| Title: | Analysis of Small RNA Expression Changes in Hybrid Plants |
| Version: | 0.1.0 |
| Description: | The most important function of the R package is the genetic effects analysis of small RNA in hybrid plants via two methods, and at the same time, it provides various forms of graph related to data characteristics and expression analysis. In terms of two classification methods, one is the calculation of the additive (a) and dominant (d), the other is the evaluation of expression level dominance by comparing the total expression of the small RNA in progeny with the expression level in the parent species. |
| License: | GPL (≥ 3) |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.1.2 |
| Imports: | DESeq2, futile.logger, ggplot2, ggsci, plyr, VennDiagram |
| Depends: | R (≥ 2.10) |
| LazyData: | true |
| NeedsCompilation: | no |
| Packaged: | 2022-03-17 11:12:19 UTC; wuyuqing |
| Author: | Yu qing Wu  [aut,
cre] |
| Maintainer: | Yu qing Wu <wuyuqing0104@163.com> |
| Repository: | CRAN |
| Date/Publication: | 2022-03-21 08:00:02 UTC |
sRNAGenetic: Analysis of Small RNA Expression Changes in Hybrid Plants
Description
The most important function of the R package sRNAGenetic is the genetic effects analysis of miRNA after plant polyploidization via two methods, and at the same time, it provides various forms of graph related to data characteristics and expression analysis. In terms of two classification methods, one is the calculation of the additive (a) and dominant (d), the other is the evaluation of ELD by comparing the total expression of the miRNA in allotetraploid with the expression level in the parent species.
Author(s)
Maintainer: Yu qing Wu wuyuqing0104@163.com (ORCID)
Filitering low expressed miRNAs based on count: Countfiliter
Description
Filitering low expressed miRNAs based on count: Countfiliter
Usage
Countfiliter(P1_count, P2_count, F1_count, count_threshold = 5)
Arguments
P1_count |
A dataframe. The count data of miRNA from the P1 species. The first column must be the miRNA sequence. Others are listed as the count of miRNA, and each column denotes one biological replicate of the sample. |
P2_count |
A dataframe. Similar with P1_count, the count data of miRNA from the P2 species. |
F1_count |
A dataframe. Similar with P1_count, the count data of miRNA from the F1 species. |
count_threshold |
A numeric. In all samples, there is at least one sample whose count value is more than or equal to count_threshold to be retained. By default, the count value more than or equal to 5 is retained. |
Value
A dataframe. The result includes all miRNAs that fulfill the count value requirement (count >= count_threshold) in at least one sample.
Examples
##Get the filitered mirna count table (default: Count >= 5 in at least one sample)
Count5result <- Countfiliter(P1_count = P1_miRNA_count,
P2_count = P2_miRNA_count,
F1_count = F1_miRNA_count,count_threshold = 5)
Sequences of miRNAs from one species
Description
The first column of this data set is all miRNA sequences from "F1", and other columns are the corresponding count values from different samples
Format
A dataframe containing all miRNAs with count value in "F1" (the S3 generations of Parents (Maternal parent: Triticum turgidum; Male parent: Aegilops tauschii)).
Source
Generated from the S3 generations of Triticum turgidum (AABB) and Aegilops tauschii (DD).
References
Li, A., et al., mRNA and Small RNA Transcriptomes Reveal Insights into Dynamic Homoeolog Regulation of Allopolyploid Heterosis in Nascent Hexaploid Wheat. Plant Cell, 2014. 26(5): p. 1878-1900.
Examples
data(F1_miRNA_count)
Sequences of miRNAs from one species
Description
The first column of this data set is all miRNA sequences from "F1", and other columns are the corresponding RPM values of different samples
Format
A dataframe containing all miRNAs with RPM value in "F1" (the S3 generations of Parents (Maternal parent: Triticum turgidum; Male parent: Aegilops tauschii)).
Source
Generated from the S3 generations of Triticum turgidum (AABB) and Aegilops tauschii (DD).
References
Li, A., et al., mRNA and Small RNA Transcriptomes Reveal Insights into Dynamic Homoeolog Regulation of Allopolyploid Heterosis in Nascent Hexaploid Wheat. Plant Cell, 2014. 26(5): p. 1878-1900.
Examples
data(F1_miRNA_rpm)
Sequences of sRNAs from one species
Description
The first column of this data set is all sequences from one speceie for the data statistics
Format
A dataframe containing numerous sequences of all sRNAs in "F1" (the S3 generations of Parents (Maternal parent: Triticum turgidum; Male parent: Aegilops tauschii)). However, only 400 sRNAs are selected as test data due to the large data of sRNA.
Details
This data.frame is very useful for the functional demonstration of "srnapredata"
Source
Generated from the S3 generations of Triticum turgidum (AABB) and Aegilops tauschii (DD).
References
Li, A., et al., mRNA and Small RNA Transcriptomes Reveal Insights into Dynamic Homoeolog Regulation of Allopolyploid Heterosis in Nascent Hexaploid Wheat. Plant Cell, 2014. 26(5): p. 1878-1900.
Examples
data(F1_sRNA_seq)
Genetic effects analysis: Twelve bins of expression analysis (method2)
Description
Genetic effects analysis: Twelve bins of expression analysis (method2)
Usage
Get12Bins(P1_count, P2_count, F1_count, count_threshold = 5, Pvalue = 0.05)
Arguments
P1_count |
A dataframe. The count data of miRNA from the P1 species. The first column must be the miRNA sequence. Others are listed as the count of miRNA, and each column denotes one biological replicate of the sample. |
P2_count |
A dataframe. Similar with P1_count, the count data of miRNA from the P2 species. |
F1_count |
A dataframe. Similar with P1_count, the count data of miRNA from the F1 species. |
count_threshold |
A numeric. In all samples, there is at least one sample whose count value is more than or equal to count_threshold to be retained. By default, the count value more than or equal to 5 is retained. |
Pvalue |
A numeric. The threshold of significance test among different groups. Default is 0.05. |
Value
A dataframe. The output results contain the P value, log2FoldChange and grouping information for each miRNA expressed in all species (count >= count_threshold). F1_vs_P1(P value: pv11,log2FoldChange: fc11), F1_vs_P2(P value: pv12,log2FoldChange: fc12), P2_vs_P1(P value: pv21,log2FoldChange: fc21)
Genetic effects analysis of miRNA: |d/a| (method 1)
Description
The additive (a) and dominant (d) values were calculated by the expression level of each miRNA. Edwards et al. proposed that the "|d/a|" can be used as the criterion to estimate the expression patterns of miRNAs. Specific classification criteria are as follows, |d/a| <= 0.2, additivity; |d/a| > 0.2 and |d/a| <= 0.8, partial dominance; |d/a| > 0.8 and |d/a| <= 1.2, dominance; |d/a| > 1.2, overdominance.
Usage
GetDAtable(P1_RPM, P2_RPM, F1_RPM, rpm_threshold = 1)
Arguments
P1_RPM |
A dataframe. The rpm data of miRNA from the P1 species. The first column must be the miRNA sequence. Others are listed as the rpm of miRNA, and each column denotes one biological replicate of the sample. |
P2_RPM |
A dataframe. Similar with P1_RPM, the rpm data of miRNA from the P2 species. |
F1_RPM |
A dataframe. Similar with P1_RPM, the rpm data of miRNA from the F1 species. |
rpm_threshold |
A numeric. the average of rpm value among all the biological replicates. By default, the average of rpm more than or equal to 1 is retained. |
Value
A dataframe. The output results contain the value of "|d/a|" and grouping results for each miRNA expressed in all species (average_rpm >= rpm_threshold).
Examples
##Get the classification results based on the value of |d/a|
DAresult <- GetDAtable(P1_RPM = P1_miRNA_rpm,
P2_RPM = P2_miRNA_rpm,
F1_RPM = F1_miRNA_rpm,rpm_threshold = 1)
Sequences of miRNAs from one species
Description
The first column of this data set is all miRNA sequences from "P1", and other columns are the corresponding count values from different samples
Format
A dataframe containing all miRNAs with count value in "P1" (Male parent: Aegilops tauschii).
Source
Generated from the Aegilops tauschii (DD).
References
Li, A., et al., mRNA and Small RNA Transcriptomes Reveal Insights into Dynamic Homoeolog Regulation of Allopolyploid Heterosis in Nascent Hexaploid Wheat. Plant Cell, 2014. 26(5): p. 1878-1900.
Examples
data(P1_miRNA_count)
Sequences of miRNAs from one species
Description
The first column of this data set is all miRNA sequences from "P1", and other columns are the corresponding RPM values of different samples
Format
A dataframe containing all miRNAs with RPM value in "P1" (Male parent: Aegilops tauschii).
Source
Generated from the Aegilops tauschii (DD).
References
Li, A., et al., mRNA and Small RNA Transcriptomes Reveal Insights into Dynamic Homoeolog Regulation of Allopolyploid Heterosis in Nascent Hexaploid Wheat. Plant Cell, 2014. 26(5): p. 1878-1900.
Examples
data(P1_miRNA_rpm)
Sequences of sRNAs from one species
Description
The first column of this data set is all sequences from one speceie for the data statistics
Format
A dataframe containing numerous sequences of all sRNAs in "P1" (Male parent: Aegilops tauschii). However, only 400 sRNAs are selected as test data due to the large data of sRNA.
Details
This data.frame is very useful for the functional demonstration of "srnapredata"
Source
Generated from the Aegilops tauschii (DD).
References
Li, A., et al., mRNA and Small RNA Transcriptomes Reveal Insights into Dynamic Homoeolog Regulation of Allopolyploid Heterosis in Nascent Hexaploid Wheat. Plant Cell, 2014. 26(5): p. 1878-1900.
Examples
data(P1_sRNA_seq)
Sequences of miRNAs from one species
Description
The first column of this data set is all miRNA sequences from "P2", and other columns are the corresponding count values from different samples
Format
A dataframe containing all miRNAs with count value in "P2" (Maternal parent: Triticum turgidum).
Source
Generated from the Triticum turgidum (AABB).
References
Li, A., et al., mRNA and Small RNA Transcriptomes Reveal Insights into Dynamic Homoeolog Regulation of Allopolyploid Heterosis in Nascent Hexaploid Wheat. Plant Cell, 2014. 26(5): p. 1878-1900.
Examples
data(P2_miRNA_count)
Sequences of miRNAs from one species
Description
The first column of this data set is all miRNA sequences from "P2", and other columns are the corresponding RPM values of different samples
Format
A dataframe containing all miRNAs with RPM value in "P2" (Maternal parent: Triticum turgidum).
Source
Generated from the Triticum turgidum (AABB).
References
Li, A., et al., mRNA and Small RNA Transcriptomes Reveal Insights into Dynamic Homoeolog Regulation of Allopolyploid Heterosis in Nascent Hexaploid Wheat. Plant Cell, 2014. 26(5): p. 1878-1900.
Examples
data(P2_miRNA_rpm)
Sequences of sRNAs from one species
Description
The first column of this data set is all sequences from one species for the data statistics
Format
A dataframe containing numerous sequences of all sRNAs in "P2" (Maternal parent: Triticum turgidum). However, only 400 sRNAs are selected as test data due to the large data of sRNA.
Details
This data.frame is very useful for the functional demonstration of "srnapredata"
Source
Generated from the Triticum turgidum (AABB).
References
Li, A., et al., mRNA and Small RNA Transcriptomes Reveal Insights into Dynamic Homoeolog Regulation of Allopolyploid Heterosis in Nascent Hexaploid Wheat. Plant Cell, 2014. 26(5): p. 1878-1900.
Examples
data(P2_sRNA_seq)
Filitering low expressed miRNAs based on RPM: Rpmfiliter
Description
Filitering low expressed miRNAs based on RPM: Rpmfiliter
Usage
Rpmfiliter(P1_RPM, P2_RPM, F1_RPM, rpm_threshold = 1)
Arguments
P1_RPM |
A dataframe. The rpm data of miRNA from the P1 species. The first column must be the miRNA sequence. Others are listed as the rpm of miRNA, and each column denotes one biological replicate of the sample. |
P2_RPM |
A dataframe. Similar with P1_RPM, the rpm data of miRNA from the P2 species. |
F1_RPM |
A dataframe. Similar with P1_RPM, the rpm data of miRNA from the F1 species. |
rpm_threshold |
A numeric. the average of rpm value among all the biological replicates. By default, the average of rpm more than or equal to 1 is retained. |
Value
A dataframe. The result includes all miRNAs that fulfill the average rpm value requirement (Average rpm >= rpm_threshold) among all species.
Examples
##Get the filitered mirna rpm table (default: the average rpm >= 1 in three species)
Rpm1result <- Rpmfiliter(P1_RPM = P1_miRNA_rpm,
P2_RPM = P2_miRNA_rpm,
F1_RPM = F1_miRNA_rpm,rpm_threshold = 1)
Generate the base frequency plot of miRNA
Description
Generate the base frequency plot of miRNA
Usage
basepreplot(file_dataframe, width = 0.6, size = 12)
Arguments
file_dataframe |
A dataframe. The output result after running mirnapredata. |
width |
A numeric. The width of the output bar plot, and default is 0.6. |
size |
A numeric. The size of axis text, and default is 0.6. |
Value
The miRNA base frequency plot
Examples
##P1
P1_miRNA_data <- mirnapredata(mirnaseq_dataframe = P1_miRNA_count)
##Drawing
basepreplot(file_dataframe = P1_miRNA_data)
Classification for 12 expression patterns
Description
The input data is generated from the analysis result of DESeq2.
Usage
genetic(pv11, pv12, pv21, fc11, fc12, fc21, Pvalue)
Arguments
pv11 |
A numeric. The P value of F1_vs_P1 (Treatment:F1; Control:P1). |
pv12 |
A numeric. The P value of F1_vs_P2 (Treatment:F1; Control:P2) |
pv21 |
A numeric. The P value of P2_vs_P1 (Treatment:P2; Control:P1) |
fc11 |
A numeric. The Log2(FoldChange) value of F1_vs_P1 (Treatment:F1; Control:P1) |
fc12 |
A numeric. The Log2(FoldChange) value of F1_vs_P2 (Treatment:F1; Control:P2) |
fc21 |
A numeric. The Log2(FoldChange) value of P2_vs_P1 (Treatment:P2; Control:P1) |
Pvalue |
A numeric. Filtration criteria of P value for Classification. |
Value
A dataframe.
Generate the sRNA length distribution plot
Description
Generate the sRNA length distribution plot
Usage
lenplot(file_dataframe, width = 0.6, size = 12)
Arguments
file_dataframe |
A dataframe. The output result after running "srnapredata". |
width |
A numeric. The width of the output bar plot, and default is 0.6. |
size |
A numeric. The size of text in the outpot plot, and default is 12. |
Value
The sRNA length distribution plot
Examples
##F1
F1_sRNA <- srnapredata(srnaseq_dataframe = F1_sRNA_seq, group = "F1")
##P1
P1_sRNA <- srnapredata(srnaseq_dataframe = P1_sRNA_seq, group = "P1")
##P2
P2_sRNA <- srnapredata(srnaseq_dataframe = P2_sRNA_seq, group = "P2")
##integrate all sRNA data from P1, P2, and F1
sRNA_data <- rbind(F1_sRNA,P1_sRNA,P2_sRNA)
##plot
lenplot(file_dataframe = sRNA_data)
Species specific expression analysis: miVennData
Description
miVennData: Extract the species-specific miRNAs and the shared miRNAs among parents and offspring.
Usage
miVennData(
P1_RPM,
P2_RPM,
F1_RPM,
rpm_threshold = 1,
output_file = "venn_list"
)
Arguments
P1_RPM |
A dataframe. The rpm data of miRNA from the P1 species. The first column must be the miRNA sequence. Others are listed as the rpm of miRNA, and each column denotes one biological replicate of the sample. |
P2_RPM |
A dataframe. Similar with P1_RPM, the rpm data of miRNA from the P2 species. |
F1_RPM |
A dataframe. Similar with P1_RPM, the rpm data of miRNA from the F1 species. |
rpm_threshold |
A numeric. the average of rpm value among all the biological replicates. By default, the average of rpm more than or equal to 1 is retained. |
output_file |
Specify the output file. "venn_list" is the default option, which outputs all the information of the Venn diagram. "all_common" is one of options, which outputs the miRNAs shared by parents and offspring. "P1_specific" is one of options, which outputs P1 specific expression miRNA. "P2_specific" is one of options, which outputs P2 specific expression miRNA. "F1_specific" is one of options, which outputs F1 specific expression miRNA. |
Value
A dataframe. The output results is based on your selection (output_file).
Examples
##Extract the species-specific miRNAs and the shared miRNAs among parents and offspring.
##output_file = "venn_list"
venn_list <- miVennData(P1_RPM = P1_miRNA_rpm,
P2_RPM = P2_miRNA_rpm,
F1_RPM = F1_miRNA_rpm,
rpm_threshold = 1,output_file = "venn_list")
##output_file = "P1_specific"
P1_specific <- miVennData(P1_RPM = P1_miRNA_rpm,
P2_RPM = P2_miRNA_rpm,
F1_RPM = F1_miRNA_rpm,
rpm_threshold = 1,output_file = "P1_specific")
##output_file = "P2_specific"
P2_specific <- miVennData(P1_RPM = P1_miRNA_rpm,
P2_RPM = P2_miRNA_rpm,
F1_RPM = F1_miRNA_rpm,
rpm_threshold = 1,output_file = "P2_specific")
##output_file = "F1_specific"
F1_specific <- miVennData(P1_RPM = P1_miRNA_rpm,
P2_RPM = P2_miRNA_rpm,
F1_RPM = F1_miRNA_rpm,
rpm_threshold = 1,output_file = "F1_specific")
##output_file = "all_common"
all_common <- miVennData(P1_RPM = P1_miRNA_rpm,
P2_RPM = P2_miRNA_rpm,
F1_RPM = F1_miRNA_rpm,
rpm_threshold = 1,output_file = "all_common")
Species specific expression analysis: miVennPlot
Description
miVennPlot: generate the Venn diagram with the specific expression information of miRNAs.
Usage
miVennPlot(P1_RPM, P2_RPM, F1_RPM, rpm_threshold = 1)
Arguments
P1_RPM |
A dataframe. The rpm data of miRNA from the P1 species. The first column must be the miRNA sequence. Others are listed as the rpm of miRNA, and each column denotes one biological replicate of the sample. |
P2_RPM |
A dataframe. Similar with P1_RPM, the rpm data of miRNA from the P2 species. |
F1_RPM |
A dataframe. Similar with P1_RPM, the rpm data of miRNA from the F1 species. |
rpm_threshold |
A numeric. the average of rpm value among all the biological replicates. By default, the average of rpm more than or equal to 1 is retained. |
Value
The Venn diagram with the specific expression information of miRNAs.
Examples
##Drawing
miVennPlot(P1_RPM = P1_miRNA_rpm,
P2_RPM = P2_miRNA_rpm,
F1_RPM = F1_miRNA_rpm,rpm_threshold = 1)
Generate the data of miRNA base frequence in each position
Description
Generally, the "T" base account for the highest percentage of miRNA in the first position.The function of "mirnapredata" can provide the input data for the next drawing of miRNA base distribution in each position.
Usage
mirnapredata(mirnaseq_dataframe)
Arguments
mirnaseq_dataframe |
A dataframe. The first column must be the sRNA sequence. |
Value
A dataframe. About the output results, the first column is the base, the second column is the base frequency, the third column is the position.
Examples
##P1
P1_miRNA_data <- mirnapredata(mirnaseq_dataframe = P1_miRNA_count)
##P2
P2_miRNA_data <- mirnapredata(mirnaseq_dataframe = P2_miRNA_count)
##F1
F1_miRNA_data <- mirnapredata(mirnaseq_dataframe = F1_miRNA_count)
Differential expression analysis
Description
Differential expression analysis
Usage
polyDESeq(P1_count, P2_count, F1_count, count_threshold = 5, Pvalue = 0.05)
Arguments
P1_count |
A dataframe. The count data of miRNA from the P1 species. The first column must be the miRNA sequence. Others are listed as the count of miRNA, and each column denotes one biological replicate of the sample. |
P2_count |
A dataframe. Similar with P1_count, the count data of miRNA from the P2 species. |
F1_count |
A dataframe. Similar with P1_count, the count data of miRNA from the F1 species. |
count_threshold |
A numeric. In all samples, there is at least one sample whose count value is more than or equal to count_threshold to be retained. By default, the count value more than or equal to 5 is retained. |
Pvalue |
A numeric. The threshold of significance test among different groups. Default is 0.05. |
Value
A dataframe. Differential expression analysis results of miRNA expressed in each two species (count >= count_threshold).
Generate the data of sRNA length distribution
Description
Generally, the length interval of sRNA is 21-24. The function of "srnapredata" can provide the input data for the next drawing of sRNA length distribution among different species.
Usage
srnapredata(srnaseq_dataframe, group)
Arguments
srnaseq_dataframe |
A dataframe. The first column must be the sRNA sequence. |
group |
A character. You an select a representative group name for next drawing. |
Value
A dataframe. The output results are consist of three columns, the first column is the length of sRNA, the second column id the frequency, and the third column is the group name.
Examples
##Only 400 sRNAs are selected as test data due to the large data of sRNA.
##Recommended to use the "data.table" package for reading data quickly.
##F1
F1_sRNA <- srnapredata(srnaseq_dataframe = F1_sRNA_seq, group = "F1")
##P1
P1_sRNA <- srnapredata(srnaseq_dataframe = P1_sRNA_seq, group = "P1")
##P2
P2_sRNA <- srnapredata(srnaseq_dataframe = P2_sRNA_seq, group = "P2")