fitPoisthNorm-methods: Poisson threshold model based normalization-log2...

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

Poisson threshold model based normalization-log2 transformation for single slide or for multiple slides

Description

Poisson threshold model based normalization-log2 transformation for single slide or for multiple slides

Usage

fitPoisthNorm(object, ...)

## S4 method for signature 'NanoStringGeoMxSet'
fitPoisthNorm(
  object,
  split = FALSE,
  ROIs_high = NULL,
  features_high = NULL,
  features_all = NULL,
  sizefact_start = NULL,
  sizefact_BG = NULL,
  threshold_mean = NULL,
  preci2 = 10000,
  iterations = 2,
  prior_type = c("contrast", "equal"),
  sizefactrec = TRUE,
  size_scale = c("sum", "first"),
  sizescalebythreshold = FALSE,
  covrob = FALSE,
  preci1con = 1/25,
  cutoff = 15,
  confac = 1,
  calhes = FALSE
)

## S4 method for signature 'matrix'
fitPoisthNorm(
  object,
  probenum = rep(1, NROW(object)),
  features_high,
  features_all,
  sizefact_start,
  sizefact_BG,
  threshold_mean,
  preci2 = 10000,
  iterations = 2,
  prior_type = c("contrast", "equal"),
  sizefactrec = TRUE,
  size_scale = c("sum", "first"),
  sizescalebythreshold = FALSE,
  covrob = FALSE,
  preci1con = 1/25,
  cutoff = 15,
  confac = 1,
  calhes = FALSE
)

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)
ROIs_high	ROIs with high expressions defined based on featfact and featfact
features_high	subset of features which are well above the background
features_all	full feature vector to apply the normalization on
sizefact_start	initial value for size factors
sizefact_BG	size factor for background
threshold_mean	average threshold level
preci2	precision for threshold, default=10000
iterations	iteration number, default=2, the first iteration using the features_high to construct the prior for parameters then refit the model on all features. precision matrix for threshold: preci2
prior_type	prior type for preci1, "equal" or "contrast", default="contrast"
sizefactrec	XXXX, default = TRUE
size_scale	method to scale the sizefact, sum(sizefact)=1 when size_scale="sum", sizefact[1]=1 when size_scale="first"
sizescalebythreshold	XXXX, default = FALSE
covrob	whether to use robust covariance in calculating the prior precision matrix 1, default = FALSE
preci1con	The user input constant term in specifying precision matrix 1, default=1/25
cutoff	term in calculating precision matrix 1, default=15
confac	The user input factor for contrast in precision matrix 1, default=1
calhes	The user input whether to calculate hessian: calhes, default=FALSE
probenum	a vector of numbers of probes in each gene

Value

if split is FALSE, a valid GeoMx S4 object including the following items

• para0_norm, matrix of estimated parameters for iter=1, features in columns and parameters(log2 expression, threshold) in rows, in featureData.

• para_norm, matrix of estimated parameters for iter=2, features in columns and parameters(log2 expression, threshold) in rows, in featureData.

• normmat0, matrix of log2 expression for iter=1, features in columns and log2 expression in rows, in assay slot.

• normmat, matrix of log2 expression for iter=2, features in columns and log2 expression in rows, in assay lot.

• sizefact_norm, estimated sizefact, in phenoData.

• sizefact0_norm, estimated sizefact in iter=1, in phenoData.

• preci1, precision matrix 1, in experimentData.

• conv0, vector of convergence for iter=1, 0 converged, 1 not converged, in featureData

• conv, vector of convergence for iter=2, 0 converged, 1 not converged, in featureData

• features_high, same as the input features_high, in featureData

• features_all, same as the input features_all, in featureData

if split is TRUE, a valid GeoMx S4 object with the following items appended.

• threshold0, matrix of estimated threshold for iter=1, features in columns and threshold for different slides in rows, in featureData.

• threshold, matrix of estimated threshold for iter=2, features in columns and threshold for different slides in rows, in featureData.

• normmat0_sp, matrix of log2 expression for iter=1, features in columns and log2 expression in rows, in assay slot.

• normmat_sp, matrix of log2 expression for iter=2, features in columns and log2 expression in rows, in assay slot.

• sizefact_norm_sp, estimated sizefact, in phenoData

• sizefact0_norm_sp, estimated sizefact in iter=1, in phenoData

• preci1, precision matrix 1, in experimentData

• conv0_sp_XX, vector of convergence for each unique slide value for iter=1, 0 converged, 1 not converged, in featureData for each unique slide.

• conv_sp_XX, vector of convergence for each unique slide value for iter=2, 0 converged, 1 not converged, in featureData for each unique slide.

• features_high_sp, same as the input features_high, in featureData.

• features_all_sp, same as the input features_all, in featureData.

a list of following items

• para0, matrix of estimated parameters for iter=1, features in columns and parameters(log2 expression, threshold) in rows.

• para, matrix of estimated parameters for iter=2, features in columns and parameters(log2 expression, threshold) in rows.

• normmat0, matrix of log2 expression for iter=1, features in columns and log2 expression in rows.

• normmat, matrix of log2 expression for iter=2, features in columns and log2 expression in rows.

• sizefact, estimated sizefact

• sizefact0, estimated sizefact in iter=1

• preci1, precision matrix 1

• Im0, Information matrix of parameters in iter=1

• Im, Information matrix of parameters in iter=2

• conv0, vector of convergence for iter=1, 0 converged, 1 not converged

• conv, vector of convergence for iter=2, 0 converged, 1 not converged

• features_high, same as the input features_high

• features_all, same as the input features_all

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)
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 = "contrast",
    covrob = FALSE,
    preci1con = 1 / 25
)

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