.73e-02	3.15e+02	7.39e+00	8.25e-02	7.90e+01	4.40e+01	3.70e+01	6.32e+00	5.49e+00	5.25e+00	2.07e-02	1.15e-02	9.68e-03	1.26e+02	1.30e+01	2.70e+01	6.99e+00	3.81e+00	4.81e+00	3.30e-02	3.40e-03	7.06e-03	8.60e+01	6.10e+01	3.95e+02	6.44e+00	5.95e+00	8.63e+00	2.25e-02	1.60e-02	1.03e-01	7.30e+01	7.90e+01	7.94e+02	6.21e+00	6.32e+00	9.63e+00	1.91e-02	2.07e-02	2.08e-01
chr2	129364570	129371139	ENSMUSG00000027398	45.34	-	Il1b	protein_coding	2.44e-04	3.10e-01	0.3942	-1.342888	0.486	-1.04122	1.38e+02	6.76e+00	4.50e-02	1.41e+02	6.24e+00	4.61e-02	5.40e+01	5.73e+00	1.75e-02	6.80e+01	6.10e+00	2.22e-02	2.21e+02	1.57e+02	3.50e+01	7.79e+00	7.30e+00	5.17e+00	7.22e-02	5.13e-02	1.14e-02	3.52e+02	4.60e+01	2.50e+01	8.46e+00	5.55e+00	4.70e+00	1.15e-01	1.50e-02	8.17e-03	7.30e+01	4.80e+01	4.00e+01	6.21e+00	5.61e+00	5.36e+00	2.39e-02	1.57e-02	1.31e-02	6.20e+01	6.20e+01	8.00e+01	5.98e+00	5.98e+00	6.34e+00	2.03e-02	2.03e-02	2.61e-02
chr17	46772635	46773407	ENSMUSG00000062619	57.96	+	2310039H08Rik	protein_coding	2.76e-04	3.25e-01	2.6616	1.412303	5.054	2.33734	6.50e+01	6.00e+00	5.20e-02	4.90e+01	5.49e+00	3.90e-02	1.75e+02	7.43e+00	1.40e-01	2.50e+02	7.81e+00	2.00e-01	4.90e+01	8.90e+01	5.70e+01	5.64e+00	6.49e+00	5.86e+00	3.92e-02	7.13e-02	4.56e-02	7.90e+01	2.60e+01	4.10e+01	6.32e+00	4.75e+00	5.39e+00	6.33e-02	2.08e-02	3.28e-02	1.32e+02	2.11e+02	1.81e+02	7.06e+00	7.73e+00	7.51e+00	1.06e-01	1.69e-01	1.45e-01	1.13e+02	2.72e+02	3.65e+02	6.83e+00	8.09e+00	8.52e+00	9.05e-02	2.18e-01	2.92e-01
chr5	134600022	134615025	ENSMUSG00000040751	52.23	-	Lat2	protein_coding	3.30e-04	3.63e-01	0.3176	-1.654865	0.370	-1.43558	4.80e+01	5.48e+00	6.92e-03	5.40e+01	5.00e+00	7.73e-03	1.50e+01	3.95e+00	2.10e-03	1.90e+01	4.32e+00	2.77e-03	8.10e+01	3.10e+01	3.30e+01	6.36e+00	5.00e+00	5.09e+00	1.16e-02	4.44e-03	4.73e-03	1.29e+02	9.00e+00	2.40e+01	7.02e+00	3.32e+00	4.64e+00	1.85e-02	1.29e-03	3.44e-03	1.60e+01	1.70e+01	1.10e+01	4.09e+00	4.17e+00	3.58e+00	2.29e-03	2.43e-03	1.58e-03	1.40e+01	2.20e+01	2.20e+01	3.91e+00	4.52e+00	4.52e+00	2.01e-03	3.15e-03	3.15e-03
chr4	130913125	130936141	ENSMUSG00000028581	53.09	+	Laptm5	protein_coding	4.04e-04	3.96e-01	0.3455	-1.533105	0.557	-0.84513	3.09e+02	8.05e+00	2.97e-02	2.67e+02	7.53e+00	2.57e-02	1.06e+02	6.72e+00	1.02e-02	1.48e+02	7.10e+00	1.42e-02	3.68e+02	4.46e+02	1.12e+02	8.53e+00	8.80e+00	6.82e+00	3.54e-02	4.29e-02	1.08e-02	5.88e+02	1.31e+02	8.10e+01	9.20e+00	7.04e+00	6.36e+00	5.66e-02	1.26e-02	7.80e-03	1.17e+02	8.30e+01	1.18e+02	6.88e+00	6.39e+00	6.89e+00	1.13e-02	7.99e-03	1.14e-02	1.00e+02	1.07e+02	2.37e+02	6.66e+00	6.75e+00	7.89e+00	9.62e-03	1.03e-02	2.28e-02
chr8	72134975	72153142	ENSMUSG00000031799	46.79	+	Tpm4	protein_coding	4.07e-04	3.96e-01	2.3878	1.255687	4.559	2.18866	2.56e+02	7.81e+00	6.69e-02	1.92e+02	7.30e+00	5.01e-02	6.13e+02	9.23e+00	1.60e-01	8.77e+02	9.61e+00	2.29e-01	2.31e+02	4.22e+02	1.15e+02	7.86e+00	8.72e+00	6.86e+00	6.04e-02	1.10e-01	3.01e-02	3.68e+02	1.24e+02	8.30e+01	8.53e+00	6.97e+00	6.39e+00	9.62e-02	3.24e-02	2.17e-02	4.43e+02	7.66e+02	6.29e+02	8.79e+00	9.58e+00	9.30e+00	1.16e-01	2.00e-01	1.64e-01	3.78e+02	9.88e+02	1.27e+03	8.57e+00	9.95e+00	1.03e+01	9.88e-02	2.58e-01	3.31e-01
chr10	117287796	117292868	ENSMUSG00000069515	46.09	-	Lyz1	protein_coding	5.01e-04	4.59e-01	0.5062	-0.982078	0.507	-0.97873	1.06e+02	6.09e+00	5.18e-02	1.34e+02	5.61e+00	6.55e-02	5.30e+01	5.70e+00	2.60e-02	6.70e+01	6.07e+00	3.30e-02	2.30e+02	6.80e+01	1.90e+01	7.85e+00	6.11e+00	4.32e+00	1.13e-01	3.33e-02	9.31e-03	3.67e+02	2.00e+01	1.40e+01	8.52e+00	4.39e+00	3.91e+00	1.80e-01	9.80e-03	6.86e-03	7.50e+01	4.20e+01	4.20e+01	6.25e+00	5.43e+00	5.43e+00	3.67e-02	2.06e-02	2.06e-02	6.40e+01	5.40e+01	8.40e+01	6.02e+00	5.78e+00	6.41e+00	3.14e-02	2.65e-02	4.12e-02
chr14	54642161	54686931	ENSMUSG00000022185	45.59	-	Acin1	protein_coding	5.44e-04	4.72e-01	2.5747	1.364411	4.474	2.16156	1.44e+02	7.14e+00	8.05e-03	1.14e+02	6.62e+00	6.39e-03	3.72e+02	8.53e+00	2.08e-02	5.15e+02	8.91e+00	2.88e-02	1.05e+02	1.40e+02	1.87e+02	6.73e+00	7.14e+00	7.55e+00	5.87e-03	7.82e-03	1.04e-02	1.67e+02	4.10e+01	1.35e+02	7.39e+00	5.39e+00	7.09e+00	9.33e-03	2.29e-03	7.54e-03	3.25e+02	4.51e+02	3.41e+02	8.35e+00	8.82e+00	8.42e+00	1.82e-02	2.52e-02	1.91e-02	2.77e+02	5.81e+02	6.87e+02	8.12e+00	9.18e+00	9.43e+00	1.55e-02	3.25e-02	3.84e-02
chr17	43568098	43612201	ENSMUSG00000023913	43.77	+	Pla2g7	protein_coding	6.30e-04	5.20e-01	0.2258	-2.146841	0.233	-2.10236	2.00e+01	4.06e+00	2.54e-03	2.30e+01	3.62e+00	3.01e-03	4.00e+00	2.13e+00	4.73e-04	5.00e+00	2.39e+00	6.02e-04	3.80e+01	1.40e+01	7.00e+00	5.29e+00	3.91e+00	3.00e+00	4.91e-03	1.81e-03	9.04e-04	6.10e+01	4.00e+00	5.00e+00	5.95e+00	2.32e+00	2.58e+00	7.88e-03	5.16e-04	6.45e-04	6.00e+00	2.00e+00	3.00e+00	2.81e+00	1.58e+00	2.00e+00	7.75e-04	2.58e-04	3.87e-04	5.00e+00	2.00e+00	7.00e+00	2.58e+00	1.58e+00	3.00e+00	6.45e-04	2.58e-04	9.04e-04
chr14	69029289	69041401	ENSMUSG00000014813	44.45	+	Stc1	protein_coding	7.71e-04	6.06e-01	3.1552	1.657719	5.865	2.55210	1.80e+01	4.11e+00	4.42e-03	1.10e+01	3.60e+00	2.73e-03	6.00e+01	5.63e+00	1.45e-02	7.10e+01	6.01e+00	1.72e-02	9.00e+00	3.10e+01	1.50e+01	3.32e+00	5.00e+00	4.00e+00	2.17e-03	7.47e-03	3.61e-03	1.40e+01	9.00e+00	1.10e+01	3.91e+00	3.32e+00	3.58e+00	3.37e-03	2.17e-03	2.65e-03	7.10e+01	9.20e+01	1.70e+01	6.17e+00	6.54e+00	4.17e+00	1.71e-02	2.22e-02	4.09e-03	6.10e+01	1.18e+02	3.50e+01	5.95e+00	6.89e+00	5.17e+00	1.47e-02	2.84e-02	8.43e-03
chr7	4965260	4971168	ENSMUSG00000085272	49.42	-	Sbk3	protein_coding	8.35e-04	6.10e-01	0.0638	-3.969626	0.114	-3.12928	1.50e+01	1.83e+00	4.40e-03	1.10e+01	1.68e+00	3.20e-03	0.00e+00	0.00e+00	0.00e+00	0.00e+00	3.33e-01	1.00e-04	0.00e+00	0.00e+00	4.40e+01	0.00e+00	0.00e+00	5.49e+00	0.00e+00	0.00e+00	1.32e-02	0.00e+00	0.00e+00	3.20e+01	0.00e+00	0.00e+00	5.04e+00	0.00e+00	0.00e+00	9.60e-03	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	3.00e-04
chr8	84699876	84800344	ENSMUSG00000001911	53.14	-	Nfix	protein_coding	8.83e-04	6.10e-01	2.3537	1.234943	4.319	2.11068	3.26e+02	8.33e+00	4.22e-02	2.54e+02	7.81e+00	3.29e-02	7.69e+02	9.58e+00	9.95e-02	1.10e+03	9.96e+00	1.42e-01	2.69e+02	4.19e+02	2.90e+02	8.08e+00	8.71e+00	8.18e+00	3.48e-02	5.42e-02	3.75e-02	4.30e+02	1.23e+02	2.09e+02	8.75e+00	6.95e+00	7.71e+00	5.56e-02	1.59e-02	2.70e-02	6.39e+02	8.28e+02	8.39e+02	9.32e+00	9.70e+00	9.71e+00	8.27e-02	1.07e-01	1.09e-01	5.45e+02	1.07e+03	1.69e+03	9.09e+00	1.01e+01	1.07e+01	7.05e-02	1.38e-01	2.19e-01
chr9	110741861	110769798	ENSMUSG00000059741	53.16	+	Myl3	protein_coding	9.16e-04	6.10e-01	0.2121	-2.237039	0.444	-1.16993	1.09e+02	5.02e+00	2.92e-02	8.00e+01	4.60e+00	2.14e-02	2.20e+01	4.45e+00	5.99e-03	3.50e+01	4.82e+00	9.38e-03	9.00e+00	1.00e+01	3.08e+02	3.32e+00	3.46e+00	8.27e+00	2.41e-03	2.68e-03	8.26e-02	1.50e+01	3.00e+00	2.22e+02	4.00e+00	2.00e+00	7.80e+00	4.02e-03	8.04e-04	5.95e-02	1.40e+01	1.90e+01	3.40e+01	3.91e+00	4.32e+00	5.13e+00	3.75e-03	5.09e-03	9.12e-03	1.20e+01	2.40e+01	6.90e+01	3.70e+00	4.64e+00	6.13e+00	3.22e-03	6.43e-03	1.85e-02
chr2	24336853	24351494	ENSMUSG00000026981	42.77	+	Il1rn	protein_coding	9.41e-04	6.10e-01	0.4213	-1.247160	0.559	-0.84006	1.56e+02	7.23e+00	2.67e-02	1.44e+02	6.72e+00	2.47e-02	6.50e+01	5.93e+00	1.11e-02	8.00e+01	6.31e+00	1.37e-02	1.94e+02	1.74e+02	9.90e+01	7.61e+00	7.45e+00	6.64e+00	3.33e-02	2.98e-02	1.70e-02	3.10e+02	5.10e+01	7.10e+01	8.28e+00	5.70e+00	6.17e+00	5.31e-02	8.74e-03	1.22e-02	1.04e+02	4.50e+01	4.60e+01	6.71e+00	5.52e+00	5.55e+00	1.78e-02	7.71e-03	7.88e-03	8.90e+01	5.80e+01	9.30e+01	6.49e+00	5.88e+00	6.55e+00	1.53e-02	9.94e-03	1.59e-02
chr16	13833148	13903131	ENSMUSG00000022680	41.35	-	Pdxdc1	protein_coding	9.61e-04	6.10e-01	0.3580	-1.481869	0.365	-1.45568	2.60e+01	3.55e+00	1.67e-03	3.10e+01	3.61e+00	1.99e-03	9.00e+00	2.95e+00	5.57e-04	1.10e+01	3.19e+00	6.86e-04	4.20e+01	0.00e+00	3.60e+01	5.43e+00	0.00e+00	5.21e+00	2.70e-03	0.00e+00	2.32e-03	6.70e+01	0.00e+00	2.60e+01	6.09e+00	0.00e+00	4.75e+00	4.31e-03	0.00e+00	1.67e-03	1.60e+01	2.00e+00	8.00e+00	4.09e+00	1.58e+00	3.17e+00	1.03e-03	1.29e-04	5.15e-04	1.40e+01	2.00e+00	1.60e+01	3.91e+00	1.58e+00	4.09e+00	9.00e-04	1.29e-04	1.03e-03
chr3	53099000	53103175	ENSMUSG00000104735	43.46	+	C130075A20Rik	TEC	1.00e-03	6.12e-01	4.5000	2.169925	9.400	3.23266	4.00e+00	1.72e+00	2.92e-03	2.00e+00	1.27e+00	1.86e-03	2.00e+01	4.33e+00	1.59e-02	3.00e+01	4.69e+00	2.42e-02	0.00e+00	3.00e+00	8.00e+00	0.00e+00	2.00e+00	3.17e+00	0.00e+00	2.39e-03	6.38e-03	0.00e+00	1.00e+00	6.00e+00	0.00e+00	1.00e+00	2.81e+00	0.00e+00	7.97e-04	4.78e-03	1.50e+01	1.60e+01	2.90e+01	4.00e+00	4.09e+00	4.91e+00	1.20e-02	1.28e-02	2.31e-02	1.30e+01	2.00e+01	5.80e+01	3.81e+00	4.39e+00	5.88e+00	1.04e-02	1.59e-02	4.63e-02
chr3	66562805	66563551	ENSMUSG00000103600	42.70	-	Gm17952	processed_pseudogene	1.05e-03	6.19e-01	0.0652	-3.938599	0.118	-3.08746	1.40e+01	1.82e+00	1.14e-02	1.00e+01	1.67e+00	8.24e-03	0.00e+00	0.00e+00	0.00e+00	0.00e+00	3.33e-01	2.66e-04	0.00e+00	0.00e+00	4.30e+01	0.00e+00	0.00e+00	5.46e+00	0.00e+00	0.00e+00	3.43e-02	0.00e+00	0.00e+00	3.10e+01	0.00e+00	0.00e+00	5.00e+00	0.00e+00	0.00e+00	2.47e-02	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	7.97e-04
chr16	76287400	76373827	ENSMUSG00000048490	42.99	-	Nrip1	protein_coding	1.12e-03	6.35e-01	2.7386	1.453458	4.941	2.30485	2.80e+01	4.83e+00	3.14e-03	2.20e+01	4.34e+00	2.40e-03	7.90e+01	6.31e+00	8.81e-03	1.11e+02	6.69e+00	1.23e-02	1.90e+01	3.10e+01	3.50e+01	4.32e+00	5.00e+00	5.17e+00	2.11e-03	3.44e-03	3.88e-03	3.10e+01	9.00e+00	2.50e+01	5.00e+00	3.32e+00	4.70e+00	3.44e-03	9.99e-04	2.77e-03	6.70e+01	9.50e+01	7.60e+01	6.09e+00	6.58e+00	6.27e+00	7.44e-03	1.05e-02	8.44e-03	5.70e+01	1.23e+02	1.53e+02	5.86e+00	6.95e+00	7.27e+00	6.33e-03	1.37e-02	1.70e-02
chr14	31208312	31211729	ENSMUSG00000091898	52.78	+	Tnnc1	protein_coding	1.21e-03	6.67e-01	0.1898	-2.397435	0.481	-1.05614	1.17e+02	5.64e+00	3.04e-02	8.60e+01	5.16e+00	2.25e-02	2.10e+01	2.75e+00	5.56e-03	4.10e+01	2.97e+00	1.07e-02	1.80e+01	2.00e+01	3.12e+02	4.25e+00	4.39e+00	8.29e+00	4.69e-03	5.21e-03	8.13e-02	2.80e+01	6.00e+00	2.25e+02	4.86e+00	2.81e+00	7.82e+00	7.30e-03	1.56e-03	5.86e-02	4.00e+00	0.00e+00	6.00e+01	2.32e+00	0.00e+00	5.93e+00	1.04e-03	0.00e+00	1.56e-02	3.00e+00	0.00e+00	1.20e+02	2.00e+00	0.00e+00	6.92e+00	7.82e-04	0.00e+00	3.13e-02
chr12	104247905	104253669	ENSMUSG00000041449	47.72	+	Serpina3h	transcribed_unprocessed_pseudogene	1.26e-03	6.69e-01	0.3204	-1.642106	0.458	-1.12553	3.30e+01	4.84e+00	1.33e-02	3.10e+01	4.34e+00	1.24e-02	1.00e+01	3.21e+00	3.99e-03	1.40e+01	3.52e+00	5.45e-03	4.60e+01	4.40e+01	1.00e+01	5.55e+00	5.49e+00	3.46e+00	1.83e-02	1.75e-02	3.99e-03	7.30e+01	1.30e+01	7.00e+00	6.21e+00	3.81e+00	3.00e+00	2.91e-02	5.18e-03	2.79e-03	4.00e+00	1.90e+01	7.00e+00	2.32e+00	4.32e+00	3.00e+00	1.59e-03	7.58e-03	2.79e-03	3.00e+00	2.40e+01	1.40e+01	2.00e+00	4.64e+00	3.91e+00	1.20e-03	9.57e-03	5.58e-03
chr12	33111711	33147597	ENSMUSG00000052125	46.11	-	F730043M19Rik	processed_transcript	1.31e-03	6.69e-01	0.0938	-3.415037	0.167	-2.58496	1.00e+01	2.74e+00	1.28e-03	7.00e+00	2.39e+00	9.30e-04	0.00e+00	0.00e+00	0.00e+00	0.00e+00	3.33e-01	4.43e-05	2.00e+00	3.00e+00	2.40e+01	1.58e+00	2.00e+00	4.64e+00	2.66e-04	3.99e-04	3.19e-03	3.00e+00	1.00e+00	1.70e+01	2.00e+00	1.00e+00	4.17e+00	3.99e-04	1.33e-04	2.26e-03	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	1.00e+00	0.00e+00	0.00e+00	1.33e-04
chr16	37882542	37883631	ENSMUSG00000083405	56.33	+	Gm15725	transcribed_processed_pseudogene	1.34e-03	6.69e-01	1.8623	0.897066	4.211	2.07425	1.10e+02	6.63e+00	8.80e-02	7.00e+01	6.12e+00	5.58e-02	2.06e+02	7.62e+00	1.65e-01	2.98e+02	8.00e+00	2.38e-01	4.90e+01	1.70e+02	1.12e+02	5.64e+00	7.42e+00	6.82e+00	3.91e-02	1.36e-01	8.93e-02	7.90e+01	5.00e+01	8.10e+01	6.32e+00	5.67e+00	6.36e+00	6.30e-02	3.99e-02	6.46e-02	1.27e+02	2.82e+02	2.10e+02	7.00e+00	8.14e+00	7.72e+00	1.01e-01	2.25e-01	1.67e-01	1.08e+02	3.64e+02	4.22e+02	6.77e+00	8.51e+00	8.72e+00	8.61e-02	2.90e-01	3.37e-01
chr1	118458652	118459267	ENSMUSG00000107722	42.37	-	2900060B14Rik	antisense	1.49e-03	7.19e-01	1.4694	0.555274	3.951	1.98219	5.66e+02	8.63e+00	4.45e-01	2.91e+02	8.11e+00	2.29e-01	8.33e+02	9.59e+00	6.55e-01	1.15e+03	9.97e+00	9.06e-01	1.08e+02	1.04e+03	5.46e+02	6.77e+00	1.00e+01	9.10e+00	8.49e-02	8.22e-01	4.29e-01	1.73e+02	3.07e+02	3.93e+02	7.44e+00	8.27e+00	8.62e+00	1.36e-01	2.41e-01	3.09e-01	5.27e+02	1.32e+03	6.47e+02	9.04e+00	1.04e+01	9.34e+00	4.14e-01	1.04e+00	5.09e-01	4.50e+02	1.71e+03	1.30e+03	8.82e+00	1.07e+01	1.03e+01	3.54e-01	1.34e+00	1.02e+00
chr2	164891882	164911757	ENSMUSG00000039834	53.59	-	Zfp335	protein_coding	1.58e-03	7.19e-01	0.4030	-1.311202	0.508	-0.97841	6.60e+01	5.98e+00	3.10e-03	6.50e+01	5.50e+00	3.06e-03	2.60e+01	4.69e+00	1.22e-03	3.30e+01	5.06e+00	1.53e-03	7.80e+01	3.70e+01	8.30e+01	6.30e+00	5.25e+00	6.39e+00	3.66e-03	1.74e-03	3.89e-03	1.25e+02	1.10e+01	6.00e+01	6.98e+00	3.58e+00	5.93e+00	5.86e-03	5.16e-04	2.81e-03	3.30e+01	2.90e+01	1.60e+01	5.09e+00	4.91e+00	4.09e+00	1.55e-03	1.36e-03	7.51e-04	2.80e+01	3.70e+01	3.30e+01	4.86e+00	5.25e+00	5.09e+00	1.31e-03	1.74e-03	1.55e-03
chr17	34728380	34743882	ENSMUSG00000073418	54.98	-	C4b	protein_coding	1.59e-03	7.19e-01	0.4489	-1.155689	0.602	-0.73301	1.59e+02	7.25e+00	2.21e-02	1.20e+02	6.73e+00	1.67e-02	7.10e+01	5.46e+00	9.83e-03	7.20e+01	5.83e+00	1.00e-02	9.80e+01	1.60e+02	2.18e+02	6.63e+00	7.33e+00	7.77e+00	1.36e-02	2.23e-02	3.03e-02	1.57e+02	4.70e+01	1.57e+02	7.30e+00	5.58e+00	7.30e+00	2.18e-02	6.54e-03	2.18e-02	1.69e+02	2.00e+01	2.30e+01	7.41e+00	4.39e+00	4.58e+00	2.35e-02	2.78e-03	3.20e-03	1.44e+02	2.60e+01	4.60e+01	7.18e+00	4.75e+00	5.55e+00	2.00e-02	3.62e-03	6.40e-03
chr1	134182176	134190181	ENSMUSG00000064246	50.34	+	Chil1	protein_coding	1.61e-03	7.19e-01	1.7710	0.824571	3.979	1.99244	1.58e+02	6.97e+00	4.69e-02	9.50e+01	6.45e+00	2.81e-02	2.80e+02	8.12e+00	8.32e-02	3.80e+02	8.50e+00	1.13e-01	4.10e+01	2.18e+02	2.14e+02	5.39e+00	7.77e+00	7.75e+00	1.22e-02	6.48e-02	6.36e-02	6.60e+01	6.40e+01	1.54e+02	6.07e+00	6.02e+00	7.28e+00	1.96e-02	1.90e-02	4.58e-02	2.60e+02	3.45e+02	2.35e+02	8.03e+00	8.43e+00	7.88e+00	7.73e-02	1.03e-01	6.99e-02	2.22e+02	4.45e+02	4.72e+02	7.80e+00	8.80e+00	8.89e+00	6.60e-02	1.32e-01	1.40e-01
chr17	32113429	32141839	ENSMUSG00000097305	49.67	+	Gm17276	lincRNA	1.67e-03	7.24e-01	0.1190	-3.070389	0.208	-2.26303	1.30e+01	3.52e+00	1.04e-02	7.00e+00	2.99e+00	5.58e-03	1.00e+00	5.28e-01	5.32e-04	1.00e+00	5.28e-01	5.32e-04	4.00e+00	2.40e+01	1.10e+01	2.32e+00	4.64e+00	3.58e+00	3.19e-03	1.91e-02	8.77e-03	6.00e+00	7.00e+00	8.00e+00	2.81e+00	3.00e+00	3.17e+00	4.78e-03	5.58e-03	6.38e-03	0.00e+00	2.00e+00	0.00e+00	0.00e+00	1.58e+00	0.00e+00	0.00e+00	1.59e-03	0.00e+00	0.00e+00	2.00e+00	0.00e+00	0.00e+00	1.58e+00	0.00e+00	0.00e+00	1.59e-03	0.00e+00
chr9	44043384	44048579	ENSMUSG00000032011	53.87	+	Thy1	protein_coding	1.78e-03	7.52e-01	4.5000	2.169925	6.000	2.58496	1.00e+00	6.67e-01	1.92e-04	1.00e+00	7.74e-01	2.57e-04	8.00e+00	2.83e+00	1.54e-03	1.30e+01	3.07e+00	2.50e-03	3.00e+00	0.00e+00	0.00e+00	2.00e+00	0.00e+00	0.00e+00	5.77e-04	0.00e+00	0.00e+00	4.00e+00	0.00e+00	0.00e+00	2.32e+00	0.00e+00	0.00e+00	7.70e-04	0.00e+00	0.00e+00	6.00e+00	2.00e+00	1.60e+01	2.81e+00	1.58e+00	4.09e+00	1.15e-03	3.85e-04	3.08e-03	5.00e+00	2.00e+00	3.20e+01	2.58e+00	1.58e+00	5.04e+00	9.62e-04	3.85e-04	6.16e-03
chr14	32028989	32062197	ENSMUSG00000021903	48.44	+	Galnt15	protein_coding	1.87e-03	7.73e-01	0.3010	-1.732188	0.632	-0.66216	2.67e+02	7.50e+00	5.18e-02	1.97e+02	6.98e+00	3.83e-02	8.00e+01	6.22e+00	1.54e-02	1.24e+02	6.60e+00	2.41e-02	7.80e+01	1.23e+02	6.00e+02	6.30e+00	6.95e+00	9.23e+00	1.51e-02	2.38e-02	1.16e-01	1.24e+02	3.60e+01	4.32e+02	6.97e+00	5.21e+00	8.76e+00	2.40e-02	6.98e-03	8.37e-02	6.10e+01	5.20e+01	1.26e+02	5.95e+00	5.73e+00	6.99e+00	1.18e-02	1.01e-02	2.44e-02	5.20e+01	6.70e+01	2.54e+02	5.73e+00	6.09e+00	7.99e+00	1.01e-02	1.30e-02	4.92e-02
chr17	56100591	56109803	ENSMUSG00000002831	55.19	-	Plin4	protein_coding	1.97e-03	7.94e-01	0.2353	-2.087463	0.320	-1.64386	2.70e+01	4.66e+00	1.02e-02	2.40e+01	4.19e+00	8.99e-03	6.00e+00	2.69e+00	2.12e-03	7.00e+00	2.99e+00	2.62e-03	2.20e+01	1.40e+01	4.60e+01	4.52e+00	3.91e+00	5.55e+00	8.24e-03	5.24e-03	1.72e-02	3.50e+01	4.00e+00	3.30e+01	5.17e+00	2.32e+00	5.09e+00	1.31e-02	1.50e-03	1.24e-02	8.00e+00	5.00e+00	4.00e+00	3.17e+00	2.58e+00	2.32e+00	3.00e-03	1.87e-03	1.50e-03	7.00e+00	6.00e+00	8.00e+00	3.00e+00	2.81e+00	3.17e+00	2.62e-03	2.25e-03	3.00e-03
chrX	60045111	60046229	ENSMUSG00000046993	57.55	-	Gm5637	processed_pseudogene	2.26e-03	8.88e-01	0.3310	-1.595158	0.492	-1.02346	4.60e+01	5.56e+00	3.69e-02	4.00e+01	5.06e+00	3.22e-02	1.50e+01	3.94e+00	1.17e-02	1.90e+01	4.28e+00	1.54e-02	4.40e+01	4.10e+01	5.40e+01	5.49e+00	5.39e+00	5.78e+00	3.51e-02	3.27e-02	4.31e-02	7.00e+01	1.20e+01	3.90e+01	6.15e+00	3.70e+00	5.32e+00	5.58e-02	9.57e-03	3.11e-02	1.90e+01	1.10e+01	1.40e+01	4.32e+00	3.58e+00	3.91e+00	1.52e-02	8.77e-03	1.12e-02	1.60e+01	1.40e+01	2.80e+01	4.09e+00	3.91e+00	4.86e+00	1.28e-02	1.12e-02	2.23e-02
chr12	90724554	90738438	ENSMUSG00000007682	42.09	-	Dio2	protein_coding	2.48e-03	9.40e-01	2.7027	1.434403	4.037	2.01338	6.10e+01	5.93e+00	9.93e-03	5.30e+01	5.42e+00	8.62e-03	1.66e+02	7.29e+00	2.71e-02	2.16e+02	7.67e+00	3.53e-02	6.50e+01	6.80e+01	4.90e+01	6.04e+00	6.11e+00	5.64e+00	1.06e-02	1.11e-02	8.02e-03	1.03e+02	2.00e+01	3.50e+01	6.70e+00	4.39e+00	5.17e+00	1.69e-02	3.27e-03	5.73e-03	1.52e+02	2.45e+02	1.00e+02	7.26e+00	7.94e+00	6.66e+00	2.49e-02	4.01e-02	1.64e-02	1.30e+02	3.16e+02	2.01e+02	7.03e+00	8.31e+00	7.66e+00	2.13e-02	5.17e-02	3.29e-02
chr7	25461707	25477603	ENSMUSG00000074272	46.13	-	Ceacam1	protein_coding	2.51e-03	9.40e-01	0.0857	-3.544321	0.136	-2.87447	1.10e+01	2.60e+00	9.95e-04	6.00e+00	2.23e+00	5.91e-04	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	8.00e+00	2.40e+01	0.00e+00	3.17e+00	4.64e+00	0.00e+00	7.46e-04	2.24e-03	0.00e+00	1.20e+01	7.00e+00	0.00e+00	3.70e+00	3.00e+00	0.00e+00	1.12e-03	6.53e-04	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00	0.00e+00

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

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.

	metaseqR analysis report metaseqr_quantseq_run463_SL_LL_comparisons