MultiQC Report

pmultiqc

pmultiqc is a MultiQC module to show the pipeline performance of mass spectrometry based quantification pipelines such as nf-core/quantms, MaxQuant, DIA-NN, and FragPipe.https://github.com/bigbio/pmultiqc

Experimental Design and Metadata

Experimental Design

This table shows the design of the experiment. I.e., which files and channels correspond to which sample/condition/fraction.

You can see details about it in https://abibuilder.informatik.uni-tuebingen.de/archive/openms/Documentation/release/latest/html/classOpenMS_1_1ExperimentalDesign.html

Showing ¹⁶⁰/₁₆₀ rows and ⁵/₅ columns.

Sample Name	MSstats Condition	MSstats BioReplicate	Fraction Group	Fraction	Label
Sample 1	blood plasma	1
↳ FP-1A1			1	1	1
Sample 2	blood plasma	2
↳ FP-1A2			2	1	1
Sample 3	blood plasma	3
↳ FP-1A3			3	1	1
Sample 4	blood plasma	4
↳ FP-1A4			4	1	1
Sample 5	blood plasma	5
↳ FP-1B1			5	1	1
Sample 6	blood plasma	6
↳ FP-1B2			6	1	1
Sample 7	blood plasma	7
↳ FP-1B3			7	1	1
Sample 8	blood plasma	8
↳ FP-1B4			8	1	1
Sample 9	blood plasma	9
↳ FP-1C1			9	1	1
Sample 10	blood plasma	10
↳ FP-1C2			10	1	1
Sample 11	blood plasma	11
↳ FP-1C3			11	1	1
Sample 12	blood plasma	12
↳ FP-1C4			12	1	1
Sample 13	blood plasma	13
↳ FP-1D1			13	1	1
Sample 14	blood plasma	14
↳ FP-1D2			14	1	1
Sample 15	blood plasma	15
↳ FP-1D3			15	1	1
Sample 16	blood plasma	16
↳ FP-1D4			16	1	1
Sample 17	blood plasma	17
↳ FP-1E1			17	1	1
Sample 18	blood plasma	18
↳ FP-1E2			18	1	1
Sample 19	blood plasma	19
↳ FP-1E3			19	1	1
Sample 20	blood plasma	20
↳ FP-1E4			20	1	1
Sample 21	blood plasma	21
↳ FP-2A1			21	1	1
Sample 22	blood plasma	22
↳ FP-2A2			22	1	1
Sample 23	blood plasma	23
↳ FP-2A3			23	1	1
Sample 24	blood plasma	24
↳ FP-2A4			24	1	1
Sample 25	blood plasma	25
↳ FP-2B1			25	1	1
Sample 26	blood plasma	26
↳ FP-2B2			26	1	1
Sample 27	blood plasma	27
↳ FP-2B3			27	1	1
Sample 28	blood plasma	28
↳ FP-2B4			28	1	1
Sample 29	blood plasma	29
↳ FP-2C1			29	1	1
Sample 30	blood plasma	30
↳ FP-2C2			30	1	1
Sample 31	blood plasma	31
↳ FP-2C3			31	1	1
Sample 32	blood plasma	32
↳ FP-2C4			32	1	1
Sample 33	blood plasma	33
↳ FP-2D1			33	1	1
Sample 34	blood plasma	34
↳ FP-2D2			34	1	1
Sample 35	blood plasma	35
↳ FP-2D3			35	1	1
Sample 36	blood plasma	36
↳ FP-2D4			36	1	1
Sample 37	blood plasma	37
↳ FP-2E1			37	1	1
Sample 38	blood plasma	38
↳ FP-2E2			38	1	1
Sample 39	blood plasma	39
↳ FP-2E3			39	1	1
Sample 40	blood plasma	40
↳ FP-2E4			40	1	1
Sample 41	blood plasma	41
↳ FP-3A1			41	1	1
Sample 42	blood plasma	42
↳ FP-3A2			42	1	1
Sample 43	blood plasma	43
↳ FP-3A3			43	1	1
Sample 44	blood plasma	44
↳ FP-3A4			44	1	1
Sample 45	blood plasma	45
↳ FP-3B1			45	1	1
Sample 46	blood plasma	46
↳ FP-3B2			46	1	1
Sample 47	blood plasma	47
↳ FP-3B3			47	1	1
Sample 48	blood plasma	48
↳ FP-3B4			48	1	1
Sample 49	blood plasma	49
↳ FP-3C1			49	1	1
Sample 50	blood plasma	50
↳ FP-3C2			50	1	1
Sample 51	blood plasma	51
↳ FP-3C3			51	1	1
Sample 52	blood plasma	52
↳ FP-3C4			52	1	1
Sample 53	blood plasma	53
↳ FP-3D1			53	1	1
Sample 54	blood plasma	54
↳ FP-3D2			54	1	1
Sample 55	blood plasma	55
↳ FP-3D3			55	1	1
Sample 56	blood plasma	56
↳ FP-3D4			56	1	1
Sample 57	blood plasma	57
↳ FP-3E1			57	1	1
Sample 58	blood plasma	58
↳ FP-3E2			58	1	1
Sample 59	blood plasma	59
↳ FP-3E3			59	1	1
Sample 60	blood plasma	60
↳ FP-3E4			60	1	1
Sample 61	blood plasma	61
↳ FP-4A1			61	1	1
Sample 62	blood plasma	62
↳ FP-4A2			62	1	1
Sample 63	blood plasma	63
↳ FP-4A3			63	1	1
Sample 64	blood plasma	64
↳ FP-4A4			64	1	1
Sample 65	blood plasma	65
↳ FP-4B1			65	1	1
Sample 66	blood plasma	66
↳ FP-4B2			66	1	1
Sample 67	blood plasma	67
↳ FP-4B3			67	1	1
Sample 68	blood plasma	68
↳ FP-4B4			68	1	1
Sample 69	blood plasma	69
↳ FP-4C1			69	1	1
Sample 70	blood plasma	70
↳ FP-4C2			70	1	1
Sample 71	blood plasma	71
↳ FP-4C3			71	1	1
Sample 72	blood plasma	72
↳ FP-4C4			72	1	1
Sample 73	blood plasma	73
↳ FP-4D1			73	1	1
Sample 74	blood plasma	74
↳ FP-4D2			74	1	1
Sample 75	blood plasma	75
↳ FP-4D3			75	1	1
Sample 76	blood plasma	76
↳ FP-4D4			76	1	1
Sample 77	blood plasma	77
↳ FP-4E1			77	1	1
Sample 78	blood plasma	78
↳ FP-4E2			78	1	1
Sample 79	blood plasma	79
↳ FP-4E3			79	1	1
Sample 80	blood plasma	80
↳ FP-4E4			80	1	1
Sample 81	blood plasma	81
↳ MP-1A1			81	1	1
Sample 82	blood plasma	82
↳ MP-1A2			82	1	1
Sample 83	blood plasma	83
↳ MP-1A3			83	1	1
Sample 84	blood plasma	84
↳ MP-1A4			84	1	1
Sample 85	blood plasma	85
↳ MP-1B1			85	1	1
Sample 86	blood plasma	86
↳ MP-1B2			86	1	1
Sample 87	blood plasma	87
↳ MP-1B3			87	1	1
Sample 88	blood plasma	88
↳ MP-1B4			88	1	1
Sample 89	blood plasma	89
↳ MP-1C1			89	1	1
Sample 90	blood plasma	90
↳ MP-1C2			90	1	1
Sample 91	blood plasma	91
↳ MP-1C3			91	1	1
Sample 92	blood plasma	92
↳ MP-1C4			92	1	1
Sample 93	blood plasma	93
↳ MP-1D1			93	1	1
Sample 94	blood plasma	94
↳ MP-1D2			94	1	1
Sample 95	blood plasma	95
↳ MP-1D3			95	1	1
Sample 96	blood plasma	96
↳ MP-1D4			96	1	1
Sample 97	blood plasma	97
↳ MP-1E1			97	1	1
Sample 98	blood plasma	98
↳ MP-1E2			98	1	1
Sample 99	blood plasma	99
↳ MP-1E3			99	1	1
Sample 100	blood plasma	100
↳ MP-1E4			100	1	1
Sample 101	blood plasma	101
↳ MP-2A1			101	1	1
Sample 102	blood plasma	102
↳ MP-2A2			102	1	1
Sample 103	blood plasma	103
↳ MP-2A3			103	1	1
Sample 104	blood plasma	104
↳ MP-2A4			104	1	1
Sample 105	blood plasma	105
↳ MP-2B1			105	1	1
Sample 106	blood plasma	106
↳ MP-2B2			106	1	1
Sample 107	blood plasma	107
↳ MP-2B3			107	1	1
Sample 108	blood plasma	108
↳ MP-2B4			108	1	1
Sample 109	blood plasma	109
↳ MP-2C1			109	1	1
Sample 110	blood plasma	110
↳ MP-2C2			110	1	1
Sample 111	blood plasma	111
↳ MP-2C3			111	1	1
Sample 112	blood plasma	112
↳ MP-2C4			112	1	1
Sample 113	blood plasma	113
↳ MP-2D1			113	1	1
Sample 114	blood plasma	114
↳ MP-2D2			114	1	1
Sample 115	blood plasma	115
↳ MP-2D3			115	1	1
Sample 116	blood plasma	116
↳ MP-2D4			116	1	1
Sample 117	blood plasma	117
↳ MP-2E1			117	1	1
Sample 118	blood plasma	118
↳ MP-2E2			118	1	1
Sample 119	blood plasma	119
↳ MP-2E3			119	1	1
Sample 120	blood plasma	120
↳ MP-2E4			120	1	1
Sample 121	blood plasma	121
↳ MP-3A1			121	1	1
Sample 122	blood plasma	122
↳ MP-3A2			122	1	1
Sample 123	blood plasma	123
↳ MP-3A3			123	1	1
Sample 124	blood plasma	124
↳ MP-3A4			124	1	1
Sample 125	blood plasma	125
↳ MP-3B1			125	1	1
Sample 126	blood plasma	126
↳ MP-3B2			126	1	1
Sample 127	blood plasma	127
↳ MP-3B3			127	1	1
Sample 128	blood plasma	128
↳ MP-3B4			128	1	1
Sample 129	blood plasma	129
↳ MP-3C1			129	1	1
Sample 130	blood plasma	130
↳ MP-3C2			130	1	1
Sample 131	blood plasma	131
↳ MP-3C3			131	1	1
Sample 132	blood plasma	132
↳ MP-3C4			132	1	1
Sample 133	blood plasma	133
↳ MP-3D1			133	1	1
Sample 134	blood plasma	134
↳ MP-3D2			134	1	1
Sample 135	blood plasma	135
↳ MP-3D3			135	1	1
Sample 136	blood plasma	136
↳ MP-3D4			136	1	1
Sample 137	blood plasma	137
↳ MP-3E1			137	1	1
Sample 138	blood plasma	138
↳ MP-3E2			138	1	1
Sample 139	blood plasma	139
↳ MP-3E3			139	1	1
Sample 140	blood plasma	140
↳ MP-3E4			140	1	1
Sample 141	blood plasma	141
↳ MP-4A1			141	1	1
Sample 142	blood plasma	142
↳ MP-4A2			142	1	1
Sample 143	blood plasma	143
↳ MP-4A3			143	1	1
Sample 144	blood plasma	144
↳ MP-4A4			144	1	1
Sample 145	blood plasma	145
↳ MP-4B1			145	1	1
Sample 146	blood plasma	146
↳ MP-4B2			146	1	1
Sample 147	blood plasma	147
↳ MP-4B3			147	1	1
Sample 148	blood plasma	148
↳ MP-4B4			148	1	1
Sample 149	blood plasma	149
↳ MP-4C1			149	1	1
Sample 150	blood plasma	150
↳ MP-4C2			150	1	1
Sample 151	blood plasma	151
↳ MP-4C3			151	1	1
Sample 152	blood plasma	152
↳ MP-4C4			152	1	1
Sample 153	blood plasma	153
↳ MP-4D1			153	1	1
Sample 154	blood plasma	154
↳ MP-4D2			154	1	1
Sample 155	blood plasma	155
↳ MP-4D3			155	1	1
Sample 156	blood plasma	156
↳ MP-4D4			156	1	1
Sample 157	blood plasma	157
↳ MP-4E1			157	1	1
Sample 158	blood plasma	158
↳ MP-4E2			158	1	1
Sample 159	blood plasma	159
↳ MP-4E3			159	1	1
Sample 160	blood plasma	160
↳ MP-4E4			160	1	1

Expand table

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

Sort	Column	Description	ID
\|\|	MSstats Condition	MSstats Condition	`MSstats_Condition`
\|\|	MSstats BioReplicate	MSstats BioReplicate	`MSstats_BioReplicate`
\|\|	Fraction Group	Fraction Group	`Fraction_Group`
\|\|	Fraction	Fraction Identifier	`Fraction`
\|\|	Label	Label	`Label`

Results Overview

Summary Table

This table shows the summary statistics of the submitted data.

Showing ¹/₁ rows.

#Peptides Quantified	#Proteins Quantified
5447	865

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

Sort	Visible	Group	Column	Description	ID	Scale
\|\|			#Proteins Quantified	#Proteins Quantified	`Proteins_Quantified`

HeatMap

This heatmap provides an overview of the performance of the quantms DIA (DIA-NN) results.

This plot shows the pipeline performance overview. Some metrics are calculated. *Heatmap score[Contaminants]: as fraction of summed intensity with 0 = sample full of contaminants; 1 = no contaminants *Heatmap score[Pep Intensity (>23.0)]: Linear scale of the median intensity reaching the threshold, i.e. reaching 2^21 of 2^23 gives score 0.25. *Heatmap score[Charge]: Deviation of the charge 2 proportion from a representative Raw file (median). For typtic digests, peptides of charge 2 (one N-terminal and one at tryptic C-terminal R or K residue) should be dominant. Ionization issues (voltage?), in-source fragmentation, missed cleavages and buffer irregularities can cause a shift (see Bittremieux 2017, DOI: 10.1002/mas.21544) *Heatmap score[RT Alignment]: Compute 1 minus the mean absolute difference between 'RT' and 'Predicted.RT', and take the maximum of this value and 0. 1: |RT - Predicted.RT| = 0 *Heatmap score [ID rate over RT]: Judge column occupancy over retention time. Ideally, the LC gradient is chosen such that the number of identifications (here, after FDR filtering) is uniform over time, to ensure consistent instrument duty cycles. Sharp peaks and uneven distribution of identifications over time indicate potential for LC gradient optimization.Scored using 'Uniform' scoring function. i.e. constant receives good score, extreme shapes are bad *Heatmap score [Norm Factor]: Computes the mean absolute deviation (MAD) of 'Normalisation.Factor' from its mean. 0 = high variability in normalization factors; 1 = perfectly consistent normalization factors *Heatmap score [Peak Width]: Average peak width (RT.Stop - RT.Start). 1 = peak width equals 0; 0 = peak width equals 1 or greater

Created with MultiQC

Pipeline Result Statistics

This plot shows the final pipeline results.

Including Sample Name, Possible Study Variables, identified the number of peptide in the pipeline, and identified the number of modified peptide in the pipeline, eg. All data in this table are obtained from the out_msstats file. You can also remove the decoy with the `remove_decoy` parameter. In the FragPipe results summary, the data were obtained from psm.tsv.

Showing ¹⁶⁰/₁₆₀ rows and ⁶/₆ columns.

Sample Name	MSstats_Condition	Fraction	#Peptide IDs	#Unambiguous Peptide IDs	#Protein (group) IDs
Sample 1	blood plasma		3710	3432	585
↳ FP-1A1		1	3710	3432	585
Sample 2	blood plasma		3856	3572	598
↳ FP-1A2		1	3856	3572	598
Sample 3	blood plasma		3767	3503	576
↳ FP-1A3		1	3767	3503	576
Sample 4	blood plasma		3798	3526	579
↳ FP-1A4		1	3798	3526	579
Sample 5	blood plasma		3855	3567	652
↳ FP-1B1		1	3855	3567	652
Sample 6	blood plasma		3921	3621	632
↳ FP-1B2		1	3921	3621	632
Sample 7	blood plasma		3931	3660	611
↳ FP-1B3		1	3931	3660	611
Sample 8	blood plasma		3832	3565	585
↳ FP-1B4		1	3832	3565	585
Sample 9	blood plasma		3717	3443	565
↳ FP-1C1		1	3717	3443	565
Sample 10	blood plasma		3706	3433	573
↳ FP-1C2		1	3706	3433	573
Sample 11	blood plasma		3787	3516	580
↳ FP-1C3		1	3787	3516	580
Sample 12	blood plasma		3865	3539	582
↳ FP-1C4		1	3865	3539	582
Sample 13	blood plasma		3543	3304	520
↳ FP-1D1		1	3543	3304	520
Sample 14	blood plasma		3867	3566	599
↳ FP-1D2		1	3867	3566	599
Sample 15	blood plasma		3775	3503	578
↳ FP-1D3		1	3775	3503	578
Sample 16	blood plasma		3826	3533	583
↳ FP-1D4		1	3826	3533	583
Sample 17	blood plasma		3543	3294	558
↳ FP-1E1		1	3543	3294	558
Sample 18	blood plasma		3653	3390	574
↳ FP-1E2		1	3653	3390	574
Sample 19	blood plasma		3753	3473	587
↳ FP-1E3		1	3753	3473	587
Sample 20	blood plasma		3827	3550	601
↳ FP-1E4		1	3827	3550	601
Sample 21	blood plasma		3221	2967	521
↳ FP-2A1		1	3221	2967	521
Sample 22	blood plasma		3326	3058	529
↳ FP-2A2		1	3326	3058	529
Sample 23	blood plasma		3361	3096	550
↳ FP-2A3		1	3361	3096	550
Sample 24	blood plasma		3309	3052	523
↳ FP-2A4		1	3309	3052	523
Sample 25	blood plasma		3366	3117	526
↳ FP-2B1		1	3366	3117	526
Sample 26	blood plasma		3278	3029	504
↳ FP-2B2		1	3278	3029	504
Sample 27	blood plasma		3254	2983	529
↳ FP-2B3		1	3254	2983	529
Sample 28	blood plasma		3201	2955	495
↳ FP-2B4		1	3201	2955	495
Sample 29	blood plasma		3174	2936	519
↳ FP-2C1		1	3174	2936	519
Sample 30	blood plasma		3358	3068	528
↳ FP-2C2		1	3358	3068	528
Sample 31	blood plasma		3320	3062	528
↳ FP-2C3		1	3320	3062	528
Sample 32	blood plasma		3731	3453	589
↳ FP-2C4		1	3731	3453	589
Sample 33	blood plasma		3543	3302	526
↳ FP-2D1		1	3543	3302	526
Sample 34	blood plasma		3539	3290	538
↳ FP-2D2		1	3539	3290	538
Sample 35	blood plasma		3375	3147	507
↳ FP-2D3		1	3375	3147	507
Sample 36	blood plasma		3476	3228	531
↳ FP-2D4		1	3476	3228	531
Sample 37	blood plasma		3642	3373	529
↳ FP-2E1		1	3642	3373	529
Sample 38	blood plasma		3635	3339	549
↳ FP-2E2		1	3635	3339	549
Sample 39	blood plasma		3565	3294	514
↳ FP-2E3		1	3565	3294	514
Sample 40	blood plasma		3553	3293	529
↳ FP-2E4		1	3553	3293	529
Sample 41	blood plasma		3392	3127	513
↳ FP-3A1		1	3392	3127	513
Sample 42	blood plasma		3548	3281	528
↳ FP-3A2		1	3548	3281	528
Sample 43	blood plasma		3523	3260	520
↳ FP-3A3		1	3523	3260	520
Sample 44	blood plasma		3574	3291	523
↳ FP-3A4		1	3574	3291	523
Sample 45	blood plasma		3520	3263	502
↳ FP-3B1		1	3520	3263	502
Sample 46	blood plasma		3464	3214	522
↳ FP-3B2		1	3464	3214	522
Sample 47	blood plasma		3544	3290	512
↳ FP-3B3		1	3544	3290	512
Sample 48	blood plasma		3418	3161	499
↳ FP-3B4		1	3418	3161	499
Sample 49	blood plasma		3643	3342	526
↳ FP-3C1		1	3643	3342	526
Sample 50	blood plasma		3496	3243	511
↳ FP-3C2		1	3496	3243	511
Sample 51	blood plasma		3496	3248	499
↳ FP-3C3		1	3496	3248	499
Sample 52	blood plasma		3554	3299	505
↳ FP-3C4		1	3554	3299	505
Sample 53	blood plasma		3532	3251	508
↳ FP-3D1		1	3532	3251	508
Sample 54	blood plasma		3588	3311	537
↳ FP-3D2		1	3588	3311	537
Sample 55	blood plasma		3560	3317	496
↳ FP-3D3		1	3560	3317	496
Sample 56	blood plasma		3524	3271	507
↳ FP-3D4		1	3524	3271	507
Sample 57	blood plasma		3623	3338	535
↳ FP-3E1		1	3623	3338	535
Sample 58	blood plasma		3727	3432	540
↳ FP-3E2		1	3727	3432	540
Sample 59	blood plasma		3650	3384	534
↳ FP-3E3		1	3650	3384	534
Sample 60	blood plasma		3776	3476	548
↳ FP-3E4		1	3776	3476	548
Sample 61	blood plasma		3628	3346	525
↳ FP-4A1		1	3628	3346	525
Sample 62	blood plasma		3557	3273	538
↳ FP-4A2		1	3557	3273	538
Sample 63	blood plasma		3519	3268	511
↳ FP-4A3		1	3519	3268	511
Sample 64	blood plasma		3485	3229	520
↳ FP-4A4		1	3485	3229	520
Sample 65	blood plasma		3392	3140	492
↳ FP-4B1		1	3392	3140	492
Sample 66	blood plasma		3567	3285	539
↳ FP-4B2		1	3567	3285	539
Sample 67	blood plasma		3414	3163	509
↳ FP-4B3		1	3414	3163	509
Sample 68	blood plasma		3687	3389	546
↳ FP-4B4		1	3687	3389	546
Sample 69	blood plasma		3444	3181	508
↳ FP-4C1		1	3444	3181	508
Sample 70	blood plasma		3357	3107	481
↳ FP-4C2		1	3357	3107	481
Sample 71	blood plasma		3550	3276	528
↳ FP-4C3		1	3550	3276	528
Sample 72	blood plasma		3548	3287	528
↳ FP-4C4		1	3548	3287	528
Sample 73	blood plasma		2950	2702	445
↳ FP-4D1		1	2950	2702	445
Sample 74	blood plasma		3547	3272	535
↳ FP-4D2		1	3547	3272	535
Sample 75	blood plasma		3577	3295	526
↳ FP-4D3		1	3577	3295	526
Sample 76	blood plasma		3563	3286	522
↳ FP-4D4		1	3563	3286	522
Sample 77	blood plasma		3554	3282	524
↳ FP-4E1		1	3554	3282	524
Sample 78	blood plasma		3462	3199	517
↳ FP-4E2		1	3462	3199	517
Sample 79	blood plasma		3528	3271	528
↳ FP-4E3		1	3528	3271	528
Sample 80	blood plasma		3598	3326	520
↳ FP-4E4		1	3598	3326	520
Sample 81	blood plasma		3947	3636	653
↳ MP-1A1		1	3947	3636	653
Sample 82	blood plasma		3619	3352	573
↳ MP-1A2		1	3619	3352	573
Sample 83	blood plasma		3835	3540	622
↳ MP-1A3		1	3835	3540	622
Sample 84	blood plasma		3828	3566	584
↳ MP-1A4		1	3828	3566	584
Sample 85	blood plasma		3949	3618	593
↳ MP-1B1		1	3949	3618	593
Sample 86	blood plasma		3759	3484	588
↳ MP-1B2		1	3759	3484	588
Sample 87	blood plasma		3757	3494	588
↳ MP-1B3		1	3757	3494	588
Sample 88	blood plasma		3663	3385	583
↳ MP-1B4		1	3663	3385	583
Sample 89	blood plasma		3813	3547	587
↳ MP-1C1		1	3813	3547	587
Sample 90	blood plasma		3846	3556	588
↳ MP-1C2		1	3846	3556	588
Sample 91	blood plasma		3914	3623	612
↳ MP-1C3		1	3914	3623	612
Sample 92	blood plasma		3748	3474	574
↳ MP-1C4		1	3748	3474	574
Sample 93	blood plasma		3570	3314	550
↳ MP-1D1		1	3570	3314	550
Sample 94	blood plasma		3788	3495	578
↳ MP-1D2		1	3788	3495	578
Sample 95	blood plasma		3814	3553	595
↳ MP-1D3		1	3814	3553	595
Sample 96	blood plasma		3764	3470	580
↳ MP-1D4		1	3764	3470	580
Sample 97	blood plasma		3935	3636	625
↳ MP-1E1		1	3935	3636	625
Sample 98	blood plasma		3692	3392	578
↳ MP-1E2		1	3692	3392	578
Sample 99	blood plasma		3821	3533	588
↳ MP-1E3		1	3821	3533	588
Sample 100	blood plasma		3663	3394	589
↳ MP-1E4		1	3663	3394	589
Sample 101	blood plasma		3787	3509	607
↳ MP-2A1		1	3787	3509	607
Sample 102	blood plasma		3789	3494	592
↳ MP-2A2		1	3789	3494	592
Sample 103	blood plasma		3620	3353	553
↳ MP-2A3		1	3620	3353	553
Sample 104	blood plasma		3825	3493	605
↳ MP-2A4		1	3825	3493	605
Sample 105	blood plasma		3286	3026	532
↳ MP-2B1		1	3286	3026	532
Sample 106	blood plasma		3194	2948	504
↳ MP-2B2		1	3194	2948	504
Sample 107	blood plasma		3087	2846	489
↳ MP-2B3		1	3087	2846	489
Sample 108	blood plasma		3276	2994	528
↳ MP-2B4		1	3276	2994	528
Sample 109	blood plasma		3314	3047	561
↳ MP-2C1		1	3314	3047	561
Sample 110	blood plasma		3134	2900	496
↳ MP-2C2		1	3134	2900	496
Sample 111	blood plasma		3270	3006	521
↳ MP-2C3		1	3270	3006	521
Sample 112	blood plasma		3302	3037	529
↳ MP-2C4		1	3302	3037	529
Sample 113	blood plasma		3651	3390	566
↳ MP-2D1		1	3651	3390	566
Sample 114	blood plasma		3815	3504	603
↳ MP-2D2		1	3815	3504	603
Sample 115	blood plasma		3587	3317	551
↳ MP-2D3		1	3587	3317	551
Sample 116	blood plasma		3580	3328	539
↳ MP-2D4		1	3580	3328	539
Sample 117	blood plasma		3524	3269	555
↳ MP-2E1		1	3524	3269	555
Sample 118	blood plasma		3335	3087	504
↳ MP-2E2		1	3335	3087	504
Sample 119	blood plasma		3431	3192	506
↳ MP-2E3		1	3431	3192	506
Sample 120	blood plasma		3411	3176	497
↳ MP-2E4		1	3411	3176	497
Sample 121	blood plasma		3774	3472	568
↳ MP-3A1		1	3774	3472	568
Sample 122	blood plasma		3728	3443	578
↳ MP-3A2		1	3728	3443	578
Sample 123	blood plasma		3523	3243	533
↳ MP-3A3		1	3523	3243	533
Sample 124	blood plasma		3338	3085	506
↳ MP-3A4		1	3338	3085	506
Sample 125	blood plasma		3484	3214	525
↳ MP-3B1		1	3484	3214	525
Sample 126	blood plasma		3381	3133	498
↳ MP-3B2		1	3381	3133	498
Sample 127	blood plasma		3486	3213	533
↳ MP-3B3		1	3486	3213	533
Sample 128	blood plasma		3159	2919	488
↳ MP-3B4		1	3159	2919	488
Sample 129	blood plasma		3556	3310	510
↳ MP-3C1		1	3556	3310	510
Sample 130	blood plasma		3531	3262	508
↳ MP-3C2		1	3531	3262	508
Sample 131	blood plasma		3567	3315	510
↳ MP-3C3		1	3567	3315	510
Sample 132	blood plasma		3556	3293	511
↳ MP-3C4		1	3556	3293	511
Sample 133	blood plasma		3708	3404	547
↳ MP-3D1		1	3708	3404	547
Sample 134	blood plasma		3486	3237	502
↳ MP-3D2		1	3486	3237	502
Sample 135	blood plasma		3545	3280	513
↳ MP-3D3		1	3545	3280	513
Sample 136	blood plasma		3608	3324	526
↳ MP-3D4		1	3608	3324	526
Sample 137	blood plasma		3715	3411	545
↳ MP-3E1		1	3715	3411	545
Sample 138	blood plasma		3654	3388	536
↳ MP-3E2		1	3654	3388	536
Sample 139	blood plasma		3569	3312	520
↳ MP-3E3		1	3569	3312	520
Sample 140	blood plasma		3514	3261	511
↳ MP-3E4		1	3514	3261	511
Sample 141	blood plasma		546	446	136
↳ MP-4A1		1	546	446	136
Sample 142	blood plasma		3609	3323	542
↳ MP-4A2		1	3609	3323	542
Sample 143	blood plasma		3579	3283	528
↳ MP-4A3		1	3579	3283	528
Sample 144	blood plasma		3554	3278	513
↳ MP-4A4		1	3554	3278	513
Sample 145	blood plasma		3562	3270	540
↳ MP-4B1		1	3562	3270	540
Sample 146	blood plasma		3553	3291	503
↳ MP-4B2		1	3553	3291	503
Sample 147	blood plasma		3440	3202	503
↳ MP-4B3		1	3440	3202	503
Sample 148	blood plasma		3460	3225	505
↳ MP-4B4		1	3460	3225	505
Sample 149	blood plasma		3659	3368	555
↳ MP-4C1		1	3659	3368	555
Sample 150	blood plasma		3437	3152	524
↳ MP-4C2		1	3437	3152	524
Sample 151	blood plasma		3484	3218	542
↳ MP-4C3		1	3484	3218	542
Sample 152	blood plasma		3348	3082	524
↳ MP-4C4		1	3348	3082	524
Sample 153	blood plasma		3669	3398	536
↳ MP-4D1		1	3669	3398	536
Sample 154	blood plasma		3552	3304	506
↳ MP-4D2		1	3552	3304	506
Sample 155	blood plasma		3506	3235	542
↳ MP-4D3		1	3506	3235	542
Sample 156	blood plasma		2876	2628	449
↳ MP-4D4		1	2876	2628	449
Sample 157	blood plasma		3342	3087	515
↳ MP-4E1		1	3342	3087	515
Sample 158	blood plasma		3450	3174	514
↳ MP-4E2		1	3450	3174	514
Sample 159	blood plasma		3640	3330	534
↳ MP-4E3		1	3640	3330	534
Sample 160	blood plasma		3585	3289	540
↳ MP-4E4		1	3585	3289	540

Expand table

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

Sort	Column	Description	ID
\|\|	MSstats_Condition	MSstats Condition	`MSstats_Condition`
\|\|	Fraction	Fraction Identifier	`Fraction`
\|\|	#Peptide IDs	The number of identified PSMs in the pipeline	`Peptide_Num`
\|\|	#Unambiguous Peptide IDs	The number of unique peptides in the pipeline. Those that match only one protein in the provided database	`Unique_Peptide_Num`
\|\|	#Modified Peptide IDs	Number of modified identified peptides in the pipeline	`Modified_Peptide_Num`
\|\|	#Protein (group) IDs	The number of identified protein(group)s in the pipeline	`Protein_Num`

Identification Summary

Number of Peptides identified Per Protein

This plot shows the number of peptides per protein in quantms pipeline final result

This statistic is extracted from the out_msstats file. Proteins supported by more peptide identifications can constitute more confident results.

Created with MultiQC

ProteinGroups Count

Number of protein groups per raw file.

Based on statistics calculated from mzTab, mzIdentML (mzid), DIA-NN report files, or FragPipe psm.tsv.

Created with MultiQC

Peptide ID Count

Number of unique (i.e. not counted twice) peptide sequences including modifications per Raw file.

Based on statistics calculated from mzTab, mzIdentML (mzid), DIA-NN report files, or FragPipe psm.tsv.

Created with MultiQC

Peptide Length Distribution

Peptide length distribution per Run.

Peptide length distribution.
FragPipe: psm.tsv ('Peptide Length': number of residues in the peptide sequence).
MaxQuant: evidence.txt ('Length': the length of the sequence stored in the column 'Sequence').
DIA-NN: report.tsv (the length of the 'Stripped.Sequence').
quantms: *.mzTab (the length of sequence).

Created with MultiQC

Quantification Analysis

Peptides Quantification Table

This plot shows the quantification information of peptides in the final result (mainly the mzTab file).

The quantification information of peptides is obtained from the MSstats input file. The table shows the quantitative level and distribution of peptides in different study variables, run and peptiforms. The distribution show all the intensity values in a bar plot above and below the average intensity for all the fractions, runs and peptiforms. * BestSearchScore: It is equal to 1 - min(Q.Value) for DIA datasets. Then it is equal to 1 - min(best_search_engine_score[1]), which is from best_search_engine_score[1] column in mzTab peptide table for DDA datasets. * Average Intensity: Average intensity of each peptide sequence across all conditions with NA=0 or NA ignored. * Peptide intensity in each condition (Eg. `CT=Mixture;CN=UPS1;QY=0.1fmol`): Summarize intensity of fractions, and then mean intensity in technical replicates/biological replicates separately.

Showing ⁵⁰/₅₀ rows and ⁵/₅ columns.

PeptideID	Protein Name	Peptide Sequence	Best Search Score	Average Intensity	blood plasma
1	IPSP_HUMAN	AAAATGTLFTFR	1.0000	7.2108	7.2108
2	TFR1_HUMAN	AAAEVAGQFVLK	1.0000	6.2663	6.2663
3	TLN1_HUMAN	AAAFEEQENETVVVK	0.9928	6.6869	6.6869
4	AOC3_HUMAN	AAALAHLDR	1.0000	5.5533	5.5533
5	POSTN_HUMAN	AAALTSDLLEALGR	1.0000	6.0312	6.0312
6	ANAG_HUMAN	AAAVSEAEADFYEQNSR	1.0000	5.0776	5.0776
7	TTHY_HUMAN	AADDTWEPFASGK	1.0000	9.7129	9.7129
8	FA5_HUMAN	AADLEQQAVFAVFDENK	1.0000	7.1805	7.1805
9	PHB1_HUMAN	AAELLANSLATAGDGLLELR	1.0000	5.8910	5.8910
10	1433E_HUMAN	AAFDDALAELDTLSEESYK	1.0000	5.6967	5.6967
11	COMD3_HUMAN	AAFQSLLDAQADEAVLDHPDLK	0.9959	6.9196	6.9196
12	PTPRF_HUMAN	AAGTEGPFQEVDGVATTR	1.0000	5.8582	5.8582
13	NCAM1_HUMAN	AAHFVFR	0.9997	6.0236	6.0236
14	APOB_HUMAN	AALGKLPQQANDYLNSFNWER	1.0000	8.2900	8.2900
15	ACE_HUMAN	AALPAQELEEYNK	1.0000	5.6296	5.6296
16	LRP1_HUMAN	AALSGANVLTLLEK	1.0000	5.7929	5.7929
17	ITIH1_HUMAN	AALSGENAGLVR	1.0000	9.1727	9.1727
18	APOB_HUMAN	AALTELSLGSAYQAMLLGVDSK	1.0000	7.5942	7.5942
19	ANGT_HUMAN	AAMVGMLANFLGFR	1.0000	6.5836	6.5836
20	HYOU1_HUMAN	AANSLEAFLFETQDK	1.0000	6.3650	6.3650
21	COR1A_HUMAN	AAPEASGTPSSDAVSR	1.0000	4.9994	4.9994
22	QSOX1_HUMAN	AAPGQEPPEHMAELQR	1.0000	6.9875	6.9875
23	CO6A3_HUMAN	AAPLQGMLPGLLAPLR	1.0000	5.4851	5.4851
24	IGLC7_HUMAN	AAPSVTLFPPSSEELQANK	1.0000	8.2159	8.2159
25	LCAT_HUMAN	AAPYDWR	0.9997	7.1839	7.1839
26	FCG2A_HUMAN	AAQFEPPGR	1.0000	6.2442	6.2442
27	A2MG_HUMAN	AAQVTLQSSGTFSSK	1.0000	7.2145	7.2145
28	CO7_HUMAN	AASGTQNNVLR	1.0000	6.1424	6.1424
29	APOB_HUMAN	AASGTTGTYQEWK	1.0000	8.5080	8.5080
30	APOB_HUMAN	AASGTTGTYQEWKDK	1.0000	6.8078	6.8078
31	PHB1_HUMAN	AATFGLLLDDVSLTHLTFGK	0.9989	6.5604	6.5604
32	APOE_HUMAN	AATVGSLAGQPLQER	1.0000	8.5561	8.5561
33	HEM2_HUMAN	AAVLEAMTAFR	1.0000	5.8607	5.8607
34	ENOA_HUMAN	AAVPSGASTGLYEALELR	1.0000	6.5722	6.5722
35	CO3_HUMAN	AAVYHHFLSDGVR	1.0000	9.3946	9.3946
36	CO3_HUMAN	AAVYHHFLSDGVRK	1.0000	7.6217	7.6217
37	CONTAM_HBA_HUMAN;HBA_HUMAN	AAWGKVGAHAGEYGAEALER	1.0000	6.7950	6.7950
38	ALDOA_HUMAN	ADDGRPFPQVLK	1.0000	6.6966	6.6966
39	ALBU_HUMAN;CONTAM_ALBU_HUMAN	ADDKETCFAEEGK	1.0000	6.8517	6.8517
40	CONTAM_PRDX1_HUMAN;PRDX1_HUMAN	ADEGLSFR	0.9948	5.8632	5.8632
41	PCSK9_HUMAN	ADEYQPPDGGSLVEVYLLDTSLQSDHRELEGR	1.0000	5.3807	5.3807
42	B3AT_HUMAN	ADFLEQPVLGFVR	1.0000	6.2023	6.2023
43	CAH1_HUMAN;CONTAM_CAH1_HUMAN	ADGLAVLGVLMK	1.0000	7.6744	7.6744
44	CD109_HUMAN	ADGNQLTLEER	1.0000	5.8432	5.8432
45	CO4B_HUMAN	ADGSYAAWLSR	1.0000	8.7034	8.7034
46	ASGR2_HUMAN	ADHDALLFHLK	1.0000	5.8520	5.8520
47	BGH3_HUMAN	ADHHATNGVVHLLDK	1.0000	6.6119	6.6119
48	FA5_HUMAN	ADKPLSLHPQGLR	1.0000	6.7216	6.7216
49	CONTAM_K1C17_HUMAN;K1C17_HUMAN	ADLEMQLENLK	0.9932	6.9798	6.9798
50	CONTAM_K1C14_HUMAN;K1C14_HUMAN	ADLEMQLESLKEELAYLK	1.0000	6.6003	6.6003

Expand table

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

Sort	Column	Description	ID
\|\|	Protein Name	Name/Identifier(s) of the protein (group)	`ProteinName`
\|\|	Peptide Sequence	Peptide Sequence	`PeptideSequence`
\|\|	Best Search Score	Best Search Score	`BestSearchScore`
\|\|	Average Intensity	Average intensity across all conditions	`Average_Intensity`
\|\|	blood plasma	blood plasma	`blood_plasma`

Protein Quantification Table

This plot shows the quantification information of proteins in the final result (mainly the mzTab file).

The quantification information of proteins is obtained from the msstats input file. The table shows the quantitative level and distribution of proteins in different study variables and run. * Peptides_Number: The number of peptides for each protein. * Average Intensity: Average intensity of each protein across all conditions with NA=0 or NA ignored. * Protein intensity in each condition (Eg. `CT=Mixture;CN=UPS1;QY=0.1fmol`): Summarize intensity of peptides.

Showing ⁵⁰/₅₀ rows and ⁴/₄ columns.

ProteinID	Protein Name	Number of Peptides	Average Intensity	blood plasma
1	1433B_HUMAN	1	5.5428	5.5428
2	1433E_HUMAN	6	7.0074	7.0074
3	1433T_HUMAN	1	5.3322	5.3322
4	1433Z_HUMAN	5	7.0499	7.0499
5	4F2_HUMAN	8	7.2806	7.2806
6	6PGD_HUMAN	2	6.0220	6.0220
7	A1AG1_HUMAN	10	8.4759	8.4759
8	A1AG2_HUMAN	6	8.6081	8.6081
9	A1AT_HUMAN	36	10.3121	10.3121
10	A1BG_HUMAN	14	10.0529	10.0529
11	A2AP_HUMAN	15	9.5034	9.5034
12	A2GL_HUMAN	10	9.3554	9.3554
13	A2MG_HUMAN	63	9.5265	9.5265
14	A4_HUMAN	1	5.6606	5.6606
15	AACT_HUMAN	28	10.3909	10.3909
16	AATC_HUMAN	2	5.4660	5.4660
17	ABCD3_HUMAN	1	6.0229	6.0229
18	ACADV_HUMAN	1	7.3439	7.3439
19	ACES_HUMAN	1	5.3543	5.3543
20	ACE_HUMAN	8	7.7527	7.7527
21	ACTBL_HUMAN	6	7.8915	7.8915
22	ACTB_HUMAN;ACTG_HUMAN	8	8.4190	8.4190
23	ACTC_HUMAN	1	5.8513	5.8513
24	ACTN1_HUMAN	19	7.6279	7.6279
25	ACTS_HUMAN	9	8.1438	8.1438
26	ADA2_HUMAN	5	6.8083	6.8083
27	ADEC1_HUMAN	1	5.4188	5.4188
28	ADIPO_HUMAN	5	7.9267	7.9267
29	ADT2_HUMAN	2	6.8402	6.8402
30	ADT3_HUMAN	1	5.8031	5.8031
31	AFAM_HUMAN	22	9.7319	9.7319
32	AGRE1_HUMAN	1	6.1030	6.1030
33	AGRF5_HUMAN	3	6.5983	6.5983
34	AGRG6_HUMAN	3	6.3593	6.3593
35	AGRL4_HUMAN	2	5.5922	5.5922
36	AHSP_HUMAN	2	6.2100	6.2100
37	AL1A1_HUMAN	4	6.3021	6.3021
38	AL4A1_HUMAN	1	6.4031	6.4031
39	ALBU_HUMAN;CONTAM_ALBU_HUMAN	46	10.8600	10.8600
40	ALDOA_HUMAN	8	7.6226	7.6226
41	ALDOB_HUMAN	8	7.7182	7.7182
42	ALS_HUMAN	22	9.2564	9.2564
43	AMBP_HUMAN	9	9.1926	9.1926
44	AMD_HUMAN	4	5.9594	5.9594
45	AMPE_HUMAN	4	6.6902	6.6902
46	AMPN_HUMAN	24	7.9286	7.9286
47	AMY1A_HUMAN;AMY1B_HUMAN;AMY1C_HUMAN	1	4.5475	4.5475
48	AMYP_HUMAN	6	6.3282	6.3282
49	ANAG_HUMAN	9	7.0657	7.0657
50	ANGI_HUMAN	3	6.9475	6.9475

Expand table

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

Sort	Column	Description	ID
\|\|	Protein Name	Name/Identifier(s) of the protein (group)	`ProteinName`
\|\|	Number of Peptides	Number of peptides per proteins	`Peptides_Number`
\|\|	Average Intensity	Average intensity across all conditions	`Average_Intensity`
\|\|	blood plasma	blood plasma	`blood_plasma`

Intensity Distribution

log2(Precursor.Quantity) for each Run (or Sample).

[DIA-NN: main report] log2(Precursor.Quantity) for each Run (or Sample).

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

Standard Deviation of Intensity

Standard deviation of intensity by sample (experimental conditions).

[DIA-NN: report.tsv] Sample grouping is derived from the SDRF when available; otherwise, it is parsed from "Run" names. First, identify the experimental conditions from the "Run" name. Then, group the data by experimental condition and Modified.Sequence, and calculate the standard deviation of log2(Precursor.Quantity).

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

MS1 Analysis

MS1 Area Distribution

log2(Ms1.Area) for each Run.

[DIA-NN: report.tsv] log2(Ms1.Area) for each Run. Ms1.Area: non-normalised MS1 peak area.

Flat image plot. Toolbox functions such as highlighting / hiding samples will not work (see the docs).

MS2 and Spectral Stats

Distribution of Precursor Charges

This is a bar chart representing the distribution of the precursor ion charges for a given whole experiment.

[DIA-NN: main report] distribution of the precursor ion charges for a given whole experiment. Precursor.Charge: the charge of the precursor.

Created with MultiQC

Charge-state

The distribution of the charge-state of the precursor ion.

[DIA-NN: main report] The distribution of the charge-state of the precursor ion (Precursor.Charge).

Created with MultiQC

Mass Error Trends

Delta Mass

This plot is based on the "Ms1.Apex.Mz.Delta" column from the DIA-NN main report.

[DIA-NN: main report] Ms1.Apex.Mz.Delta: difference between observed precursor m/z and the theoretical value.

Created with MultiQC

RT Quality Control

IDs over RT

Distribution of retention time, derived from the main report.

[DIA-NN: main report] Distribution of retention time (RT) for each run.

This plot allows to judge column occupancy over retention time. Ideally, the LC gradient is chosen such that the number of identifications (here, after FDR filtering) is uniform over time, to ensure consistent instrument duty cycles. Sharp peaks and uneven distribution of identifications over time indicate potential for LC gradient optimization. See [Moruz 2014, DOI: 10.1002/pmic.201400036](https://pubmed.ncbi.nlm.nih.gov/24700534/) for details.

Created with MultiQC

Normalisation Factor over RT

Distribution of Normalisation.Factor with retention time, derived from the main report.

[DIA-NN: main report] Distribution of Normalisation.Factor with retention time (RT) for each run. RT: the retention time (RT) of the PSM in minutes. Normalisation.Factor: normalisation factor applied to the precursor in the specific run, i.e. normalised quantity = normalisation factor X non-normalised quantity

Created with MultiQC

FWHM over RT

Distribution of FWHM with retention time, derived from the main report. FWHM: estimated peak width at half-maximum.

[DIA-NN: main report] Distribution of FWHM with retention time (RT) for each run. RT: the retention time (RT) of the PSM in minutes. FWHM: estimated peak width at half-maximum; note that the accuracy of such estimates sometimes strongly depends on the DIA cycle time and sample injection amount, i.e. they can only be used to evaluate chromatographic performance in direct comparisons with similar settings, including the scan window; another caveat is that FWHM does not reflect any peak tailing.

Created with MultiQC

Peak Width over RT

Distribution of peak width with retention time, derived from the main report. Peak Width = RT.Stop - RT.Start.

[DIA-NN: main report] Distribution of peak width with retention time (RT) for each run. RT: the retention time (RT) of the PSM in minutes. RT.Start and RT.Stop: peak boundaries.

Created with MultiQC

Absolute RT Error over RT

Distribution of rt error with retention time, derived from the main report.

[DIA-NN: main report] Distribution of absolute RT error (|RT - Predicted.RT|) with retention time (RT) for each run. RT: the retention time (RT) of the PSM in minutes. Predicted.RT: predicted RT based on the iRT.

Created with MultiQC

LOESS RT ~ iRT

Distribution of LOESS RT ~ iRT for each run, derived from the main report.

[DIA-NN: main report] Distribution of LOESS RT ~ iRT for each run. RT: the retention time (RT) of the PSM in minutes. iRT: reference RT as recorded in the spectral library.

Created with MultiQC

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Pipeline Result Statistics Help

AI Summary

Pipeline Result Statistics: Columns

Identification Summary

Number of Peptides identified Per Protein Help

AI Summary

ProteinGroups Count Help

AI Summary

Peptide ID Count Help

AI Summary

Peptide Length Distribution Help

AI Summary

Quantification Analysis

Peptides Quantification Table Help

AI Summary

Peptides Quantification Table: Columns

Protein Quantification Table Help

AI Summary

Protein Quantification Table: Columns

Intensity Distribution Help

AI Summary

Standard Deviation of Intensity Help

AI Summary

MS1 Analysis

MS1 Area Distribution Help

AI Summary

MS2 and Spectral Stats

Distribution of Precursor Charges Help

AI Summary

Charge-state Help

AI Summary

Mass Error Trends

Delta Mass Help

AI Summary

RT Quality Control

IDs over RT Help

AI Summary

Normalisation Factor over RT Help

AI Summary

FWHM over RT Help

AI Summary

Peak Width over RT Help

AI Summary

Absolute RT Error over RT Help

AI Summary

LOESS RT ~ iRT Help

AI Summary

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Experimental Design

Summary Table

HeatMap

Pipeline Result Statistics

Number of Peptides identified Per Protein

ProteinGroups Count

Peptide ID Count

Peptide Length Distribution

Peptides Quantification Table

Protein Quantification Table

Intensity Distribution

Standard Deviation of Intensity

MS1 Area Distribution

Distribution of Precursor Charges

Charge-state

Delta Mass

IDs over RT

Normalisation Factor over RT

FWHM over RT

Peak Width over RT

Absolute RT Error over RT

LOESS RT ~ iRT