Visualizing Fraction Proportions
Fraction proportions across replicates
The plot shows: - The proportion of RNA in each fraction - The “Lost” fraction (grey) representing unrecoverable material - Consistency across replicates
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FracFixR is a compositional statistical framework for analyzing RNA-seq data from fractionation experiments. It addresses the fundamental challenge where library preparation and sequencing depth obscure the original proportions of RNA fractions.
This vignette demonstrates:
Let’s simulate a simple polysome profiling experiment:
# Simulate count data for 500 genes across 12 samples
n_genes <- 100
n_samples <- 12
# Generate count matrix with varying expression levels
total_counts <- matrix(
rnbinom(n_genes * 4, mu = 100, size = 10),
nrow = n_genes,
dimnames = list(
paste0("Gene", 1:n_genes),
paste0("Sample", 1:4)
)
)
prob=c(1/4,1/4,1/2)
# distribute counts evenly accross different fractions in Control
multinom_samples <- apply(total_counts, c(1, 2), function(x) rmultinom(1, size = x, prob = prob))
reshaped <- aperm(multinom_samples, c(3, 2, 1)) # now (n, 4, 3)
reshaped_matrix1 <- matrix(reshaped, nrow = n_genes, ncol = 12)
colnames(reshaped_matrix1) <- paste0("V", rep(1:4, each = 3), "_p", 1:3)
counts<-cbind(total_counts[,1:2],reshaped_matrix1[,c(2:3,5:6)],total_counts[,3:4],reshaped_matrix1[,c(8:9,11:12)])
# Create annotation data frame
annotation <- data.frame(
Sample = colnames(counts),
Condition = rep(c("Control", "Treatment"), each = 6),
Type = rep(c("Total", "Total", "Light_Polysome", "Heavy_Polysome","Light_Polysome", "Heavy_Polysome"), 2),
Replicate = c("Rep1","Rep2", rep("Rep1", 2), rep("Rep2", 2), "Rep1","Rep2", rep("Rep1", 2), rep("Rep2", 2))
)
print(head(annotation))
#> Sample Condition Type Replicate
#> 1 Sample1 Control Total Rep1
#> 2 Sample2 Control Total Rep2
#> 3 V1_p2 Control Light_Polysome Rep1
#> 4 V1_p3 Control Heavy_Polysome Rep1
#> 5 V2_p2 Control Light_Polysome Rep2
#> 6 V2_p3 Control Heavy_Polysome Rep2# Run the main analysis
results <- FracFixR(MatrixCounts = counts, Annotation = annotation)
#> Setting up parallel processing...
#> Filtering transcripts based on Total samples...
#> Retained 100 transcripts present in Total samples
#>
#> Processing condition 1/2: Control
#> Selecting transcripts with 70-96% quantiles (range: 214.3 - 296.1)
#> Selected 26 transcripts for regression
#> Processing replicate 1/2: Rep1
#> Processing replicate 2/2: Rep2
#>
#> Processing condition 2/2: Treatment
#> Selecting transcripts with 70-96% quantiles (range: 217.3 - 280.0)
#> Selected 26 transcripts for regression
#> Processing replicate 1/2: Rep1
#> Processing replicate 2/2: Rep2
#>
#> Finalizing results...
#> FracFixR analysis complete!
# View the structure of results
names(results)
#> [1] "OriginalData" "Annotation" "Propestimates" "Coefficients"
#> [5] "Fractions" "plots"The FracFixR output contains several components:
# 1. Fraction proportions for each replicate
print(results$Fractions)
#> Light_Polysome Heavy_Polysome Lost Replicate
#> 1 0.08560270 0.00123615 0.9131612 Control_Rep1
#> 2 0.04790483 0.06823600 0.8838592 Control_Rep2
#> 3 0.03838147 0.05640819 0.9052103 Treatment_Rep1
#> 4 0.08793944 0.10989656 0.8021640 Treatment_Rep2
# 2. Regression coefficients
print(results$Coefficients)
#> Lost Light_Polysome Heavy_Polysome Replicate
#> 1 226.1183 0.6616193 0.009933183 Control_Rep1
#> 2 219.9686 0.3981329 0.547867870 Control_Rep2
#> 3 223.3712 0.3011167 0.234843211 Treatment_Rep1
#> 4 201.5765 0.3582424 0.424722648 Treatment_Rep2
# 3. Estimated Proportions (first 5 genes, first 6 samples)
print(results$Propestimates[1:5, 1:6])
#> Sample1 Sample2 V1_p2 V1_p3 V2_p2 V2_p3
#> Gene1 253 250 0.17673392 0.0021771360 0.05453876 0.15010079
#> Gene2 237 236 0.05319552 0.0006988169 0.04201403 0.03579036
#> Gene3 249 238 0.07548677 0.0007666551 0.02538431 0.03309269
#> Gene4 243 248 0.10875934 0.0031976685 0.05999263 0.12758567
#> Gene5 241 237 0.06175113 0.0011478345 0.02123376 0.05113433The plot shows: - The proportion of RNA in each fraction - The “Lost” fraction (grey) representing unrecoverable material - Consistency across replicates
Now let’s identify genes with differential polysome association between conditions:
# Test for differential proportion in heavy polysomes
diff_heavy <- DiffPropTest(
NormObject = results,
Conditions = c("Control", "Treatment"),
Types = "Heavy_Polysome",
Test = "Logit"
)
#> Performing Logit test comparing Conditions Control and Treatment in the Heavy_Polysome fraction
#> Applying FDR correction...
#> Analysis complete. Found 14 transcripts with padj < 0.01
# View top differentially associated genes
top_genes <- diff_heavy[order(diff_heavy$padj), ]
print(head(top_genes, 10))
#> transcript mean_success_cond1 mean_success_cond2 mean_diff log2FC
#> 72 Gene72 0.02617524 0.1390912 0.11291599 2.828417
#> 82 Gene82 0.04533465 0.1535843 0.10824963 2.352886
#> 50 Gene50 0.06573491 0.1267624 0.06102752 2.633829
#> 6 Gene6 0.02014019 0.1492147 0.12907447 2.603087
#> 11 Gene11 0.01350989 0.1079133 0.09440345 3.669058
#> 29 Gene29 0.02035831 0.1017006 0.08134232 2.841302
#> 33 Gene33 0.01628909 0.1063028 0.09001371 3.070389
#> 35 Gene35 0.02986100 0.1431196 0.11325857 2.242306
#> 40 Gene40 0.03166690 0.1083089 0.07664199 2.710986
#> 46 Gene46 0.02526712 0.1347122 0.10944504 2.256062
#> pval padj
#> 72 3.099748e-05 0.001549874
#> 82 1.675630e-05 0.001549874
#> 50 5.916326e-05 0.001972109
#> 6 8.567553e-05 0.002141888
#> 11 7.595966e-04 0.006124418
#> 29 6.455671e-04 0.006124418
#> 33 7.840268e-04 0.006124418
#> 35 7.961743e-04 0.006124418
#> 40 6.586108e-04 0.006124418
#> 46 7.268264e-04 0.006124418Positive mean_diff indicates higher proportion in Treatment.
volcano <- PlotComparison(
diff_heavy,
Conditions = c("Control", "Treatment"),
Types = "Heavy_Polysome",
cutoff = 20
)
print(volcano)
For real experiments, ensure your data meets these requirements:
sessionInfo()
#> R version 4.4.2 (2024-10-31)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.3 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.12.0
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
#>
#> locale:
#> [1] LC_CTYPE=fr_FR.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=fr_FR.UTF-8 LC_COLLATE=C
#> [5] LC_MONETARY=fr_FR.UTF-8 LC_MESSAGES=fr_FR.UTF-8
#> [7] LC_PAPER=fr_FR.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: Australia/Sydney
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] FracFixR_1.0.0
#>
#> loaded via a namespace (and not attached):
#> [1] future.apply_1.20.0 gtable_0.3.6 jsonlite_2.0.0
#> [4] dplyr_1.1.4 compiler_4.4.2 tidyselect_1.2.1
#> [7] parallel_4.4.2 tidyr_1.3.1 jquerylib_0.1.4
#> [10] globals_0.18.0 scales_1.4.0 yaml_2.3.10
#> [13] fastmap_1.2.0 ggplot2_3.5.2 R6_2.6.1
#> [16] labeling_0.4.3 generics_0.1.4 knitr_1.50
#> [19] future_1.67.0 tibble_3.3.0 bslib_0.9.0
#> [22] pillar_1.11.0 RColorBrewer_1.1-3 rlang_1.1.6
#> [25] cachem_1.1.0 xfun_0.52 nnls_1.6
#> [28] sass_0.4.10 aod_1.3.3 cli_3.6.5
#> [31] withr_3.0.2 magrittr_2.0.3 digest_0.6.37
#> [34] grid_4.4.2 lifecycle_1.0.4 vctrs_0.6.5
#> [37] evaluate_1.0.4 glue_1.8.0 farver_2.1.2
#> [40] listenv_0.9.1 codetools_0.2-20 parallelly_1.45.1
#> [43] purrr_1.1.0 rmarkdown_2.29 matrixStats_1.5.0
#> [46] tools_4.4.2 pkgconfig_2.0.3 htmltools_0.5.8.1Cleynen A, Ravindran A, Shirokikh N (2025). FracFixR: A compositional statistical framework for absolute proportion estimation between fractions in RNA sequencing data.
For more examples and advanced usage, see the FracFixR GitHub repository.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.