CircadianTools: CircadianTools: A Collection of Tools for Detecting Rhythmic...
In nathansam/CircadianTools: Collection of Tools for Detecting Rhythmic Genes
Description
Simple Plots
Clustering
Correlation
Cosinor
Cytoscape
Fasta Files
Filtering
RAIN
Utility Functions
Description
Allows analysis of rhythmic genes to be easily carried out on transcriptomics
data using R. Designed to be as flexible as possible such as by allowing an
unequal number of replicates.
——————————————————————————
Simple Plots
BasicPlot: Plots activity data as points and average activity
as lines
CompPlot: Plots two genes from a gene activity dataset.
DatasetPlot: Saves plots of all genes in a dataset.
TurningPlot: Fits a spline to a given gene in a given dataset.
Finds the turning points and then plots the turning points and spline.
——————————————————————————
Clustering
AgglomClustering: Applies hierarchical clustering, clustering to a
transcriptomics dataset and appends a cluster column to this dataset for all
genes.
AgglomParamSelection: Runs hierarchical clustering with
differing numbers of partitions and returns validation metrics.
ClusterCenterGenerator: Finds the center of every cluster in a
dataset
ClusterCorDatasetPlot: Uses ClusterCorPlot to plot all
of the clusters generated by a clustering method when absolute
Pearson's correlation was used as a distance measure.
ClusterCorPlot: Plots the activity level for a cluster
generated by using absolute Pearson's correlation as a distance measure.
Plots positively and negatively correlated genes as two different lines.
ClusterDatasetPlot: Plots the mean and error bars for all
clusters across time.
ClusterParamSelection: Calculates validation metrics for
different clustering methods and different numbers of partitions.
The validation metrics are plotted.
ClusterPlot: Plots the mean and error bars for the genes in a
cluster across time.
ClusterSpread: Shows how many genes are in each cluster after
clustering has been applied.
ClusterText: Takes a dataframe of clusters and stores the name
of all genes in a text file. The row number deontes the cluster number.
ClusterTimeProfile: Provides a dataframe of median values at
each time point for each cluster.
CommonSingletonFinder: Finds the genes which belong to common
singleton clusters in two different clustered datasets.
DendogramDatasetPlot: Plots the dendogram for every cluster in
a clustered dataset.
DendogramPlot: Plots the dendogram for a cluster in a
clustered dataset
DianaClustering: Applies DIANA (DIvisive ANAlysis) clustering
to a transcriptomics dataset and appends a cluster column to this dataset
for all genes.
DianaParamSelection: Runs DIANA (DIvisive ANAlysis) clustering
with differing numbers of partitions and returns validation metrics.
FindClusterDistanceQuantiles: Finds The distances between the
center of each cluster and the centers of all the other clusters.
FindClusterMedian: Finds the center of a cluster
FindClusterQuantile: Finds The distances between the center of
a cluster and the centers of all other clusters.
MDSPlot: Applies multidimensional scaling to a clustered
transcriptomics dataset to reduce the clusters to two dimensions and
then plots the clusters.
PamClustering: Applies PAM (Partitioning around Medoids)
clustering to a transcriptomics dataset and appends a cluster column to this
dataset for all genes.
PamParamSelection: Runs PAM with differing numbers of
partitions and returns validation metrics.
QuantilePlots: Finds the quartiles for intercluster distances
and plots these distances as a set of histograms
SingletonNameFinder: Finds the genes which belong to
singleton clusters.
—————————————————————————-
Correlation
CorAnalysis: Ranks correlation between a given gene and all
other genes in a dataset. Plots both the given gene and highly correlated
genes for a given correlation value.
CorAnalysisCluster: Correlates the average activity of a
cluster with the average activity of every other cluster.
CorAnalysisClusterDataset: Correlates the average activity of
each cluster with every other cluster in a dataset.
CorAnalysisDataset: Correlates every gene in a dataset with
every other gene in the same dataset. Allows a timelag between genes to be
correlated.
CorAnalysisPar: Parallel Implementation of CorAnalysis.
CorSignificantPlot: Prints or saves the genes found to be
most significant by CorAnalysis.
—————————————————————————-
Cosinor
CosinorAnalysis: Fits cosinor models to transcriptomics data
and plots the best-fitting models using ggplot2.
CosinorAnalysisPar: Parallel Implementation of
CosinorAnalysis.
CosinorPlot: Fits a cosinor model to a given gene in a given
dataset and plots the model.
CosinorResidualDatasetPlot: Fits cosinor models and plots the
residuals for multiple genes in a dataset
CosinorResidualPlot: Fits a cosinor model to a gene and plots
the residuals
CosinorSignificantPlot: Prints or saves the genes found to be
most significant by cosinoranalysis.
MultiCosinorTest: Fits a cosinor model and carries out ANOVA
using raw coefficients. Then fits a cosinor model with additonal sine and
cosine terms with a different period. ANOVA tests are carried out on the
more complex model as well as directly comparing the two models.
—————————————————————————-
Cytoscape
CytoscapeFile: Converts a correlation dataframe object into a
format suitable for cytoscape and saves as a csv file.
CytoscapeFilter: Reduces the size of a file intended for
Cytoscape by filtering out the genes/clusters which are not correlated
—————————————————————————–
Fasta Files
ContigGen: Finds all unique contig IDs in a transcriptomics
dataset
FastaSub: Creates a fasta file from only certain sequences in
another fasta file
—————————————————————————-
Filtering
AnovaFilter: Filters a gene activity dataframe via ANOVA.
CombiFilter: Filters a transcriptomics dataset by using
Zerofilter, Anovafilter and Sizefilter.
SizeFilter: Filters the genes with the smallest range from a
transcriptomics dataset.
TFilter: Applies a filter where a t-test is carried out on gene
activity levels between time points.
ZeroFilter: Filters a transcriptomics dataset such that there
is a minimum number of non-zero activity readings for each gene in the
resultant dataset.
—————————————————————————-
RAIN
RainAnalysis: Carries out RAIN analysis on a transcriptomics
dataset.
RainSignificantPlot: Prints or saves plots of the genes found
to be most significant by rainanalysis.
—————————————————————————–
Utility Functions
AbsCorDist: Calculates a distance matrix based on the distance
measure of:
1 - |cor(x, y)|
ActivitySelect: Returns gene activity by either gene name or
row number
FileConflict: Checks if a file which will be created already
exists and, if necessary asks the user if this file should be overwritten.
GeneClean: Removes columns and rows which show no gene activity
over time.
GeneRange: Finds the range of gene activity for each gene in a
dataframe. The median for the replicates is used for each time point.
GeneScale: Centers/scales every gene in a transcriptomics
dataset.
GeneSub: Takes an object where the first column is genenames
(IE a column of known Circadian genes) and subsets from a dataset containing
activity readings for these genes.
ggplot.cosinor.lm: Adapted from the Cosinor package by
Michael Sachs. Given a cosinor.lm model fit, generate a plot of the data with
the fitted values.
MakeTimevector: Produces a vector of time values for the gene
activity readings.
MedList: Provides a dataframe of median values at each
time point for each gene from a transcriptomics dataset.
TAnalysis: A t.test is carried out on gene
activity levels between time points and the number of significant increases
& decreases is returned.
nathansam/CircadianTools documentation built on Dec. 26, 2019, 11:30 a.m.
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Description Simple Plots Clustering Correlation Cosinor Cytoscape Fasta Files Filtering RAIN Utility Functions
Description
Allows analysis of rhythmic genes to be easily carried out on transcriptomics
data using R. Designed to be as flexible as possible such as by allowing an
unequal number of replicates.
——————————————————————————
Simple Plots
BasicPlot: Plots activity data as points and average activity
as lines
CompPlot: Plots two genes from a gene activity dataset.
DatasetPlot: Saves plots of all genes in a dataset.
TurningPlot: Fits a spline to a given gene in a given dataset.
Finds the turning points and then plots the turning points and spline.
——————————————————————————
Clustering
AgglomClustering: Applies hierarchical clustering, clustering to a
transcriptomics dataset and appends a cluster column to this dataset for all
genes.
AgglomParamSelection: Runs hierarchical clustering with
differing numbers of partitions and returns validation metrics.
ClusterCenterGenerator: Finds the center of every cluster in a
dataset
ClusterCorDatasetPlot: Uses ClusterCorPlot to plot all
of the clusters generated by a clustering method when absolute
Pearson's correlation was used as a distance measure.
ClusterCorPlot: Plots the activity level for a cluster
generated by using absolute Pearson's correlation as a distance measure.
Plots positively and negatively correlated genes as two different lines.
ClusterDatasetPlot: Plots the mean and error bars for all
clusters across time.
ClusterParamSelection: Calculates validation metrics for
different clustering methods and different numbers of partitions.
The validation metrics are plotted.
ClusterPlot: Plots the mean and error bars for the genes in a
cluster across time.
ClusterSpread: Shows how many genes are in each cluster after
clustering has been applied.
ClusterText: Takes a dataframe of clusters and stores the name
of all genes in a text file. The row number deontes the cluster number.
ClusterTimeProfile: Provides a dataframe of median values at
each time point for each cluster.
CommonSingletonFinder: Finds the genes which belong to common
singleton clusters in two different clustered datasets.
DendogramDatasetPlot: Plots the dendogram for every cluster in
a clustered dataset.
DendogramPlot: Plots the dendogram for a cluster in a
clustered dataset
DianaClustering: Applies DIANA (DIvisive ANAlysis) clustering
to a transcriptomics dataset and appends a cluster column to this dataset
for all genes.
DianaParamSelection: Runs DIANA (DIvisive ANAlysis) clustering
with differing numbers of partitions and returns validation metrics.
FindClusterDistanceQuantiles: Finds The distances between the
center of each cluster and the centers of all the other clusters.
FindClusterMedian: Finds the center of a cluster
FindClusterQuantile: Finds The distances between the center of
a cluster and the centers of all other clusters.
MDSPlot: Applies multidimensional scaling to a clustered
transcriptomics dataset to reduce the clusters to two dimensions and
then plots the clusters.
PamClustering: Applies PAM (Partitioning around Medoids)
clustering to a transcriptomics dataset and appends a cluster column to this
dataset for all genes.
PamParamSelection: Runs PAM with differing numbers of
partitions and returns validation metrics.
QuantilePlots: Finds the quartiles for intercluster distances
and plots these distances as a set of histograms
SingletonNameFinder: Finds the genes which belong to
singleton clusters.
—————————————————————————-
Correlation
CorAnalysis: Ranks correlation between a given gene and all
other genes in a dataset. Plots both the given gene and highly correlated
genes for a given correlation value.
CorAnalysisCluster: Correlates the average activity of a
cluster with the average activity of every other cluster.
CorAnalysisClusterDataset: Correlates the average activity of
each cluster with every other cluster in a dataset.
CorAnalysisDataset: Correlates every gene in a dataset with
every other gene in the same dataset. Allows a timelag between genes to be
correlated.
CorAnalysisPar: Parallel Implementation of CorAnalysis.
CorSignificantPlot: Prints or saves the genes found to be
most significant by CorAnalysis.
—————————————————————————-
Cosinor
CosinorAnalysis: Fits cosinor models to transcriptomics data
and plots the best-fitting models using ggplot2.
CosinorAnalysisPar: Parallel Implementation of
CosinorAnalysis.
CosinorPlot: Fits a cosinor model to a given gene in a given
dataset and plots the model.
CosinorResidualDatasetPlot: Fits cosinor models and plots the
residuals for multiple genes in a dataset
CosinorResidualPlot: Fits a cosinor model to a gene and plots
the residuals
CosinorSignificantPlot: Prints or saves the genes found to be
most significant by cosinoranalysis.
MultiCosinorTest: Fits a cosinor model and carries out ANOVA
using raw coefficients. Then fits a cosinor model with additonal sine and
cosine terms with a different period. ANOVA tests are carried out on the
more complex model as well as directly comparing the two models.
—————————————————————————-
Cytoscape
CytoscapeFile: Converts a correlation dataframe object into a
format suitable for cytoscape and saves as a csv file.
CytoscapeFilter: Reduces the size of a file intended for
Cytoscape by filtering out the genes/clusters which are not correlated
—————————————————————————–
Fasta Files
ContigGen: Finds all unique contig IDs in a transcriptomics
dataset
FastaSub: Creates a fasta file from only certain sequences in
another fasta file
—————————————————————————-
Filtering
AnovaFilter: Filters a gene activity dataframe via ANOVA.
CombiFilter: Filters a transcriptomics dataset by using
Zerofilter, Anovafilter and Sizefilter.
SizeFilter: Filters the genes with the smallest range from a
transcriptomics dataset.
TFilter: Applies a filter where a t-test is carried out on gene
activity levels between time points.
ZeroFilter: Filters a transcriptomics dataset such that there
is a minimum number of non-zero activity readings for each gene in the
resultant dataset.
—————————————————————————-
RAIN
RainAnalysis: Carries out RAIN analysis on a transcriptomics
dataset.
RainSignificantPlot: Prints or saves plots of the genes found
to be most significant by rainanalysis.
—————————————————————————–
Utility Functions
AbsCorDist: Calculates a distance matrix based on the distance
measure of:
1 - |cor(x, y)|
ActivitySelect: Returns gene activity by either gene name or
row number
FileConflict: Checks if a file which will be created already
exists and, if necessary asks the user if this file should be overwritten.
GeneClean: Removes columns and rows which show no gene activity
over time.
GeneRange: Finds the range of gene activity for each gene in a
dataframe. The median for the replicates is used for each time point.
GeneScale: Centers/scales every gene in a transcriptomics
dataset.
GeneSub: Takes an object where the first column is genenames
(IE a column of known Circadian genes) and subsets from a dataset containing
activity readings for these genes.
ggplot.cosinor.lm: Adapted from the Cosinor package by
Michael Sachs. Given a cosinor.lm model fit, generate a plot of the data with
the fitted values.
MakeTimevector: Produces a vector of time values for the gene
activity readings.
MedList: Provides a dataframe of median values at each
time point for each gene from a transcriptomics dataset.
TAnalysis: A t.test is carried out on gene
activity levels between time points and the number of significant increases
& decreases is returned.
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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