Package 'projectR'

Title: Functions for the projection of weights from PCA, CoGAPS, NMF, correlation, and clustering
Description: Functions for the projection of data into the spaces defined by PCA, CoGAPS, NMF, correlation, and clustering.
Authors: Gaurav Sharma, Charles Shin, Jared Slosberg, Loyal Goff, Genevieve Stein-O'Brien
Maintainer: Genevieve Stein-O'Brien <[email protected]>
License: GPL (==2)
Version: 1.21.0
Built: 2024-09-28 03:28:31 UTC
Source: https://github.com/bioc/projectR

Help Index

alluvialMat

Description

Function to provide alluvial matrix for generating alluvial plot

Usage

alluvialMat(
  projection,
  annotations,
  annotationName = "Cell type",
  annotationType = "Cell",
  plot = TRUE,
  minPropExplained = 0.75,
  pvalThreshold = 0.05,
  qvalThreshold = 0.05
)

Arguments

projection

a projection generated from projectR, ensure that full = TRUE while generating projection
annotations

a character vector of annotations for the data
annotationName

a charcter fof collective name of the annotations, default is "Cell type"
annotationType

a character indicating the type of data annotated, default is "Cell"
plot

logical indicating whether to return the alluvial plot, default is TRUE
minPropExplained

threshold for minimum proportion of samples that correspond to a pattern to be used for plotting
pvalThreshold

theshold level of significance for p-value
qvalThreshold

theshold level of significance for Benjamini-Hochberg corrected p-value

Value

A matrix to generate alluvial plots

Examples

projection <- projectR(data=p.ESepiGen4c1l$mRNA.Seq,loadings=AP.RNAseq6l3c3t$Amean,
dataNames = map.ESepiGen4c1l[["GeneSymbols"]], full = TRUE)
alluvialMat(projection,pd.ESepiGen4c1l$Condition)

CoGAPS patterns and genes weights for p.RNAseq6l3c3t

Description

AP.RNAseq6l3c3t contains the output of the gapsRun function in the CoGAPS package for data = p.RNAseq6l3c3t

Usage

AP.RNAseq6l3c3t

Format

A list of 12 items

aucMat

Description

Calculates AUC values for each set of weights for each label and outputs the results as a matrix

Usage

aucMat(labels, weights)

Arguments

labels

a vector of labels whose length is equal to the number of columns in the weight matrix
weights

a matrix of weights from projection analysis

Value

A matrix of AUC values for each set of weights classifying each label.

Examples

projectR(data=p.ESepiGen4c1l$mRNA.Seq,loadings=AP.RNAseq6l3c3t$Amean, 
dataNames = map.ESepiGen4c1l[["GeneSymbols"]]) -> projection
aucMat(pd.ESepiGen4c1l$Condition,projection)

bonferroniCorrectedDifferences

Description

Calculate weighted/unweighted mean difference for each gene between 2 groups

Usage

bonferroniCorrectedDifferences(
  group1,
  group2,
  pvalue,
  diff_weights = NULL,
  mode = "CI"
)

Arguments

group1

count matrix 1
group2

count matrix 2
pvalue

significance value to threshold
diff_weights

loadings to weight the differential expression
mode

statistical approach, confidence intervals(CI) or pvalues(PV)

Generic cluster2pattern function

Description

Function to make patterns of continuous weights from clusters.

Usage

cluster2pattern(clusters, NP, data, ...)

## S4 method for signature 'character'
cluster2pattern(clusters, data)

## S4 method for signature 'numeric'
cluster2pattern(clusters, data)

## S4 method for signature 'kmeans'
cluster2pattern(clusters, data)

## S4 method for signature 'hclust'
cluster2pattern(clusters, NP, data = NA)

Arguments

clusters

a cluster object which could be either an hclust or a kmeans object
NP

number of desired patterns
data

data used to make clusters object
...

Additional arguments to cluster2pattern

Value

An object of class pclust containing pattern weights corresponding for each cluster.

Examples

k.RNAseq6l3c3t<-kmeans(t(p.RNAseq6l3c3t),3)
cluster2pattern(clusters=k.RNAseq6l3c3t,data=p.RNAseq6l3c3t)

distp <- dist(t(p.RNAseq6l3c3t))
hc.RNAseq6l3c3t <- hclust(distp)
cluster2pattern(clusters=hc.RNAseq6l3c3t,NP=3,data=p.RNAseq6l3c3t)

cluster2pattern

Description

class of cluster2pattern output.

Slots

clusterMatrix

matrix of continous values for projection that is output of cluster2pattern function

Generic clusterPlotR function

Description

plotting function for clustering objects

Usage

clusterPlotR(cData, cls, x, NC, ...)

## S4 method for signature 'ANY,kmeans'
clusterPlotR(
  cData = NA,
  cls = NA,
  x = NA,
  NC = NA,
  annoIndx = NA,
  label = NULL,
  ...
)

## S4 method for signature 'ANY,hclust'
clusterPlotR(
  cData = NA,
  cls = NA,
  x = NA,
  NC = NA,
  annoIndx = NA,
  label = NULL,
  ...
)

Arguments

cData

data used to get clusters
cls

a cluster (kmeans or hclust) object
x

a vector of length equal to number of samples to use for plotting
NC

vector of integers indicating which clusters to use
...

additional parameters for plotting. ex. pch,cex,col,labels, xlab, etc.
annoIndx

vector indexing into subsets for plotting
label

character vector to use for plotting text, defaults is NULL

Value

A plot of the mean behavior for each cluster

Examples

## Not run: 
 k.RNAseq6l3c3t<-kmeans(p.RNAseq6l3c3t,22)
 clusterPlotR(p.RNAseq6l3c3t, cls=k.RNAseq6l3c3t,NC=1,x=pd.RNAseq6l3c3t$days,
col=pd.RNAseq6l3c3t$color)

## End(Not run)

correlateR

Description

Function to extract genes highly correlated with a gene or reference expression pattern.

Usage

correlateR(genes, dat, threshtype = "R", threshold = 0.7, absR = FALSE, ...)

Arguments

genes

gene or character vector of genes for reference expression pattern
dat

matrix or data frame with genes to be used for to calculate correlation
threshtype

Default "R" indicates thresholding by R value or equivalent. Alternatively, "N" indicates a numerical cut off.
threshold

numeric indicating value at which to make threshold.
absR

logical indicating where to include both positive and negatively correlated genes
...

addtion inputs to cor, such as method

Details

If threshtype is "R" than threshold must be between -1 and 1. Otherwise if top N correlated genes are required, set threshtype as "N" and set threshold = N, i.e, the number of correlated genes required.

Value

A correlation matrix

Examples

cor2T<-correlateR(genes="T", dat=p.RNAseq6l3c3t, threshtype="N", threshold=10, absR=TRUE)

correlateR

Description

class of correlateR output.

Slots

corM

correlation matrix obtained from correlateR

CogapsResult object for microglial_counts

Description

cr_microglia contains the output of the CoGAPS function in the CoGAPS package for data = microglial_counts

Usage

cr_microglial

Format

A CogapsResult object

CogapsResult object for p.RNAseq6l3c3t

Description

CR.RNAseq6l3c3t contains the output of the CoGAPS function in the CoGAPS package for data = p.RNAseq6l3c3t

Usage

CR.RNAseq6l3c3t

Format

A CogapsResult object

Generic geneMatchR function

Description

Matches genes accross datasets

Usage

geneMatchR(
  data1,
  data2,
  data1Names = NULL,
  data2Names = NULL,
  merge = FALSE,
  ...
)

Arguments

data1

a data matrix, typically genes by samples
data2

an amplitude matrix, typically genes by factors
data1Names

rownames of data matrix, for eg genenames
data2Names

rownames of amplitude matrix to be matched to rownames of datamatrix
merge

logical indicating wether or not to merged data sets
...

Additional arguments to geneMatchR

Value

A list of genes (intersection) in both datasets. (if merge = TRUE, Also returns merged data.)

Examples

geneMatchR(data1=p.ESepiGen4c1l$mRNA.Seq,data2=p.RNAseq6l3c3t,
data1Names=map.ESepiGen4c1l[["GeneSymbols"]])

getTSNE

Description

Function to provide tSNE of projection

Usage

getTSNE(projection, axis = 2, ...)

Arguments

projection

martrix, a projection generated from projectR
axis

integer, either 1 umap of projection or 2 for umap of transpose of projection
...

addtional arguments passed to tsne

Examples

projection <- projectR(data=p.ESepiGen4c1l$mRNA.Seq,loadings=AP.RNAseq6l3c3t$Amean,
dataNames = map.ESepiGen4c1l[["GeneSymbols"]], full = TRUE)
projectionTSNE <- getTSNE(projection)

getUMAP

Description

Function to provide umap of projection

Usage

getUMAP(projection, axis = 2, umapMethod = "naive", umapConfig = umap.defaults)

Arguments

projection

martrix, a projection generated from projectR
axis

integer, either 1 umap of projection or 2 for umap of transpose of projection
umapMethod

character, implementation. Available methods are 'naive' (an implementation written in pure R) and 'umap-learn' (requires python package 'umap-learn')
umapConfig

umap.config, a list of parameters to customize umap embedding

Value

A umap of projection

Examples

library(umap)
projection <- projectR(data=p.ESepiGen4c1l$mRNA.Seq,loadings=AP.RNAseq6l3c3t$Amean,
dataNames = map.ESepiGen4c1l[["GeneSymbols"]], full = TRUE)
umapConfig = umap.defaults
umapConfig$n_neighbors = 3
projectionUMAP <- getUMAP(projection,umapConfig = umapConfig)

log-normalized count data from astrocytes and oligodendrocytes in the p6 mouse cortex.

Description

log-normalized count data from astrocytes and oligodendrocytes in the p6 mouse cortex.

Usage

glial_counts

Format

A gene (rows) by cell (column) matrix

Constructor for cluster2pattern

Description

Constructor for cluster2pattern

Usage

## S4 method for signature 'cluster2pattern'
initialize(.Object, clusterMatrix, ...)

Arguments

.Object

clusterMatrix object
clusterMatrix

matrix of continous values for projection that is output of cluster2pattern function
...

additional arguments to intialize cluster2pattern

Value

initialized cluster2pattern object

Constructor for correlateR

Description

Constructor for correlateR

Usage

## S4 method for signature 'correlateR'
initialize(.Object, corM, ...)

Arguments

.Object

correlateR object
corM

correlation matrix obtained from correlateR
...

additional arguments to intialize correlateR

Value

initialized correlateR object

Constructor for rotatoR

Description

Constructor for rotatoR

Usage

## S4 method for signature 'rotatoR'
initialize(.Object, rotatedM, ...)

Arguments

.Object

rotatoR object
rotatedM

rotated matrix from rotatoR function
...

additional arguments to intialize rotatoR

Value

initialized rotatoR object

Generic intersectoR function

Description

A function to find and test the intersecting values of two sets of objects, presumably the genes associated with patterns in two different datasets. Both the input objects need to be of the same type either kmeans or hclust.

Usage

intersectoR(pSet1, pSet2, pval, ...)

## S4 method for signature 'kmeans,kmeans'
intersectoR(pSet1 = NA, pSet2 = NA, pval = 0.05, full = FALSE)

## S4 method for signature 'hclust,hclust'
intersectoR(pSet1 = NA, pSet2 = NA, pval = 0.05, full = FALSE, k = NULL)

Arguments

pSet1

an object for a set of patterns where each entry is a set of genes associated with a single pattern
pSet2

an object for a second set of patterns where each entry is a set of genes associated with a single pattern
pval

the maximum p-value considered significant
...

additional parameters depending on input object
full

logical indicating whether to return full data frame of signigicantly overlapping sets. Default is false will return summary matrix.
k

Numeric giving cut height for hclust objects, if a vector is given arguments will be applied to pSet1 and pSet2 in that order

Value

A list containing: Overlap matrix, overlap index, and overlapping sets.

Examples

ESepiGen4c1lmRNASeq <- p.ESepiGen4c1l$mRNA.Seq
rownames(ESepiGen4c1lmRNASeq) <- map.ESepiGen4c1l$GeneSymbols

k.RNAseq6l3c3t<-kmeans(p.RNAseq6l3c3t,22)
k.ESepiGen4c1l<-kmeans(ESepiGen4c1lmRNASeq,10)
intersectoR(k.RNAseq6l3c3t, k.ESepiGen4c1l, pval=.05)

h.RNAseq6l3c3t<-hclust(as.dist(1-(cor(t(p.RNAseq6l3c3t)))))
h.ESepiGen4c1l<-hclust(as.dist(1-(cor(t(ESepiGen4c1lmRNASeq)))))
intersectoR(pSet1=h.ESepiGen4c1l, pSet2=h.RNAseq6l3c3t, pval=.05, k=c(3,4))

RNAseqing and ChIPSeq of matched genes in differentiated human iPS cells

Description

map.ESepiGen4c1l contains gene annotations

Usage

map.ESepiGen4c1l

Format

A data frames with 93 rows and 9 variables:

References

1. Gifford, C. A. et al. Transcriptional and epigenetic dynamics during specification of human embryonic stem cells. Cell 153, 1149-1163 (2013).

RNAseqing from human 3 iPS & 3 ES cell lines in 3 experimental condition at 3 time points

Description

map.RNAseq6l3c3 contains gene annotations for polyA bulk sequencing of 6 cell lines in 3 experimental condition at 3 time points.

Usage

map.RNAseq6l3c3t

Format

A data frames with 108 rows and 54 variables:

log-normalized count data from microglial cells in the p6 mouse cortex.

Description

log-normalized count data from microglial cells in the p6 mouse cortex.

Usage

microglial_counts

Format

A gene (rows) by cell (column) matrix

Generic multivariateAnalysisR function

Description

Performs multivariate analysis across specified clusters in datasets

Usage

multivariateAnalysisR(
  significanceLevel = 0.05,
  patternKeys,
  seuratobj,
  dictionaries,
  customNames = NULL,
  exclusive = TRUE,
  exportFolder = "",
  ANOVAwidth = 1000,
  ANOVAheight = 1000,
  CIwidth = 1000,
  CIheight = 1000,
  CIspacing = 1
)

Arguments

significanceLevel

double value for testing significance in ANOVA test
patternKeys

list of strings indicating pattern subsets from seuratobj to be analyzed
seuratobj

Seurat Object Data containing patternKeys in meta.data
dictionaries

list of dictionaries indicating clusters to be compared
customNames

list of custom names for clusters in corresponding order
exclusive

boolean value for determining interpolation between params in clusters
exportFolder

name of folder to store exported graphs and CSV files
ANOVAwidth

width of ANOVA png
ANOVAheight

height of ANOVA png
CIwidth

width of CI png
CIheight

height of CI png
CIspacing

spacing between each CI in CI graph

Value

a sorted list of ANOVA and CI results; ANOVA and Confidence Intervals are visualized and exported in both PNG and CSV

Truncated Seurat Object with latent space projection done to unspecified cells in different stages for multivariateAnalysisR analysis

Description

Truncated Seurat Object with latent space projection done to unspecified cells in different stages for multivariateAnalysisR analysis

Usage

multivariateAnalysisR_seurat_test

Format

A Seurat Object with 31034 observations of 4 variables in meta.data:

RNAseqing and ChIPSeq of matched genes in differentiated human iPS cells

Description

p.ESepiGen4c1l contains log2(RPKM + 1) values for polyA bulk sequencing and log2 counts of normalized ChIPSeq reads of 1 cell lines with 2 replicates in 4 experimental conditions at a single time point.

Usage

p.ESepiGen4c1l

Format

p.ESepiGen4c1l is a list of 6 data frames each with with 93 rows and between 4 and 9 variables:

References

1. Gifford, C. A. et al. Transcriptional and epigenetic dynamics during specification of human embryonic stem cells. Cell 153, 1149-1163 (2013).

RNAseqing from human 3 iPS & 3 ES cell lines in 3 experimental condition at 3 time points

Description

p.RNAseq6l3c3 contains log2(RPKM + 1) values for polyA bulk sequencing of 6 cell lines in 3 experimental condition at 3 time points.

Usage

p.RNAseq6l3c3t

Format

A data frames with 108 rows and 54 variables:

RNAseqing and ChIPSeq of matched genes in differentiated human iPS cells

Description

pd.ESepiGen4c1l.4cond contains sample phenotype and experimental information

Usage

pd.ESepiGen4c1l

Format

A data frames with 9 rows and 2 variables:

References

1. Gifford, C. A. et al. Transcriptional and epigenetic dynamics during specification of human embryonic stem cells. Cell 153, 1149-1163 (2013).

RNAseqing from human 3 iPS & 3 ES cell lines in 3 experimental condition at 3 time points

Description

pd.RNAseq6l3c3t contains sample phenotype and experimental information for polyA bulk sequencing of 6 cell lines in 3 experimental condition at 3 time points.

Usage

pd.RNAseq6l3c3t

Format

A data frames with 54 rows and 38 variables:

pdVolcano

Description

Generate volcano plot and gate genes based on fold change and pvalue, includes vectors that can be used with fast gene set enrichment (fgsea)

Usage

pdVolcano(
  result,
  FC = 0.2,
  pvalue = NULL,
  subset = NULL,
  filter.inf = FALSE,
  label.num = 5L,
  display = TRUE
)

Arguments

result

result output from projectionDriveR function in PV mode
FC

fold change threshold, default at 0.2
pvalue

significance threshold, default set stored pvalue
subset

vector of gene names to subset the plot by
filter.inf

remove genes that have pvalues below machine double minimum value
label.num

Number of genes to label on either side of the volcano plot, default 5
display

boolean. Whether or not to plot and display volcano plots

Value

A list with weighted and unweighted differential expression metrics

plotConfidenceIntervals

Description

Generate point and line confidence intervals from provided estimates.

Usage

plotConfidenceIntervals(
  confidence_intervals,
  interval_name = c("low", "high"),
  pattern_name = NULL,
  sort = TRUE,
  genes = NULL,
  weights = NULL,
  weights_clip = 0.99,
  weights_vis_norm = "none",
  weighted = FALSE
)

Arguments

confidence_intervals

A dataframe of features x estimates.
interval_name

Estimate column names. Default: c("low","high")
pattern_name

string to use as the title for plots.
sort

Boolean. Sort genes by their estimates (default = TRUE)
genes

a vector with names of genes to include in plot. If sort=F, estimates will be plotted in this order.
weights

optional. weights of features to include as annotation.
weights_clip

optional. quantile of data to clip color scale for improved visualization. Default: 0.99
weights_vis_norm

Which version of weights to visualize as a heatmap. Options are "none" (uses provided weights) or "quantiles". Default: none
weighted

specifies whether the confidence intervals in use are weighted by the pattern and labels plots accordingly

Value

A list with pointrange estimates and a heatmap of pattern weights.

plotVolcano

Description

Volcano plotting function

Usage

plotVolcano(stats, metadata, FC, pvalue, title)

Arguments

stats

data frame with differential expression statistics
metadata

#metadata from pdVolcano
FC

Fold change threshold
pvalue

p value threshold
title

plot title

projectionDriveR

Description

Calculate weighted expression difference between two groups (group1 - group2)

Usage

projectionDriveR(
  cellgroup1,
  cellgroup2,
  loadings,
  pattern_name,
  loadingsNames = NULL,
  pvalue = 1e-05,
  display = TRUE,
  normalize_pattern = TRUE,
  mode = "CI"
)

Arguments

cellgroup1

gene x cell count matrix for cell group 1
cellgroup2

gene x cell count matrix for cell group 2
loadings

A matrix of continuous values defining the features
pattern_name

column of loadings for which drivers will be calculated
loadingsNames

a vector with names of loading rows defaults to rownames
pvalue

confidence level. Default 1e-5
display

boolean. Whether or not to display confidence intervals
normalize_pattern

Boolean. Whether or not to normalize pattern weights
mode

statistical approach, confidence intervals or pvalues. default CI

Value

A list with unweighted/weighted mean differences and differential genes that meet the provided signficance threshold.

Generic projectR function

Description

A function for the projection of new data into a previously defined feature space.

Usage

projectR(data, loadings, dataNames = NULL, loadingsNames = NULL, ...)

## S4 method for signature 'matrix,matrix'
projectR(
  data,
  loadings,
  dataNames = NULL,
  loadingsNames = NULL,
  NP = NA,
  full = FALSE,
  family = "gaussianff",
  bootstrapPval = FALSE,
  bootIter = 1000
)

## S4 method for signature 'dgCMatrix,matrix'
projectR(
  data,
  loadings,
  dataNames = NULL,
  loadingsNames = NULL,
  NP = NA,
  full = FALSE,
  family = "gaussianff"
)

## S4 method for signature 'matrix,LinearEmbeddingMatrix'
projectR(
  data,
  loadings,
  dataNames = NULL,
  loadingsNames = NULL,
  NP = NA,
  full = FALSE,
  model = NA,
  family = "gaussianff",
  bootstrapPval = FALSE,
  bootIter = 1000
)

## S4 method for signature 'matrix,prcomp'
projectR(
  data,
  loadings,
  dataNames = NULL,
  loadingsNames = NULL,
  NP = NA,
  full = FALSE
)

## S4 method for signature 'matrix,rotatoR'
projectR(
  data,
  loadings,
  dataNames = NULL,
  loadingsNames = NULL,
  NP = NA,
  full = FALSE
)

## S4 method for signature 'matrix,correlateR'
projectR(
  data,
  loadings,
  dataNames = NULL,
  loadingsNames = NULL,
  NP = NA,
  full = FALSE,
  bootstrapPval = FALSE,
  bootIter = 1000
)

## S4 method for signature 'matrix,hclust'
projectR(
  data,
  loadings,
  dataNames = NULL,
  loadingsNames = NULL,
  full = FALSE,
  targetNumPatterns,
  sourceData,
  bootstrapPval = FALSE,
  bootIter = 1000
)

## S4 method for signature 'matrix,kmeans'
projectR(
  data,
  loadings,
  dataNames = NULL,
  loadingsNames = NULL,
  full = FALSE,
  sourceData,
  bootstrapPval = FALSE,
  bootIter = 1000
)

## S4 method for signature 'matrix,cluster2pattern'
projectR(
  data,
  loadings,
  dataNames = NULL,
  loadingsNames = NULL,
  full = FALSE,
  sourceData,
  bootstrapPval = FALSE,
  bootIter = 1000
)

Arguments

data

Target dataset into which you will project. It must of type matrix.
loadings

loadings learned from source dataset.
dataNames

a vector containing unique name, i.e. gene names, for the rows of the target dataset to be used to match features with the loadings, if not provided by rownames(data). Order of names in vector must match order of rows in data.
loadingsNames

a vector containing unique names, i.e. gene names, for the rows ofloadings to be used to match features with the data, if not provided by rownames(loadings). Order of names in vector must match order of rows in loadings.
...

Additional arguments to projectR
NP

vector of integers indicating which columns of loadings object to use. The default of NP=NA will use entire matrix.
full

logical indicating whether to return the full model solution. By default only the new pattern object is returned.
family

VGAM family function for model fitting (default: "gaussianff")
bootstrapPval

logical to indicate whether to generate p-values using bootstrap, not available for prcomp and rotatoR objects
bootIter

number of bootstrap iterations, default = 1000
model

Optional arguements to choose method for projection
targetNumPatterns

desired number of patterns with hclust
sourceData

data used to create cluster object

Details

loadings can belong to one of several classes depending on upstream analysis. Currently permitted classes are matrix, CogapsResult, CoGAPS, pclust, prcomp, rotatoR, and correlateR. Please note that loadings should not contain NA.

Value

A matrix of sample weights for each input basis in the loadings matrix (if full=TRUE, full model solution is returned).

Examples

projectR(data=p.ESepiGen4c1l$mRNA.Seq,loadings=AP.RNAseq6l3c3t$Amean,
dataNames = map.ESepiGen4c1l[["GeneSymbols"]])

library("CoGAPS")
# CR.RNAseq6l3c3t <- CoGAPS(p.RNAseq6l3c3t, params = new("CogapsParams", nPatterns=5))
projectR(data=p.ESepiGen4c1l$mRNA.Seq,loadings=CR.RNAseq6l3c3t,
dataNames = map.ESepiGen4c1l[["GeneSymbols"]])

pca.RNAseq6l3c3t<-prcomp(t(p.RNAseq6l3c3t))
pca.ESepiGen4c1l<-projectR(data=p.ESepiGen4c1l$mRNA.Seq,
loadings=pca.RNAseq6l3c3t, dataNames = map.ESepiGen4c1l[["GeneSymbols"]])

pca.RNAseq6l3c3t<-prcomp(t(p.RNAseq6l3c3t))
r.RNAseq6l3c3t<-rotatoR(1,1,-1,-1,pca.RNAseq6l3c3t$rotation[,1:2])
pca.ESepiGen4c1l<-projectR(data=p.ESepiGen4c1l$mRNA.Seq,
loadings=r.RNAseq6l3c3t, dataNames = map.ESepiGen4c1l[["GeneSymbols"]])

c.RNAseq6l3c3t<-correlateR(genes="T", dat=p.RNAseq6l3c3t, threshtype="N",
threshold=10, absR=TRUE)
cor.ESepiGen4c1l<-projectR(data=p.ESepiGen4c1l$mRNA.Seq, loadings=c.RNAseq6l3c3t,
NP="PositiveCOR", dataNames = map.ESepiGen4c1l[["GeneSymbols"]])

library("projectR")
data(p.RNAseq6l3c3t)
nP<-3
kClust<-kmeans(t(p.RNAseq6l3c3t),centers=nP)
kpattern<-cluster2pattern(clusters = kClust, NP = nP, data = p.RNAseq6l3c3t)
p<-as.matrix(p.RNAseq6l3c3t)
projectR(p,kpattern)

CoGAPS patterns learned from the developing mouse retina.

Description

CoGAPS patterns learned from the developing mouse retina.

Usage

retinal_patterns

Format

A gene (rows) by pattern (column) matrix

References

1. Clark, B.S., & Stein-O'Brien G.L., et al. Single-Cell RNA-Seq Analysis of Development Identifies NFI Factors as Regulating Mitotic Exit and Late-Born Cell Specification. Cell 102, 1111-1126 (2019).

rotatoR

Description

a function for rotating two basis about a point or line in that plane

Usage

rotatoR(x1, y1, x2, y2, basisSET)

Arguments

x1

a value describing a the coordinate of a point in the first basis. If no values are provided for x2
y1

a value describing a the coordinate of a point in the second basis
x2

a value describing a the coordinate of the second point in the second basis
y2

a value describing a the coordinate of the second point in the second basis
basisSET

the basis to be rotated

Value

An object of class rotatoR.

Examples

pca.RNAseq6l3c3t<-prcomp(t(p.RNAseq6l3c3t))
 r.RNAseq6l3c3t<-rotatoR(1,1,-1,-1,pca.RNAseq6l3c3t$rotation[,1:2])

rotatoR

Description

class of rotatoR output.

Slots

rotatedM

rotated basis set (matrix) that is output of rotatoR function