Learn R Programming

nanny: high-level data analysis and manipulation in tidyverse style.

It tidies up your playground!

Functions/utilities available

FunctionDescription
reduce_dimensionsPerform dimensionality reduction (PCA, MDS, tSNE)
rotate_dimensionsRotate two dimensions of a degree
cluster_elementsLabels elements with cluster identity
remove_redundancyFilter out elements with highly correlated features
fill_missingFill values of missing element/feature pairs
impute_missingImpute values of missing element/feature pairs
permute_nestFrom one column build a two permuted columns with nested information
combine_nestFrom one column build a two combination columns with nested information
keep_variableKeep top variable features
lower_triangularkeep rows corresponding to a lower triangular matrix
UtilitiesDescription
as_matrixRobustly convert a tibble to matrix
subsetSelect columns with information relative to a column of interest

Minimal input data frame

elementfeaturevalue
chr or fctrchr or fctrnumeric

Output data frame

elementfeaturevaluenew information
chr or fctrchr or fctrnumeric…

Installation

devtools::install_github("stemangiola/nanny")

Introduction

nanny is a collection of wrapper functions for high level data analysis and manipulation following the tidy paradigm.

Tidy data

mtcars_tidy = 
    mtcars %>% 
    as_tibble(rownames="car_model") %>% 
    mutate_at(vars(-car_model,- hp, -vs), scale) %>%
    gather(feature, value, -car_model, -hp, -vs)

mtcars_tidy
## # A tibble: 288 x 5
##    car_model            hp    vs feature  value
##    <chr>             <dbl> <dbl> <chr>    <dbl>
##  1 Mazda RX4           110     0 mpg      0.151
##  2 Mazda RX4 Wag       110     0 mpg      0.151
##  3 Datsun 710           93     1 mpg      0.450
##  4 Hornet 4 Drive      110     1 mpg      0.217
##  5 Hornet Sportabout   175     0 mpg     -0.231
##  6 Valiant             105     1 mpg     -0.330
##  7 Duster 360          245     0 mpg     -0.961
##  8 Merc 240D            62     1 mpg      0.715
##  9 Merc 230             95     1 mpg      0.450
## 10 Merc 280            123     1 mpg     -0.148
## # … with 278 more rows

reduce_dimensions

We may want to reduce the dimensions of our data, for example using PCA, MDS of tSNE algorithms. reduce_dimensions takes a tibble, column names (as symbols; for element, feature and value) and a method (e.g., MDS, PCA or tSNE) as arguments and returns a tibble with additional columns for the reduced dimensions.

MDS

mtcars_tidy_MDS =
  mtcars_tidy %>%
  reduce_dimensions(car_model, feature, value, method="MDS", .dims = 3)

On the x and y axes axis we have the reduced dimensions 1 to 3, data is coloured by cell type.

mtcars_tidy_MDS %>% subset(car_model)  %>% select(contains("Dim"), everything())
## # A tibble: 32 x 6
##       Dim1    Dim2     Dim3 car_model            hp    vs
##      <dbl>   <dbl>    <dbl> <chr>             <dbl> <dbl>
##  1 -0.342   0.499  -0.0853  Mazda RX4           110     0
##  2 -0.323   0.439   0.00461 Mazda RX4 Wag       110     0
##  3 -0.787  -0.140  -0.130   Datsun 710           93     1
##  4  0.164  -0.670  -0.133   Hornet 4 Drive      110     1
##  5  0.602  -0.205  -0.339   Hornet Sportabout   175     0
##  6  0.284  -0.777  -0.0186  Valiant             105     1
##  7  0.810   0.172  -0.179   Duster 360          245     0
##  8 -0.417  -0.452   0.335   Merc 240D            62     1
##  9 -0.526  -0.715   0.679   Merc 230             95     1
## 10 -0.0277  0.0696  0.387   Merc 280            123     1
## # … with 22 more rows
mtcars_tidy_MDS %>%
    subset(car_model) %>%
  GGally::ggpairs(columns = 4:6, ggplot2::aes(colour=factor(vs)))

PCA

mtcars_tidy_PCA =
  mtcars_tidy %>%
  reduce_dimensions(car_model, feature, value, method="PCA", .dims = 3)

On the x and y axes axis we have the reduced dimensions 1 to 3, data is coloured by cell type.

mtcars_tidy_PCA %>% subset(car_model) %>% select(contains("PC"), everything())
## # A tibble: 32 x 6
##        PC1     PC2       PC3 car_model            hp    vs
##      <dbl>   <dbl>     <dbl> <chr>             <dbl> <dbl>
##  1 -0.136   0.274  -0.0602   Mazda RX4           110     0
##  2 -0.145   0.253  -0.0261   Mazda RX4 Wag       110     0
##  3 -0.212  -0.0619 -0.146    Datsun 710           93     1
##  4  0.0767 -0.331  -0.130    Hornet 4 Drive      110     1
##  5  0.205  -0.0498 -0.196    Hornet Sportabout   175     0
##  6  0.0920 -0.306  -0.0449   Valiant             105     1
##  7  0.210   0.130   0.000724 Duster 360          245     0
##  8 -0.124  -0.252   0.219    Merc 240D            62     1
##  9 -0.0997 -0.283   0.243    Merc 230             95     1
## 10 -0.0242  0.0185  0.547    Merc 280            123     1
## # … with 22 more rows
mtcars_tidy_PCA %>%
     subset(car_model) %>%
  GGally::ggpairs(columns = 4:6, ggplot2::aes(colour=factor(vs)))

tSNE

mtcars_tidy_tSNE =
    mtcars_tidy %>% 
    reduce_dimensions(car_model, feature, value, method = "tSNE")

Plot

mtcars_tidy_tSNE %>%
    subset(car_model) %>%
    select(contains("tSNE"), everything()) 
## # A tibble: 32 x 5
##     tSNE1   tSNE2 car_model            hp    vs
##     <dbl>   <dbl> <chr>             <dbl> <dbl>
##  1 -63.2    0.149 Mazda RX4           110     0
##  2 -65.6   -7.21  Mazda RX4 Wag       110     0
##  3 -60.8  -50.2   Datsun 710           93     1
##  4  28.4    0.525 Hornet 4 Drive      110     1
##  5  98.9   23.6   Hornet Sportabout   175     0
##  6  36.0    2.21  Valiant             105     1
##  7  74.7   48.5   Duster 360          245     0
##  8  -5.49  -6.48  Merc 240D            62     1
##  9  -7.30 -15.1   Merc 230             95     1
## 10 -33.1    1.88  Merc 280            123     1
## # … with 22 more rows
mtcars_tidy_tSNE %>%
    subset(car_model) %>%
    ggplot(aes(x = `tSNE1`, y = `tSNE2`, color=factor(vs))) + geom_point() + my_theme

rotate_dimensions

We may want to rotate the reduced dimensions (or any two numeric columns really) of our data, of a set angle. rotate_dimensions takes a tibble, column names (as symbols; for element, feature and value) and an angle as arguments and returns a tibble with additional columns for the rotated dimensions. The rotated dimensions will be added to the original data set as <NAME OF DIMENSION> rotated <ANGLE> by default, or as specified in the input arguments.

mtcars_tidy_MDS.rotated =
  mtcars_tidy_MDS %>%
    rotate_dimensions(`Dim1`, `Dim2`, .element = car_model, rotation_degrees = 45, action="get")

Original On the x and y axes axis we have the first two reduced dimensions, data is coloured by cell type.

mtcars_tidy_MDS.rotated %>%
    ggplot(aes(x=`Dim1`, y=`Dim2`, color=factor(vs) )) +
  geom_point() +
  my_theme

Rotated On the x and y axes axis we have the first two reduced dimensions rotated of 45 degrees, data is coloured by cell type.

mtcars_tidy_MDS.rotated %>%
    ggplot(aes(x=`Dim1 rotated 45`, y=`Dim2 rotated 45`, color=factor(vs) )) +
  geom_point() +
  my_theme

cluster_elements

We may want to cluster our data (e.g., using k-means element-wise). cluster_elements takes as arguments a tibble, column names (as symbols; for element, feature and value) and returns a tibble with additional columns for the cluster annotation. At the moment only k-means clustering is supported, the plan is to introduce more clustering methods.

k-means

mtcars_tidy_cluster = mtcars_tidy_MDS %>%
  cluster_elements(car_model, feature, value, method="kmeans",  centers = 2, action="get" )

We can add cluster annotation to the MDS dimesion reduced data set and plot.

 mtcars_tidy_cluster %>%
    ggplot(aes(x=`Dim1`, y=`Dim2`, color=cluster_kmeans)) +
  geom_point() +
  my_theme

drop_redundant

We may want to remove redundant elements from the original data set (e.g., elements or features), for example if we want to define cell-type specific signatures with low element redundancy. remove_redundancy takes as arguments a tibble, column names (as symbols; for element, feature and value) and returns a tibble dropped recundant elements (e.g., elements). Two redundancy estimation approaches are supported:

removal of highly correlated clusters of elements (keeping a representative) with method=“correlation”

mtcars_tidy_non_redundant =
    mtcars_tidy_MDS %>%
  remove_redundancy(car_model, feature, value)

We can visualise how the reduced redundancy with the reduced dimentions look like

mtcars_tidy_non_redundant %>%
    subset(car_model) %>%
    ggplot(aes(x=`Dim1`, y=`Dim2`, color=factor(vs))) +
  geom_point() +
  my_theme

fill_missing

This function allows to obtain a rectangular underlying data structure, where every element has one feature, filling missing element/feature pairs with a value of choice (e.g., 0)

We create a non-rectangular data frame

mtcars_tidy_non_rectangular = mtcars_tidy %>% slice(-1)

We fill the missing value with the value of 0

mtcars_tidy_non_rectangular %>% fill_missing(car_model, feature, value, fill_with = 0)
## # A tibble: 288 x 5
##    car_model            hp    vs feature  value
##    <chr>             <dbl> <dbl> <chr>    <dbl>
##  1 Mazda RX4           110     0 mpg      0    
##  2 Mazda RX4 Wag       110     0 mpg      0.151
##  3 Datsun 710           93     1 mpg      0.450
##  4 Hornet 4 Drive      110     1 mpg      0.217
##  5 Hornet Sportabout   175     0 mpg     -0.231
##  6 Valiant             105     1 mpg     -0.330
##  7 Duster 360          245     0 mpg     -0.961
##  8 Merc 240D            62     1 mpg      0.715
##  9 Merc 230             95     1 mpg      0.450
## 10 Merc 280            123     1 mpg     -0.148
## # … with 278 more rows

impute_missing

This function allows to obtain a rectangular underlying data structure, where every element has one feature, imputig missing element/feature pairs with a function of choice (e.g., median)

We impute the missing value with the a summary value (median by default) according to a grouping

mtcars_tidy_non_rectangular %>% mutate(vs = factor(vs)) %>% 
    impute_missing( car_model, feature, value,  ~ vs) %>%
    
    # Print imputed first
    arrange(car_model != "Mazda RX4" | feature != "mpg")
## # A tibble: 288 x 5
##    car_model              hp vs    feature  value
##    <chr>               <dbl> <fct> <chr>    <dbl>
##  1 Mazda RX4             110 0     mpg     -0.762
##  2 Mazda RX4 Wag         110 0     mpg      0.151
##  3 Hornet Sportabout     175 0     mpg     -0.231
##  4 Duster 360            245 0     mpg     -0.961
##  5 Merc 450SE            180 0     mpg     -0.612
##  6 Merc 450SL            180 0     mpg     -0.463
##  7 Merc 450SLC           180 0     mpg     -0.811
##  8 Cadillac Fleetwood    205 0     mpg     -1.61 
##  9 Lincoln Continental   215 0     mpg     -1.61 
## 10 Chrysler Imperial     230 0     mpg     -0.894
## # … with 278 more rows

permute_nest

From one column build a two permuted columns with nested information

mtcars_tidy_permuted = 
    mtcars_tidy %>%
    permute_nest(car_model, c(feature,value))

mtcars_tidy_permuted
## # A tibble: 992 x 3
##    car_model_1 car_model_2        data             
##    <fct>       <fct>              <list>           
##  1 AMC Javelin Cadillac Fleetwood <tibble [18 × 3]>
##  2 AMC Javelin Camaro Z28         <tibble [18 × 3]>
##  3 AMC Javelin Chrysler Imperial  <tibble [18 × 3]>
##  4 AMC Javelin Datsun 710         <tibble [18 × 3]>
##  5 AMC Javelin Dodge Challenger   <tibble [18 × 3]>
##  6 AMC Javelin Duster 360         <tibble [18 × 3]>
##  7 AMC Javelin Ferrari Dino       <tibble [18 × 3]>
##  8 AMC Javelin Fiat 128           <tibble [18 × 3]>
##  9 AMC Javelin Fiat X1-9          <tibble [18 × 3]>
## 10 AMC Javelin Ford Pantera L     <tibble [18 × 3]>
## # … with 982 more rows

combine_nest

From one column build a two combination columns with nested information

mtcars_tidy %>%
    combine_nest(car_model, value)
## # A tibble: 496 x 3
##    car_model_1 car_model_2        data             
##    <fct>       <fct>              <list>           
##  1 AMC Javelin Cadillac Fleetwood <tibble [18 × 2]>
##  2 AMC Javelin Camaro Z28         <tibble [18 × 2]>
##  3 AMC Javelin Chrysler Imperial  <tibble [18 × 2]>
##  4 AMC Javelin Datsun 710         <tibble [18 × 2]>
##  5 AMC Javelin Dodge Challenger   <tibble [18 × 2]>
##  6 AMC Javelin Duster 360         <tibble [18 × 2]>
##  7 AMC Javelin Ferrari Dino       <tibble [18 × 2]>
##  8 AMC Javelin Fiat 128           <tibble [18 × 2]>
##  9 AMC Javelin Fiat X1-9          <tibble [18 × 2]>
## 10 AMC Javelin Ford Pantera L     <tibble [18 × 2]>
## # … with 486 more rows

lower_triangular

keep rows corresponding to a lower triangular matrix

mtcars_tidy_permuted %>%
    
    # Summarise mpg
    mutate(data = map(data, ~ .x %>% filter(feature == "mpg") %>% summarise(mean(value)))) %>%
    unnest(data) %>%
    
    # Lower triangular
    lower_triangular(car_model_1, car_model_2,  `mean(value)`)
## # A tibble: 496 x 3
##    car_model_1    car_model_2       `mean(value)`
##    <chr>          <chr>                     <dbl>
##  1 Mazda RX4      Mazda RX4 Wag           0.151  
##  2 Mazda RX4      Datsun 710              0.300  
##  3 Mazda RX4 Wag  Datsun 710              0.300  
##  4 Mazda RX4      Hornet 4 Drive          0.184  
##  5 Mazda RX4 Wag  Hornet 4 Drive          0.184  
##  6 Datsun 710     Hornet 4 Drive          0.333  
##  7 Mazda RX4      Hornet Sportabout      -0.0399 
##  8 Mazda RX4 Wag  Hornet Sportabout      -0.0399 
##  9 Datsun 710     Hornet Sportabout       0.109  
## 10 Hornet 4 Drive Hornet Sportabout      -0.00674
## # … with 486 more rows

keep_variable

Keep top variable features

mtcars_tidy %>%
    keep_variable(car_model, feature, value, top=10)
## # A tibble: 288 x 5
##    car_model            hp    vs feature  value
##    <chr>             <dbl> <dbl> <chr>    <dbl>
##  1 Mazda RX4           110     0 mpg      0.151
##  2 Mazda RX4 Wag       110     0 mpg      0.151
##  3 Datsun 710           93     1 mpg      0.450
##  4 Hornet 4 Drive      110     1 mpg      0.217
##  5 Hornet Sportabout   175     0 mpg     -0.231
##  6 Valiant             105     1 mpg     -0.330
##  7 Duster 360          245     0 mpg     -0.961
##  8 Merc 240D            62     1 mpg      0.715
##  9 Merc 230             95     1 mpg      0.450
## 10 Merc 280            123     1 mpg     -0.148
## # … with 278 more rows

as_matrix

Robustly convert a tibble to matrix

mtcars_tidy %>%
    select(car_model, feature, value) %>%
    spread(feature, value) %>%
    as_matrix(rownames = car_model) %>%
    head()
##                            am       carb       cyl       disp       drat
## AMC Javelin        -0.8141431 -0.5030337  1.014882  0.5912449 -0.8351978
## Cadillac Fleetwood -0.8141431  0.7352031  1.014882  1.9467538 -1.2466598
## Camaro Z28         -0.8141431  0.7352031  1.014882  0.9623962  0.2495658
## Chrysler Imperial  -0.8141431  0.7352031  1.014882  1.6885616 -0.6855752
## Datsun 710          1.1899014 -1.1221521 -1.224858 -0.9901821  0.4739996
## Dodge Challenger   -0.8141431 -0.5030337  1.014882  0.7042040 -1.5646078
##                          gear        mpg        qsec         wt
## AMC Javelin        -0.9318192 -0.8114596 -0.30708866  0.2225442
## Cadillac Fleetwood -0.9318192 -1.6078826  0.07344945  2.0775048
## Camaro Z28         -0.9318192 -1.1267104 -1.36476075  0.6364610
## Chrysler Imperial  -0.9318192 -0.8944204 -0.23993487  2.1745964
## Datsun 710          0.4235542  0.4495434  0.42600682 -0.9170046
## Dodge Challenger   -0.9318192 -0.7616832 -0.54772305  0.3094156

subset

Select columns with information relative to a column of interest

mtcars_tidy %>%
    subset(car_model)
## # A tibble: 32 x 3
##    car_model            hp    vs
##    <chr>             <dbl> <dbl>
##  1 Mazda RX4           110     0
##  2 Mazda RX4 Wag       110     0
##  3 Datsun 710           93     1
##  4 Hornet 4 Drive      110     1
##  5 Hornet Sportabout   175     0
##  6 Valiant             105     1
##  7 Duster 360          245     0
##  8 Merc 240D            62     1
##  9 Merc 230             95     1
## 10 Merc 280            123     1
## # … with 22 more rows

nest_subset

Nest a data frame based on the columns with information relative to the column provided to nest

mtcars_tidy %>% nest_subset(data = -car_model)
## # A tibble: 32 x 4
##    car_model            hp    vs data            
##    <chr>             <dbl> <dbl> <list>          
##  1 Mazda RX4           110     0 <tibble [9 × 2]>
##  2 Mazda RX4 Wag       110     0 <tibble [9 × 2]>
##  3 Datsun 710           93     1 <tibble [9 × 2]>
##  4 Hornet 4 Drive      110     1 <tibble [9 × 2]>
##  5 Hornet Sportabout   175     0 <tibble [9 × 2]>
##  6 Valiant             105     1 <tibble [9 × 2]>
##  7 Duster 360          245     0 <tibble [9 × 2]>
##  8 Merc 240D            62     1 <tibble [9 × 2]>
##  9 Merc 230             95     1 <tibble [9 × 2]>
## 10 Merc 280            123     1 <tibble [9 × 2]>
## # … with 22 more rows

ADD versus GET versus ONLY modes

Every function takes a tidyfeatureomics structured data as input, and (i) with action=“add” outputs the new information joint to the original input data frame (default), (ii) with action=“get” the new information with the element or feature relative informatin depending on what the analysis is about, or (iii) with action=“only” just the new information. For example, from this data set

  mtcars_tidy
## # A tibble: 288 x 5
##    car_model            hp    vs feature  value
##    <chr>             <dbl> <dbl> <chr>    <dbl>
##  1 Mazda RX4           110     0 mpg      0.151
##  2 Mazda RX4 Wag       110     0 mpg      0.151
##  3 Datsun 710           93     1 mpg      0.450
##  4 Hornet 4 Drive      110     1 mpg      0.217
##  5 Hornet Sportabout   175     0 mpg     -0.231
##  6 Valiant             105     1 mpg     -0.330
##  7 Duster 360          245     0 mpg     -0.961
##  8 Merc 240D            62     1 mpg      0.715
##  9 Merc 230             95     1 mpg      0.450
## 10 Merc 280            123     1 mpg     -0.148
## # … with 278 more rows

action=“add” (Default) We can add the MDS dimensions to the original data set

  mtcars_tidy %>%
    reduce_dimensions(
        car_model, feature, value, 
        method="MDS" ,
        .dims = 3,
        action="add"
    )
## # A tibble: 288 x 8
##    car_model            hp    vs feature  value    Dim1    Dim2     Dim3
##    <chr>             <dbl> <dbl> <chr>    <dbl>   <dbl>   <dbl>    <dbl>
##  1 Mazda RX4           110     0 mpg      0.151 -0.342   0.499  -0.0853 
##  2 Mazda RX4 Wag       110     0 mpg      0.151 -0.323   0.439   0.00461
##  3 Datsun 710           93     1 mpg      0.450 -0.787  -0.140  -0.130  
##  4 Hornet 4 Drive      110     1 mpg      0.217  0.164  -0.670  -0.133  
##  5 Hornet Sportabout   175     0 mpg     -0.231  0.602  -0.205  -0.339  
##  6 Valiant             105     1 mpg     -0.330  0.284  -0.777  -0.0186 
##  7 Duster 360          245     0 mpg     -0.961  0.810   0.172  -0.179  
##  8 Merc 240D            62     1 mpg      0.715 -0.417  -0.452   0.335  
##  9 Merc 230             95     1 mpg      0.450 -0.526  -0.715   0.679  
## 10 Merc 280            123     1 mpg     -0.148 -0.0277  0.0696  0.387  
## # … with 278 more rows

action=“get” We can add the MDS dimensions to the original data set selecting just the element-wise column

  mtcars_tidy %>%
    reduce_dimensions(
        car_model, feature, value, 
        method="MDS" ,
        .dims = 3,
        action="get"
    )
## # A tibble: 32 x 6
##    car_model            hp    vs    Dim1    Dim2     Dim3
##    <chr>             <dbl> <dbl>   <dbl>   <dbl>    <dbl>
##  1 Mazda RX4           110     0 -0.342   0.499  -0.0853 
##  2 Mazda RX4 Wag       110     0 -0.323   0.439   0.00461
##  3 Datsun 710           93     1 -0.787  -0.140  -0.130  
##  4 Hornet 4 Drive      110     1  0.164  -0.670  -0.133  
##  5 Hornet Sportabout   175     0  0.602  -0.205  -0.339  
##  6 Valiant             105     1  0.284  -0.777  -0.0186 
##  7 Duster 360          245     0  0.810   0.172  -0.179  
##  8 Merc 240D            62     1 -0.417  -0.452   0.335  
##  9 Merc 230             95     1 -0.526  -0.715   0.679  
## 10 Merc 280            123     1 -0.0277  0.0696  0.387  
## # … with 22 more rows

action=“only” We can get just the MDS dimensions relative to each element

  mtcars_tidy %>%
    reduce_dimensions(
        car_model, feature, value, 
        method="MDS" ,
        .dims = 3,
        action="only"
    )
## # A tibble: 32 x 4
##    car_model            Dim1    Dim2     Dim3
##    <chr>               <dbl>   <dbl>    <dbl>
##  1 Mazda RX4         -0.342   0.499  -0.0853 
##  2 Mazda RX4 Wag     -0.323   0.439   0.00461
##  3 Datsun 710        -0.787  -0.140  -0.130  
##  4 Hornet 4 Drive     0.164  -0.670  -0.133  
##  5 Hornet Sportabout  0.602  -0.205  -0.339  
##  6 Valiant            0.284  -0.777  -0.0186 
##  7 Duster 360         0.810   0.172  -0.179  
##  8 Merc 240D         -0.417  -0.452   0.335  
##  9 Merc 230          -0.526  -0.715   0.679  
## 10 Merc 280          -0.0277  0.0696  0.387  
## # … with 22 more rows

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Version

Install

install.packages('nanny')

Monthly Downloads

8

Version

0.1.8

License

GPL-3

Maintainer

Stefano Mangiola

Last Published

June 13th, 2020

Functions in nanny (0.1.8)

error_if_wrong_input

Check whether there are NA counts
keep_variable

Keep top variable features across elements
rotate_dimensions

Rotate two dimensions (e.g., principal components) of an arbitrary angle
lower_triangular

Keep rows corresponding of a lower triangular matrix built from two columns
fill_NA_using_formula

This function is needed for DE in case the matrix is not rectangular, but includes NA
fill_missing

Fill feature value if missing from element-feature pairs
permute_nest

Permute columns and nest data for each permutation
get_reduced_dimensions_TSNE_bulk

Get principal component information to a tibble using tSNE
select_closest_pairs

Sub function of remove_redundancy_elements_though_reduced_dimensions
get_rotated_dimensions

Get rotated dimensions of two principal components or MDS dimension of choice, of an angle
ifelse2_pipe

This is a generalisation of ifelse that accepts an object and return an objects
fill_NA_using_value

This function is needed for DE in case the matrix is not rectangular, but includes NA
error_if_duplicated_genes

Check whether there are duplicated genes/transcripts
get_x_y_annotation_columns

get_x_y_annotation_columns
reduce_dimensions

Dimension reduction of the feature value data
subset

Extract selected-column-wise information
error_if_log_transformed

Check whether a numeric vector has been log transformed
nest_subset

Nest according to selected-column-wise information
test_data

Example data set
check_if_duplicated_genes

Check whether there are duplicated genes/transcripts
mtcars_tidy

Cibersort reference
quo_names

Convert array of quosure (e.g. c(col_a, col_b)) into character vector
get_clusters_SNN_bulk

Get SNN shared nearest neighbour clusters to a tibble
remove_redundancy_elements_through_correlation

Drop redundant elements (e.g., elements) for which feature (e.g., genes) aboundances are correlated
remove_redundancy_elements_though_reduced_dimensions

Identifies the closest pairs in a MDS contaxt and return one of them
get_reduced_dimensions_MDS_bulk

Get dimensionality information to a tibble using MDS
get_clusters_kmeans_bulk

Get K-mean clusters to a tibble
remove_redundancy

Drop redundant elements (e.g., elements) for which feature (e.g., feature/gene) aboundances are correlated
nest_subset,tbl-method

nest_subset
parse_formula

.formula parser
impute_missing

Impute feature value if missing from element-feature pairs
ifelse_pipe

This is a generalisation of ifelse that acceots an object and return an objects
test_data2

Example data set reduced
get_reduced_dimensions_PCA_bulk

Get principal component information to a tibble using PCA
check_if_counts_is_na

Check whether there are NA counts
cluster_elements

Get clusters of elements (e.g., elements or features)
check_if_wrong_input

Check whether there are NA counts
combine_nest

Combine columns and nest data for each permutation
drop_attr

Drop attribute to abject
error_if_counts_is_na

Check whether there are NA counts
drop_class

Remove class to abject
add_attr

Add attribute to abject
as_matrix

Get matrix from tibble