RLSeq Report
SH-SY5Y shCTR
2022-07-07
Summary
This report was generated with RLSeq v1.0.3.
Sample information
Sample name: SH-SY5Y shCTR
Sample type: DRIP
Label: POS
Genome: hg38
Time: Thu Jul 7 20:26:41 2022
Results summary
| Result | Available |
|---|---|
| RLFS analysis | TRUE |
| Sample classification | TRUE |
| Noise analysis | TRUE |
| Feature enrichment test | TRUE |
| Transcript feature overlap | TRUE |
| Correlation analysis | TRUE |
| Gene annotations | TRUE |
| RL-Regions test | TRUE |
Results
1. RLFS analysis
Z-Score distribution
R-loop forming sequences (RLFS) were compared to the ranges in SH-SY5Y shCTR to measure enrichment. The resulting Z-score distribution is visualized below:
Note: for samples which map R-loop successfully, enrichment is expected. See representative examples for POS and NEG sample types here.
Details
Additional details
RLFS were derived across the genome using QmRLFS-finder.py. R-loop broad peaks
were called with macs and then compared with RLFS using
permTest from the regioneR R package. An empirical
distribution of RLFS was generated using the
circularRandomizeRegions method and compared to the peaks
in order to calculate enrichment p value and zscore (effect size of
enrichment). For additional detail, please refer to the
RLSeq::analyzeRLFS documentation (link).
From this analysis, the empirically-determined p value was 0.009901 (with 100 permutations, the minimum possible p value was 0.009901). The enrichment z-score was 40.3393.
2. Sample classification
Predicted label for sample SH-SY5Y shCTR is “POS” (i.e., robust R-loop mapping).
Details
Additional Details
To evaluate sample quality, a binary classifier was developed via the online-learning approach described in the RLSuite manuscript. The classifier evaluates features engineered from the RLFS Z score distribution, specifically, the following features:
| feature | description | raw_value | processed_value |
|---|---|---|---|
| Z1 | mean of Z | 1.2186307 | 37.2195411 |
| Z2 | variance of Z | 0.8306204 | 529.9093513 |
| Zacf1 | mean of Z ACF | -0.8338110 | 0.0292045 |
| Zacf2 | variance of Z ACF | -0.4505824 | 69.4893772 |
| ReW1 | mean of FT of Z (real part) | 1.7594982 | 32.1432798 |
| ReW2 | variance of FT of Z (real part) | 0.8288778 | 7512.6761798 |
| ImW1 | mean of FT of Z (imaginary part) | -0.1184944 | 0.0000000 |
| ImW2 | variance of FT of Z (imaginary part) | -1.3255137 | 35.8420653 |
| ReWacf1 | mean of FT of Z ACF (real part) | -0.5486679 | 11.7401943 |
| ReWacf2 | variance of FT of Z ACF (real part) | -0.3981260 | 835.4160391 |
| ImWacf1 | mean of FT of Z ACF (imaginary part) | -0.5221044 | 0.0000000 |
| ImWacf2 | variance of FT of Z ACF (imaginary part) | -0.5032904 | 522.1719304 |
From these features, classification was performed to derive a prediction (predicted label) regarding whether the sample mapped R-loops or not. In short, “POS” indicates any sample for which all the following are true:
- Criteria 1: The RLFS Permutation test P value is significant (p < .05)
- Criteria 2: The Z-score distribution middle is > 0.
- Criteria 3: The Z-score distribution middle is > the start and the end.
- Criteria 4: The model predicts a label of “POS”.
The criteria for SH-SY5Y shCTR are shown below:
| Criteria | Result |
|---|---|
|
TRUE |
|
TRUE |
|
TRUE |
|
TRUE |
These results led to the final prediction: “POS” (i.e., robust R-loop mapping).
For additional detail, please refer to the
RLSeq::predictCondition documentation (link).
3. Noise analysis
Fingerprint plot
To visualize the results of noiseAnalyze we can use a
“fingerprint plot” (named after the deepTools
implementation by the same name).
This plot shows the proportion of signal contained in the corresponding proportion of coverage bins. In the plot above, we can observe that relatively few bins contain nearly all the signal. This is exactly what we would expect to see when our sample has good signal-to-noise ratio, a sign of good quality in R-loop mapping datasets.
Noise comparison plot
While a fingerprint plot is useful for getting a quick view of the
dataset, it is also useful to compare the analyzed sample to
publicly-available the datasets provided by RLBase. The
noiseComparisonPlot enables this comparison.
4. Feature enrichment test
Enrichment plots
The results were then visualized with the
plotEnrichment() function:
CpG_Islands
Encode_CREs
G4Qpred
knownGene_RNAs
PolyA
Repeat_Masker
skewr
snoRNA_miRNA_scaRNA
Transcript_Features
tRNAs
Note: If < 200 peaks in user-supplied sample, ◇ will be missing from plots.
Summary table
Additional Details
Annotations were derived from a variety of sources and accessed using
RLHub (unless custom annotations were supplied by the user).
Detailed explanations of each database and type can be found here. The valr R package was implemented to test
the enrichment of these features within the supplied ranges for
SH-SY5Y shCTR. For additional detail, please refer to
the RLSeq::featureEnrich documentation (link).
5. Transcript feature overlap
Transcript feature overlap plot
The results were then visualized with the
plotTxFeatureOverlap() function:
6. Correlation analysis
Using the method described in Chedin et al. 2020, the inter-sample
correlations between SH-SY5Y shCTR and the samples in
RLBase were calculated. For additional detail, please refer to
the RLSeq::corrAnalyze documentation (link).
In the resulting heatmap, SH-SY5Y shCTR is
identified via the group annotation.
Note: In the plot legend (mode panel), misc includes the modes with < 12 samples: BisMapR, DREAM, DRIP-RNA-Seq, DRIPc-HBD, DRIVE, DRNA, m6A-DIP, RNH-CnR, RR-ChIP, S1-DRIP.
7. Gene annotations
hg38 Gene annotations were downloaded from AnnotationHub and overlapped with R-loop
ranges in SH-SY5Y shCTR. For additional detail, please
refer to the RLSeq::geneAnnotation documentation (link). The resulting gene table was then filtered
for the top 2000 peaks (by p-adjusted value) and is observed here:
8. RL-Regions test
RL-Regions are consensus R-loop sites derived from a meta-analysis of
all high-confidence R-loop mapping samples in RLBase (see the RLSuite
manuscript for a full description). The ranges supplied for
SH-SY5Y shCTR were compared to the RL-Regions to
determine the degree and significance of overlap. For additional detail,
please refer to the RLSeq::rlRegionTest documentation (link).
Other
For more information about RLSeq please visit the package homepage here.
Note: if you use RLSeq in published research, please reference:
Miller et al., RLSeq, (2021), GitHub repository, Bishop-Laboratory/RLSeq
Session info
Session info
## R version 4.2.0 (2022-04-22)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 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:
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## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices datasets utils methods base
##
## other attached packages:
## [1] RLHub_1.2.0 RLSeq_1.0.3 dplyr_1.0.9 magrittr_2.0.3
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.2 tidyselect_1.1.2
## [3] RSQLite_2.2.14 AnnotationDbi_1.58.0
## [5] htmlwidgets_1.5.4 grid_4.2.0
## [7] BiocParallel_1.30.3 pROC_1.18.0
## [9] aws.signature_0.6.0 munsell_0.5.0
## [11] codetools_0.2-18 DT_0.23
## [13] future_1.26.1 withr_2.5.0
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## [17] filelock_1.0.2 highr_0.9
## [19] knitr_1.39 rstudioapi_0.13
## [21] stats4_4.2.0 listenv_0.8.0
## [23] MatrixGenerics_1.8.1 labeling_0.4.2
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## [179] renv_0.15.5 future.apply_1.9.0RLSeq © 2022, Bishop Lab, UT Health San Antonio
RLSeq maintainer: Henry Miller