smallRNAseq Sample Report: MultiQC Report

General Statistics

Showing ⁹/₉ rows and ⁶/₁₂ columns.

Sample Name	M Seqs	% Trimmed GC	% Reads PF	% miRNA	No. miRNA	% miRNA	% non-miRNA RNA types	% Dups	% GC	Length	% Failed	% BP Trimmed
CD4_S1	3.2	45.0%	82.2%	59.8%	695	55.6%	32.0%	91.5%	50%	51 bp	45%	54.2%
CD4_S2	2.5	47.0%	81.1%	33.9%	569	31.1%	16.0%	93.9%	51%	51 bp	36%	58.7%
CD4_S4	3.4	46.0%	81.8%	54.1%	671	50.2%	37.7%	92.0%	51%	51 bp	45%	52.6%
NK_S2	3.2	49.0%	45.3%	34.1%	531	32.8%	7.4%	95.4%	54%	51 bp	36%	75.4%
NK_S3	3.1	50.0%	81.5%	41.7%	627	40.8%	20.4%	93.0%	52%	51 bp	36%	57.8%
NK_S4	6.9	50.0%	69.7%	37.5%	791	36.1%	33.3%	93.7%	53%	51 bp	36%	59.0%
monocytes_S2	1.9	48.0%	77.8%	43.8%	648	42.2%	13.0%	92.0%	52%	51 bp	36%	59.0%
monocytes_S3	6.1	52.0%	54.2%	37.7%	778	36.0%	28.3%	93.3%	55%	51 bp	36%	70.4%
monocytes_S4	4.4	50.0%	85.4%	30.3%	770	28.2%	35.8%	92.1%	52%	51 bp	36%	51.4%

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Sort	Group	Column	Description	ID	Scale
\|\|	FastQC	M Seqs	Total Sequences (millions)	`total_sequences`	read_count
\|\|	Trim_Galore	% Trimmed GC	% GC Content After Fastq Trimming by Trim Galore	`trimmed_gcperc`	None
\|\|	Trim_Galore	% Reads PF	% Reads pass various length and quality filters	`percent_pass_filter`	None
\|\|	Pipeline	% miRNA	Percentage of miRNA reads out of total reads that passed filters	`perc_miRNA_quantifyrna`	None
\|\|	miRTrace	No. miRNA	Number of different miRNA hairpins detected in each sample	`no_miRNA`	None
\|\|	miRTrace	% miRNA	Percentage of miRNA reads in reads passing filter	`perc_miRNA`	None
\|\|	Pipeline	% non-miRNA RNA types	Percentage of non-miRNA RNA type reads identified out of total reads that passed filters	`perc_RNAtypes_quantifyrna`	None
\|\|	FastQC	% Dups	% Duplicate Reads	`percent_duplicates`	None
\|\|	FastQC	% GC	Average % GC Content	`percent_gc`	None
\|\|	FastQC	Length	Average Sequence Length (bp)	`avg_sequence_length`	None
\|\|	FastQC	% Failed	Percentage of modules failed in FastQC report (includes those not plotted here)	`percent_fails`	None
\|\|	Trim_Galore	% BP Trimmed	% Total Base Pairs trimmed	`percent_trimmed`	None

FastQC

FastQC is a quality control tool for high throughput sequence data, written by Simon Andrews at the Babraham Institute in Cambridge.

Sequence Quality Histograms

The mean quality value across each base position in the read.

To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

Taken from the FastQC help:

The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

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Per Sequence Quality Scores

The number of reads with average quality scores. Shows if a subset of reads has poor quality.

From the FastQC help:

The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

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Per Base N Content

The percentage of base calls at each position for which an N was called.

From the FastQC help:

If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

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Sequence Length Distribution

All samples have sequences of a single length (51bp).

Adapter Content

The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

Note that only samples with ≥ 0.1% adapter contamination are shown.

There may be several lines per sample, as one is shown for each adapter detected in the file.

From the FastQC Help:

The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

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Trim Galore

Trim Galore is a wrapper around Cutadapt and FastQC to consistently apply adpater and quality trimming to FastQ files.

Filtered Reads

This plot shows the number of reads (SE) / pairs (PE) removed by Trim Galore.

Read filtering criteria such as minimum and maximum read length can be found in Workflow Summary section.

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Trimmed Sequence Lengths

This plot shows the number of reads with certain lengths of adapter trimmed. Quality trimmed and hard trimmed sequences are not included.

Obs/Exp shows the raw counts divided by the number expected due to sequencing errors. A defined peak may be related to adapter length. See the cutadapt documentation for more information on how these numbers are generated.

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miRTrace

miRTrace is a quality control software for small RNA sequencing data developed by Friedländer lab (KTH, Sweden).

Read Length Distribution

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Contamination Check

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miRNA Complexity

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Estimated RNA Type Counts

The following bargraph provides an estimate of RNA types in the sample. Reads were mapped to RNA reference sequences with Bowtie, and subsequent transcript quantification was completed with RSEM. Current piRNA database entries may contain significant sequential overlap with miRNA entries and negatively affect miRNA results. To preserve miRNA results, piRNA were not included in this analysis. "Unmapped" reads may include RNA types that the pipeline does not test for, such as circRNA and piRNA. "miscellaneous" RNA, or miscRNA, includes unclassified RNA types, such as vault RNA and YRNA.

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miRNA sample similarities

The following sample similarities plots were generated from only miRNA transcript data using isomiRs.

Similarity matrix of samples

The similarities (Pearson correlation coefficient) between samples are visualized here in the form of heatmap. Larger values indicate higher similarity between samples. The similarities were calculated using Log2 values of normalized read counts of all mature miRNAs using isomiRs.

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Multidimensional scaling analysis of samples

Multidimensional scaling was conducted to visualize the distance/similarity between samples.

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Sample similarities in non-coding genes excluding miRNAs and rRNAs

The following sample similarities plots were generated from tRNA, mitochondrial tRNA, lncRNA, snoRNA, scaRNA, snRNA, and miscRNA gene data using DeSeq2.

Similarity matrix of samples

The similarities (Pearson correlation coefficient) between samples are visualized here in the form of heatmap. Larger values indicate higher similarity between samples. The similarities were calculated using Log2 values of normalized read counts of genes using DESeq2.

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Multidimensional scaling analysis of samples

Multidimensional scaling was conducted to visualize the distance/similarity between samples.

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miRNA heatmap

The following heatmap was generated from only miRNA data using isomiRs.

Normalized read counts of top miRNAs with highest variance, calculated using isomiRs. Values plotted in Log2 scale after centering per miRNA. A static version of this figure can be download in the Download data section.

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Gene heatmap for non-coding genes excluding miRNAs and rRNAs

The following heatmap was generated from tRNA, mitochondrial tRNA, lncRNA, snoRNA, scaRNA, snRNA, and miscRNA gene data using DeSeq2. For simplification, this report treats tRNA gene copies with duplicate sequences as belonging to one gene.

Normalized read counts of top genes with highest variance, calculated using DESeq2. Values plotted in Log2 scale. A static version of this figure can be download in the Download data section.

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miRNA Differential Expression

isomiRs The following differential expression analysis was generated using only miRNA data with isomiRs.

Summary table of miRNAs differential expression

General statistics of differentially expressed miRNAs in pairwise comparisons. miRNAs with adjusted p-values smaller than 0.05 were considered differentially expressed.

Showing ³/₃ rows and ⁴/₄ columns.

Comparison(group1_vs_group2)	Higher in group 1	Higher in group 2	Not differentially expressed	Did not pass filter
CD4_vs_NK	43	27	344	238
CD4_vs_monocytes	73	71	270	238
monocytes_vs_NK	51	50	339	212

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Sort	Column	Description	ID	Scale
\|\|	Higher in group 1	Numbers of differentially expressed miRNAs with higher expression in group 1	`up_deg`	None
\|\|	Higher in group 2	Numbers of differentially expressed miRNAs with higher expression in group 2	`down_deg`	None
\|\|	Not differentially expressed	Numbers of miRNAs that were not differentially expressed between groups 1 and 2	`not_deg`	None
\|\|	Did not pass filter	Numbers of miRNAs that failed to pass filtering because low expression level or outliers	`filtered`	None

Scatter plot

Scatter plot is a simple and straightforward way to visualize differential gene expression results. Expression levels of miRNAs in one group are shown on X-axis while those in other are shown on Y-axis.
Red dots represent differentially expressed miRNAs (adjusted p-values<0.05). Grey dots represent non-differentially expressed miRNAs.

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MA plot

MA plot is a type of visualization of differential gene expression results often used in publications. Mean expression levels are shown on X-axis while Log2 of fold changes are shown on Y-axis.
Red dots represent differentially expressed miRNAs (adjusted p-values<0.05). Grey dots represent non-differentially expressed miRNAs. Shrinkage of effect size was carried out using the 'ashr' method in DESeq2

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Top differentially expressed miRNAs in comparison CD4 vs. NK

Top 50 differentially expressed miRNAs, ranked by FDR, in comparison CD4 vs. NK. Full isomiRs results can be downloaded in the Download data section.
miRNAs with positive Log2 fold changes have higher expression in CD4. miRNAs with negative Log2 fold changes have higher expression in NK.

Showing ⁵⁰/₅₀ rows and ⁴/₄ columns.

Rank	Gene name	Mean normalized counts	Log2 Fold change	False discovery rate
1	hsa-miR-125a-5p	1733	6.85	1.7e-42
2	hsa-miR-99b-5p	234	5.38	1.0e-20
3	hsa-miR-181a-2-3p	2718	-4.08	2.0e-20
4	hsa-miR-152-3p	132	-3.96	2.4e-17
5	hsa-miR-4772-5p	74	-5.39	1.7e-15
6	hsa-miR-99a-5p	70	5.63	1.3e-13
7	hsa-miR-125b-5p	114	5.45	1.4e-12
8	hsa-miR-338-3p	277	-3.66	5.8e-12
9	hsa-miR-181b-5p	4918	-2.72	8.1e-12
10	hsa-miR-92b-3p	168	3.56	9.9e-12
11	hsa-miR-21-3p	2182	2.65	1.4e-11
12	hsa-miR-148a-3p	2843	2.94	2.1e-08
13	hsa-let-7e-5p	62	3.67	8.8e-08
14	hsa-miR-151a-3p	692	2.31	3.5e-07
15	hsa-miR-335-3p	32	3.48	7.7e-07
16	hsa-miR-150-3p	225	2.26	1.1e-06
17	hsa-miR-151b	39	3.36	1.3e-06
18	hsa-miR-4772-3p	36	-5.26	1.3e-06
19	hsa-miR-29a-3p	7279	2.41	1.3e-06
20	hsa-miR-181a-5p	145539	-2.33	1.7e-06
21	hsa-miR-873-3p	28	-7.55	1.7e-06
22	hsa-miR-17-3p	100	2.05	3.7e-06
23	hsa-miR-101-3p	10578	2.30	9.9e-06
24	hsa-miR-340-5p	1762	-1.72	1.0e-05
25	hsa-miR-486-5p	656	3.62	2.2e-05
26	hsa-miR-335-5p	77	2.96	2.8e-05
27	hsa-miR-361-3p	1187	1.62	3.6e-05
28	hsa-miR-92a-3p	70835	2.12	5.1e-05
29	hsa-miR-125b-2-3p	19	7.02	9.0e-05
30	hsa-let-7c-5p	93	3.11	1.1e-04
31	hsa-miR-132-3p	114	-2.30	1.3e-04
32	hsa-miR-20a-5p	457	1.56	1.4e-04
33	hsa-miR-26a-5p	88387	1.68	1.4e-04
34	hsa-miR-29c-3p	2107	2.24	1.6e-04
35	hsa-let-7g-5p	26334	1.48	2.1e-04
36	hsa-miR-193b-3p	12	5.19	2.1e-04
37	hsa-miR-150-5p	51634	2.37	3.0e-04
38	hsa-miR-21-5p	54255	1.66	4.9e-04
39	hsa-miR-153-3p	13	6.44	5.2e-04
40	hsa-miR-31-5p	387	3.33	5.2e-04
41	hsa-miR-223-3p	3529	-2.53	6.4e-04
42	hsa-miR-16-2-3p	249	1.54	9.4e-04
43	hsa-miR-22-3p	27319	-1.76	1.3e-03
44	hsa-miR-155-5p	1072	1.56	1.7e-03
45	hsa-miR-23b-3p	109	-2.23	1.8e-03
46	hsa-miR-101-5p	34	2.23	1.8e-03
47	hsa-miR-338-5p	14	-5.59	1.9e-03
48	hsa-miR-20b-5p	64	-2.11	4.2e-03
49	hsa-miR-29b-3p	320	2.05	4.2e-03
50	hsa-miR-148b-3p	928	-1.45	6.6e-03

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Sort	Column	Description	ID	Scale
\|\|	Gene name	Gene name	`gene_link`	None
\|\|	Mean normalized counts	Mean normalized read counts in both conditions	`baseMean`	None
\|\|	Log2 Fold change	Log2 Fold change	`log2FoldChange`	None
\|\|	False discovery rate	False discovery rate	`padj`	None

Top differentially expressed miRNAs in comparison CD4 vs. monocytes

Top 50 differentially expressed miRNAs, ranked by FDR, in comparison CD4 vs. monocytes. Full isomiRs results can be downloaded in the Download data section.
miRNAs with positive Log2 fold changes have higher expression in CD4. miRNAs with negative Log2 fold changes have higher expression in monocytes.

Showing ⁵⁰/₅₀ rows and ⁴/₄ columns.

Rank	Gene name	Mean normalized counts	Log2 Fold change	False discovery rate
1	hsa-miR-361-3p	1187	5.90	1.8e-106
2	hsa-miR-150-5p	51634	10.04	4.3e-105
3	hsa-miR-192-5p	4454	4.82	1.9e-72
4	hsa-miR-340-5p	1762	-4.48	9.3e-66
5	hsa-miR-342-5p	301	5.91	1.3e-60
6	hsa-miR-125a-5p	1733	8.08	1.3e-60
7	hsa-miR-29a-3p	7279	5.57	1.9e-47
8	hsa-miR-146b-3p	611	6.23	1.2e-45
9	hsa-miR-150-3p	225	8.52	7.8e-36
10	hsa-miR-141-3p	407	5.26	6.3e-35
11	hsa-miR-223-3p	3529	-6.50	1.5e-32
12	hsa-miR-146b-5p	28609	5.03	4.8e-32
13	hsa-miR-99b-5p	234	6.16	1.7e-29
14	hsa-miR-194-5p	215	4.77	1.4e-27
15	hsa-let-7g-5p	26334	2.68	2.2e-22
16	hsa-miR-342-3p	4870	4.32	1.1e-20
17	hsa-miR-10a-5p	4887	4.11	2.3e-20
18	hsa-miR-31-5p	387	10.53	3.6e-20
19	hsa-miR-874-3p	77	5.03	9.7e-20
20	hsa-miR-29c-3p	2107	4.11	3.9e-19
21	hsa-miR-92b-3p	168	4.29	8.2e-19
22	hsa-miR-301a-3p	370	-3.06	2.4e-17
23	hsa-miR-99a-5p	70	5.54	3.8e-17
24	hsa-miR-16-2-3p	249	2.69	2.3e-16
25	hsa-miR-582-3p	88	-5.30	1.3e-15
26	hsa-miR-142-5p	87345	3.18	4.5e-14
27	hsa-miR-29b-3p	320	3.75	1.1e-12
28	hsa-miR-125b-5p	114	4.93	1.3e-12
29	hsa-miR-155-5p	1072	2.50	2.8e-12
30	hsa-miR-148b-3p	928	-2.46	4.4e-12
31	hsa-miR-151b	39	7.38	5.7e-12
32	hsa-let-7e-5p	62	4.21	1.0e-11
33	hsa-miR-146a-5p	4025	2.86	1.3e-11
34	hsa-miR-326	106	-3.80	2.8e-11
35	hsa-miR-32-5p	527	4.19	3.3e-11
36	hsa-miR-582-5p	69	-5.01	5.7e-11
37	hsa-miR-26a-5p	88387	2.29	6.5e-11
38	hsa-miR-500a-3p	327	-4.02	7.4e-11
39	hsa-miR-301b-3p	65	-4.17	1.4e-10
40	hsa-miR-186-5p	11826	1.60	1.1e-09
41	hsa-miR-199b-5p	119	-5.28	1.6e-09
42	hsa-miR-363-3p	1831	2.31	3.5e-09
43	hsa-miR-101-3p	10578	2.69	7.8e-09
44	hsa-miR-1249-3p	27	-8.33	8.3e-09
45	hsa-miR-6503-3p	30	-8.58	1.2e-08
46	hsa-miR-143-3p	286	-5.05	4.6e-08
47	hsa-miR-23b-3p	109	-3.12	4.9e-08
48	hsa-miR-424-5p	73	-5.15	5.5e-08
49	hsa-miR-215-5p	25	4.41	6.4e-08
50	hsa-miR-223-5p	95	-5.51	1.5e-07

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Sort	Column	Description	ID	Scale
\|\|	Gene name	Gene name	`gene_link`	None
\|\|	Mean normalized counts	Mean normalized read counts in both conditions	`baseMean`	None
\|\|	Log2 Fold change	Log2 Fold change	`log2FoldChange`	None
\|\|	False discovery rate	False discovery rate	`padj`	None

Top differentially expressed miRNAs in comparison monocytes vs. NK

Top 50 differentially expressed miRNAs, ranked by FDR, in comparison monocytes vs. NK. Full isomiRs results can be downloaded in the Download data section.
miRNAs with positive Log2 fold changes have higher expression in monocytes. miRNAs with negative Log2 fold changes have higher expression in NK.

Showing ⁵⁰/₅₀ rows and ⁴/₄ columns.

Rank	Gene name	Mean normalized counts	Log2 Fold change	False discovery rate
1	hsa-miR-150-5p	51634	-7.67	1.7e-58
2	hsa-miR-361-3p	1187	-4.28	1.1e-50
3	hsa-miR-181a-2-3p	2718	-5.34	4.6e-38
4	hsa-miR-342-5p	301	-4.78	7.7e-37
5	hsa-miR-21-3p	2182	4.22	6.9e-36
6	hsa-miR-146b-3p	611	-5.52	1.3e-34
7	hsa-miR-192-5p	4454	-3.47	1.1e-33
8	hsa-miR-10a-5p	4887	-5.10	4.8e-33
9	hsa-miR-146b-5p	28609	-4.78	2.2e-28
10	hsa-miR-363-3p	1831	-3.65	2.0e-27
11	hsa-miR-340-5p	1762	2.76	3.9e-22
12	hsa-miR-150-3p	225	-6.26	4.2e-18
13	hsa-miR-582-3p	88	6.93	1.4e-16
14	hsa-miR-301a-3p	370	3.02	1.4e-16
15	hsa-miR-342-3p	4870	-3.89	3.3e-16
16	hsa-miR-181b-5p	4918	-2.97	4.4e-15
17	hsa-miR-181a-5p	145539	-3.22	1.1e-14
18	hsa-miR-152-3p	132	-3.25	6.5e-14
19	hsa-miR-141-3p	407	-3.54	6.5e-14
20	hsa-miR-29a-3p	7279	-3.16	5.7e-13
21	hsa-miR-582-5p	69	6.36	6.8e-13
22	hsa-miR-194-5p	215	-3.41	1.9e-12
23	hsa-miR-4772-5p	74	-9.81	2.0e-12
24	hsa-miR-21-5p	54255	2.50	5.6e-12
25	hsa-miR-223-3p	3529	3.96	2.8e-11
26	hsa-miR-199b-5p	119	5.88	6.6e-11
27	hsa-miR-31-5p	387	-7.20	4.5e-09
28	hsa-miR-301b-3p	65	3.74	6.2e-09
29	hsa-miR-424-5p	73	5.54	1.2e-08
30	hsa-miR-4772-3p	36	-8.77	1.7e-08
31	hsa-miR-873-3p	28	-8.47	1.8e-08
32	hsa-miR-345-5p	287	3.42	2.0e-08
33	hsa-miR-874-3p	77	-3.45	4.1e-08
34	hsa-miR-6503-3p	30	5.92	9.2e-08
35	hsa-miR-27a-5p	104	3.31	5.2e-07
36	hsa-miR-146a-5p	4025	-2.31	7.7e-07
37	hsa-miR-1249-3p	27	3.80	7.8e-07
38	hsa-miR-574-3p	52	5.17	3.7e-06
39	hsa-miR-450b-5p	22	5.43	4.0e-06
40	hsa-miR-18a-5p	105	3.88	5.5e-06
41	hsa-miR-873-5p	210	-11.35	1.3e-05
42	hsa-miR-652-3p	254	1.86	1.3e-05
43	hsa-miR-28-5p	1425	-1.98	2.1e-05
44	hsa-miR-142-5p	87345	-2.14	2.3e-05
45	hsa-miR-132-3p	114	-2.35	2.5e-05
46	hsa-miR-186-5p	11826	-1.33	2.6e-05
47	hsa-miR-148a-3p	2843	2.35	3.0e-05
48	hsa-miR-20b-5p	64	-2.55	4.1e-05
49	hsa-miR-450a-5p	17	5.30	1.0e-04
50	hsa-miR-1468-5p	21	-3.53	1.4e-04

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order.

Sort	Column	Description	ID	Scale
\|\|	Gene name	Gene name	`gene_link`	None
\|\|	Mean normalized counts	Mean normalized read counts in both conditions	`baseMean`	None
\|\|	Log2 Fold change	Log2 Fold change	`log2FoldChange`	None
\|\|	False discovery rate	False discovery rate	`padj`	None

Differential expression of non-coding genes except miRNAs and rRNAs

The following differential expression analysis was generated from tRNA, mitochondrial tRNA, lncRNA, snoRNA, scaRNA, snRNA, and miscRNA gene data using DeSeq2. For simplification, this report treats tRNA gene copies with duplicate sequences as belonging to one gene.

Summary table of differential expression in non-coding genes except miRNAs and rRNAs

General statistics of differentially expressed genes in pairwise comparisons. Genes with adjusted p-values smaller than 0.05 were considered differentially expressed.

Showing ³/₃ rows and ⁴/₄ columns.

Comparison(group1_vs_group2)	Higher in group 1	Higher in group 2	Not differentially expressed	Did not pass filter
CD4_vs_NK	0	0	8650	14496
CD4_vs_monocytes	13	4	1112	22017
monocytes_vs_NK	0	1	8649	14496

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Sort	Column	Description	ID	Scale
\|\|	Higher in group 1	Numbers of differentially expressed genes with higher expression in group 1	`up_deg`	None
\|\|	Higher in group 2	Numbers of differentially expressed genes with higher expression in group 2	`down_deg`	None
\|\|	Not differentially expressed	Numbers of genes that were not differentially expressed between groups 1 and 2	`not_deg`	None
\|\|	Did not pass filter	Numbers of genes that failed to pass filtering because low expression level or outliers	`filtered`	None

Scatter plot

Scatter plot is a simple and straightforward way to visualize differential expression results. Expression levels of genes in one group are shown on X-axis while those in other are shown on Y-axis.
Red dots represent differentially expressed genes (adjusted p-values<0.05). Grey dots represent non-differentially expressed genes. Non-differentially expressed genes are downsampled to 1000 randomly chosen data points.

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MA plot

MA plot is a type of visualization of differential expression results often used in publications. Mean expression levels are shown on X-axis while Log2 of fold changes are shown on Y-axis.
Red dots represent differentially expressed genes (adjusted p-values<0.05). Grey dots represent non-differentially expressed genes. Shrinkage of effect size was carried out using the 'ashr' method in DESeq2.
Non-differentially expressed genes are downsampled to 1000 randomly chosen data points.

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Top differentially expressed genes in comparison CD4 vs. NK

No significant differential expression results were found for this comparison.

Top differentially expressed genes in comparison CD4 vs. monocytes

Top differentially expressed genes, ranked by FDR, in comparison CD4 vs. monocytes. Full DeSeq2 results can be downloaded in the Download data section.
Genes with positive Log2 fold changes have higher expression in CD4. Genes with negative Log2 fold changes have higher expression in monocytes.

Showing ¹⁷/₁₇ rows and ⁵/₅ columns.

Rank	Gene ID	Gene Name	Mean normalized counts	Log2 Fold change	False discovery rate
1	ENSG00000274554	ENSG00000274554	154	9.96	1.5e-06
2	ENSG00000201800	Y_RNA	32	-9.62	7.7e-05
3	ENSG00000276610	SNORD64	161	5.31	7.7e-05
4	ENSG00000276314	SNORD107	73	5.40	6.0e-04
5	ENSG00000252139	ENSG00000252139	171	4.61	3.9e-03
6	ENSG00000200084	SNORD68	2493	4.53	8.1e-03
7	ENSG00000265236	SNORD84	1435	4.68	8.1e-03
8	ENSG00000230630	DNM3OS	13	-6.35	2.5e-02
9	ENSG00000257764	ENSG00000257764	12	-7.04	2.5e-02
10	ENSG00000275084	SNORD91B	210	3.66	4.8e-02
11	ENSG00000234741	GAS5	35899	3.53	4.8e-02
12	ENSG00000209645	SNORD105	354	3.60	4.8e-02
13	ENSG00000202400	SNORD82	3283	3.82	4.8e-02
14	ENSG00000273885	ENSG00000273885	125	3.94	4.8e-02
15	ENSG00000260464	ENSG00000260464	375	-5.24	4.8e-02
16	ENSG00000264549	SNORD95	3408	3.60	4.8e-02
17	ENSG00000212163	SNORD91A	888	3.54	4.9e-02

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order.

Sort	Column	Description	ID	Scale
\|\|	Gene ID	Gene Ensembl ID	`gene_link`	None
\|\|	Gene Name	Gene Name	`gene_name`	None
\|\|	Mean normalized counts	Mean normalized read counts in both conditions	`baseMean`	None
\|\|	Log2 Fold change	Log2 Fold change	`log2FoldChange`	None
\|\|	False discovery rate	False discovery rate	`padj`	None

Top differentially expressed genes in comparison monocytes vs. NK

Top differentially expressed genes, ranked by FDR, in comparison monocytes vs. NK. Full DeSeq2 results can be downloaded in the Download data section.
Genes with positive Log2 fold changes have higher expression in monocytes. Genes with negative Log2 fold changes have higher expression in NK.

Showing ¹/₁ rows and ⁵/₅ columns.

Rank	Gene ID	Gene Name	Mean normalized counts	Log2 Fold change	False discovery rate
1	ENSG00000274554	ENSG00000274554	154	-7.77	3.1e-02

Uncheck the tick box to hide columns. Click and drag the handle on the left to change order.

Sort	Column	Description	ID	Scale
\|\|	Gene ID	Gene Ensembl ID	`gene_link`	None
\|\|	Gene Name	Gene Name	`gene_name`	None
\|\|	Mean normalized counts	Mean normalized read counts in both conditions	`baseMean`	None
\|\|	Log2 Fold change	Log2 Fold change	`log2FoldChange`	None
\|\|	False discovery rate	False discovery rate	`padj`	None

Software Versions

Software versions are collected at run time from the software output. This pipeline is adapted from nf-core smRNAseq pipeline.

smrnaseq pipeline: v2.2.0
Nextflow: v22.10.4
R: v4.0.5
FastQC: v0.11.9
Trim Galore!: v0.6.6
Bowtie: v1.3.0
Samtools: v1.16.1
FASTX: v0.0.14
miRTrace: v1.0.1
rsem: v1.2.28
tximport: v1.18.0
mirtop: v0.4.23
isomiRs: v1.18.1
DESeq2: v1.30.1

Workflow Summary

This section summarizes important parameters used in the pipeline. They were collected when the pipeline was started.

Genome: GRCh38
Protocol: zymo
Downsampling cutoff: None
Min Trimmed Length: 18
Max Trimmed Length: None
isomiRs FDR cutoff: 0.05
isomiRs Log2FC cutoff: 0.585
Trim 5' R1: 1
Trim 3' R1: 0
3' adapter: TGGAATTCTCGGGTGCCAAGG

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smallRNAseq Sample Report

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About MultiQC

smallRNAseq Sample Report

General Statistics

General Statistics: Columns

FastQC

Sequence Quality Histograms Help

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Sequence Length Distribution

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Trim Galore

Filtered Reads

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miRTrace

Read Length Distribution

Contamination Check

miRNA Complexity

Estimated RNA Type Counts

miRNA sample similarities

Similarity matrix of samples

Multidimensional scaling analysis of samples

Sample similarities in non-coding genes excluding miRNAs and rRNAs

Similarity matrix of samples

Multidimensional scaling analysis of samples

miRNA heatmap

Gene heatmap for non-coding genes excluding miRNAs and rRNAs

miRNA Differential Expression

Summary table of miRNAs differential expression

Differential Expression Table: Columns

Scatter plot

MA plot

Top differentially expressed miRNAs in comparison CD4 vs. NK

Top Degs Cd4 Vs Nk: Columns

Top differentially expressed miRNAs in comparison CD4 vs. monocytes

Top Degs Cd4 Vs Monocytes: Columns

Top differentially expressed miRNAs in comparison monocytes vs. NK

Top Degs Monocytes Vs Nk: Columns

Differential expression of non-coding genes except miRNAs and rRNAs

Summary table of differential expression in non-coding genes except miRNAs and rRNAs

Differential Expression Table-1: Columns

Scatter plot

MA plot

Top differentially expressed genes in comparison CD4 vs. NK

Top differentially expressed genes in comparison CD4 vs. monocytes

Top Degs Cd4 Vs Monocytes-1: Columns

Top differentially expressed genes in comparison monocytes vs. NK

Top Degs Monocytes Vs Nk-1: Columns

Software Versions

Workflow Summary

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Sequence Quality Histograms

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