A TCGA dataset application
1. Introduction
This vignette is about the integration of gene and miRNA pairs and their expression dataset and analysis. The sample dataset in this demonstration, which contains human miRNA:target pairs, was retrieved from miRTarBase website (Release 7.0).
2. Installation
3. Integration of dataset which includes only miRNA and gene expression values
3.1. miRNA:target pairs
data("mirtarbasegene")
head(mirtarbasegene)
#> # A tibble: 6 × 2
#> miRNA Target
#> <chr> <chr>
#> 1 hsa-miR-20a-5p HIF1A
#> 2 hsa-miR-146a-5p CXCR4
#> 3 hsa-miR-122-5p CYP7A1
#> 4 hsa-miR-222-3p STAT5A
#> 5 hsa-miR-21-5p RASGRP1
#> 6 hsa-miR-148a-3p DNMT1NOTE if the mirna:target dataset includes miRNA
genes as targets, the priming_graph() function can fail.
Because, the function define to miRNAs and targets without
distinguishing between uppercase or lowercase.
3.2. Gene expression in normal and tumor samples
The gene and mirna expression counts of patient barcoded with
TCGA-E9-A1N5 is retrieved from TCGA via TCGAbiolinks
package (Colaprico et al. 2015) from
Bioconductor. The instructions of retrieving data can be
found at TCGAbiolinks manual.
For this step you don’t have to use TCGA data, any other source or package can be utilized.
data("TCGA_E9_A1N5_normal")
head(TCGA_E9_A1N5_normal)
#> # A tibble: 6 × 7
#> patient sample barcode definition ensembl_gene_id external_gene_name
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 TCGA-E9-A1N5 TCGA-E9-A1… TCGA-E… Solid Tis… ENSG00000000003 TSPAN6
#> 2 TCGA-E9-A1N5 TCGA-E9-A1… TCGA-E… Solid Tis… ENSG00000000005 TNMD
#> 3 TCGA-E9-A1N5 TCGA-E9-A1… TCGA-E… Solid Tis… ENSG00000000419 DPM1
#> 4 TCGA-E9-A1N5 TCGA-E9-A1… TCGA-E… Solid Tis… ENSG00000000457 SCYL3
#> 5 TCGA-E9-A1N5 TCGA-E9-A1… TCGA-E… Solid Tis… ENSG00000000460 C1orf112
#> 6 TCGA-E9-A1N5 TCGA-E9-A1… TCGA-E… Solid Tis… ENSG00000000938 FGR
#> # ℹ 1 more variable: gene_expression <dbl>data("TCGA_E9_A1N5_tumor")
head(TCGA_E9_A1N5_tumor)
#> # A tibble: 6 × 7
#> patient sample barcode definition ensembl_gene_id external_gene_name
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 TCGA-E9-A1N5 TCGA-E9-A1… TCGA-E… Primary s… ENSG00000000003 TSPAN6
#> 2 TCGA-E9-A1N5 TCGA-E9-A1… TCGA-E… Primary s… ENSG00000000005 TNMD
#> 3 TCGA-E9-A1N5 TCGA-E9-A1… TCGA-E… Primary s… ENSG00000000419 DPM1
#> 4 TCGA-E9-A1N5 TCGA-E9-A1… TCGA-E… Primary s… ENSG00000000457 SCYL3
#> 5 TCGA-E9-A1N5 TCGA-E9-A1… TCGA-E… Primary s… ENSG00000000460 C1orf112
#> 6 TCGA-E9-A1N5 TCGA-E9-A1… TCGA-E… Primary s… ENSG00000000938 FGR
#> # ℹ 1 more variable: gene_expression <dbl>3.3. miRNA expression data
data("TCGA_E9_A1N5_mirnatumor")
head(TCGA_E9_A1N5_mirnatumor)
#> # A tibble: 6 × 6
#> barcode mirbase_ID miRNA Precusor total_read total_RPM
#> <chr> <chr> <chr> <chr> <int> <dbl>
#> 1 TCGA-E9-A1N5-01A-11R-A14C-13 MIMAT0000062 hsa-l… MI00000… 45725 20802.
#> 2 TCGA-E9-A1N5-01A-11R-A14C-13 MIMAT0004481 hsa-l… MI00000… 100 45.5
#> 3 TCGA-E9-A1N5-01A-11R-A14C-13 MIMAT0010195 hsa-l… MI00000… 6 2.73
#> 4 TCGA-E9-A1N5-01A-11R-A14C-13 MIMAT0000063 hsa-l… MI00000… 43489 19785.
#> 5 TCGA-E9-A1N5-01A-11R-A14C-13 MIMAT0004482 hsa-l… MI00000… 126 57.3
#> 6 TCGA-E9-A1N5-01A-11R-A14C-13 MIMAT0000064 hsa-l… MI00000… 2002 911.data("TCGA_E9_A1N5_mirnanormal")
head(TCGA_E9_A1N5_mirnanormal)
#> # A tibble: 6 × 6
#> barcode mirbase_ID miRNA Precusor total_read total_RPM
#> <chr> <chr> <chr> <chr> <int> <dbl>
#> 1 TCGA-E9-A1N5-11A-41R-A14C-13 MIMAT0000062 hsa-l… MI00000… 67599 37068.
#> 2 TCGA-E9-A1N5-11A-41R-A14C-13 MIMAT0004481 hsa-l… MI00000… 132 72.4
#> 3 TCGA-E9-A1N5-11A-41R-A14C-13 MIMAT0010195 hsa-l… MI00000… 57 31.3
#> 4 TCGA-E9-A1N5-11A-41R-A14C-13 MIMAT0000063 hsa-l… MI00000… 47266 25918.
#> 5 TCGA-E9-A1N5-11A-41R-A14C-13 MIMAT0004482 hsa-l… MI00000… 126 69.1
#> 6 TCGA-E9-A1N5-11A-41R-A14C-13 MIMAT0000064 hsa-l… MI00000… 14554 7981.Here’s the summary of size of each dataset
| Dataset name | Number of rows |
|---|---|
mirtarbasegene |
380627 |
TCGA_E9_A1N5_normal |
56830 |
TCGA_E9_A1N5_tumor |
56830 |
TCGA_E9_A1N5_mirnanormal |
750 |
TCGA_E9_A1N5_mirnatumor |
648 |
3.4. Integrating and analysing data
All of these datasets are integrated using the code below resulting in miRNA:target dataset that contains miRNA and gene expression values.
TCGA_E9_A1N5_mirnagene <- TCGA_E9_A1N5_mirnanormal %>%
inner_join(mirtarbasegene, by= "miRNA") %>%
inner_join(TCGA_E9_A1N5_normal,
by = c("Target"= "external_gene_name")) %>%
select(Target, miRNA, total_read, gene_expression) %>%
distinct()
#> Warning in inner_join(., TCGA_E9_A1N5_normal, by = c(Target = "external_gene_name")): Detected an unexpected many-to-many relationship between `x` and `y`.
#> ℹ Row 3405 of `x` matches multiple rows in `y`.
#> ℹ Row 842 of `y` matches multiple rows in `x`.
#> ℹ If a many-to-many relationship is expected, set `relationship =
#> "many-to-many"` to silence this warning.Note: Some of genes have expression values more than one because some of tissue samples were sequenced in two medium separately. So, we select maximum expression values of that genes at following:
#> # A tibble: 26 × 3
#> # Groups: Target, miRNA [26]
#> Target miRNA n
#> <chr> <chr> <int>
#> 1 COG8 hsa-miR-186-5p 2
#> 2 GOLGA8M hsa-miR-1270 2
#> 3 GOLGA8M hsa-miR-5703 2
#> 4 MATR3 hsa-let-7e-5p 2
#> 5 MATR3 hsa-miR-1-3p 2
#> 6 MATR3 hsa-miR-10b-3p 2
#> 7 MATR3 hsa-miR-125b-5p 2
#> 8 MATR3 hsa-miR-149-5p 2
#> 9 MATR3 hsa-miR-155-5p 2
#> 10 MATR3 hsa-miR-16-1-3p 2
#> # ℹ 16 more rowshead(TCGA_E9_A1N5_mirnagene)
#> # A tibble: 6 × 4
#> Target miRNA total_read gene_expression
#> <chr> <chr> <int> <dbl>
#> 1 CDK6 hsa-let-7a-5p 67599 4669
#> 2 MYC hsa-let-7a-5p 67599 11593
#> 3 BCL2 hsa-let-7a-5p 67599 2445
#> 4 NKIRAS2 hsa-let-7a-5p 67599 1519
#> 5 ITGB3 hsa-let-7a-5p 67599 196
#> 6 NF2 hsa-let-7a-5p 67599 1755When we compared the two gene expression dataset of TCGA-E9A1N5 patient, and selected a gene which has 30-fold increased expression, (gene name: HIST1H3H), this gene node will be used in the example. Note that the selected node must not be isolated one. If the an isolated node is selected the change in expression will not propagate in network. (You can see commands for node selection in the vignette The auxiliary commands which can help to the users)
Optionally, you can filter the low expressed gene nodes because they are not effective elements.
The analysis is performed based on amounts of miRNAs and targets as
seen. Firstly, we tried to find optimal iteration for the network when
simulation start with HIST1H3H node. As an example,
simulation() function was used with cycle = 5
argument, this argument can be arranged according to network. Note that
it can be appropriate that using greater number of cycle
for comprehensive network objects.
simulation_res_HIST <- TCGA_E9_A1N5_mirnagene %>%
priming_graph(competing_count = gene_expression,
miRNA_count = total_read) %>%
update_how(node_name = "HIST1H3H", how =30) %>%
simulate(5)
simulation_res_HIST%>%
find_iteration(plot=TRUE)
The graph was shown that the change in expression level of HIST1H3H results in weak perturbation efficiency, despite 30-fold change. The code shown below can be used for calculation of fold changes after simulation HIST1H3H gene to 30 fold:
simulation_res_HIST%>%
as_tibble()%>%
mutate(FC= count_current/initial_count)%>%
arrange(desc(FC))
#> # A tibble: 13,432 × 8
#> name type node_id initial_count count_pre count_current changes_variable
#> <chr> <chr> <int> <dbl> <dbl> <dbl> <chr>
#> 1 HIST1H3H Comp… 9705 27 808 808 Competing
#> 2 CDK6 Comp… 1 4669 4669 4669 Competing
#> 3 MYC Comp… 2 11593 11593 11593 Competing
#> 4 BCL2 Comp… 3 2445 2445 2445 Competing
#> 5 NKIRAS2 Comp… 4 1519 1519 1519 Competing
#> 6 ITGB3 Comp… 5 196 196 196 Competing
#> 7 NF2 Comp… 6 1755 1755 1755 Competing
#> 8 NRAS Comp… 7 2311 2311 2311 Competing
#> 9 KRAS Comp… 8 1204 1204 1204 Competing
#> 10 PRDM1 Comp… 9 456 456 456 Competing
#> # ℹ 13,422 more rows
#> # ℹ 1 more variable: FC <dbl>And then, we tried to simulate the network with the gene which has higher expression value. For this, we selected ACTB node as shown in The auxiliary commands which can help to the users
simulation_res_ACTB <- TCGA_E9_A1N5_mirnagene %>%
priming_graph(competing_count = gene_expression,
miRNA_count = total_read) %>%
update_how(node_name = "ACTB", how =1.87) %>%
simulate(5)
simulation_res_ACTB%>%
find_iteration(plot=TRUE)
Following codes are shown entire gene fold changes after simulation ACTB gene to 1.87 fold:
simulation_res_ACTB%>%
as_tibble()%>%
mutate(FC= count_current/initial_count)%>%
arrange(desc(FC))
#> # A tibble: 13,432 × 8
#> name type node_id initial_count count_pre count_current changes_variable
#> <chr> <chr> <int> <dbl> <dbl> <dbl> <chr>
#> 1 ACTB Compe… 84 101917 183705 183705 Competing
#> 2 YOD1 Compe… 472 470 472 472 Competing
#> 3 ADIPOR2 Compe… 319 3015 3026 3026 Competing
#> 4 FAM105A Compe… 385 569 571 571 Competing
#> 5 RRM2 Compe… 67 1479 1484 1484 Competing
#> 6 NKIRAS2 Compe… 4 1519 1524 1524 Competing
#> 7 IGF1R Compe… 324 3875 3887 3887 Competing
#> 8 NRAS Compe… 7 2311 2318 2318 Competing
#> 9 SOCS7 Compe… 592 1331 1335 1335 Competing
#> 10 SRD5A3 Compe… 2887 676 678 678 Competing
#> # ℹ 13,422 more rows
#> # ℹ 1 more variable: FC <dbl>Note: it can be useful that you look at The auxiliary commands which can help to the users for perturbation efficiency of ACTB gene by simulation with same conditions and different expression changes.
3.5. The sum of two conditions:
In a real biological sample, we tested perturbation efficiencies of two genes; * one with low expression but high fold change (HIST1H3H, 30-fold increase in tumor) * another one with high expression but small change in expression level (ACTB, 1.87-fold increase in tumor)
With these two samples, it has been obtained that expression values of genes, rest of the perturbed gene, changed slightly.
Despite high fold change, former gene caused little perturbation. When the perturbation efficiencies of both of these genes are analysed, it has been oberved that HIST1H3H does not affect the other genes in given limit. On the contrary, high expressing gene with very low fold increase in tumor causes greater perturbation in the network. Additionaly, the perturbation efficiency of ACTB gene is quite high from HIST1H3H with 30-fold change, when ACTB is simulated with 30 fold-change.
Thus, if the perturbed node has lower target:total target ratio in group or groups, the efficiency of it can be weak, or vice versa. The efficiency of ACTB gene may be high for this reason, in comparison with HIST1H3H perturbation. In fact, it has been observed that ACTB has not strong perturbation efficiency too. This could be arisen from low miRNA:target ratio or ineffective target nodes which have very low expression levels.
4. Dataset (huge_example) which includes miRNA and gene
expressions and miRNA:target interaction factors
4.1. Description of the huge_example dataset
Interactions between miRNAs and their targets can be analyzed after the integration of miRNA and targets via various datasets. As an example, we prepared the huge_example dataset. It was generated by integrating:
- Next-generation RNA sequencing data of a breast cancer patient from TCGA (patient id:TCGA_A7_A0CE)
- The microRNA expression values of the same breast cancer patient.
- High-throughput miRNA:target determination datasets. We utilised the microRNA:target gene dataset which was integrated from high-throughput experimental studies(CLASH & CLEAR-CLiP methods). These datasets give exact microRNA:target pairs because of these methods base on chimeric reading of pairs after binding of microRNAs and their targets. For this reason, the datasets contain detailed information for microRNA:target pairs such as interacted bases (in terms of degree of complementarity) on microRNAs and target mRNAs, location of interaction region (seed) on mRNA and estimated binding energies of pairs.
Below, only 6 rows from total of 26,176 rows are shown.
data("huge_example")
head(huge_example)
#> competing miRNA competing_counts mirnaexpression_normal Energy
#> 1 TSPAN6 hsa-miR-484 5404 23.058807 -21.4
#> 2 DPM1 hsa-miR-18b-5p 2472 0.256209 -10.0
#> 3 SCYL3 hsa-miR-149-5p 1483 34.844420 -5.5
#> 4 SCYL3 hsa-miR-30a-5p 1483 63031.505507 -13.0
#> 5 C1orf112 hsa-miR-1296-5p 312 1.793463 -8.1
#> 6 CFH hsa-miR-17-5p 5760 89.929349 -17.2
#> region_effect seed_type_effect
#> 1 0.42 0.43
#> 2 0.84 0.01
#> 3 0.42 0.01
#> 4 0.84 0.43
#> 5 0.42 0.01
#> 6 0.42 0.014.2. Select a node as trigger
The node that initiates simulation can be determined according your interest or research.
The dataset, which is a data frame, can be manipulated with tidyverse packages. As an example, competing RNAs targeted by less than 5 miRNAs are eliminated to make the network manageable size.
filtered_example <- huge_example %>%
add_count(competing) %>%
filter(n > 5) %>%
select(-n)
head(filtered_example)
#> competing miRNA competing_counts mirnaexpression_normal Energy
#> 1 MAD1L1 hsa-miR-149-5p 1909 34.84442 -18.6
#> 2 MAD1L1 hsa-miR-16-5p 1909 296.68999 -19.3
#> 3 MAD1L1 hsa-miR-196a-5p 1909 55.85356 -15.4
#> 4 MAD1L1 hsa-miR-20a-5p 1909 65.84570 -13.6
#> 5 MAD1L1 hsa-miR-30c-5p 1909 602.60349 -26.7
#> 6 MAD1L1 hsa-miR-92a-3p 1909 5112.64996 -30.6
#> region_effect seed_type_effect
#> 1 0.42 0.01
#> 2 0.01 0.07
#> 3 0.01 0.07
#> 4 0.42 0.01
#> 5 0.01 0.01
#> 6 0.01 0.05On the other hand, we chose the node GAPDH according to interaction count of the nodes. With the simulation, the graph was visualized after node GAPDH was increased to five fold.
simulation_GAPDH <- filtered_example %>%
priming_graph(competing_count = competing_counts,
miRNA_count = mirnaexpression_normal,
aff_factor = Energy) %>%
update_how("GAPDH", 5)
simulation_GAPDH%>%
vis_graph(title = "Distribution of GAPDH gene node")
Let’s visualize each step of simulation via
simulate_vis() function.
simulation_GAPDH%>%
simulate_vis(title = "GAPDH over expression in the real dataset", 3)
#> # A tbl_graph: 1526 nodes and 11384 edges
#> #
#> # A directed acyclic simple graph with 1 component
#> #
#> # Node Data: 1,526 × 7 (active)
#> name type node_id initial_count count_pre count_current changes_variable
#> <chr> <chr> <int> <dbl> <dbl> <dbl> <chr>
#> 1 MAD1L1 Comp… 1 1909 1909. 1909. Up
#> 2 TFPI Comp… 2 3377 3377. 3377. Down
#> 3 SLC7A2 Comp… 3 8706 8707. 8707. Down
#> 4 FKBP4 Comp… 4 20583 20584. 20584. Down
#> 5 SLC25A13 Comp… 5 1637 1638. 1638. Down
#> 6 ST7 Comp… 6 1962 1962. 1962. Up
#> 7 UPF1 Comp… 7 10094 10095. 10095. Up
#> 8 SLC25A5 Comp… 8 13819 13827. 13824. Down
#> 9 THSD7A Comp… 9 558 558. 558. Up
#> 10 MSL3 Comp… 10 2029 2030. 2030. Down
#> # ℹ 1,516 more rows
#> #
#> # Edge Data: 11,384 × 21
#> from to Competing_name miRNA_name competing_counts mirnaexpression_normal
#> <int> <int> <chr> <chr> <dbl> <dbl>
#> 1 1 1255 MAD1L1 hsa-miR-14… 1909 34.8
#> 2 1 1256 MAD1L1 hsa-miR-16… 1909 297.
#> 3 1 1257 MAD1L1 hsa-miR-19… 1909 55.9
#> # ℹ 11,381 more rows
#> # ℹ 15 more variables: Energy <dbl>, dummy <dbl>, afff_factor <dbl>,
#> # degg_factor <dbl>, comp_count_list <list>, comp_count_pre <dbl>,
#> # comp_count_current <dbl>, mirna_count_list <list>, mirna_count_pre <dbl>,
#> # mirna_count_current <dbl>, mirna_count_per_dep <dbl>, effect_current <dbl>,
#> # effect_pre <dbl>, effect_list <list>, mirna_count_per_comp <dbl>
Now, we can track changes in expression levels at every node for 3 cycles when GAPDH is overexpressed 5-fold.
After increase in GAPDH expression level in the first graph, the responses of the other competing elements to the GAPDH distributions were calculated.
The changing regulations (up or down) were observed as a result of interactions in the second graph.
When three graphs were carefully compared to each other, it can be observed that the expression levels of nodes change continuously at each stage.
5. Finding perturbation efficiency on an experimental dataset
find_node_perturbation() runs calc_perturb
on all nodes in the network in parallel with help of the
future and furrr packages. In this vignette,
the function is demonstrated on the midsamp data. This
dataset is not comparable to actual biological miRNA:target gene
datasets in size and complexity. Although
find_node_perturbation() runs in parallel it might take
long time to run in real huge biological datasets.
In real biological datasets, more complex interactions whether
functional or non-functional could be observed. We have improved our
approach with fast argument in
find_node_perturbation() based on selection of elements
that could be affected from perturbation. In this fucntion,
fast argument specifies the percentage of the competing
amount that can be affected within the initial competing amount and acts
as a selection parameter. For instance, in filtered example data:
entire_perturbation <- filtered_example%>%
priming_graph(competing_count = competing_counts, miRNA_count = mirnaexpression_normal)%>%
find_node_perturbation(how=5, cycle=3, fast = 15)%>%
select(name, perturbation_efficiency, perturbed_count)
entire_perturbation%>%
filter(!is.na(perturbation_efficiency), !is.na(perturbed_count))%>%
select(name, perturbation_efficiency, perturbed_count)
#> # A tibble: 53 × 3
#> name perturbation_efficiency perturbed_count
#> <chr> <dbl> <dbl>
#> 1 SLC25A5 1.34 43
#> 2 MSL3 0.198 43
#> 3 SCMH1 0.354 43
#> 4 RALBP1 0.650 43
#> 5 THUMPD1 0.648 43
#> 6 ARFGEF1 0.481 43
#> 7 PABPC1 6.35 43
#> 8 CNOT4 0 0
#> 9 PPP1R13B 0.262 43
#> 10 CSNK2A1 0.545 43
#> # ℹ 43 more rows6. Session Info
sessionInfo()
#> R version 4.4.1 (2024-06-14)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
#> [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
#>
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] purrr_1.0.2 png_0.1-8 ceRNAnetsim_1.19.0 tidygraph_1.3.1
#> [5] dplyr_1.1.4 rmarkdown_2.28
#>
#> loaded via a namespace (and not attached):
#> [1] viridis_0.6.5 sass_0.4.9 utf8_1.2.4 future_1.34.0
#> [5] generics_0.1.3 tidyr_1.3.1 listenv_0.9.1 digest_0.6.37
#> [9] magrittr_2.0.3 evaluate_1.0.1 grid_4.4.1 fastmap_1.2.0
#> [13] jsonlite_1.8.9 ggrepel_0.9.6 gridExtra_2.3 fansi_1.0.6
#> [17] viridisLite_0.4.2 scales_1.3.0 tweenr_2.0.3 codetools_0.2-20
#> [21] jquerylib_0.1.4 cli_3.6.3 graphlayouts_1.2.0 rlang_1.1.4
#> [25] polyclip_1.10-7 parallelly_1.38.0 munsell_0.5.1 withr_3.0.2
#> [29] cachem_1.1.0 yaml_2.3.10 tools_4.4.1 parallel_4.4.1
#> [33] memoise_2.0.1 colorspace_2.1-1 ggplot2_3.5.1 globals_0.16.3
#> [37] buildtools_1.0.0 vctrs_0.6.5 R6_2.5.1 lifecycle_1.0.4
#> [41] MASS_7.3-61 ggraph_2.2.1 furrr_0.3.1 pkgconfig_2.0.3
#> [45] pillar_1.9.0 bslib_0.8.0 gtable_0.3.6 Rcpp_1.0.13
#> [49] glue_1.8.0 ggforce_0.4.2 highr_0.11 xfun_0.48
#> [53] tibble_3.2.1 tidyselect_1.2.1 sys_3.4.3 knitr_1.48
#> [57] farver_2.1.2 htmltools_0.5.8.1 igraph_2.1.1 labeling_0.4.3
#> [61] maketools_1.3.1 compiler_4.4.1