Sample carryover … not ready yet…

Heatmap score [EVD: Carryover]: The percentage of peptide identifications whose sequence gives rise to a large ‘retention span’.

Charge distribution per Raw file. 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). The charge distribution should be similar across Raw files. Consistent charge distribution is paramount for comparable 3D-peak intensities across samples.

Heatmap score [EVD: Charge]: Deviation of the charge 2 proportion from a representative Raw file (‘qualMedianDist’ function).

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 for details.

Heatmap score [EVD: ID rate over RT]: Scored using ‘Uniform’ scoring function, i.e. constant receives good score, extreme shapes are bad.

MBR Alignment: First of two steps (1=align, 2=transfer) during Match-between-runs. This plot is based purely on real MS/MS ids. Ideally, RTs of identical peptides should be equal (i.e. very small residual RT delta) across Raw files after alignment.

MaxQuants RT correction is shown in blue – it should be well within the alignment search window (20min by default) set during MaxQuant configuration. The resulting residual RT delta after RT alignment (compared to a reference Raw file), is shown as green/red dots. One dot represents one peptide (incl. charge). Every dot (peptide) outside an allowed residual delta RT (1min by default) is colored red. All others are green. The ratio of ‘green’ vs. ‘green+red’ peptides is annotated using ‘sc:’ (for ‘score’) in the plot subtitles. High values are better (green peptides dominate).

If moving ‘red’ dots to the horizontal zero-line (to make them green) requires large RT shifts, then increasing the alignment search window might help MaxQuant to find a better alignment.

Heatmap score [EVD: MBR Align]: ratio of ‘green’ vs. ‘green+red’ peptides

MBR Transfer: Last of two steps (1=align, 2=transfer) during Match-between-runs. If MaxQuant only transfers peptide ID’s which are not present in the target file, then each Raw file should not have any duplicates of identical peptides (incl. charge). Sometimes, a single or split 3D-peak gets annotated multiple times, that’s ok. However, the same peptide should not be annotated twice (or more) at vastly different points in RT.

This plot shows three columns: - left: the ‘genuine’ situation (pretending that no MBR was computed) - middle: looking only at transferred IDs - right: combined picture (a mixture of left+middle, usually)

Each peptide falls into three categories (the colors): - single (good, because it has either one genuine OR a transferred ID). - in-group (also good, because all ID’s are very close in RT) - out-group (bad, spread across the RT gradient – should not be possible; a false ID)

Heatmap score [EVD: MBR ID-Transfer]: The fraction of non-out-group peptides (i.e. good peptides) in the middle column. This score is ‘pessimistic’ because if few ID’s were transferred, but all of them are bad, the score is bad, even though the majority of peptides is still ok (because they are genuine). However, in this case MBR provides few (and wrong) additional information, and should be disabled.

Auxililiary plots – experimental – without scores.

Return a tree plot with a possible alignment tree. This allows the user to judge which Raw files have similar corrected RT’s (i.e. where aligned successfully). If there are clear sub-clusters, it might be worth introducing artifical fractions into MaxQuant, to avoid ID-transfer between these clusters (use the MBR-Align and MBR-ID-Transfer metrics to support the decision).

If the input contains fractions, leaf nodes will be colored accordingly. Distinct sub-clusters should have their own color. If not, MaxQuant’s fraction settings should be optimized. Note that introducing fractions in MaxQuant will naturally lead to a clustering here (it’s somewhat circular).

Heatmap score: none.

An oversampled 3D-peak is defined as a peak whose peptide ion (same sequence and same charge state) was identified by at least two distinct MS² spectra in the same Raw file. For high complexity samples, oversampling of individual 3D-peaks automatically leads to undersampling or even omission of other 3D-peaks, reducing the number of identified peptides. Oversampling occurs in low-complexity samples or long LC gradients, as well as undersized dynamic exclusion windows for data independent acquisitions.

Heatmap score [EVD: MS² Oversampling]: The percentage of non-oversampled 3D-peaks.

Missing peptide intensities per Raw file from evidence.txt. This metric shows the fraction of missing peptides compared to all peptides seen in the whole experiment. The more Raw files you have, the higher this fraction is going to be (because there is always going to be some exotic [low intensity?] peptide which gets [falsely] identified in only a single Raw file). A second plot shows how many peptides (Y-axis) are covered by at least X Raw files. A third plot shows the density of the observed (line) and the missing (filled area) data. To reconstruct the distribution of missing values, an imputation strategy is required, so the argument is somewhat circular here. If all Raw files are (technical) replicates, i.e. we can expect that missing peptides are indeed present and have an intensity similar to the peptides we do see, then the median is a good estimator. This method performs a global normalization across Raw files (so their observed intensitiy distributions have the same mean), before computing the imputed values. Afterwards, the distributions are de-normalized again (shifting them back to their) original locations – but this time with imputed peptides.

Peptides obtained via Match-between-run (MBR) are accounted for (i.e. are considered as present = non-missing). Thus, make sure that MBR is working as intended (see MBR metrics).

Warning: this metric is meaningless for fractionated data! TODO: compensate for lower scores in large studies (with many Raw files), since peptide FDR is accumulating!?

Heatmap score [EVD: Pep Missing]: Linear scale of the fraction of missing peptides.

Number of unique (i.e. not counted twice) peptide sequences including modifications (after FDR) per Raw file. A configurable target threshold is indicated as dashed line.

If MBR was enabled, three categories (‘genuine (exclusive)’, ‘genuine + transferred’, ‘transferred (exclusive)’ are shown, so the user can judge the gain that MBR provides.
Peptides in the ‘genuine + transferred’ category were identified within the Raw file by MS/MS, but at the same time also transferred to this Raw file using MBR. This ID transfer can be correct (e.g. in case of different charge states), or incorrect – see MBR-related metrics to tell the difference. Ideally, the ‘genuine + transferred’ category should be rather small, the other two should be large.

If MBR would be switched off, you can expect to see the number of peptides corresponding to ‘genuine (exclusive)’ + ‘genuine + transferred’. In general, if the MBR gain is low and the MBR scores are bad (see the two MBR-related metrics), MBR should be switched off for the Raw files which are affected (could be a few or all).

Heatmap score [EVD: Pep Count (>%1.0f)]: Linear scoring from zero. Reaching or exceeding the target threshold gives a score of 100%%.

Peptide precursor intensity per Raw file from evidence.txt WITHOUT match-between-runs evidence. Low peptide intensity usually goes hand in hand with low MS/MS identifcation rates and unfavourable signal/noise ratios, which makes signal detection harder. Also instrument acquisition time increases for trapping instruments.

Failing to reach the intensity threshold is usually due to unfavorable column conditions, inadequate column loading or ionization issues. If the study is not a dilution series or pulsed SILAC experiment, we would expect every condition to have about the same median log-intensity (of 2^%1.1f). The relative standard deviation (RSD) gives an indication about reproducibility across files and should be below 5%%.

Depending on your setup, your target thresholds might vary from PTXQC’s defaults. Change the threshold using the YAML configuration file.

Heatmap score [EVD: Pep Intensity (>%1.1f)]: Linear scale of the median intensity reaching the threshold, i.e. reaching 2²¹ of 2²³ gives score 0.25.

Precursor mass accuracy after calibration. Failed samples from precalibration data are still marked here. Ppm errors should be centered on zero and their spread is expected to be significantly smaller than before calibration.

Heatmap score [EVD: MS Cal-Post]: The variance and centeredness around zero of the calibrated distribution (function GaussDev).

Mass accurary before calibration. Outliers are marked as such (‘out-of-search-tol’) using ID rate and standard deviation as additional information (if available). If any Raw file is flagged ‘failed’, increasing MaxQuant’s first-search tolerance (20ppm by default, here: %1.1f ppm) might help to enable successful recalibration. A bug in MaxQuant sometimes leads to excessively high ppm mass errors (>10⁴) reported in the output data. However, this can sometimes be corrected for by re-computing the delta mass error from other data. If this is the case, a warning (‘bugfix applied’) will be shown.

Heatmap score [EVD: MS Cal Pre (%1.1f)]: the centeredness (function CenteredRef) of uncalibrated masses in relation to the search window size.

Number of Protein groups (after FDR) per Raw file. A configurable target threshold is indicated as dashed line.

If MBR was enabled, three categories (‘genuine (exclusive)’, ‘genuine + transferred’, ‘transferred (exclusive)’ are shown, so the user can judge the gain that MBR provides. Here, ‘transferred (exclusive)’ means that this protein group has peptide evidence which originates only from transferred peptide IDs. The quantification is (of course) always from the local Raw file. Proteins in the ‘genuine + transferred’ category have peptide evidence from within the Raw file by MS/MS, but at the same time also peptide IDs transferred to this Raw file using MBR were used. It is not unusual to see the ‘genuine + transferred’ category be the rather large, since a protein group usually has peptide evidence from both sources. To see of MBR worked, it is better to look at the two MBR-related metrics.

If MBR would be switched off, you can expect to see the number of protein groups corresponding to ‘genuine (exclusive)’ + ‘genuine + transferred’. In general, if the MBR gain is low and the MBR scores are bad (see the two MBR-related metrics), MBR should be switched off for the Raw files which are affected (could be a few or all).

Heatmap score [EVD: Prot Count (>%1.0f)]: Linear scoring from zero. Reaching or exceeding the target threshold gives a score of 100%%.

One parameter of optimal and reproducible chromatographic separation is the distribution of widths of peptide elution peaks, derived from the evidence table. Ideally, all Raw files show a similar distribution, e.g. to allow for equal conditions during dynamic precursor exclusion, RT alignment or peptide quantification.

Heatmap score [EVD: RT Peak Width]: Scored using BestKS function, i.e. the D statistic of a Kolmogoriv-Smirnoff test.

ITRAQ/TMT reporter intensity violin plots of all PSMs for each channel and Raw file. The second subplot shows labeling efficiency (LE), i.e the fraction of PSMs with non-zero abundance (100% = full labeling of all PSMs; 0% = no reporter ions at all). This is used for heatmap scoring. See below.

There is a similar ‘Experimental Group’ based metric/plot based on proteins.txt.

PTXQC uses isotope-corrected intensities (eliminating channel carry-over) to allow for detection of empty channels, e.g. due to mis-labeling. If MaxQuant did no isotope correction (i.e. corrected and uncorrected channels are equal), the plot title will show a warning. The scores are too optimistic in this case (since carry-over will be mistaken for actual signal).

Note: global labelling efficiency can only be judged indirectly with this metric, since isobaric reporters where set as fixed modification. Thus, MaxQuant. will only identify labeled peptides in the first place. Observing only very few peptides (see peptide count metric), is a good indicator. However, if only the labeling of a few channels failed, this will be noticable here!

Labeling can still be poor, even though identification was successful.

A labeling efficiency (LE) is computed per Raw file AND channel as: the percentage of PSMs which have non-zero reporter intensity. Ideally LE reaches 100 percent (all peptides have an intensity in the channel; biological missingness ignored).

Heatmap score: minimum labeling efficiency per Raw file across all channels. I.e. for 4-plex ITRAQ and two Raw files, there will be 8 labeling efficiency (LE) values. Each Raw file is now scored by the minimum LE of all its 4 channels.

PTXQC will explicitly show the five most abundant external protein contaminants (as detected via MaxQuant’s contaminants FASTA file) by Raw file, and summarize the remaining contaminants as ‘other’. This allows to track down which proteins exactly contaminate your sample. Low contamination is obviously better. The ‘Abundance class’ models the average peptide intensity in each Raw file and is visualized using varying degrees of transparency. It is not unusual to see samples with low sample content to have higher contamination. If you see only one abundance class (‘mid’), this means all your Raw files have roughly the same peptide intensity distribution.

If you see less than 5 contaminants, it either means there are actually less, or that one (or more) of the shortened contaminant names subsume multiple of the top5 contaminants (since they start with the same prefix).

Heatmap score [EVD: Contaminants]: as fraction of summed intensity with 0 = sample full of contaminants; 1 = no contaminants

The metric shows an upSet plot based on the number of modified peptide sequences per Raw file, intersected or merged with other Raw files (see below for details).

If the number of Raw files is >=6, only the distinct plot is generated (the other two are skipped for performance reasons). See here for an example plot showing how the set size is computed .

Definition: An active set is the set of black dots in a column of the plot – as opposed to the grey dots (you will understand when you see it).

distinct: shows the number of sequences that are present in ALL active sets. For three Raw files and active sets A and B, this would mean all sequences which occur in A and B (intersect), but not in C (setdiff).
intersection: shows the number of sequences that occurs in all active sets (intersection).
union: shows the number of sequences that occurs in total. For two files that are all sequences that occurs either in A or in B (union).

Heatmap score [EVD: UpSet]: The proportion of sequences that the file has in common with all other files.