fitNBth | R Documentation |
Estimate the signal size factor for features above the background
Estimate the signal size factor for features above the background
fitNBth(object, ...) ## S4 method for signature 'NanoStringGeoMxSet' fitNBth( object, split = TRUE, features_high = NULL, sizefact_BG = NULL, sizefact_start = sizefact_BG, size_scale = c("sum", "first"), threshold_start = NULL, threshold_fix = FALSE, tol = 1e-07, iterations = 8, start_para = c(threshold_start, 0.5), lower_sizefact = 0, lower_threshold = threshold_start/5 ) ## S4 method for signature 'matrix' fitNBth( object, features_high, probenum, sizefact_BG, sizefact_start = sizefact_BG, size_scale = c("sum", "first"), threshold_start, threshold_fix = FALSE, tol = 1e-07, iterations = 8, start_para = c(threshold_start, 1), lower_sizefact = 0, lower_threshold = threshold_start/5 )
object |
count matrix with features in rows and samples in columns |
... |
additional argument list that might be used |
split |
indicator variable on whether it is for multiple slides (Yes, TRUE; No, FALSE) |
features_high |
subset of features which are well above the background |
sizefact_BG |
size factors for the background |
sizefact_start |
initial value for size factors |
size_scale |
method to scale the sizefact, sum(sizefact)=1 when size_scale="sum", sizefact[1]=1 when size_scale="first" |
threshold_start |
initial value for threshold |
threshold_fix |
whether to fix the threshold, default=FALSE |
tol |
tolerance to determine convergence, default=1e-3 |
iterations |
maximum iterations to be run, default=5 |
start_para |
starting values for parameter estimation, default=c(threshold_start, 1) |
lower_sizefact |
lower limit for sizefact, default=0 |
lower_threshold |
lower limit for threshold |
probenum |
a vector of numbers of probes in each gene |
a valid GeoMx S4 object
para0 = "NA", in experimentData
para, estimated parameters, "signal" "r" in rows and features in columns, in featureData
sizefact, estimated size factor, in phenoData
preci1 = "NA", in experimentData
conv0 = "NA", in experimentData
conv = "NA", in experimentData
Im = "NA", in experimentData
features_high, a vector of indicators, in featureData (0: No; 1: Yes; NA: not included in features_high)
features_all = "NA", in experimentData
threshold, estimated threshold, when threshold_fix, equals to threshold_start, in experimentData
a list of following items, some items are place holders = NA
para0 = NA,
para, estimated parameters, "signal" "r" in rows and features in columns
sizefact, estimated size factor
preci1 = NA
conv0 = NA
conv = NA
Im = NA
features_high = features_high
features_all = NA
threshold, estimated threshold, when threshold_fix, equals to threshold_start
library(Biobase) library(dplyr) data(demoData) demoData <- fitPoisBG(demoData, size_scale = "sum") 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)
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