DeltaCC12: Difference between revisions

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ΔCC<sub>1/2</sub> is a quantity, that detects datasets/frames, that are non-isomorphous. As described in [https://scripts.iucr.org/cgi-bin/paper?zw5005 Assmann and Diederichs (2016)], ΔCC<sub>1/2</sub> is calculated with the σ-τ method. This method is a way to calculate the Pearson correlation coefficient for the special case of two sets of values (intensities) that randomly deviate from their true values, but is not influenced by a random number sequence as shown in [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3457925/ Karplus and Diederichs (2012)]. For the σ-τ method CC<sub>1/2</sub> is calculated for all datasets/frames, which will be called CC<sub>1/2_overall</sub> and CC<sub>1/2</sub> is calculated for all datasets/frames except for one dataset i, which is omitted from calculations and denoted as CC<sub>1/2_i</sub>. The difference of the two quantities is ΔCC<sub>1/2</sub>.
ΔCC<sub>1/2</sub> is a quantity that detects datasets/frames which are non-isomorphous. As described in [https://scripts.iucr.org/cgi-bin/paper?zw5005 Assmann and Diederichs (2016)], ΔCC<sub>1/2</sub> is calculated with the σ-τ method. This method is a way to calculate the Pearson correlation coefficient for the special case of two sets of values (intensities) that randomly deviate from their true values. The σ-τ method is not influenced by a random number sequence as shown in [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3457925/ Karplus and Diederichs (2012)]. For the σ-τ method CC<sub>1/2</sub> is calculated for all datasets/frames, which will be called CC<sub>1/2_overall</sub> and CC<sub>1/2</sub> is calculated for all datasets/frames except for one dataset i, which is omitted from calculations and denoted as CC<sub>1/2_i</sub>. The difference of the two quantities is ΔCC<sub>1/2</sub>.


: <math>\Delta CC_{1/2}= CC_{1/2 overall}-CC_{1/2 i} </math>
: <math>\Delta CC_{1/2}= CC_{1/2 overall}-CC_{1/2_i} </math>


If ΔCC<sub>1/2_</sub> is > 0 -CC<sub>1/2_overall</sub> is bigger than CC<sub>1/2_i</sub>- that means if omitting dataset i from calculations, a lower CC<sub>1/2</sub> results, which is why we want to keep it. Thus it is improving the whole merged dataset. If Δ CC<sub>1/2</sub> is < 0, -CC<sub>1/2_overall</sub> is smaller than CC<sub>1/2_i</sub> - that means that by omitting dataset i from calculations a higher CC<sub>1/2</sub> results, which is why we want to exclude it from calculations, because it is impairing the whole merged dataset.  
If ΔCC<sub>1/2</sub> is > 0 (CC<sub>1/2_overall</sub> is bigger than CC<sub>1/2_i</sub>) it means that by omitting dataset i from calculations a lower CC<sub>1/2</sub> results. As we want to maximize CC<sub>1/2</sub> the dataset is kept for calculations, it is improving the whole merged dataset. If Δ CC<sub>1/2</sub> is < 0 (CC<sub>1/2_overall</sub> is smaller than CC<sub>1/2_i</sub>) it means that by omitting dataset i from calculations a higher CC<sub>1/2</sub> results, which is why we want to exclude it from calculations, because it is impairing the whole merged dataset.  




== Applications ==
== Applications ==


The ΔCC<sub>1/2</sub>  method is applicable for single frames, SSX data and SFX data.
The ΔCC<sub>1/2</sub>  method is applicable for single frames, SSX data and SFX data. ΔCC<sub>1/2</sub> can be calculated by [[XDSCC12]].

Revision as of 09:38, 6 September 2018

ΔCC1/2 is a quantity that detects datasets/frames which are non-isomorphous. As described in Assmann and Diederichs (2016), ΔCC1/2 is calculated with the σ-τ method. This method is a way to calculate the Pearson correlation coefficient for the special case of two sets of values (intensities) that randomly deviate from their true values. The σ-τ method is not influenced by a random number sequence as shown in Karplus and Diederichs (2012). For the σ-τ method CC1/2 is calculated for all datasets/frames, which will be called CC1/2_overall and CC1/2 is calculated for all datasets/frames except for one dataset i, which is omitted from calculations and denoted as CC1/2_i. The difference of the two quantities is ΔCC1/2.

[math]\displaystyle{ \Delta CC_{1/2}= CC_{1/2 overall}-CC_{1/2_i} }[/math]

If ΔCC1/2 is > 0 (CC1/2_overall is bigger than CC1/2_i) it means that by omitting dataset i from calculations a lower CC1/2 results. As we want to maximize CC1/2 the dataset is kept for calculations, it is improving the whole merged dataset. If Δ CC1/2 is < 0 (CC1/2_overall is smaller than CC1/2_i) it means that by omitting dataset i from calculations a higher CC1/2 results, which is why we want to exclude it from calculations, because it is impairing the whole merged dataset.


Applications

The ΔCC1/2 method is applicable for single frames, SSX data and SFX data. ΔCC1/2 can be calculated by XDSCC12.