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Comments (16)

ejfertig avatar ejfertig commented on July 22, 2024

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rpalaganas avatar rpalaganas commented on July 22, 2024

I did not filter genes with zero expression

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ejfertig avatar ejfertig commented on July 22, 2024

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rpalaganas avatar rpalaganas commented on July 22, 2024

Good morning,
Sorry for the delay. After removing zero variance and zero expression genes with

#drop zero expression genes 
row_sums <- rowSums(Hoxd10.mat)
zeroindx <- which(row_sums == 0)
Hoxd10mat_filtered <- Hoxd10.mat[-zeroindx,]

#drop zero variance genes 
row_var <- rowVars(Hoxd10mat_filtered)
zervarindx <- which(row_var == 0)
Hoxd10mat_filtered <- Hoxd10mat_filtered[-zervarindx,]

I reran CoGAPS with and without sparse optimization. Looks like it gave a similar result with -nan ChiSq

> Hoxd10_mat <- readRDS('~Hoxd10mat_filtered.RDS')

> params <- CogapsParams(nPatterns=5, nIterations=30000, seed=42, sparseOptimization=TRUE, distributed="genome-wide")
> params <- setDistributedParams(params, nSets=5)
setting distributed parameters - call this again if you change nPatterns

> Hoxd10_matnp5 <- CoGAPS(Hoxd10_mat, params)

This is CoGAPS version 3.19.1 
Running genome-wide CoGAPS on Hoxd10_mat (27277 genes and 380 samples) with parameters:

-- Standard Parameters --
nPatterns            5 
nIterations          30000 
seed                 42 
sparseOptimization   TRUE 
distributed          genome-wide 

-- Sparsity Parameters --
alpha          0.01 
maxGibbsMass   100 

-- Distributed CoGAPS Parameters -- 
nSets          5 
cut            5 
minNS          3 
maxNS          8 

Creating subsets...
set sizes (min, mean, max): (5455, 5455.4, 5457)
Running Across Subsets...

Data Model: Sparse, Normal
Sampler Type: Sequential
Loading Data...Done! (00:00:00)
    worker 1 is starting!
    worker 2 is starting!
    worker 4 is starting!
    worker 3 is starting!
    worker 5 is starting!
-- Equilibration Phase --
1000 of 30000, Atoms: 16620(A), 1167(P), ChiSq: -nan, Time: 00:00:52 / 01:28:04
...
30000 of 30000, Atoms: 25197(A), 1328(P), ChiSq: -nan, Time: 00:41:15 / 01:28:38
-- Sampling Phase --
1000 of 30000, Atoms: 25152(A), 1329(P), ChiSq: -nan, Time: 00:42:42 / 01:28:29
...
30000 of 30000, Atoms: 25114(A), 1344(P), ChiSq: -nan, Time: 01:24:45 / 01:24:45
    worker 1 is finished! Time: 01:24:45
    worker 2 is finished! Time: 01:24:49
    worker 3 is finished! Time: 01:25:26
    worker 5 is finished! Time: 01:28:35
    worker 4 is finished! Time: 01:30:20

Matching Patterns Across Subsets...
Running Final Stage...

Data Model: Sparse, Normal
Sampler Type: Sequential
Loading Data...Done! (00:00:00)
    worker 1 is starting!
    worker 2 is starting!
    worker 5 is starting!
    worker 3 is starting!
    worker 4 is starting!
-- Equilibration Phase --
1000 of 30000, Atoms: 12640(A), 0(P), ChiSq: -nan, Time: 00:00:34 / 00:57:35
...
30000 of 30000, Atoms: 19022(A), 0(P), ChiSq: -nan, Time: 00:25:15 / 00:54:15
-- Sampling Phase --
1000 of 30000, Atoms: 19126(A), 0(P), ChiSq: -nan, Time: 00:26:07 / 00:54:07
...
30000 of 30000, Atoms: 18941(A), 0(P), ChiSq: -nan, Time: 00:51:43 / 00:51:43
    worker 1 is finished! Time: 00:51:43
    worker 3 is finished! Time: 00:52:06
    worker 5 is finished! Time: 00:52:45
    worker 2 is finished! Time: 00:52:56
    worker 4 is finished! Time: 00:53:10
Warning message:
In checkInputs(data, uncertainty, allParams) :
  running distributed cogaps without mtx/tsv/csv/gct data

While running without sparse optimization looked normal.

> Hoxd10_mat <- readRDS('~Hoxd10mat_filtered.RDS')
> params <- CogapsParams(nPatterns=5, nIterations=30000, seed=42, distributed="genome-wide")
> params <- setDistributedParams(params, nSets=5)
setting distributed parameters - call this again if you change nPatterns

> Hoxd10_matnp5 <- CoGAPS(Hoxd10_mat, params)

This is CoGAPS version 3.19.1 
Running genome-wide CoGAPS on Hoxd10_mat (27277 genes and 380 samples) with parameters:

-- Standard Parameters --
nPatterns            5 
nIterations          30000 
seed                 42 
sparseOptimization   FALSE 
distributed          genome-wide 

-- Sparsity Parameters --
alpha          0.01 
maxGibbsMass   100 

-- Distributed CoGAPS Parameters -- 
nSets          5 
cut            5 
minNS          3 
maxNS          8 

Creating subsets...
set sizes (min, mean, max): (5455, 5455.4, 5457)
Running Across Subsets...

    worker 2 is starting!
Data Model: Dense, Normal
Sampler Type: Sequential
Loading Data...Done! (00:00:00)
    worker 1 is starting!
    worker 4 is starting!
    worker 3 is starting!
    worker 5 is starting!
-- Equilibration Phase --
1000 of 30000, Atoms: 5351(A), 1181(P), ChiSq: 6223970, Time: 00:01:14 / 02:05:20
...
30000 of 30000, Atoms: 12093(A), 2528(P), ChiSq: 5907389, Time: 00:55:27 / 01:59:09
-- Sampling Phase --
1000 of 30000, Atoms: 12133(A), 2524(P), ChiSq: 5907130, Time: 00:57:20 / 01:58:48
...
22000 of 30000, Atoms: 12115(A), 2522(P), ChiSq: 5906996, Time: 01:38:09 / 01:54:53
    worker 5 is finished! Time: 01:39:32
23000 of 30000, Atoms: 12027(A), 2495(P), ChiSq: 5907204, Time: 01:40:02 / 01:54:40
24000 of 30000, Atoms: 12127(A), 2530(P), ChiSq: 5906893, Time: 01:41:46 / 01:54:16
25000 of 30000, Atoms: 12067(A), 2549(P), ChiSq: 5907026, Time: 01:43:30 / 01:53:54
26000 of 30000, Atoms: 12093(A), 2520(P), ChiSq: 5907063, Time: 01:45:13 / 01:53:31
    worker 4 is finished! Time: 01:46:33
27000 of 30000, Atoms: 12146(A), 2533(P), ChiSq: 5907351, Time: 01:46:57 / 01:53:09
    worker 3 is finished! Time: 01:48:18
28000 of 30000, Atoms: 12094(A), 2504(P), ChiSq: 5906844, Time: 01:48:37 / 01:52:44
    worker 2 is finished! Time: 01:49:34
29000 of 30000, Atoms: 12145(A), 2491(P), ChiSq: 5907016, Time: 01:50:00 / 01:52:03
30000 of 30000, Atoms: 12155(A), 2493(P), ChiSq: 5907008, Time: 01:51:34 / 01:51:34
    worker 1 is finished! Time: 01:51:34

Matching Patterns Across Subsets...
Running Final Stage...

    worker 5 is starting!
    worker 3 is starting!
    worker 4 is starting!
    worker 2 is starting!
Data Model: Dense, Normal
Sampler Type: Sequential
Loading Data...Done! (00:00:00)
    worker 1 is starting!
-- Equilibration Phase --
1000 of 30000, Atoms: 6726(A), 0(P), ChiSq: 18248056, Time: 00:00:08 / 00:13:33
...
30000 of 30000, Atoms: 11486(A), 0(P), ChiSq: 18248056, Time: 00:07:05 / 00:15:13
-- Sampling Phase --
1000 of 30000, Atoms: 11367(A), 0(P), ChiSq: 18248056, Time: 00:07:20 / 00:15:11
...
30000 of 30000, Atoms: 11467(A), 0(P), ChiSq: 18248056, Time: 00:14:32 / 00:14:32
    worker 1 is finished! Time: 00:14:32
    worker 2 is finished! Time: 00:15:12
    worker 5 is finished! Time: 00:16:59
    worker 3 is finished! Time: 00:17:06
    worker 4 is finished! Time: 00:17:30
Warning message:
In checkInputs(data, uncertainty, allParams) :
  running distributed cogaps without mtx/tsv/csv/gct data

This time, each run generated the same number of patterns, however the values differed.

> range(sparseTRUE@featureLoadings)
[1] 0.000000 9.607491
> range(sparseFALSE@featureLoadings)
[1] 7.684777e-09 5.845305e+00

PatternMarkers with threshold = 'all' also did not work on the CoGAPS object generated with sparseOptimization = "TRUE". PatternMarkers worked on the object generated without sparse optimization.

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ejfertig avatar ejfertig commented on July 22, 2024

from cogaps.

dimalvovs avatar dimalvovs commented on July 22, 2024

Chisq is still not nan if we run on exact same dimensions and parameters

c <- 380
r <- 27277
simdata <- matrix(runif(r*c), nrow=r, ncol=c)
params <- CogapsParams(nPatterns=5, nIterations=30000, seed=42, distributed="genome-wide", sparseOptimization=TRUE)
params <- setDistributedParams(params, nSets=5)
res <- CoGAPS(simdata, params = params, outputFrequency = 1000)

This is CoGAPS version 3.22.0 
Running genome-wide CoGAPS on simdata (27277 genes and 380 samples) with parameters:

-- Standard Parameters --
nPatterns            5 
nIterations          30000 
seed                 42 
sparseOptimization   TRUE 
distributed          genome-wide 

-- Sparsity Parameters --
alpha          0.01 
maxGibbsMass   100 

-- Distributed CoGAPS Parameters -- 
nSets          5 
cut            5 
minNS          3 
maxNS          8 

Creating subsets...
set sizes (min, mean, max): (5455, 5455.4, 5457)
Running Across Subsets...

Data Model: Sparse, Normal
Sampler Type: Sequential
Loading Data...Done! (00:00:00)
    worker 1 is starting!
    worker 3 is starting!
-- Equilibration Phase --
    worker 4 is starting!
    worker 2 is starting!
    worker 5 is starting!
1000 of 30000, Atoms: 28296(A), 1016(P), ChiSq: 171672640, Time: 00:00:55 / 01:33:09
2000 of 30000, Atoms: 31014(A), 983(P), ChiSq: 171320192, Time: 00:02:02 / 01:32:27
3000 of 30000, Atoms: 32194(A), 934(P), ChiSq: 171210704, Time: 00:03:07 / 01:28:59
4000 of 30000, Atoms: 33225(A), 922(P), ChiSq: 171167024, Time: 00:04:12 / 01:26:24

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dimalvovs avatar dimalvovs commented on July 22, 2024

Making data 50% sparse still runs fine. @rpalaganas what's the sparsity of your data?

c <- 380
r <- 27277
dense <- runif(r*c)
sparse <- sample(c(dense, rep(0, length(dense))),
                size = length(dense), replace = T)
sum(sparse==0)/length(sparse)
simdata <- matrix(dense, nrow=r, ncol=c)
params <- CogapsParams(nPatterns = 5, nIterations = 30000, seed = 42, distributed = "genome-wide", 
                       sparseOptimization = TRUE)
params <- setDistributedParams(params, nSets=5)
res <- CoGAPS(simdata, params = params, outputFrequency = 1000)

This is CoGAPS version 3.22.0 
Running genome-wide CoGAPS on simdata (27277 genes and 380 samples) with parameters:

-- Standard Parameters --
nPatterns            5 
nIterations          30000 
seed                 42 
sparseOptimization   TRUE 
distributed          genome-wide 

-- Sparsity Parameters --
alpha          0.01 
maxGibbsMass   100 

-- Distributed CoGAPS Parameters -- 
nSets          5 
cut            5 
minNS          3 
maxNS          8 

Creating subsets...
set sizes (min, mean, max): (5455, 5455.4, 5457)
Running Across Subsets...

Data Model: Sparse, Normal
Sampler Type: Sequential
Loading Data...Done! (00:00:00)
    worker 1 is starting!
-- Equilibration Phase --
    worker 2 is starting!
    worker 4 is starting!
    worker 3 is starting!
    worker 5 is starting!
1000 of 30000, Atoms: 28519(A), 990(P), ChiSq: 171619760, Time: 00:00:56 / 01:34:51
2000 of 30000, Atoms: 30973(A), 967(P), ChiSq: 171261920, Time: 00:02:04 / 01:33:58

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rpalaganas avatar rpalaganas commented on July 22, 2024

The sparsity of the matrix that gave the -nans is 0.71.
coop::sparsity(Hoxd10) #0.7125972

I also do not get -nan ChiSq when testing a matrix that is almost exactly as sparse.

x <- matrix(0, 380, 27277)
x[sample(length(x), size = round(0.29 * length(x)))] <- 1
coop::sparsity(x)  #0.7099992

params <- CogapsParams(nPatterns = 5, nIterations = 30000, seed = 42, distributed = "genome-wide", 
                       sparseOptimization = TRUE)
params <- setDistributedParams(params, nSets=5)
res <- CoGAPS(t(x), params = params, outputFrequency = 1000)


This is CoGAPS version 3.21.5 
Running genome-wide CoGAPS on t(x) (27277 genes and 380 samples) with parameters:

-- Standard Parameters --
nPatterns            5 
nIterations          30000 
seed                 42 
sparseOptimization   TRUE 
distributed          genome-wide 

-- Sparsity Parameters --
alpha          0.01 
maxGibbsMass   100 

-- Distributed CoGAPS Parameters -- 
nSets          5 
cut            5 
minNS          3 
maxNS          8 

Creating subsets...
set sizes (min, mean, max): (5455, 5455.4, 5457)
Running Across Subsets...

Data Model: Sparse, Normal
Sampler Type: Sequential
Loading Data...    worker 5 is starting!
    worker 4 is starting!
    worker 2 is starting!
Done! (00:00:00)
    worker 1 is starting!
-- Equilibration Phase --
    worker 3 is starting!
1000 of 30000, Atoms: 10004(A), 1354(P), ChiSq: 42395544, Time: 00:00:15 / 00:25:24
2000 of 30000, Atoms: 12855(A), 1780(P), ChiSq: 42189580, Time: 00:00:38 / 00:28:47

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dimalvovs avatar dimalvovs commented on July 22, 2024

I thought that something is wrong in some genes' distributions, and used this fun to remove genes that would yield chisq nan in the results.

failRemoveRowsAll <- function(data){
  i <- 1
  j <- 6 #to keep genes > patterns 
  failnames <- c()
  params <- CogapsParams(nPatterns = 5, nIterations = 10, seed = 1,
                         sparseOptimization = TRUE)
  while (j <= nrow(data)) {
    res <- CoGAPS(data[c(i:j), ], params = params, outputFrequency = 10,
                  messages = FALSE)
    if (sum(is.na(res@metadata$chisq)) > 0) {
      failname <- rownames(data)[j]
      message(failname, ", at: ", j, " fails: ", length(failnames))
      failnames <- c(failnames, failname)
      data <- data[-c(j),]
    } else {
        j <- j + 1
    }
  }
  return(failnames)
}

afterwards, the chisq is not nan anymore, but the value itself is huge:

This is CoGAPS version 3.22.0 
Running Standard CoGAPS on hoxdata[!(rownames(hoxdata) %in% failed_by_all), ] (28470 genes and 380 samples) with parameters:

-- Standard Parameters --
nPatterns            5 
nIterations          10000 
seed                 1 
sparseOptimization   TRUE 

-- Sparsity Parameters --
alpha          0.01 
maxGibbsMass   100 

Data Model: Sparse, Normal
Sampler Type: Sequential
Loading Data...Done! (00:00:00)
-- Equilibration Phase --
1000 of 10000, Atoms: 108128(A), 625(P), ChiSq: 1839920873269298576727893606400, Time: 00:01:01 / 00:30:39
2000 of 10000, Atoms: 115884(A), 588(P), ChiSq: 2292072881336368004474865713152, Time: 00:02:15 / 00:30:21
3000 of 10000, Atoms: 120147(A), 593(P), ChiSq: 2783612750416574213534292901888, Time: 00:03:29 / 00:29:30
4000 of 10000, Atoms: 123364(A), 572(P), ChiSq: 2550784517891173166148455759872, Time: 00:04:44 / 00:28:53
5000 of 10000, Atoms: 126268(A), 568(P), ChiSq: 2541503896605446561631530123264, Time: 00:06:01 / 00:28:30
6000 of 10000, Atoms: 126646(A), 563(P), ChiSq: 2905690684144169274910709907456, Time: 00:07:20 / 00:28:16
7000 of 10000, Atoms: 126687(A), 555(P), ChiSq: 2674804590947979129294038237184, Time: 00:08:38 / 00:27:57
8000 of 10000, Atoms: 126574(A), 561(P), ChiSq: 3147559868411558326579587186688, Time: 00:09:56 / 00:27:41
9000 of 10000, Atoms: 126684(A), 553(P), ChiSq: 2698749482425631185700149788672, Time: 00:11:13 / 00:27:23
10000 of 10000, Atoms: 126485(A), 553(P), ChiSq: 2957405810624188978088997027840, Time: 00:12:30 / 00:27:06
-- Sampling Phase --
1000 of 10000, Atoms: 126720(A), 556(P), ChiSq: 2646788641772774809142103638016, Time: 00:13:47 / 00:26:51
2000 of 10000, Atoms: 126701(A), 559(P), ChiSq: 2966075017676645483900815015936, Time: 00:15:05 / 00:26:40
3000 of 10000, Atoms: 126748(A), 561(P), ChiSq: 2824885175666762749901468073984, Time: 00:16:23 / 00:26:29
4000 of 10000, Atoms: 126825(A), 548(P), ChiSq: 2910313314246920713217492647936, Time: 00:17:41 / 00:26:19
5000 of 10000, Atoms: 126857(A), 542(P), ChiSq: 3040585044948408827482774437888, Time: 00:18:58 / 00:26:08
6000 of 10000, Atoms: 126800(A), 551(P), ChiSq: 3114951210047638030192943824896, Time: 00:20:16 / 00:25:59
7000 of 10000, Atoms: 126686(A), 548(P), ChiSq: 2923385429134413698483590529024, Time: 00:21:33 / 00:25:49
8000 of 10000, Atoms: 126779(A), 559(P), ChiSq: 2855560157182209446923168907264, Time: 00:22:51 / 00:25:41
9000 of 10000, Atoms: 126407(A), 551(P), ChiSq: 2890611147933206197900012421120, Time: 00:24:09 / 00:25:34
10000 of 10000, Atoms: 126802(A), 552(P), ChiSq: 2753871361865324913892758913024, Time: 00:25:26 / 00:25:26

compared to results on the same data with sparseOptimization = FALSE:

-- Equilibration Phase --
10000 of 10000, Atoms: 57241(A), 3038(P), ChiSq: 26866870, Time: 00:29:54 / 01:04:51

sparse sampler cannot find a proper solution? btw is that normal for chisq to increase over iterations?

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jeanettejohnson avatar jeanettejohnson commented on July 22, 2024

hey all-- a few notes regarding this issue
@rpalaganas since the run is distributed, the P atoms being 0 in the second phase is correct, because at that point one matrix has been fixed and cogaps is learning the cognate (A) matrix

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jeanettejohnson avatar jeanettejohnson commented on July 22, 2024

Is this data more than 80% sparse?

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rpalaganas avatar rpalaganas commented on July 22, 2024

Is this data more than 80% sparse?

Slightly less, ~71% sparse

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dimalvovs avatar dimalvovs commented on July 22, 2024

so, we have technically addressed all the points addressed in the issue report:

  1. ChiSq value was -nan: these -nans appear as the actual value of ChiSq are too large, as can be confirmed here
  2. during the equilibration phase, the P matrix was 0: it is expected as in the distributed run one of the matrices is fixed (see comment above)
  3. SparseOptimization = TRUE gave 5 patterns while SparseOptimization = FALSE gave 6 patterns: number of patterns returned can differ from the number of patterns requested in the distributed mode, as the number of patterns is a superset of patterns matched across nSets, controlled by maxNS parameter. We may want to set the maxNS parameter to nPatterns by default to avoid this confusion.

The unsolved problem that is motivated by this issue is why the ChisQ is so large for a given dataset compared to a simulated dataset with similar dimensions and sparsity parameters, as demonstrated here.

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dimalvovs avatar dimalvovs commented on July 22, 2024

Interestingly the boostrapped version of the original data also fails

> resamp <- sample(hoxdata, size = length(hoxdata), replace = T)
> resamp <- matrix(resamp, ncol = 380)
> params <- CogapsParams(nPatterns=5, nIterations=30000, seed=42, sparseOptimization=TRUE, distributed="genome-wide")
> params <- setDistributedParams(params, nSets=5)
setting distributed parameters - call this again if you change nPatterns
> res <- CoGAPS(resamp, params)

This is CoGAPS version 3.23.1 
Running genome-wide CoGAPS on resamp (30407 genes and 380 samples) with parameters:

-- Standard Parameters --
nPatterns            5 
nIterations          30000 
seed                 42 
sparseOptimization   TRUE 
distributed          genome-wide 

-- Sparsity Parameters --
alpha          0.01 
maxGibbsMass   100 

-- Distributed CoGAPS Parameters -- 
nSets          5 
cut            5 
minNS          3 
maxNS          8 

Creating subsets...
set sizes (min, mean, max): (6081, 6081.4, 6083)
Running Across Subsets...

Data Model: Sparse, Normal
Sampler Type: Sequential
    worker 3 is starting!
    worker 4 is starting!
Loading Data...Done! (00:00:00)
    worker 1 is starting!
-- Equilibration Phase --
    worker 5 is starting!
    worker 2 is starting!
1000 of 30000, Atoms: 24149(A), 137(P), ChiSq: nan, Time: 00:00:15 / 00:25:24

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dimalvovs avatar dimalvovs commented on July 22, 2024

Also sparseOptimization=TRUE fails for non-distributed mode:

#sparse optimized and not distributed
params <- CogapsParams(nPatterns=5, nIterations=30000, seed=42, sparseOptimization=TRUE)
params <- setDistributedParams(params, nSets=5)
res <- CoGAPS(hoxdata, params)

This is CoGAPS version 3.22.0 
Running Standard CoGAPS on hoxdata (30407 genes and 380 samples) with parameters:

-- Standard Parameters --
nPatterns            5 
nIterations          30000 
seed                 42 
sparseOptimization   TRUE 

-- Sparsity Parameters --
alpha          0.01 
maxGibbsMass   100 

Data Model: Sparse, Normal
Sampler Type: Sequential
Loading Data...Done! (00:00:00)
-- Equilibration Phase --
1000 of 30000, Atoms: 86929(A), 369(P), ChiSq: nan, Time: 00:00:38 / 01:04:21
2000 of 30000, Atoms: 99716(A), 375(P), ChiSq: nan, Time: 00:01:29 / 01:07:26
3000 of 30000, Atoms: 103343(A), 386(P), ChiSq: nan, Time: 00:02:23 / 01:08:03

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dimalvovs avatar dimalvovs commented on July 22, 2024

Interestingly, sampling from a histogram does not fail:

#sample from histogram of data
hox_hist <- hist(hoxdata, breaks = 100, plot = FALSE)

hox_sim <- sample(hox_hist$mids, size = length(hoxdata),
  replace = T, prob = hox_hist$density)
hox_sim <- matrix(jitter(hox_sim), ncol = 380)

params <- CogapsParams(nPatterns=5, nIterations=30000, seed=42, sparseOptimization=TRUE)
res <- CoGAPS(hox_sim, params)

This is CoGAPS version 3.22.0 
Running Standard CoGAPS on hox_sim (30407 genes and 380 samples) with parameters:

-- Standard Parameters --
nPatterns            5 
nIterations          30000 
seed                 42 
sparseOptimization   TRUE 

-- Sparsity Parameters --
alpha          0.01 
maxGibbsMass   100 

Data Model: Sparse, Normal
Sampler Type: Sequential
Loading Data...Done! (00:00:00)
-- Equilibration Phase --
1000 of 30000, Atoms: 73945(A), 1873(P), ChiSq: 221560368, Time: 00:05:48 / 09:49:26
2000 of 30000, Atoms: 83256(A), 1960(P), ChiSq: 220883600, Time: 00:11:54 / 09:01:04
3000 of 30000, Atoms: 94329(A), 1951(P), ChiSq: 220712496, Time: 00:19:01 / 09:03:02
4000 of 30000, Atoms: 103541(A), 1927(P), ChiSq: 220577920, Time: 00:26:22 / 09:02:24
5000 of 30000, Atoms: 111937(A), 1922(P), ChiSq: 220456768, Time: 01:02:02 / 16:30:34
^C

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