In espors/idepGolem: Integrated Differential Expression and Pathway Analysis

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
options(rmarkdown.html_vignette.check_title = FALSE)

# library(idepGolem)
devtools::load_all()

# Make data for pre-processing functions
idep_data <- get_idep_data()

DATABASE <- Sys.getenv("GE_DATABASE")[1]
YOUR_DATA_PATH <- paste0(DATABASE, "data_go/BcellGSE71176_p53.csv")
YOUR_EXPERIMENT_PATH <- paste0(DATABASE, "data_go/BcellGSE71176_p53_sampleInfo.csv")

expression_file <- data.frame(
  datapath = YOUR_DATA_PATH
)
experiment_file <- data.frame(
  datapath = YOUR_EXPERIMENT_PATH
)

load_data <- input_data(
  expression_file = expression_file,
  experiment_file = experiment_file,
  go_button = FALSE,
  demo_data_file = idep_data$demo_data_file,
  demo_metadata_file = idep_data$demo_metadata_file
)

converted <- convert_id(
  query = rownames(load_data$data),
  idep_data = idep_data,
  select_org = "BestMatch"
)

all_gene_info <- gene_info(
  converted = converted,
  select_org = "BestMatch",
  idep_data = idep_data
)

converted_data <- convert_data(
  converted = converted,
  no_id_conversion = FALSE,
  data = load_data$data
)

gene_names <- get_all_gene_names(
  mapped_ids = converted_data$mapped_ids,
  all_gene_info = all_gene_info
)

This instruction builds off the objects and functions from the "Load_Data" vignette. Please see that document before using the functions described below. We will now transform and process the data based off the type that is being studied. The package also provides visualization functions that will provide a foundation for exploratory data analysis and good overview of the data.

pre_process Function

Processing the data requires many options to ensure the data gets to the desired form. This is the data that will be used in the exploratory analysis to come. The data to be processed is the converted_data from the "Load_Data" vignette. The parameters missing_value and data_file_format have a list of input values to select from for processing. These values and descriptions can be found in the tables below. The missing_value parameter determines how missing data entries will be filled in, and the data_file_format parameter tells the function what type of data is being processed. Depending on the data_file_format chosen, additional parameters will need to be provided. These are explained below.

Input (missing_value) Description

---

geneMedian Substitute median expression value for gene across all samples treatAsZero Substitute "0" for all missing values geneMedianInGroup Substitute median expression value for gene within sample group

Input (data_file_format) Description

---

1 Read counts data (recommended) 2 Normalized expression values (RNA-seq FPKM, micro-array, etc.) 3 Fold-changes and corrected P values from CuffDiff or any other program

Read counts data

For read counts data, the parameters min_counts, n_min_samples_counts, counts_transform and counts_log_start need input values. The parameters min_counts and n_min_samples work to filter the genes out of the data that do not have sufficient counts. The function counts the columns that have more counts than min_counts for each gene and filters out the genes with less columns than n_min_samples. For example, if you only wanted genes with more than 0 counts for each sample, you would set min_counts = .5 and n_min_samples = ncol(converted_data). The parameter counts_transform specifies the transformation to perform on the counts data. This parameter has 3 input options which are outlined below. If counts_transform = 1, counts_log_start determines the constant to add to CPM to ensure that there are no log(0) errors. All other parameters can be set to NULL

Input (counts_transform) Description

---

1 EdgeR: log2(CPM+c) (Counts per Million + constant) (Visit https://www.rdocumentation.org/packages/SparkR/versions/2.1.2/topics/log2) 2 VST: variance stabilizing transform (Visit https://www.rdocumentation.org/packages/DESeq2/versions/1.12.3/topics/vst) 3 rlog: regularized log (slow) (Visit https://www.rdocumentation.org/packages/DESeq2/versions/1.12.3/topics/rlog)

Normalized expression values Parameters

If the data being processed is for normalized expression values, the parameters low_filter_fpkm, n_min_samples_fpkm, log_transform_fpkm and log_start_fpkm need input values. The parameters low_filter_fpkm and n_min_samples_fpkm work together to filter out the genes that do not have enough counts. The function will count the columns that are greater than low_filter_fpkm and discard the gene if it is not more than n_min_samples_fpkm columns. The parameter log_transform_fpkm is TRUE/FALSE for whether to perform a log transform on the data or not. If log_transform = TRUE, then log_start_fpkm is the value that will be added to ensure there are no log(0) errors. All other values can be set to NULL.

Fold-changes and corrected P values

For fold-changes and corrected p-values, there is only one additional parameter to provide an input for, no_fdr. This is a logical TRUE/FALSE to specify whether the data contains corrected p-values or not. TRUE denotes fold-change only data. All other parameters can be set to NULL.

The demonstration loaded in the "Load_Data" instruction is read counts data. The code below shows an example use of the pre_process function using the example data. This data has the converted ensembl IDs. We will return a large list of the results of the processing from this function. The objects in this list are defined in the table below.

Output Description

---

data Data matrix that has the pre-process operations performed to it. mean_kurtosis The calculated kurtosis of the mean counts for a gene. (Visit https://www.rdocumentation.org/packages/e1071/versions/1.7-2/topics/kurtosis) raw_counts The raw counts before pre-processing. data_type_warning Set to "1" if iDEP recognizes the data as different than the inputted data_file_format. data_size Length 4 vector giving the dimensions of the data before and after processing. p_vals For fold-change data has p-values, they are returned here.

processed_data <- pre_process(
  data = converted_data$data,
  missing_value = "geneMedian",
  data_file_format = 1,
  low_filter_fpkm = NULL,
  n_min_samples_fpkm = NULL,
  log_transform_fpkm = NULL,
  log_start_fpkm = NULL,
  min_counts = .5,
  n_min_samples_count = 1,
  counts_transform = 1,
  counts_log_start = 4,
  no_fdr = NULL
)

Exploratory Data Analysis

Now that the data has been processed, we can begin data analysis. It is possible to create unique and informative plots with the processed data, and iDEP has built in functions to assist with that. These plots can then also be saved in an object and have additional ggplot features added to them.

total_counts_ggplot Function

This function will only work for read counts data, so if that is not the format of the processed data, please skip this function. For read counts data, this function creates a barplot to show the counts in millions for each sample in the data. counts_data is the raw counts that is returned from the pre_process function. sample_info is the experiment design data from the input_data function.

total_counts_ggplot(
  counts_data = processed_data$raw_counts,
  sample_info = load_data$sample_info
)

eda_scatter Function

Another useful plot is a scatter plot of the transformed expression for two samples in the data. eda_scatter provides a scatter plot of two specified sample (columns) of the processed data. plot_xaxis and plot_yaxis are string names of the columns to be plotted. The options for these inputs can be seen using colnames(processed_data$data). processed_data is the returned data from the pre_process function.

colnames(processed_data$data)

eda_scatter(
  processed_data = processed_data$data,
  plot_xaxis = "p53_IR_1",
  plot_yaxis = "p53_mock_1"
)

eda_density Function

This plotting function provides a plot of the distribution of counts values for each sample in the data. The information is displayed in a density ridge plot in the ggplot format. Again, the data provided is returned from the pre_process function. The sample_info provides information on each sample in the dataset.

eda_density(
  processed_data = processed_data$data,
  sample_info = load_data$sample_info
)

merge_data Function

The current data is using the converted ensembl IDs as rownames in a large data matrix, but the following function will create a data frame containing the gene names from the get_all_gene_names function. merge_data takes the gene_names data frame and a data matrix with ID rownames and creates a data frame with columns for each ID that idep has matched from the original IDs. This is useful to view different IDs for the same gene. It also enables writing CSV files that can be saved and be used to reproduce results from iDEP. The following code will merge the gene names with the raw counts data and the transformed data from pre_process.

merged_raw_counts <- merge_data(
  all_gene_names = gene_names,
  data = processed_data$raw_counts,
  merge_ID = "ensembl_ID"
)

merged_data <- merge_data(
  all_gene_names = gene_names,
  data = processed_data$data,
  merge_ID = "ensembl_ID"
)

Plotting Individual Genes

iDEP also has a function that visualizes individual genes and their expression behavior across the samples. It is possible to determine the genes to plot by looking at the merged or processed data. It is also possible to use the gene_names data frame to search for a gene ID. The code below demonstrates how to search for a gene ID.

gene_names[grep("H4c1", gene_names$symbol), ]

Depending on the type of gene ID that is desired for the plot, the function below will swap the gene IDs with the processed data matrix. This new data matrix will be used in the individual plot function using the selected ID type and the desired gene. The code below shows how to swap the gene IDs to the symbol name.

ind_plot_data <- rowname_id_swap(
  data_matrix = processed_data$data,
  all_gene_names = gene_names,
  select_gene_id = "symbol"
)

The function individual_plots will create the plot for the desired gene. We will use the data that we created in the code above for individual_data. If the data comes with an experiment file, the sample information that was loaded in the first instruction will be used in sample_info. The gene to be plotted can be specified in the selected_gene parameter. Multiple genes can be inputted as a vector to plot them at the same time and compare. There are two different types of plots that can be outputted with this function. The parameter gene_plot_box controls which plot will be outputted. Setting this input to TRUE will output a lineplot that gives the expression across all samples. FALSE will output a bar plot that gives the mean expression across the groups detected in the sample names. For the barplot, use_sd as TRUE will give a standard deviation bar instead of a standard error bar. lab_rotate controls the rotation of the x-axis labels. The outputted object is a formatted ggplot.

# individual_plots(
#   individual_data = ind_plot_data,
#   sample_info = load_data$sample_info,
#   selected_gene = " H4c1",
#   gene_plot_box = FALSE,
#   use_sd = FALSE,
#   lab_rotate = 90
# )
individual_plots(
  individual_data = ind_plot_data,
  sample_info = load_data$sample_info,
  selected_gene = " Ankrd13b",
  gene_plot_box = FALSE,
  use_sd = FALSE,
  lab_rotate = 90
)
individual_plots(
  individual_data = ind_plot_data,
  sample_info = load_data$sample_info,
  selected_gene = " Ankrd13b",
  gene_plot_box = TRUE,
  use_sd = FALSE,
  lab_rotate = 90
)

Conclusion

This concludes all the functions utilized in the Pre-Process portion of the iDEP package. The data can be plotted in many different ways, but hopefully the functions detailed above provide a good starting point. For troubleshooting, all functions have documentation and the code is available on Github. (https://github.com/gexijin/idepGolem)

espors/idepGolem documentation built on April 23, 2024, 1:11 p.m.