fitNBthDE error with prior_type="equal"

[maddygriz]

This error occurs when prior_type = "equal". It doesn't happen every time but I don't see a pattern for when it does.

Error in t(X) %*% preci1con : non-conformable arguments

The Traceback doesn't seem helpful but I'm adding it anyway. Traceback:

6. .local(object, ...)
7. fitNBthDE(form = form, annot = annot[ROIs_high, ], object = countmat[, ROIs_high], probenum = probenum, features_high = features_high, features_all = features_all, sizefact_start = sizefact_start, sizefact_BG = sizefact_BG, threshold_mean = threshold_mean, ...
8. fitNBthDE(form = form, annot = annot[ROIs_high, ], object = countmat[, ROIs_high], probenum = probenum, features_high = features_high, features_all = features_all, sizefact_start = sizefact_start, sizefact_BG = sizefact_BG, threshold_mean = threshold_mean, ...
9. .local(object, ...)
10. fitNBthDE(form = ~region, split = FALSE, object = kidney, preci2 = 10000, prior_type = "equal", preci1con = 1/25, sizescalebythreshold = TRUE)
11. fitNBthDE(form = ~region, split = FALSE, object = kidney, preci2 = 10000, prior_type = "equal", preci1con = 1/25, sizescalebythreshold = TRUE)

[maddygriz]

I am getting the same error with fitPoisthNorm()

[zhiiiyang]

fitNBthDE passed

> data(demoData)
> demoData <- demoData[, c(1:5, 33:37)]
> demoData <- fitPoisBG(demoData, size_scale = "sum")
Iteration = 1, squared error = 1.199470e+05
Iteration = 2, squared error = 0.000000e+00
Model converged.
> demoData <- aggreprobe(demoData, use = "cor")
> demoData <- BGScoreTest(demoData)
> demoData$slidename <- substr(demoData[["slide name"]], 12, 17)
> thmean <- 1 * mean(fData(demoData)$featfact, na.rm = TRUE)
> demo_pos <- demoData[which(!fData(demoData)$CodeClass == "Negative"), ]
> demo_neg <- demoData[which(fData(demoData)$CodeClass == "Negative"), ]
> sc1_scores <- fData(demo_pos)[, "scores"]
> names(sc1_scores) <- fData(demo_pos)[, "TargetName"]
> features_high <- ((sc1_scores > quantile(sc1_scores, probs = 0.4)) &
+                     (sc1_scores < quantile(sc1_scores, probs = 0.95))) |>
+   which() |>
+   names()
> set.seed(123)
> demoData <- fitNBth(demoData,
+                     features_high = features_high,
+                     sizefact_BG = demo_neg$sizefact,
+                     threshold_start = thmean,
+                     iterations = 5,
+                     start_para = c(200, 1),
+                     lower_sizefact = 0,
+                     lower_threshold = 100,
+                     tol = 1e-8)
Iteration = 1, squared error = 0.00211581666061465
Iteration = 2, squared error = 0.000635277560607171
Iteration = 3, squared error = 0.000136530888681976
Iteration = 4, squared error = 3.81046696630021e-05
Iteration = 5, squared error = 1.15557488250469e-05
Model converged.
> ROIs_high <- sampleNames(demoData)[which(demoData$sizefact_fitNBth * thmean > 2)]
> features_all <- rownames(demo_pos)
> 
> pData(demoData)$group <- c(rep(1, 5), rep(2, 5))
> 
> NBthDEmod1 <- fitNBthDE(
+   form = ~group,
+   split = FALSE,
+   object = demoData,
+   ROIs_high = ROIs_high,
+   features_high = features_high,
+   features_all = features_all,
+   sizefact_start = demoData[, ROIs_high][["sizefact_fitNBth"]],
+   sizefact_BG = demoData[, ROIs_high][["sizefact"]],
+   preci2 = 10000,
+   prior_type = "equal",
+   covrob = FALSE,
+   preci1con = 1/25,
+   sizescalebythreshold = TRUE
+ )
Iteration = 1, squared error = 4.081296e-05
Iteration = 2, squared error = 1.933278e-05

[zhiiiyang]

fitPoisthNorm passed

> library(Biobase)
> library(dplyr)
> data(demoData)
> demoData <- fitPoisBG(demoData, size_scale = "sum")
Iteration = 1, squared error = 9.471314e+06
Iteration = 2, squared error = 0.000000e+00
Model converged.
> demoData <- aggreprobe(demoData, use = "cor")
> demoData <- BGScoreTest(demoData)
> thmean <- 1 * mean(fData(demoData)$featfact, na.rm = TRUE)
> demo_pos <- demoData[which(!fData(demoData)$CodeClass == "Negative"), ]
> demo_neg <- demoData[which(fData(demoData)$CodeClass == "Negative"), ]
> sc1_scores <- fData(demo_pos)[, "scores"]
> names(sc1_scores) <- fData(demo_pos)[, "TargetName"]
> features_high <- ((sc1_scores > quantile(sc1_scores, probs = 0.4)) &
+                     (sc1_scores < quantile(sc1_scores, probs = 0.95))) |>
+   which() |>
+   names()
> set.seed(123)
> features_high <- sample(features_high, 100)
> demoData <- fitNBth(demoData,
+                     features_high = features_high,
+                     sizefact_BG = demo_neg$sizefact,
+                     threshold_start = thmean,
+                     iterations = 5,
+                     start_para = c(200, 1),
+                     lower_sizefact = 0,
+                     lower_threshold = 100,
+                     tol = 1e-8)
Iteration = 1, squared error = 0.00127298898031945
Iteration = 2, squared error = 3.85529407814789e-05
Iteration = 3, squared error = 2.75554613662818e-06
Iteration = 4, squared error = 3.04023597732423e-07
Iteration = 5, squared error = 6.68035435765034e-08
Model converged.
> ROIs_high <- sampleNames(demoData)[which((quantile(fData(demoData)[["para"]][, 1],
+                                                    probs = 0.90, na.rm = TRUE) -
+                                             notes(demoData)[["threshold"]]) * demoData$sizefact_fitNBth > 2)]
> features_all <- rownames(demo_pos)
> thmean <- mean(fData(demo_neg)[["featfact"]])
> demoData <- fitPoisthNorm(
+   object = demoData,
+   split = FALSE,
+   ROIs_high = ROIs_high,
+   features_high = features_high,
+   features_all = features_all,
+   sizefact_start = demoData[, ROIs_high][["sizefact_fitNBth"]],
+   sizefact_BG = demoData[, ROIs_high][["sizefact"]],
+   threshold_mean = thmean,
+   preci2 = 10000,
+   prior_type = "equal",
+   covrob = FALSE,
+   preci1con = 1 / 25
+ )
probe finished
Iteration = 1, squared error = 5.48724768110968e-05
probe finished
Iteration = 2, squared error = 5.77221071476496e-05
Model converged.