reqs.md

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

Reqs for fitPoisBG:

• The user input object, a GeoMx S4 class
• The user input the group variable name for slide id of ROIs: groupvar
• The user input the size_scale from "sum", "first" for sizefact (default "sum")
• The user input iteration tolerance: tol (default 1e-3)
• The iteration number: iterations (default 10)
• The function outputs a GeoMx S4 class
• The function outputs sample size factors vector in phenoData: sizefact or size_fact_sp if if multiple slides exist
• The function outputs feature factor vector in featureData: featfact or featfeact_sp_XXX if multiple slides exist (XXX is the slide name)
• the group variable name for slide id of ROIs in experimentData: fitPoisBG_sp_var if multiple slides exist specifications: https://github.com/Nanostring-Biostats/GeoDiff/blob/main/specs.md#specs-for-fitpoisbg

Reqs for diagPoisBG:

• The user input object, a GeoMx S4 class
• The user input an indicator variable on whether using results from multiple slides: split
• The user input whether to adjust p value: padj (default TRUE)
• The user input adjust p value method: padj_method (default "BH")
• The user input p value: cutoff (default 1e-6)
• The user input whether to generate ppplot: generate_ppplot(default TRUE)
• The function generates ppplot if generate_ppplot=TRUE
• The function outputs a GeoMx S4 class
• matrix of lower tail probability in assayData slot called lowtail_prob : lowtail_prob
• matrix of upper tail probability in assayData slot called uptail_prob: uptail_prob
• the dispersion parameter in experimentData named disper or disper_sp if multiple is TRUE: disper or disper_sp
• matrix of outlier indicator (Yes: 1; No: 0) in assayData slot called low_outlier : low_outlier
• matrix of outlier indicator (Yes: 1; No: 0) in assayData slot called up_outlier : up_outlier specifications: https://github.com/Nanostring-Biostats/GeoDiff/blob/main/specs.md#specs-for-diagpoisbg

Reqs for QuanRange

• The user input a GeoMx S4 class
• The user input an indicator variable on whether using results from multiple slides: split
• The user input vector of probabilities: probs (default FALSE)
• The function outputs a GeoMx S4 class with column names same as probs:quanrange in phenoData specifications: https://github.com/Nanostring-Biostats/GeoDiff/blob/main/specs.md#specs-for-quanrange

Reqs for BGScoreTest:

• The user input a GeoMx S4 class
• The user input an indicator variable on whether using results from multiple slides: split
• The user input adjustment factor: adj (default 1)
• The user input whether to use outlier detection and remove outlier: removeoutlier (default FALSE)
• The user input whether to implement the score test with the assumption the feature factor follows gamma distribution: useprior (default FALSE)
• The function outputs a GeoMx S4 class if split is FALSE.
• vector of p values in featureData:pvalues
• vector of Background score test statistics in featureData: scores
• The function outputs a GeoMx S4 class if split is TRUE.
• matrix of p values with column dimension equals to slide number in featureData: pvalues_XXX
• matrix of Background score test statistics with column dimension equals to slide number in featureData: scores_XXX specifications: https://github.com/Nanostring-Biostats/GeoDiff/blob/main/specs.md#specs-for-bgscoretest

Reqs for fitNBth

• The user input a GeoMx S4 class
• The user input an indicator variable on whether using results from multiple slides: split
• The user input vector of high features names: features_high (If NULL, the default value is calculated within the function)
• The user input size factor for background: sizefact_BG (If NULL, the default value is calculated within the function)
• The user input starting size factor: sizefact_start(default=sizefactor_BG)
• The user input the size_scale from "sum", "first" for sizefact (default "sum")
• The user input starting threshold: threshold_start (If NULL, the default value is calculated within the function)
• The user input whether to fix threshold: threshold_fix(default=FALSE)
• The user input iteration tolerance: tol (default=1e-3)
• The user input iteration number: iterations (default=5)
• The user input start_para: starting values for parameter estimation (default=c(threshold_start, 1))
• The user input lower_sizefact: lower limit for sizefact (default=0)
• The user input the lower end of threshold: lower_threshold(default=threshold_start/5)
• The function outputs a GeoMx S4 class
• para0 in experimentData called para0 (=NA)
• para in featureData called para, matrix of estimated parameters, features in rows and parameters(signal, r) in columns.
• sizefact in phenoData called sizefact_fitNBth, estimated sizefact
• preci1 in experimentData call preci1 (=NA)
• conv0 in experimentData call conv0 (=NA)
• conv in experimentData call conv(=NA)
• Im in experimentData call Im(=NA)
• features_high in featureData called feature_high_fitNBth, same as the input features_high
• features_all in experimentData called features_all(=NA)
• threshold in experimentData called threshold specifications: https://github.com/Nanostring-Biostats/GeoDiff/blob/main/specs.md#specs-for-fitnbth

Reqs for fitPoisthNorm

• The user input a GeoMx S4 class
• The user input an indicator variable on whether using results from multiple slides: split
• The user input vector of high ROIs names: ROIs_high (If NULL, the default value is calculated within the function)
• The user input vector of high features names: features_high (If NULL, the default value is calculated within the function)
• The user input vector of all features names need to be fitted: features_all (If NULL, the default value is calculated within the function)
• The user input size factor for background: sizefact_BG (If NULL, the default value is calculated within the function)
• The user input starting size factor: sizefact_start (If NULL, the default value is calculated within the function)
• The user input threshold_mean: threshold_mean
• The user input precision matrix for threshold: preci2
• The user input iteration number: iterations=1 or 2, default=2
• The user input prior type for preci1: prior_type from "equal" or "contrast", default="contrast"
• The user input XXX: sizefactrec, default = TRUE
• The user input the size_scale from "sum", "first" for sizefact (default "sum")
• The user input XXX: sizescalebythreshold, default = TRUE
• The user input robust covariance: covrob, default=FALSE
• The user input constant term in specifying precision matrix 1: preci1con(default=1/25)
• The user input cutoff term in calculating precision matrix 1 (default=15)
• The user input factor for contrast in precision matrix 1:confac (default=1)
• The user input whether to calculate hessian: calhes (default=FALSE)
• When split is FALSE, the function outputs a GeoMx S4 class
• para0 in featureData called para0, matrix of estimated parameters for iter=1, features in columns and parameters(log2 expression, threshold) in rows.
• para in featureData called para, matrix of estimated parameters for iter=2, features in columns and parameters(log2 expression, threshold) in rows.
• normmat0 in assayData slot called normmat0, matrix of log2 expression for iter=1, features in columns and log2 expression in rows.
• normmat in assayData slot called normmat, matrix of log2 expression for iter=2, features in columns and log2 expression in rows.
• sizefact in phenoData called sizefact_norm, estimated sizefact
• sizefact0 in phenoData called sizefact0_norm, estimated sizefact in iter=1
• preci1 in featureData called preci1_norm, precision matrix 1
• conv0 in featureData called conv0, vector of convergence for iter=1, 0 converged, 1 not converged
• conv in featureData called conv, vector of convergence for iter=2, 0 converged, 1 not converged
• features_high in featureData called features_high , same as the input features_high
• features_all in featureData called features_all, same as the input features_all
• When split is TRUE, the function outputs a GeoMx S4 class
• threshold0 in featureData called threshold0, matrix of estimated threshold for iter=1, features in columns and threshold for different slides in rows.
• threshold in featureData called threshold, matrix of estimated threshold for iter=2, features in columns and threshold for different slides in rows.
• normmat0 in assayData slot called normmat0_sp, matrix of log2 expression for iter=1, features in columns and log2 expression in rows.
• normmat in assayData slot called normmat_sp, matrix of log2 expression for iter=2, features in columns and log2 expression in rows.
• sizefact in phenoData called sizefact_norm_sp, estimated sizefact
• sizefact0 in phenoData called sizefact0_norm_sp, estimated sizefact in iter=1
• preci1 in experimentData called preci1_norm_sp, precision matrix 1
• conv0 in featureData called conv0_sp_XX, vector of convergence for iter=1, 0 converged, 1 not converged, NA if features are not present.
• conv in featureData called conv_sp_XX, vector of convergence for iter=2, 0 converged, 1 not converged, NA if features are not present.
• features_high in featureData called features_high_sp, same as the input features_high
• features_all in featureData called features_all_sp, same as the input features_all specifications: https://github.com/Nanostring-Biostats/GeoDiff/blob/main/specs.md#specs-for-fitpoisthnorm

Reqs for aggreprobe

• The user input a GeoMx S4 class
• The user input an indicator variable on whether using results from multiple slides: split
• The user input the method to determine outliers from score, cor, and both
• The function outputs a GeoMx S4 class
• the collapsed expression matrix by target in the expression slot
• the collapsed feature data by target in the featureData slot
• probenum in featureData, numerical vector of probe numbers of targets
• proberemained in featureData, the list of remaining probes of targets specifications: https://github.com/Nanostring-Biostats/GeoDiff/blob/main/specs.md#specs-for-aggreprobe

Reqs for fitNBthDE

• The user input a GeoMx S4 class
• The user input form: model formula
• The user input an indicator variable on whether using results from multiple slides: split
• The user input features_high: features with high abudance to be run in iter=1
• The user input features_all: all features to be run in iter=2
• The user input sizefact_start: initial value for size factors
• The user input sizefact_BG: size factor for background
• The user input threshold_mean: average threshold level
• The user input preci2: precision for the background
• The user input lower_threshold: lower limit for the threshold
• The user input prior_type: empirical bayes prior type, choose from c("equal", "contrast")
• The user input sizefactrec, whether to recalculate sizefact, default=TRUE
• The user input size_scale: how to scale size factor if sizefactrec=TRUE
• The user input sizescalebythreshold: whether to scale the size factor by the threshold_mean in the modeling, default=TRUE
• The user input iterations: how many iterations need to run to get final results, default=2, the first iteration apply the model only on features_high and construct the prior then refit the model using this prior for all genes.
• The user input covrob: whether to use robust covariance in calculating covariance. default=FALSE
• The user input preci1con: constant for preci1
• The user input cutoff: cutoff for calculating the precision matrix for regression coefficients
• The user input confac: contrast factor in the precision matrix for regression coefficients
• The function outputs a list of following objects
• design matrix: X = X
• parameters estimated in iter 1: para0
• parameters estimated in iter 2: para
• size factor for signal: sizefact
• size factor for background: sizefact0,
• preci matrix for regression coefficients, preci1,
• Information matrix: Im0,
• Information matrix: Im,
• vector of whether model has converged in iter=1: conv0, 0=converged, 1=not converged
• vector whether model has converged in iter=2: conv, 0=converged, 1=not converged
• features with high abundance to be run in iter=1: features_high
• all features need to be run in iter=2: features_all specifications: https://github.com/Nanostring-Biostats/GeoDiff/blob/main/specs.md#specs-for-fitnbthde

Reqs for fitNBthmDE

• The user input a GeoMx S4 class
• The user input form: model formula
• The user input an indicator variable on whether using results from multiple slides: split
• The user input features_all: vector of features to be run.
• The user input sizefact size factor: size factor for signal
• The user input sizefact_BG: size factor for background
• The user input preci1: precision matrix for regression coefficients
• The user input threshold_mean: average background level
• The user input preci2 precision for the background
• The user input seed random number generator seed
• The user input sizescalebythreshold: whether to scale the size factor, default=TRUE
• The user input controlRandom: list of random effect control parameters
• The function outputs a list of following objects
• X, design matrix for fixed effect
• Z, design matrix for random effect
• rt, random effect terms
• para0, =NA
• para, estimated parameters, including regression coefficients, r and threshold in rows and features in columns
• sizefact, same as input sizefact
• sizefact0, NA
• preci1, input precision matrix for regression coefficients
• Im0, NA
• Im, Information matrix of parameters
• conv0, NA
• conv, vector of convergence, 0 converged, 1 not converged
• features_high, NA
• features_all, same as the input features_all
• theta, list of estimated random effect parameters(for relative covariance matrix)
• varcov, list of estimated variance covariance parameter estimation
• MAP random effect specifications: https://github.com/Nanostring-Biostats/GeoDiff/blob/main/specs.md#specs-for-fitnbthmde

Reqs for coefNBth

• The user input object: DE model, output by fitNBthDE or fitNBthmDE
• The user input fullpara: fullpara whether to generate results on all parameters
• The function outputs a list of following objects
• estimate, coefficients estimate
• wald_stat, Wald test statistics
• p_value, p value of Wald test
• se, standard error specifications: https://github.com/Nanostring-Biostats/GeoDiff/blob/main/specs.md#specs-for-coefnbth

Reqs for contrastNBth

• The user input object: DE model, output by fitNBthDE or fitNBthmDE
• The user input test: statistical test, choose from c("two-sided", ">", "<")
• The user input method: contrasts methods, only matrix of contrast vector is allowed for now, default=diag(1,ncol(object$X)), i.e. testing the regression coefficients
• The user input baseline: testing baseline, default=0.
• The function outputs a list of following objects
• estimate, contrasts estimate
• wald_stat, Wald test statistics
• p_value, p value of Wald test
• se, standard error specifications: https://github.com/Nanostring-Biostats/GeoDiff/blob/main/specs.md#specs-for-contrastnbth

Reqs for DENBth

• The user input object: inference list from coefNBth or contrastNBth
• The user input variable: variable contrasts need to construct on
• The user input NAto1 whether to replace NA in pvalue by 1
• The user input padj whether to adjust p value
• The user input padj_method p value adjustment method, default="BH"
• The function outputs a data frame with following columns
• log2FC, fold change in log scale
• pvalue, unadjusted p values
• adjp, adjusted p values specifications: https://github.com/Nanostring-Biostats/GeoDiff/blob/main/specs.md#specs-for-denbth

Nanostring-Biostats/GeoDiff documentation built on April 11, 2024, 5:31 a.m.