CircModel

Comprehensive comparison of differential expression models applied to circRNA count matrix.

Methods used in the article . . .

Here we present several aspects of the circRNA data analysis, evaluating:

• the Goodness Of Fit (GOF) between real data and the distributional assumptions of some differential expression detection methods;
• the ability of differential expression detection methods to control the Type I Error;
• the ability of differential expression detection methods in terms of Consistency;
• the power of differntial expression detection methods.

Data used in this analysis was retrieved from available GEO repository of Ribo-depleted RNA-seq samples of two or more different conditions (GSE136200, GSE86356, PRJNA484546, GSE52463).

Goodness of Fit (GOF) evaluation

The directory ./goodness_of_fit/ contains the GOF.Rmd file which loads circRNA data, estimates several parametric models on the real datasets and evaluates the goodness of fit for each dataset.

Robustness

The directory ./robustness/ contains:

• getPheno.R which create a file .txt containing B combination of samples for the creation of synthetic datasets;
• glm_glmm_paired.R and DEscripts.R which estimates the Negative Binomial and GLMM models for each synthetic datasets saving the results as .RData;
• SensitivityPrecision.Rnw which computes specificity, sensitivity and other measures considering p-values generated by each method in the simulations;
• plot_eval.R which puts the information from all datasets together and then plots the results.

parametric_sim

The directory ./parametric_sim/ contains:

• datasets_and_models.R which estimates the Negative Binomial and Zero-Inflated Negative Binomial parametric distributions to use as template for the simulations in the datasets;
• simulator_New.R which creates the simulation framework for both glm and glmm models evaluation;
• eval_function_call.R which tests the differential expression detection methods;
• evalPVals.R which computes specificity, sensitivity and other measures considering p-values generated by each method in the simulations;
• plot_eval.R which puts the information from all datasets together and then plots the results.

Consinstency

The directory ./consinstency/ contains:

• consistency_replicability.Rmd which loads DECs results from robustness evaluation and then tests the differential expression detection methods in terms of Concordance At the Top.

Type I Error Control

The directory ./type_I_error_control/ contains the TIEC.Rmd file which loads DECs results estimated using glm_glmm_paired.R and DEscripts.R for the evaluation of the ability of differential expression detection methods to control the type first error using mock datasets, without differentially abundant features, generated using getSampleShuffle.R script.

Data

Since the entire data production took a long time, the ./data/ directory contains several outputs from all the analyses. This should make it easier for the user to replicate the results.

Instructions and R environment

To replicate the analyses it is strongly suggested to clone or download the entire github directory. Some of the functions used this paper are adapted from the work of: Assessment of statistical methods from single cell, bulk RNA-seq and metagenomics applied to microbiome data., their original code is available at https://github.com/mcalgaro93/sc2meta. The analyses run in many version of R during the development, R 4.1.0 was the final R version on which the methods worked.

Here the sessionInfo():

R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.2 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8   
 [6] LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
[1] compiler_4.1.0    htmltools_0.5.1.1 tools_4.1.0       rmarkdown_2.8     knitr_1.33        xfun_0.23         digest_0.6.27    
[8] rlang_0.4.11      evaluate_0.14