Package 'FracFixR'

DiffPropTest: Statistical Testing for Differential Proportions

Description

Performs statistical testing to identify transcripts with significantly different proportions between conditions in specified fraction(s). Implements three test options: GLM (most powerful), Logit, and Wald.

Usage

DiffPropTest(NormObject, Conditions, Types, Test = c("GLM", "Logit", "Wald"))

Arguments

NormObject	Output from FracFixR() function
Conditions	Character vector of exactly 2 conditions to compare
Types	Character vector of fraction type(s) to analyze. Can be single fraction or multiple (will be combined)
Test	Statistical test to use: "GLM", "Logit", or "Wald"

Details

• GLM: Uses binomial generalized linear model (most statistically powerful)

• Logit: Faster alternative using logit transformation

• Wald: Beta-binomial Wald test for overdispersed count data

Value

Data frame with columns:

• transcript: transcript identifier

• mean_success_cond1/2: mean proportions in each condition

• mean_diff: difference in proportions

• log2FC: log2 fold change

• pval: p-value from statistical test

• padj: FDR-adjusted p-value

Examples

data(example_counts)
data(example_annotation)

# Run FracFixR
results <- FracFixR(example_counts, example_annotation, parallel=FALSE)
# Run differential testing
diff_results <- DiffPropTest(results,
                            Conditions = c("Control", "Treatment"),
                            Types = "Heavy_Polysome",
                            Test = "GLM")

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Example annotation data frame

Description

A data frame containing sample annotations for the example_counts matrix. Describes the experimental design with conditions, fraction types, and replicates.

Usage

example_annotation

Format

A data frame with 12 rows and 4 columns:

Sample

Sample identifier matching column names in example_counts

Condition

Experimental condition (Control or Treatment)

Type

Fraction type (Total, Light_Polysome, or Heavy_Polysome)

Replicate

Replicate identifier (Rep1 or Rep2)

Source

Simulated data generated for package examples

Examples

data(example_annotation)
head(example_annotation)
table(example_annotation$Condition, example_annotation$Type)

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Example RNA-seq count matrix

Description

A matrix containing simulated RNA-seq counts for 100 genes across 12 samples. The data simulates a polysome profiling experiment with two conditions (Control and Treatment) and three fractions (Total, Light_Polysome, Heavy_Polysome).

Usage

example_counts

Format

A numeric matrix with 100 rows (genes) and 12 columns (samples):

rows

Gene identifiers (Gene1 to Gene100)

columns

Sample identifiers (Sample1 to Sample12)

Source

Simulated data generated for package examples

Examples

data(example_counts)
dim(example_counts)
head(example_counts[, 1:6])

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FracFixR: Main Function for Fraction Correction

Description

This is the core function that implements the FracFixR framework. It takes raw count data from total and fractionated samples and reconstructs the original fraction proportions through compositional modeling.

Usage

FracFixR(MatrixCounts, Annotation, st1 = 0.6, st2 = 0.999, parallel = TRUE)

Arguments

MatrixCounts	A numeric matrix of raw transcript/gene counts with: Rows: transcripts/genes (must have rownames) Columns: samples (must have colnames matching Annotation$Sample)
Annotation	A data.frame with required columns: Sample: sample identifiers matching column names in MatrixCounts Condition: experimental condition (e.g., "Control", "Treatment") Type: fraction type (must include at least one "Total") Replicate: replicate identifier
parallel	A boolean indicating whether to use parallel processing of the transcripts (default=TRUE).
st1	Lower quantile threshold (between 0 and 1) for selecting informative transcripts for the NNLS regression fit (default = 0.6). Transcripts below this quantile of Total abundance are considered too noisy for reliable regression.
st2	Upper quantile threshold (between 0 and 1) for selecting informative transcripts for the NNLS regression fit (default = 0.999). Transcripts above this quantile are potential outliers and are excluded from the regression.

Details

The function works by:

1. Filtering transcripts based on presence in Total samples

2. For each condition and replicate, fitting NNLS regression

3. Estimating global fraction weights and individual transcript proportions

4. Calculating the "lost" unrecoverable fraction

Value

A list containing:

• OriginalData: filtered input count matrix

• Annotation: input annotation data

• Propestimates: matrix of proportion estimates (values between 0 and 1)

• NewData: corrected count matrix (proportions multiplied by predicted total, rounded)

• Coefficients: data.frame of regression coefficients

• Fractions: data.frame of estimated fraction proportions

• plots: list of diagnostic plots

References

Cleynen et al. FracFixR: A compositional statistical framework for absolute proportion estimation between fractions in RNA sequencing data.

Examples

# Load example data
data(example_counts)
data(example_annotation)

# Run FracFixR
results <- FracFixR(example_counts, example_annotation, parallel=FALSE)

# View fraction proportions
print(results$Fractions)

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get_corrected_counts: Retrieve the Corrected Count Matrix

Description

Returns the corrected count matrix embedded in a FracFixR() result object. This matrix is computed internally by multiplying each transcript's proportion estimate by the predicted total abundance for that replicate, providing counts that are corrected for compositional bias while remaining on the original count scale.

If you need to re-scale using the raw (observed) Total counts instead of the NNLS-predicted totals, multiply fracfixr_results$Propestimates by the corresponding column of fracfixr_results$OriginalData manually.

Usage

get_corrected_counts(fracfixr_results)

Arguments

fracfixr_results

Output list from FracFixR, which must contain the element NewData (present in results from version >= 1.1.0).

Value

A numeric matrix with the same dimensions as fracfixr_results$Propestimates. Non-Total columns contain corrected counts (rounded integers, proportion estimate multiplied by the NNLS-predicted total abundance). The Total column contains the NNLS-predicted total abundance itself.

Examples

data(example_counts)
data(example_annotation)

results <- FracFixR(example_counts, example_annotation, parallel = FALSE)
corrected <- get_corrected_counts(results)
head(corrected)

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PlotComparison: Create Volcano Plot for Differential Results

Description

Generates avolcano plot showing transcripts with significant differential proportions between conditions.

Usage

PlotComparison(DiffPropResult, Conditions = NULL, Types = NULL, cutoff = NULL)

Arguments

DiffPropResult	Output from DiffPropTest() function
Conditions	Character vector of conditions being compared
Types	Character vector of fraction types analyzed
cutoff	Optional y-axis maximum for plot

Value

Volcano plot-type object

Examples

data(example_counts)
data(example_annotation)

# Run FracFixR
results <- FracFixR(example_counts, example_annotation,parallel=FALSE)
# Run differential testing
diff_results <- DiffPropTest(results,
                            Conditions = c("Control", "Treatment"),
                            Types = "Heavy_Polysome",
                            Test = "GLM")
# Create volcano plot
volcano <- PlotComparison(diff_results, 
                         Conditions = c("Control", "Treatment"),
                         Types = "Heavy_Polysome")

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PlotFractions: Visualize Fraction Proportions

Description

Creates a stacked bar plot showing the distribution of RNA across fractions for each replicate, including the "lost" fraction.

Usage

PlotFractions(FracFixed)

Arguments

FracFixed

Output from FracFixR() function

Value

ggplot2 object showing fraction proportions

Examples

data(example_counts)
data(example_annotation)

# Run FracFixR
results <- FracFixR(example_counts, example_annotation, parallel=FALSE)
# Create fraction plot
frac_plot <- PlotFractions(results)
# Save plot with ggsave("fractions.pdf", frac_plot, width = 10, height = 8)

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Polysome profiling annotation example

Description

An alternative annotation data frame for polysome profiling experiments with monosome and polysome fractions.

Usage

polysome_annotation

Format

A data frame with 12 rows and 4 columns:

Sample

Sample identifier

Condition

Experimental condition (Control or Stress)

Type

Fraction type (Total, Monosome, or Polysome)

Replicate

Replicate identifier (Rep1 or Rep2)

Source

Simulated data generated for package examples

Examples

data(polysome_annotation)
head(polysome_annotation)

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Subcellular fractionation annotation example

Description

An annotation data frame for subcellular fractionation experiments with nuclear and cytoplasmic fractions.

Usage

subcellular_annotation

Format

A data frame with 12 rows and 4 columns:

Sample

Sample identifier

Condition

Experimental condition (WT or Mutant)

Type

Fraction type (Total, Nuclear, or Cytoplasmic)

Replicate

Replicate identifier (Rep1 or Rep2)

Source

Simulated data generated for package examples

Examples

data(subcellular_annotation)
head(subcellular_annotation)

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