DeltaCC12: Difference between revisions
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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>. | 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/ | : <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>) 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. | 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. | ||
Latest revision as of 12:22, 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. The program XDSCC12 calculates ΔCC1/2 for the isomorphous and anomalous signal for XDS_ASCII.HKL and XSCALE.HKL files. Exact description of calculation and implementation are found at CC1/2.