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Abilities and Tactics
- Phase 1 Atlanta
- Phase 1 Atlanta Ga
- Phase 1 Research Studies
- Dmg Pca Phase 1 3
- Phase 1 Las Vegas
- Dmg Pca Phase 1 2017
- Dmg Pca Phase 1 2
Phase 1 – THE HARBINGER
Mind Tether (TANKS)
- Main tank and closest player get a mind tether.
- 50% dmg done to either player is reflected to their partner.
- If the 2 players are separated more than 12 yards from each other, the reflected dmg is increased.
Tactics: Mind Tether (TANKS) https://cricketbrown820.weebly.com/blog/dmg-foods.
- Both tanks stack on each other.
- Tank swap when needed.
Portal of Madness
Boss summons 3 Horrific Summoners.
They will channel and summon small adds (Horrific vision)that fixates players. Mac microsoft office 2011 product key keygen crack.
If the channel completes, the entire raid takes high dmg.
HEROIC MODE - The smalls adds will do more dmg and at the same time decrease in speed. They have 30 stacks that ticks down. When reaching 30 stacks they become Unleashed Nightmare which do 100% more dmg and a stack of Hysteria (shadow dmg every sec for 25 sec and stacks).
Tactics: Portal of Madness
- Nuke the Summoners first.
- Kill the small adds that spawned, try to group them up and AoE them down.
Crushing Grasp
- During entire fight, A big tentacle spawns from an eye, and hits the platform straight line, 2 times in a row.
- The outer circle lightens up (shines) for a short time as a warning that the tentacle will spawn.
- Getting hit does high dmg and a knockback.
Tactics: Crushing Grasp Xquartz for mac.
- The outer circle lightens up (shines) for a short time as a warning that the tentacle will spawn. Beware and dodge.
Dread
- 3 random players are feared in phase 1.
- The fear does dmg every sec for 10 sec.
- When fear expires, it does moderate dmg to entire raid.
- This can be dispelled.
Tactics: Dread
- Do NOT remove fear at the same time, then it does loads of dmg.
- Remove the fear on first player.
- Top up the health fast, then dispel the second player.
- Top up the health fast, then let the last fear just expire by itself.
Phase 2 – GRIP OF FEAR
Phase 2 starts when boss has 85% health.
Fear Realm
In this phase, we will have 2 different Realms,
The Normal Realm, and the Fear Realm.
Bos will pull random players to the fear realm.
When players are in the Fear Realm, they will gain a stack of Hysteria, every 20 sec. Hysteria – Shadow dmg every sec for 25 sec and stacks.
Manifest Nightmares
- A debuff on random players.
- This debuff does high dmg to players within 7 yards every sec for 6 sec.
- Each tick will also leave a pool on the ground.
- Then the debuff expires, a Horrific vision add spawns from the player.
Tactics Manifest Nightmares
- Make sure you run away from other players.
- Nuke the adds fast.
Mind Tether (TANKS)
- Main tank and closest player get a mind tether.
- 50% dmg done to either player is reflected to their partner.
- If the 2 players are separated more than 12 yards from each other, the reflected dmg is increased.
Tactics: Mind Tether (TANKS)
- Both tanks stack on each other.
- Tank swap when needed.
Phase 1 Atlanta
Dread
- 3 random players are feared in phase 1.
- The fear does dmg every sec for 10 sec.
- When fear expires, it does moderate dmg to entire raid.
- This can be dispelled.
Tactics: Dread
- Do NOT remove fear at the same time, then it does loads of dmg.
- Remove the fear on first player.
- Top up the health fast, then dispel the second player.
- Top up the health fast, then let the last fear just expire by itself.
Crushing Grasp
- During entire fight, A big tentacle spawns from an eye, and hits the platform straight line, 2 times in a row.
- The outer circle lightens up (shines) for a short time as a warning that the tentacle will spawn.
- Getting hit does high dmg and a knockback.
Tactics: Crushing Grasp
- The outer circle lightens up (shines) for a short time as a warning that the tentacle will spawn. Beware and dodge.
Maddening Eruption
Phase 1 Atlanta Ga
- Boss spawns tears (pools on the ground), with one pool at a time.
- If the boss is NOT moved to the pool, it will do very high dmg to all players.
- If the boss is moved to the pool, it will only do high dmg to enemies within 10 yards of the pool.
- When boss stands on the pool it will also increase dmg taken by 30% for 20 sec.
Tactics: Maddening Eruption
- The pool spawns on the outer side of the platform.
- When a new pool is spawned it always spawn on the opposite side of the platform.
- Tanks must move to the pool fast and make sure boss stands in it.
- Use speed abilities and give boosts to tanks to they move faster to the pools.
Phase 3 – DELIRIUM´S DESCENT
Phase 3 starts when boss reaches 70% health.
Delirium Realm
- During this phase, you will have 2 Realms, the Delirium Realm and the normal realm.
- Boss creates circles in a line on the ground next to him.
- Players that stands on the circles gets sent to the Delirium Realm.
- You will see the boss and adds in that realm.
- You will see other players that are in that realm.
- You will be hostile to everyone inside there.
- You will have 30% increased haste.
- You will get a stack of hysteria every 3 sec.
- When you die, you get send back to normal realm, and get stunned for 6 sec.
Tactics: Delirium Realm
- Try and stay alive as much as possible.
- Nuke
Mind Tether (TANKS)
- Main tank and closest player get a mind tether.
- 50% dmg done to either player is reflected to their partner.
- If the 2 players are separated more than 12 yards from each other, the reflected dmg is increased.
Tactics: Mind Tether (TANKS)
- Both tanks stack on each other.
- Tank swap when needed.
Dread
- 3 random players are feared in phase 1.
- The fear does dmg every sec for 10 sec.
- When fear expires, it does moderate dmg to entire raid.
- This can be dispelled.
Tactics: Dread
- Do NOT remove fear at the same time, then it does loads of dmg.
- Remove the fear on first player.
- Top up the health fast, then dispel the second player.
- Top up the health fast, then let the last fear just expire by itself.
Crushing Grasp
- During entire fight, A big tentacle spawns from an eye, and hits the platform straight line, 2 times in a row.
- The outer circle lightens up (shines) for a short time as a warning that the tentacle will spawn.
- Getting hit does high dmg and a knockback.
Tactics: Crushing Grasp
- The outer circle lightens up (shines) for a short time as a warning that the tentacle will spawn. Beware and dodge.
HEROIC MODE: Tentacle Slam
- A tentacle comes up from the ground and slams the ground.
- Players within the area take dmg and gets stunned for 2 sec.
Phase 4 – ALL PATHWAYS OPEN
This phase starts when boss reaches 60% health.
During this phase, you will have the Normal Realm, the Fear Realm and the Delirium Realm.
Reality Portal
The friendly NPC First Arcanist Thalyssra comes and opens a portal that takes you back to the normal realm during the entire fight.
Cmap download gratis italiano per mac. Mind Tether (TANKS)
- Main tank and closest player get a mind tether.
- 50% dmg done to either player is reflected to their partner.
- If the 2 players are separated more than 12 yards from each other, the reflected dmg is increased.
Tactics: Mind Tether (TANKS)
- Both tanks stack on each other.
- Tank swap when needed.
Crushing Grasp
- During entire fight, A big tentacle spawns from an eye, and hits the platform straight line, 2 times in a row.
- The outer circle lightens up (shines) for a short time as a warning that the tentacle will spawn.
- Getting hit does high dmg and a knockback.
Tactics: Crushing Grasp
- The outer circle lightens up (shines) for a short time as a warning that the tentacle will spawn. Beware and dodge.
Dark Pulse
- Boss puts a shield on himself that absorbs 5% of his max health.
- During this time, the boss gathers dark energy.
- When the energy is completed, all players in all realms take deadly dmg.
- If the absorb shield is broken before boss has gathered the dark energy, it triggers instead Mind Fracture, which stuns the boss for 1.5 sec and interrupt the cast.
Tactics: Dark Pulse
Use dps cooledowns and Heroism/Timewarp/Blood Lust and nuke the shield fast.
Manic Dread
- 3 random players are feared in phase 1.
- The fear does dmg every sec for 10 sec.
- When fear expires, it does high dmg to entire raid.
- The players location is also marked and will do very high dmg to anyone within 7 yards.
- The marked location will become a pool of Caustic Delirium, which does dmg every 1 sec and stacks to the player standing in the pool. After stacks, the player is transported to the Deliruim Realm and this removes the Caustic Delirium debuff.
Tactics: Manic Dread
- Do NOT remove fear at the same time, then it does loads of dmg.
- Remove the fear on first player.
- Top up the health fast, then dispel the second player.
- Top up the health fast, then let the last fear just expire by itself.
- Let dps stand on the pool so they enter the Delirium Realm. Once there, nuke boss and stay alive as long as possible.
Dark passage
- Boss opens a passage to the Fear Realm,
- In the Fear Realm, Boss summons 3 Horrific Summoners.
- These summoners don´t take dmg from players from other realms. Only from players inside the Fear Realm.
- The summoners channel a Void Eruption when its completed which does deadly dmg to players within 300 yards.
- During the channel, small adds (Horrific vision) that fixates players.
- HEROIC MODE - The smalls adds will do more dmg and at the same time decrease in speed. They have 30 stacks that ticks down. When reaching 30 stacks they become Unleashed Nightmare which do 100% more dmg and a stack of Hysteria (shadow dmg every sec for 25 sec and stacks).
Tactics: Dark passage
![Pca Pca](/uploads/1/3/3/9/133909767/242770935.gif)
- Dps must enter the Fear Portal and Nuke the Summoners fast.
- Kill the small adds that spawned, try to group them up and AoE them down.
When to Bloodlust/Timewarp/Heroism
When boss stands on the tear (the dark pool) it will also increase dmg taken by 30% for 20 sec.
The first time this happens, that´s when you do it.
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The first time this happens, that´s when you do it.
Other posts
Principal components analysis (PCA) is a mainstay of population genetics, providing a model-free method for exploring patterns of relatedness within a collection of individuals. PCA was introduced as a tool for genetic genetic analysis by Patterson, Price & Reich (2006). Subsequently Gil McVean (2009) provided an analytical framework for understanding PCA in terms of genetic ancestry. However, although PCA is widely used and the analytical details are worked out, there are a number of practical issues that come up when trying to run PCA on large SNP datasets from next-generation sequencing experiments. For example, small changes in how you prepare the input data can make a big difference to the outputs. The Ag1000G phase 1 data provide a concrete illustration of some of these issues, so I thought I’d try to bring together some experiences here.
Also, while PCA is fairly quick to run on smaller datasets, it can become slow and memory-intensive with larger data. A few months ago I discovered that scikit-learn includes a randomized SVD implementation, which is a potentially faster and more scalable method for approximating the top N components than using a conventional singular value decomposition. To evaluate randomized PCA I implemented some functions in scikit-allel which provide a convenience layer between underlying SVD implementations in NumPy and scikit-learn and the typical data structures I used to store genotype data. I know others have also started working with randomized PCA for genotype data (Galinsky et al. 2015) so I thought it would be interesting to apply both conventional and randomized SVD implementations to a non-human dataset and report some performance data.
Setup
I have a copy of the Ag1000G phase 1 AR3 data release on a local drive. The SNP genotype data is available in an HDF5 file.
Let’s work with chromosome arm 3L.
Phase 1 Research Studies
Setup the genotype data.
<GenotypeChunkedArray shape=(9643193, 765, 2) dtype=int8 chunks=(6553, 10, 2) nbytes=13.7G cbytes=548.0M cratio=25.7 compression=gzip compression_opts=3 values=h5py._hl.dataset.Dataset>
0 | 1 | 2 | 3 | 4 | . | 760 | 761 | 762 | 763 | 764 | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | . | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | |
1 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | . | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | |
2 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | . | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | |
. | . | |||||||||||
9643190 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | . | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | |
9643191 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | . | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | |
9643192 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | . | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
Count alleles at each variant. This takes a minute or so.
<AlleleCountsArray shape=(9643193, 4) dtype=int32>
0 | 1 | 2 | 3 | ||
---|---|---|---|---|---|
0 | 1527 | 3 | 0 | 0 | |
1 | 1529 | 1 | 0 | 0 | |
2 | 1528 | 2 | 0 | 0 | |
. | . | ||||
9643190 | 1512 | 16 | 0 | 0 | |
9643191 | 1527 | 1 | 0 | 0 | |
9643192 | 1507 | 18 | 1 | 0 |
Before going any further, I’m going to remove singletons and multiallelic SNPs. Singletons are not informative for PCA, and the analysis is simpler if we restrict to biallelic SNPs.
![Dmg Pca Phase 1 Dmg Pca Phase 1](https://www.dmgcorporate.com/wp-content/uploads/2018/02/flashlight-up-framed-between-poles.jpg)
For interest, how many multiallelic SNPs are there?
How many biallelic singletons?
Apply the filtering.
<GenotypeChunkedArray shape=(4825329, 765, 2) dtype=int8 chunks=(590, 765, 2) nbytes=6.9G cbytes=442.8M cratio=15.9 compression=blosc compression_opts={'shuffle': 1, 'cname': 'lz4', 'clevel': 5} values=zarr.core.Array>
0 | 1 | 2 | 3 | 4 | . | 760 | 761 | 762 | 763 | 764 | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | . | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | |
1 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | . | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | |
2 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | . | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | |
. | . | |||||||||||
4825326 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | . | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | |
4825327 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | . | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | |
4825328 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 | . | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
Finally, transform the genotype data into a 2-dimensional matrix where each cell has the number of non-reference alleles per call. This is what we’ll use as the input to PCA.
Note that we are still working with reasonably large amounts of data here, and so we are using chunked compressed arrays for storage. The
gn
variable above uses a Zarr array to store the data, one of several chunked storage containers that can be used with scikit-allel.
Removing correlated features (LD pruning)
As I understand it, PCA works best when the features you provide as input are independent from each other. Here each SNP is a feature, however, because DNA is transmitted from one generation to the next with some recombination between parents, genotypes at nearby SNPs tend to be correlated, with the correlation (linkage disequlibrium) decaying as you increase the separation between SNPs.
We can get a sense of that correlation structure by visualising pairwise linkage disequilibrium in the first 1000 SNPs.
The darker regions in the plot above indicate pairs of SNPs where genotypes are correlated.
Before I deal with this correlation directly, I’m going to thin down the data a bit. There are 4,825,329 SNPs left after the initial filtering steps above, and analysing this many features would be slow. Here we are more concerned with running an exploratory analysis, so I’m going to randomly choose a subset of these SNPs to work with. This should still reveal the main signals in the data, while making runtime faster.
By randomly downsampling SNPs, this should have dealt with much of the correlation between nearby features. Let’s take a look at the first 1000.
You can see that much of the correlation is gone. However, depending how dusty your screen is, you may be able to see some speckling, indicating that there are still some correlated SNPs in the dataset.
To remove this remaining correlation, I’m going to explicitly locate SNPs that are not correlated with each other, using the
locate_unlinked
function from scikit-allel. This is known as LD pruning, and works by sliding a window along the data, computing pairwise LD between all SNPs within each window, then removing one SNP from each correlated pair.
Conventionally, LD pruning is run just once, however I’m going to run several iterations. In some cases this may make a difference to the results, in others it may not, probably depending on how much long-range LD is present in your samples. Running multiple iterations does slow things down a bit, but it’s interesting to demonstrate and see what the effect is.
5 iterations is probably more than necessary for this dataset, as you can see not many SNPs are removed after the first few iterations.
I’ve used a sliding window size of 500 SNPs here, which is larger than others typically use. Out of interest, how many SNPs would be removed if we used a smaller window and just one iteration?
So with this dataset, using a larger window and multiple iterations finds and removes a lot more correlated SNPs. This is probably related to the fact that there are a lot of rare variants in the data, and so a larger window is required to find variants in linkage.
Let’s take a look at how much LD is left after LD pruning.
The data are relatively small now after downsampling and LD-pruning, so we can bring the data out of chunked storage and into memory uncompressed, which is necessary for PCA.
PCA via conventional SVD
Let’s run a conventional PCA analysis of the LD-pruned genotype data.
To help visualise the results, I need to pull in some metadata about which population each individual mosquito belongs to.
ox_code | src_code | sra_sample_accession | population | country | region | contributor | contact | year | m_s | sex | n_sequences | mean_coverage | latitude | longitude | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
index | |||||||||||||||
0 | AB0085-C | BF2-4 | ERS223996 | BFS | Burkina Faso | Pala | Austin Burt | Sam O'Loughlin | 2012 | S | F | 89905852 | 28.01 | 11.150 | -4.235 |
1 | AB0087-C | BF3-3 | ERS224013 | BFM | Burkina Faso | Bana | Austin Burt | Sam O'Loughlin | 2012 | M | F | 116706234 | 36.76 | 11.233 | -4.472 |
2 | AB0088-C | BF3-5 | ERS223991 | BFM | Burkina Faso | Bana | Austin Burt | Sam O'Loughlin | 2012 | M | F | 112090460 | 23.30 | 11.233 | -4.472 |
3 | AB0089-C | BF3-8 | ERS224031 | BFM | Burkina Faso | Bana | Austin Burt | Sam O'Loughlin | 2012 | M | F | 145350454 | 41.36 | 11.233 | -4.472 |
4 | AB0090-C | BF3-10 | ERS223936 | BFM | Burkina Faso | Bana | Austin Burt | Sam O'Loughlin | 2012 | M | F | 105012254 | 34.64 | 11.233 | -4.472 |
Looking at the left-hand plot of PC1 versus PC2, there is a clear separation of individuals into 6 different clusters. This indicates there are at least 6 genetically distinct populations represented by the mosquitoes we’ve sequenced. The plot of PC3 vs PC4 gives us additional evidence that certain populations (GAS and KES) are genetically distinct from each other and the rest, but doesn’t reveal any new clusters.
Effect of LD pruning
Thunderbird download for mac. What would happen if we ran PCA on the data without removing correlated SNPs?
Although all of the same population sub-divisions are visible in the first four components, they are resolved in a very different way. The first two components are now driven strongly by two populations, Angola (AOM) and Kenya (KES), and further population structure is not clearly resolved until PC3 and PC4.
It is interesting to note that the Kenyan and Angolan populations are the two populations with the lowest heterozygosity. In particular, almost all Kenyan samples have very long runs of homozygosity, suggesting a recent population crash. I would hazard a guess that, in particular for Kenya, there is long-range LD which is affecting the PCA. When we used the aggressively LD-pruned data in Figure 4 above, this effect is reduced.
Effect of scaling
Patterson et al. (2006) proposed scaling the data to unit variance at each SNP, assuming that the alleles are approximately binomially distributed. McVean (2009) remarks that scaling the data in this way should have little effect, although it will upweight rare variants (i.e., SNPs where the minor allele is at low frequency in the dataset). Let’s return to using the LD pruned data, and see what happens if we don’t use Patterson’s scaling method but instead just centre the data.
Here again the same clusters are visible but are resolved in a different way. Also, note more of the total variance is explained by the first four components than when using the Patterson scaler. As McVean (2009) suggests, I would guess that these effects are both due to the weighting of rare variants. When rare variants are upweighted, this resolves more clearly any subtle population structure in the data. However, there a lot of rare variants in this dataset, and so the total amount of variance explained by the first few components goes down.
Effect of unequal sample sizes
McVean (2009) provides a very elegant demonstration of what can happen if different populations are not equally represented in your dataset. If there are many more samples from one particular population, this has the effect of warping the principal components.
In Ag1000G phase one there are a lot more samples from Cameroon (CMS) than any of the other locations.
What would happen if we randomly pick a subset of CMS samples, to achieve a more even representation?
Now the results are similar to the original PCA we plotted in Figure 4, however PC2 appears to be more balanced. So sample size clearly does matter. However, there is a chicken-and-egg problem here. If you are using PCA to discover population structure in some collection of individuals, you won’t know a priori if any particular population is overrepresented. Perhaps in that situation, an initial round of PCA to discover population structure can be followed up with a second round, downsampling any populations within which you observe no differentiation.
Randomized PCA
Dmg Pca Phase 1 3
Randomized PCA is an alternative to conventional PCA. I don’t claim to understand the details, but apparently it uses an approximation to estimate the top principal components only, rather than evaluating all principal components as in a conventional SVD. So it should be faster and use less memory.
Let’s run a randomized PCA on the Ag1000G data and compare the results with the conventional PCA.
For the first four components at least, the results are indistinguishable from the conventional PCA.
Let’s compare performance.
What about memory usage?
So the randomized PCA is faster, scales better with more samples, and uses around half of the memory required by conventional SVD.
Conclusions
Phase 1 Las Vegas
- LD pruning makes a difference. For NGS data, a larger window size and/or multiple rounds of pruning may be required to deal with regions of long-range LD. LD pruning may also impact different populations in different ways, if populations have different levels of LD.
- Scaling input data to unit variance using the method of Patterson et al. (2006) makes a small but noticeable difference, increasing the ability to resolve distinct populations within higher principal components.
- Unequal sample sizes warps the principal components, as predicted by McVean (2009).
- Randomized PCA produces results that are almost indistinguishable from conventional PCA, while running faster and using less memory. However, preparing the data (LD pruning) can also take a long time, so it would be good to find a way to optimise that step too.
Further reading
- Patterson, N., Price, A. L., & Reich, D. (2006). Population structure and eigenanalysis. PLoS Genetics, 2(12), 2074–2093.
- McVean, G. (2009). A genealogical interpretation of principal components analysis. PLoS Genetics, 5(10), e1000686.
- Galinsky, K. J., Bhatia, G., Loh, P.-R., Georgiev, S., Mukherjee, S., Patterson, N. J., & Price, A. L. (2015). Fast principal components analysis reveals independent evolution of ADH1B gene in Europe and East Asia. bioRxiv.
Dmg Pca Phase 1 2017
Post-script: Randomized PCA and lower components
When I originally wrote this post, I only looked at the first four components. https://cricketbrown820.weebly.com/blog/rar-for-mac-dmg. These account for most of the variance and so capture the major signals of population subdivision.
For the first four components, conventional and randomized PCA are basically the same. However, recently I looked into the lower components, where there are some interesting signals of population structure, but less variance is captured. For these lower components, results from conventional and randomized PCA are not so similar.
Dmg Pca Phase 1 2
So an important caveat when using randomized PCA is that lower components may not be resolved very well, compared with conventional PCA.
Post-script: OpenBLAS
The original version of this post was run with NumPy built without any linear algebra optimisations. Download game ppsspp iso untuk ram 512. Following a comment from Andreas Noack I have re-run this notebook with NumPy built with OpenBLAS. This significantly speeds up the runtime for both conventional and randomized PCA. With the data size used in this notebook, the bottleneck is now more data preparation (LD pruning) than running the PCA itself.