fitNBth-methods: Negative Binomial threshold model

In Nanostring-Biostats/GeoDiff: Count model based differential expression and normalization on GeoMx RNA data

Negative Binomial threshold model

Description

Estimate the signal size factor for features above the background

Estimate the signal size factor for features above the background

Usage

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
)

Arguments

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

Value

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

Examples

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)

Nanostring-Biostats/GeoDiff documentation built on April 24, 2022, 5:54 p.m.