Recent changes to Detection Metrics

Detection Metrics modified by Dinko Soic

Dinko Soic — Wed, 18 Mar 2026 13:55:41 -0000

--- v6
+++ v7
@@ -1,120 +1,102 @@
-# Oxonium Ion Detection Metrics
+# Detection Metrics

-## Introduction to Provided Detection Metrics
+## Introduction

 Oxonium Browser provides three primary metrics to assess the strength of a sugar oxonium ion match. Understanding these metrics and how to apply them is crucial for distinguishing genuine sugar signals from random mass peak matches.
-However, keep in mind that Oxonium Browser only generates a list of oxonium ion candidates based on accurate mass, abundance values, and the occurrence of additional water or carboxylic acid loss peaks. The provided default parameters have been established based on a range of reference samples and serve as a good starting point for your analysis. Lowering them may increase the number of random matches.

-## Key Metrics Explained
+Keep in mind that Oxonium Browser generates a list of oxonium ion candidates based on accurate mass, abundance values, and the occurrence of water loss peaks. The provided default parameters have been established based on a range of reference samples and serve as a good starting point. 
+
+## Key Metrics

 ### 1. Spectral Counts

-**Definition:** The number of MS2 spectra where a specific oxonium ion was detected above the intensity threshold. The 'intensity' refers to the relative abundance of the oxonium ion compared to the total abundance of all ions present in the MS2 spectrum.
+**Definition:** The number of MS2 spectra where a specific oxonium ion was detected above the intensity threshold.

-**Best Practice:**
-Start with the default parameters, which have been established based on a range of shotgun proteomics experiments using whole-cell lysate reference samples and serve as a good starting point for your analysis. Lowering these parameters may increase the number of random matches. However, the optimal value will depend on your specific proteomics experiment.
-- Counts > 20 (default): More than 20 MS2 spectra in which the oxonium ion was detected (current default setting)
-- Counts > 10: Currently set as the lower-end default value
-- Counts < 10: Fewer than 10 MS2 spectra in which the oxonium ion was detected
+**Guidelines:**
+- Counts > 20 (default): reliable detection in most experiments
+- Counts 10–20: moderate confidence, review alongside other metrics
+- Counts < 10: low confidence, may represent rare or weak signals

-**Significance:**
-- Higher counts generally indicate a more reliable detection
-- The strength of oxonium ion detection may depend on the performed experiment, its chemical nature and abundance in the proteome sample. Some sugars (e.g., deoxy sugars) typically yield lower counts
-
+Higher counts generally indicate more reliable detection, but some sugars may naturally yield lower counts depending on their abundance and fragmentation efficiency.

 ### 2. Spectral Intensity

-**Definition:** The normalized intensity of the oxonium ion relative to the total intensity of the MS2 spectrum, averaged across all detected spectra.
+**Definition:** The normalized intensity of the oxonium ion relative to total spectrum intensity, averaged across all detected spectra. Calculated as: (average intensity of the diagnostic mass pair / total spectrum intensity) × 100.

-**Best Practice:**
-- Currently, 0.2% is set as the default, established from the analysis of a set of shotgun proteomics reference samples
-- Spectral intensity as low as 0.05% was found to be valid for most shotgun proteomics reference samples
-- The typical range for oxonium ions was found to be between 0.2% and 6%. However, in some cases, lower values may also represent valid oxonium ion matches
+**Guidelines:**
+- Default threshold: 0.2%
+- Typical range for genuine oxonium ions: 0.2–6%
+- Values as low as 0.05% have been found valid in some reference samples

-**Significance:**
-- Higher intensities suggest more confident signals
-- Low intensities may indicate either weak oxonium ion detection or random background peaks
-
+Higher intensities suggest more confident signals. Low intensities may indicate weak detection or background noise. 

 ### 3. Spectral Presence

-**Definition:** The percentage of all MS2 spectra where the oxonium ion was detected above the intensity threshold.
+**Definition:** The percentage of all MS2 spectra (excluding SAGE-identified peptides) where the oxonium ion was detected above the intensity threshold.

-**Best Practice:**
-- Currently, a threshold of 0.02% is set as the default, established from the analysis of a set of shotgun proteomics reference samples
-- >1% for sugar oxonium ions is often associated with the presence of abundant protein glycosylation
-- <0.02% is commonly related to random background peak matches
+**Guidelines:**
+- Default threshold: 0.02%
+- Presence >1% often indicates abundant protein glycosylation
+- Presence <0.02% is commonly associated with random background matches

-**Significance:**
-- Allows comparison between datasets of different sizes
-
+This metric normalizes for dataset size, making it useful for comparing results across experiments with different numbers of spectra.

 ## Using Test Masses as Negative Controls

-The Oxonium Browser includes "Ox_test" masses, which are built-in random masses serving as negative controls. These test masses can be highly useful for determining appropriate thresholds for your experiments.
+The sugar database includes `Ox_test_` entries — random masses serving as built-in negative controls. These are essential for determining appropriate thresholds. Any detections of test masses represent random chance matches, providing a direct estimate of the false positive rate at the current settings.


-   - Test masses are deliberately generated within the mass range of sugar oxonium ions (100-400 Da), with restricted mass defects similar to actual sugar fragments 
-   - Designed to fall outside the peptide fragment ions mass range, as detected in an E. coli proteome reference dataset
-   - This set of "decoy" oxonium masses can be used to assess the number of false positive matches under given settings
+Test masses are generated within the sugar oxonium ion mass range (100–400 Da) with restricted mass defects similar to actual sugar fragments. For more details on how test masses are constructed, see [Sugar Database](https://sourceforge.net/p/oxoniumbrowserx/wiki/Sugar%20Database/).

+### Threshold Optimization Workflow

-### Step-by-Step Threshold Optimization
+**1. Start with default thresholds** (counts: 20, intensity: 0.2%, presence: 0.02%)

-**1. Start with Default Thresholds**

-   - Counts: 20
-   - Intensity: 0.2%
-   - Presence: 0.02%
+**2. Observe test mass behavior** — note the distribution of test masses relative to real hits.

-**2. Observe Test Mass Behavior**

-   - In the bubble chart, test masses appear as red dots
-   - Note their distribution across the metrics
-   - These usually cluster at lower values in the bubble plot
+**3. Adjust count threshold first** — increase until most test masses disappear from the match table.

-**3. Adjust Count Threshold First**

-   - This is typically the most effective filter for random matches
-   - Gradually increase the count threshold until most test masses disappear
+**4. Fine-tune with intensity** — if test masses remain, raise the intensity threshold.

-**4. Fine-Tune with Intensity Threshold**

-   - If test masses remain, increase the intensity threshold
-   - This further helps eliminate low-intensity random matches
+**5. Use presence for final refinement** — adjust last, useful for normalizing across dataset sizes.

-**5. Use Presence for Final Refinement**

-   - Adjust the presence threshold last
-   - This helps normalize for dataset size
+**6. Verify across visualizations:**
+- Check the match table for test masses appearing in sugar groups
+- Examine RT profiles — false positives often appear only at run boundaries
+- Look at co-occurrence — genuine sugars from the same glycan should cluster together

-**6. Verify with Multiple Visualizations**

-   - Check the heatmap to see separation between genuine hits and test masses
-   - Examine retention time profiles - many false positives are generated exclusively near the end of the run
-   - Look for logical patterns (e.g., sugars from the same glycan should show identicial distribution pattern across scans)
+## Interpreting the Match Table

+The match table groups oxonium ions into ±18 Da water loss families. A group containing an intact oxonium ion, its −H₂O fragment, and its −2H₂O fragment is strong evidence of a genuine sugar For a full description of the table, see [Dashboard Guide](https://sourceforge.net/p/oxoniumbrowserx/wiki/Dashboard%20Guide/).
+
+## Interpreting the Co-occurrence Plot
+
+The clustered co-occurrence heatmap shows which selected oxonium ions appear together in the same spectra. The dendrogram groups ions by Jaccard similarity of their scan profiles. For details on how the plot is constructed, see [Dashboard Guide](https://sourceforge.net/p/oxoniumbrowserx/wiki/Dashboard%20Guide/).
+
+Key patterns to look for:
+
+- **High co-occurrence** (dark cells) between ions suggests they originate from the same glycan structure 
+- **Low or zero co-occurrence** suggests ions come from different glycopeptides or different proteins
+- **Tight dendrogram clusters** indicate ions with very similar scan profiles — strong evidence they belong to the same glycan
+- **Test masses** should show minimal co-occurrence with real ions — if they co-occur strongly, thresholds may need adjustment

 ## Interpretation Strategies

-**1. Biological Context Matters**
+### Biological Context Matters

-- Consider your sample source when setting thresholds
-- Oxonium ions from mammalian samples typically show higher metrics
-- Bacterial sugars may require lower metric settings, but may still be genuine
+Consider your sample source when evaluating results. Oxonium ions from mammalian samples typically produce higher metrics across the board. Bacterial sugars may yield lower values but can still be genuine.

-**2. Related Sugar Series**
+### Related Sugar Series

-- Sugars related to the same glycan often exhibit identical scan distribution patterns
-- Check for co-occurrence in the same MS2 spectra (scans)
-- Isolated detection of a few or a single uncommon oxonium match may indicate a false positive result
-
+Sugars belonging to the same glycan often show similar scan distribution patterns. Use the retention time profiles to check whether candidate sugars co-elute, and the co-occurrence plot to confirm they appear in the same spectra. 

 ## Common Pitfalls

-**1. Setting Thresholds Too Low**

-   - May result in many false positives
-   - Test masses will appear alongside genuine hits
+**Setting thresholds too low:** Many false positives appear; test masses mix with genuine hits in the table.

-**2. Setting Thresholds Too High**

-   - You may overlook genuine oxonium matches
-   - Rare glycans, or those with weak signals will be overlooked first
+**Setting thresholds too high:** Genuine signals from rare glycans or low-abundance sugars are missed.

-**3. Ignoring Test Mass Performance**

-   - Test masses can guide to appropriate thresholds
-   - If they persist after threshold adjustment, be cautious about interpreting results
+**Ignoring test mass performance:** Test masses are the best guide to appropriate thresholds. If they persist after adjustment, interpret results with caution.

-[Back to Home](Home)
+**Ignoring retention time profiles:** False positives often cluster at the beginning or end of the chromatographic run. Always check RT profiles for suspicious patterns.
+
+[Back to Home](https://sourceforge.net/p/oxoniumbrowserx/wiki/Home/)

Detection metrics modified by Dinko Soic

Dinko Soic — Sun, 02 Mar 2025 20:48:33 -0000

--- v5
+++ v6
@@ -117,4 +117,4 @@
    - Test masses can guide to appropriate thresholds
    - If they persist after threshold adjustment, be cautious about interpreting results

-[Back to Overview](Home)
+[Back to Home](Home)

Detection metrics modified by Dinko Soic

Dinko Soic — Sun, 02 Mar 2025 20:43:15 -0000

--- v4
+++ v5
@@ -117,4 +117,4 @@
    - Test masses can guide to appropriate thresholds
    - If they persist after threshold adjustment, be cautious about interpreting results

-[[Home|Back to Overview]]
+[Back to Overview](Home)

Detection metrics modified by Dinko Soic

Dinko Soic — Sun, 02 Mar 2025 20:42:13 -0000

--- v3
+++ v4
@@ -1,4 +1,4 @@
-# Provided Oxonium Ion Detection Metrics
+# Oxonium Ion Detection Metrics

 ## Introduction to Provided Detection Metrics

@@ -116,3 +116,5 @@
 **3. Ignoring Test Mass Performance**
    - Test masses can guide to appropriate thresholds
    - If they persist after threshold adjustment, be cautious about interpreting results
+
+[[Home|Back to Overview]]

Detection metrics modified by Dinko Soic

Dinko Soic — Sun, 02 Mar 2025 20:39:44 -0000

--- v2
+++ v3
@@ -90,13 +90,13 @@

 ## Interpretation Strategies

-### 1. Biological Context Matters
+**1. Biological Context Matters**

 - Consider your sample source when setting thresholds
 - Oxonium ions from mammalian samples typically show higher metrics
 - Bacterial sugars may require lower metric settings, but may still be genuine

-### 2. Related Sugar Series
+**2. Related Sugar Series**

 - Sugars related to the same glycan often exhibit identical scan distribution patterns
 - Check for co-occurrence in the same MS2 spectra (scans)

Detection metrics modified by Dinko Soic

Dinko Soic — Sun, 02 Mar 2025 20:38:42 -0000

--- v1
+++ v2
@@ -1,4 +1,4 @@
-# Oxonium Ion Detection Metrics
+# Provided Oxonium Ion Detection Metrics

 ## Introduction to Provided Detection Metrics

@@ -60,29 +60,29 @@

 ### Step-by-Step Threshold Optimization

-1. **Start with Default Thresholds**
+**1. Start with Default Thresholds**
    - Counts: 20
    - Intensity: 0.2%
    - Presence: 0.02%

-2. **Observe Test Mass Behavior**
+**2. Observe Test Mass Behavior**
    - In the bubble chart, test masses appear as red dots
    - Note their distribution across the metrics
    - These usually cluster at lower values in the bubble plot

-3. **Adjust Count Threshold First**
+**3. Adjust Count Threshold First**
    - This is typically the most effective filter for random matches
    - Gradually increase the count threshold until most test masses disappear

-4. **Fine-Tune with Intensity Threshold**
+**4. Fine-Tune with Intensity Threshold**
    - If test masses remain, increase the intensity threshold
    - This further helps eliminate low-intensity random matches

-5. **Use Presence for Final Refinement**
+**5. Use Presence for Final Refinement**
    - Adjust the presence threshold last
    - This helps normalize for dataset size

-6. **Verify with Multiple Visualizations**
+**6. Verify with Multiple Visualizations**
    - Check the heatmap to see separation between genuine hits and test masses
    - Examine retention time profiles - many false positives are generated exclusively near the end of the run
    - Look for logical patterns (e.g., sugars from the same glycan should show identicial distribution pattern across scans)
@@ -105,14 +105,14 @@

 ## Common Pitfalls

-1. **Setting Thresholds Too Low**
+**1. Setting Thresholds Too Low**
    - May result in many false positives
    - Test masses will appear alongside genuine hits

-2. **Setting Thresholds Too High**
+**2. Setting Thresholds Too High**
    - You may overlook genuine oxonium matches
    - Rare glycans, or those with weak signals will be overlooked first

-3. **Ignoring Test Mass Performance**
+**3. Ignoring Test Mass Performance**
    - Test masses can guide to appropriate thresholds
    - If they persist after threshold adjustment, be cautious about interpreting results

Detection metrics modified by Dinko Soic

Dinko Soic — Sun, 02 Mar 2025 20:27:25 -0000

Oxonium Ion Detection Metrics

Introduction to Provided Detection Metrics

Oxonium Browser provides three primary metrics to assess the strength of a sugar oxonium ion match. Understanding these metrics and how to apply them is crucial for distinguishing genuine sugar signals from random mass peak matches.
However, keep in mind that Oxonium Browser only generates a list of oxonium ion candidates based on accurate mass, abundance values, and the occurrence of additional water or carboxylic acid loss peaks. The provided default parameters have been established based on a range of reference samples and serve as a good starting point for your analysis. Lowering them may increase the number of random matches.

Key Metrics Explained

1. Spectral Counts

Definition: The number of MS2 spectra where a specific oxonium ion was detected above the intensity threshold. The 'intensity' refers to the relative abundance of the oxonium ion compared to the total abundance of all ions present in the MS2 spectrum.

Best Practice:
Start with the default parameters, which have been established based on a range of shotgun proteomics experiments using whole-cell lysate reference samples and serve as a good starting point for your analysis. Lowering these parameters may increase the number of random matches. However, the optimal value will depend on your specific proteomics experiment.
- Counts > 20 (default): More than 20 MS2 spectra in which the oxonium ion was detected (current default setting)
- Counts > 10: Currently set as the lower-end default value
- Counts < 10: Fewer than 10 MS2 spectra in which the oxonium ion was detected

Significance:
- Higher counts generally indicate a more reliable detection
- The strength of oxonium ion detection may depend on the performed experiment, its chemical nature and abundance in the proteome sample. Some sugars (e.g., deoxy sugars) typically yield lower counts

2. Spectral Intensity

Definition: The normalized intensity of the oxonium ion relative to the total intensity of the MS2 spectrum, averaged across all detected spectra.

Best Practice:
- Currently, 0.2% is set as the default, established from the analysis of a set of shotgun proteomics reference samples
- Spectral intensity as low as 0.05% was found to be valid for most shotgun proteomics reference samples
- The typical range for oxonium ions was found to be between 0.2% and 6%. However, in some cases, lower values may also represent valid oxonium ion matches

Significance:
- Higher intensities suggest more confident signals
- Low intensities may indicate either weak oxonium ion detection or random background peaks

3. Spectral Presence

Definition: The percentage of all MS2 spectra where the oxonium ion was detected above the intensity threshold.

Best Practice:
- Currently, a threshold of 0.02% is set as the default, established from the analysis of a set of shotgun proteomics reference samples
- >1% for sugar oxonium ions is often associated with the presence of abundant protein glycosylation
- <0.02% is commonly related to random background peak matches

Significance:
- Allows comparison between datasets of different sizes

Using Test Masses as Negative Controls

The Oxonium Browser includes "Ox_test" masses, which are built-in random masses serving as negative controls. These test masses can be highly useful for determining appropriate thresholds for your experiments.

Test masses are deliberately generated within the mass range of sugar oxonium ions (100-400 Da), with restricted mass defects similar to actual sugar fragments
Designed to fall outside the peptide fragment ions mass range, as detected in an E. coli proteome reference dataset
This set of "decoy" oxonium masses can be used to assess the number of false positive matches under given settings

Step-by-Step Threshold Optimization

Start with Default Thresholds
Counts: 20
Intensity: 0.2%
Presence: 0.02%
Observe Test Mass Behavior
In the bubble chart, test masses appear as red dots
Note their distribution across the metrics
These usually cluster at lower values in the bubble plot
Adjust Count Threshold First
This is typically the most effective filter for random matches
Gradually increase the count threshold until most test masses disappear
Fine-Tune with Intensity Threshold
If test masses remain, increase the intensity threshold
This further helps eliminate low-intensity random matches
Use Presence for Final Refinement
Adjust the presence threshold last
This helps normalize for dataset size
Verify with Multiple Visualizations
Check the heatmap to see separation between genuine hits and test masses
Examine retention time profiles - many false positives are generated exclusively near the end of the run
Look for logical patterns (e.g., sugars from the same glycan should show identicial distribution pattern across scans)

Interpretation Strategies

1. Biological Context Matters

Consider your sample source when setting thresholds
Oxonium ions from mammalian samples typically show higher metrics
Bacterial sugars may require lower metric settings, but may still be genuine

Sugars related to the same glycan often exhibit identical scan distribution patterns
Check for co-occurrence in the same MS2 spectra (scans)
Isolated detection of a few or a single uncommon oxonium match may indicate a false positive result

Common Pitfalls

Setting Thresholds Too Low
May result in many false positives
Test masses will appear alongside genuine hits
Setting Thresholds Too High
You may overlook genuine oxonium matches
Rare glycans, or those with weak signals will be overlooked first
Ignoring Test Mass Performance
Test masses can guide to appropriate thresholds
If they persist after threshold adjustment, be cautious about interpreting results