Add topic column to data frame

Description

Add topic column to a data frame.

Usage

add_col_topic(df, col.topic = "Topic", topic.name = "Topic1")

Arguments

Details

Add a topic column to a data frame. This topic column is named col.topic and contains the string topic.name.

Value

Data frame with a topic column added.

See Also

assign_topic()

Keywords - animals.

Description

Keywords to identify abstracts using animal models.

Usage

animal_keywords

Format

An object of class character of length 12.

Assign topics based on precalculated scores

Description

Assign topics to abstracts based on precalculated scores.

Usage

assign_topic(
  df,
  col.topic,
  threshold,
  topic.names = NULL,
  col.topic.name = "Topic",
  col.pmid = "PMID",
  discard = FALSE
)

Arguments

Details

Assign topics to abstracts based on precalculated scores. assign_topic() compares different precalculated topic scores and assigns the abstract to the topic with the highest score. If there is a tie between topic scores, the abstract is assigned to all topics in question. If an abstract matches no topic, it is assigned to the topic "Unknown".

Value

Data frame with topics based on precalculated topic scores.

See Also

calculate_score_topic(), plot_score_topic(), add_col_topic()

Other score functions: calculate_score_animals(), calculate_score_biomarker(), calculate_score_patients(), calculate_score_topic(), plot_score_animals(), plot_score_biomarker(), plot_score_patients(), plot_score_topic()

Assign topics based on LDA model

Description

Assign topics to abstracts based on an LDA model.

Usage

assign_topic_lda(df, lda_model, topic.names, col.pmid = PMID)

Arguments

Details

Assign topic to abstracts based on an LDA model. To identify the subject of a topic, use plot_lda_term().

Value

Data frame with topics assigned to each abstract based on an LDAmodel.

See Also

fit_lda(), plot_lda_term(), assign_topic()

Other LDA functions: fit_lda(), plot_lda_term(), plot_perplexity()

Keywords - biomarkers.

Description

Keywords to identify abstracts reporting about miRNAs as biomarkers.

Usage

biomarker_keywords

Format

An object of class character of length 18.

Calculate animal model scores for abstracts

Description

Calculate animal model score for each abstract to indicate possible use of animal models.

Usage

calculate_score_animals(
  df,
  keywords = animal_keywords,
  case = FALSE,
  threshold = NULL,
  indicate = FALSE,
  discard = FALSE,
  col.abstract = Abstract
)

Arguments

Details

Calculate animal model score for each abstract to indicate possible use of animal models. This score is added to the data frame as an additional column Animal_score, containing the calculated animal model score. To decide which abstracts are considered to contain animal models, a threshold can be set via the threshold argument. Furthermore, an additional column can be added, verbally indicating the use of animal models in an abstract. Choosing the right threshold can be facilitated using plot_score_animals().

Value

Data frame with calculated animal model scores. If discard = FALSE, adds extra columns to the original data frame with the calculated animal model scores. If discard = TRUE, only abstracts with animal models are kept.

See Also

plot_score_animals()

Other score functions: assign_topic(), calculate_score_biomarker(), calculate_score_patients(), calculate_score_topic(), plot_score_animals(), plot_score_biomarker(), plot_score_patients(), plot_score_topic()

Calculate biomarker scores for abstracts

Description

Calculate biomarker score for each abstract to indicate possible use of miRNAs as biomarker.

Usage

calculate_score_biomarker(
  df,
  keywords = biomarker_keywords,
  case = FALSE,
  threshold = NULL,
  indicate = FALSE,
  discard = FALSE,
  col.abstract = Abstract
)

Arguments

Details

Calculate biomarker score for each abstract to indicate possible use of miRNAs as biomarker. This score is added to the data frame as an additional column Biomarker_score, containing the calculated biomarker score. To decide which abstracts are considered to contain use of miRNAs as biomarker, a threshold can be set via the threshold argument. Furthermore, an additional column can be added, verbally indicating the general use of miRNAs as biomarker in an abstract. Choosing the right threshold can be facilitated using plot_score_biomarker().

Value

Data frame with calculated biomarker scores. If discard = FALSE, adds extra columns to the original data frame with calculated biomarker scores. If discard = TRUE, only abstracts are with miRNAs as biomarker are kept.

See Also

plot_score_biomarker()

Other score functions: assign_topic(), calculate_score_animals(), calculate_score_patients(), calculate_score_topic(), plot_score_animals(), plot_score_biomarker(), plot_score_patients(), plot_score_topic()

Calculate patients scores for abstracts

Description

Calculate patients score for each abstract to indicate possible use of patient material.

Usage

calculate_score_patients(
  df,
  keywords = patients_keywords,
  case = FALSE,
  threshold = NULL,
  indicate = FALSE,
  discard = FALSE,
  col.abstract = Abstract
)

Arguments

Details

Calculate patient score for each abstract to indicate possible use of patient material. This score is added to the data frame as an additional column Patient_score, containing the calculated patients score. To decide which abstracts are considered to contain patient material, a threshold can be set via the threshold argument. Furthermore, an additional column can be added, verbally indicating the general use of patient material. Choosing the right threshold can be facilitated using plot_score_patients().

Value

Data frame with calculated patient scores. If discard = FALSE, adds extra columns to the original data frame with the calculated patient tissue scores. If discard = TRUE, only abstracts with use of patient tissue are kept.

See Also

plot_score_patients()

Other score functions: assign_topic(), calculate_score_animals(), calculate_score_biomarker(), calculate_score_topic(), plot_score_animals(), plot_score_biomarker(), plot_score_patients(), plot_score_topic()

Calculate scores of a self-chosen topic

Description

Calculate score of a self-chosen topic for each abstract to identify abstracts possibly corresponding to the topic of interest.

Usage

calculate_score_topic(
  df,
  keywords,
  case = FALSE,
  col.score = "topic_score",
  col.indicate = NULL,
  threshold = NULL,
  discard = FALSE,
  col.abstract = Abstract
)

Arguments

Details

Calculate score of a self-chosen topic for each abstract to identify abstracts possibly corresponding to the topic of interest. This score is added to the data frame as an additional column, usually called topic_score, containing the calculated topic score. If there is more than one topic of interest, the column topic_score should be appropriately renamed. To decide which abstracts are considered to correspond to the topic of interest, a threshold can be set via the threshold argument. Furthermore, an additional column can be added, verbally indicating if the abstract corresponds to the topic. Choosing the right threshold can be facilitated using plot_score_topic().

Value

Data frame with calculated topic scores. If discard = FALSE, adds extra columns to the original data frame with the calculated topic scores. If discard = TRUE, only abstracts corresponding to the topic of interest are kept.

See Also

assign_topic(), plot_score_topic()

Other score functions: assign_topic(), calculate_score_animals(), calculate_score_biomarker(), calculate_score_patients(), plot_score_animals(), plot_score_biomarker(), plot_score_patients(), plot_score_topic()

Combine data frames into one data frame

Description

Combine data frames into one data frame.

Usage

combine_df(...)

Arguments

Details

Combine data frames into one data frame. combine_df() accepts several data frames that are combined into one data frame. Data frames to be combined must have the same number of columns and the same column names.

Value

Combined data frame.

See Also

Other combine functions: combine_mir()

Combine miRNA vectors into one

Description

Combine miRNA vectors into one.

Usage

combine_mir(...)

Arguments

Details

Combine miRNA vectors into one. miRNA names occurring more than once are reduced to one instance.

Value

Combined character vector containing miRNA names.

See Also

get_mir()

Other combine functions: combine_df()

Combine data frames containing stop words

Description

Combine data frames containing stop words into one data frame.

Usage

combine_stopwords(...)

Arguments

Details

Combine data frames containing stop words into one data frame. Provided data frames must have two columns named "word" and "lexicon".

Value

Combined data frame with stop words.

See Also

generate_stopwords(), stopwords_miretrieve, tidytext::stop_words

Other stopword functions: generate_stopwords()

Compare count of miRNA names between different topics

Description

Compare count of miRNA names between different topics.

Usage

compare_mir_count(
  df,
  mir,
  topic = NULL,
  normalize = TRUE,
  col.topic = Topic,
  col.mir = miRNA,
  col.pmid = PMID,
  title = NULL
)

Arguments

Details

Compare count of miRNA names between different topics by plotting the number of abstracts mentioning the miRNA in a topic. This count can either be normalized, thus plotting the proportion of abstracts mentioning a miRNA name compared to all abstracts of a topic, or it can be not normalized, thus plotting the absolute number of abstracts mentioning a miRNA per topic.

Value

Bar plot comparing the count of miRNA names between different topics.

See Also

compare_mir_count_log2(), compare_mir_count_unique()

Other compare functions: compare_mir_count_log2(), compare_mir_count_unique(), compare_mir_terms_log2(), compare_mir_terms_scatter(), compare_mir_terms_unique(), compare_mir_terms()

Compare log2-frequency count of miRNA names between two topics

Description

Compare log2-frequency count of miRNA names between two topics

Usage

compare_mir_count_log2(
  df,
  mir,
  topic = NULL,
  normalize = TRUE,
  col.topic = Topic,
  col.mir = miRNA,
  col.pmid = PMID,
  title = NULL
)

Arguments

Details

Compare log2-frequency count of miRNA names between two topics by plotting the log2-ratio of the miRNA count in two topics. The miRNA count per topic can either be normalized, thus taking the proportion of abstracts mentioning a miRNA name compared to all abstracts in a topic, or not normalized, thus taking the absolute number of abstracts mentioning a miRNA in a topic. The log2-plot is greatly inspired by the book “tidytext: Text Mining and Analysis Using Tidy Data Principles in R.” by Silge and Robinson.

Value

List containing bar plot comparing the log2-frequency count of miRNA names between two topics and its corresponding data frame.

References

Silge, Julia, and David Robinson. 2016. “tidytext: Text Mining and Analysis Using Tidy Data Principles in R.” JOSS 1 (3). The Open Journal. https://doi.org/10.21105/joss.00037.

See Also

compare_mir_count(), compare_mir_count_unique()

Other compare functions: compare_mir_count_unique(), compare_mir_count(), compare_mir_terms_log2(), compare_mir_terms_scatter(), compare_mir_terms_unique(), compare_mir_terms()

Compare top count of unique miRNA names per topic

Description

Compare top count of unique miRNA names per topic

Usage

compare_mir_count_unique(
  df,
  top = 5,
  topic = NULL,
  normalize = TRUE,
  colour = "steelblue3",
  col.topic = Topic,
  col.mir = miRNA,
  col.pmid = PMID,
  title = NULL
)

Arguments

Details

Compare top count of unique miRNA names per topic by plotting the the miRNA count of unique miRNAs per topic. Per topic, the unique miRNAs are identified and their count is plotted. The miRNA count can either be normalized, thus taking the proportion of abstracts mentioning a miRNA name compared to all abstracts in a topic, or not normalized, thus taking the absolute number of abstracts mentioning a miRNA in a topic.

Value

Bar plot comparing frequency of unique miRNA count per topic.

See Also

compare_mir_count(), compare_mir_count_log2()

Other compare functions: compare_mir_count_log2(), compare_mir_count(), compare_mir_terms_log2(), compare_mir_terms_scatter(), compare_mir_terms_unique(), compare_mir_terms()

Compare count of terms associated with a miRNA name over various topics

Description

Compare count of top terms associated with a miRNA name over various topics.

Usage

compare_mir_terms(
  df,
  mir,
  top = 20,
  token = "words",
  ...,
  topic = NULL,
  shared = TRUE,
  normalize = TRUE,
  stopwords = stopwords_miretrieve,
  stopwords_ngram = TRUE,
  position = "dodge",
  col.mir = miRNA,
  col.abstract = Abstract,
  col.topic = Topic,
  col.pmid = PMID,
  title = NULL
)

Arguments

Details

Compare count of top terms associated with a miRNA name over various topics. miRNA names and topics must be in a data frame df, while terms are taken from abstracts contained in df. Number of top terms to plot is regulated by top. Terms can either be evaluated as their raw count, e.g. in how many abstracts they are mentioned in conjunction with the miRNA name, or as their relative count, e.g. in how many abstracts containing the miRNA they are mentioned compared to all abstracts containing the miRNA. compare_mir_terms() is based on the tools available in the tidytext package.

Value

Bar plot comparing the count of terms associated with a miRNA name over two topics.

See Also

compare_mir_terms_log2(), compare_mir_terms_scatter()

Other compare functions: compare_mir_count_log2(), compare_mir_count_unique(), compare_mir_count(), compare_mir_terms_log2(), compare_mir_terms_scatter(), compare_mir_terms_unique()

Compare log2-frequency count of terms associated with a miRNA name

Description

Compare log2-frequency count of terms associated with a miRNA name over two topics.

Usage

compare_mir_terms_log2(
  df,
  mir,
  top = 20,
  token = "words",
  ...,
  topic = NULL,
  shared = TRUE,
  normalize = TRUE,
  stopwords = stopwords_miretrieve,
  stopwords_ngram = TRUE,
  col.mir = miRNA,
  col.abstract = Abstract,
  col.topic = Topic,
  col.pmid = PMID,
  title = NULL
)

Arguments

Details

Compare log2-frequency count of terms associated with a miRNA name over two topics by plotting the log2-ratio of the term count associated with a miRNA name over two topics. miRNA names and topics must be in a data frame df, while terms are taken from abstracts contained in df. Number of top terms to plot is regulated by top. Terms can either be evaluated as their raw count, e.g. in how many abstracts they are mentioned in conjunction with the miRNA name, or as their relative count, e.g. in how many abstracts containing the miRNA they are mentioned compared to all abstracts containing the miRNA. compare_mir_terms_log2() is based on the tools available in the tidytext package. The log2-plot is greatly inspired by the book “tidytext: Text Mining and Analysis Using Tidy Data Principles in R.” by Silge and Robinson.

Value

List containing bar plot comparing the log2-frequency of terms associated with a miRNA over two topics and its corresponding data frame.

References

Silge, Julia, and David Robinson. 2016. “tidytext: Text Mining and Analysis Using Tidy Data Principles in R.” JOSS 1 (3). The Open Journal. https://doi.org/10.21105/joss.00037.

See Also

compare_mir_terms(), compare_mir_terms_scatter()

Other compare functions: compare_mir_count_log2(), compare_mir_count_unique(), compare_mir_count(), compare_mir_terms_scatter(), compare_mir_terms_unique(), compare_mir_terms()

Compare shared terms associated with a miRNA name

Description

Compare shared terms associated with a miRNA name over two topics.

Usage

compare_mir_terms_scatter(
  df,
  mir,
  top = 1000,
  token = "words",
  ...,
  topic = NULL,
  stopwords = stopwords_miretrieve,
  stopwords_ngram = TRUE,
  html = TRUE,
  colour.point = "red",
  colour.term = "black",
  col.mir = miRNA,
  col.abstract = Abstract,
  col.topic = Topic,
  col.pmid = PMID,
  title = NULL
)

Arguments

Details

Compare shared terms associated with a miRNA name over two topics. These terms are displayed as a scatter plot, which is either interactive as an HTML-widget, or static. This is regulated via the html argument. miRNA names and topics must be in a data frame df, while terms are taken from abstracts contained in df. Number of top terms to choose is regulated by top. Terms are evaluated as their raw count and plotted on a log10-scale. compare_mir_terms_scatter() is based on the tools available in the tidytext package. The term-plot is greatly inspired by “tidytext: Text Mining and Analysis Using Tidy Data Principles in R.” by Silge and Robinson.

Value

Scatter plot comparing shared terms of a miRNA between two topics.

References

Silge, Julia, and David Robinson. 2016. “tidytext: Text Mining and Analysis Using Tidy Data Principles in R.” JOSS 1 (3). The Open Journal. https://doi.org/10.21105/joss.00037.

See Also

compare_mir_terms(), compare_mir_terms_log2()

Other compare functions: compare_mir_count_log2(), compare_mir_count_unique(), compare_mir_count(), compare_mir_terms_log2(), compare_mir_terms_unique(), compare_mir_terms()

Compare terms uniquely associated with a miRNA name

Description

Compare terms uniquely associated with a miRNA name over topics.

Usage

compare_mir_terms_unique(
  df,
  mir,
  top = 20,
  token = "words",
  ...,
  topic = NULL,
  stopwords = stopwords_miretrieve,
  stopwords_ngram = TRUE,
  normalize = TRUE,
  colour = "steelblue3",
  col.mir = miRNA,
  col.abstract = Abstract,
  col.topic = Topic,
  col.pmid = PMID,
  title = NULL
)

Arguments

Details

Compare terms uniquely associated with a miRNA name over topics. miRNA names and topics must be in a data frame df, while terms are taken from abstracts contained in df. Number of top terms to choose is regulated by top. Terms are evaluated either as the number of times they are mentioned in all abstracts with the miRNA name of interest, or the number of times they are relatively mentioned compared to all abstracts with the miRNA name of interest. compare_mir_terms_unique() is based on the tools available in the tidytext package.

Value

Bar plot containing unique miRNA-terms associations per topic.

See Also

compare_mir_terms(), compare_mir_terms_log2(), compare_mir_terms_scatter()

Other compare functions: compare_mir_count_log2(), compare_mir_count_unique(), compare_mir_count(), compare_mir_terms_log2(), compare_mir_terms_scatter(), compare_mir_terms()

Count miRNA names in a data frame

Description

Count occurrence of miRNA names in a data frame.

Usage

count_mir(df, col.mir = miRNA)

Arguments

Details

Count occurrence of miRNA names in a data frame. The count of miRNA names is returned as a separate data frame, only listing the miRNA names and their respective frequency.

Value

Data frame. Data frame containing miRNA names and their respective frequency.

See Also

plot_mir_count(), count_mir_threshold(), plot_mir_count_threshold()

Other count functions: count_mir_threshold(), count_snp(), plot_mir_count_threshold(), plot_mir_count()

Count occurrence of miRNA names above threshold

Description

Count occurrence of miRNA names above a threshold.

Usage

count_mir_threshold(df, threshold = 1, col.mir = miRNA, col.pmid = PMID)

Arguments

Details

Count occurrence of miRNA names above a threshold. This threshold can either be an absolute value, e.g. 3, or a float between 0 and 1, e.g. 0.2. If threshold is an absolute value, number of distinct miRNA names mentioned in at least threshold abstracts is returned. If threshold is a float between 0 and 1, number of distinct miRNA names mentioned in at least threshold abstracts of all abstracts in df is returned.

Value

Integer with the number of distinct miRNA names in df.

See Also

plot_mir_count_threshold(), count_mir(), plot_mir_count()

Other count functions: count_mir(), count_snp(), plot_mir_count_threshold(), plot_mir_count()

Count SNPs in a data frame

Description

Count occurrence of SNPs in a data frame.

Usage

count_snp(df, col.snp = SNPs, col.pmid = PMID)

Arguments

Details

Count occurrence of SNPs in a data frame. The count of SNPs is returned as a separate data frame, only listing the SNPs and their respective frequency.

Value

Data frame. Data frame containing SNPs and their respective frequency.

See Also

extract_snp(), get_snp(), subset_snp()

Other count functions: count_mir_threshold(), count_mir(), plot_mir_count_threshold(), plot_mir_count()

Count targets in data frame

Description

Count occurrence of targets in a data frame.

Usage

count_target(df, col.target = Target, add.df = TRUE)

Arguments

Details

Count occurrence of targets in a data frame. The count of targets can either be returned as a separate data frame, only listing the targets and their respective frequency, or it can be added to the data frame provided as an extra column.

Value

Data frame, either with the targets and their frequency as a new data frame, or with the frequency of targets added as a new column to the input data frame df.

See Also

join_targets(), plot_target_count()

Other target functions: join_mirtarbase(), join_targets(), plot_target_count(), plot_target_mir_scatter()

Dataset of PubMed data of miRNAs in Colorectal Cancer

Description

A dataset PubMed abstracts of miRNAs in Colorectal Cancer.

Usage

df_crc

Format

A data frame.

Source

https://pubmed.ncbi.nlm.nih.gov/

miRTarBase version 8.0

Description

The most recent miRTarBase version 8.0, containing miRNA stem, capitalized targets, and PMIDs.

Usage

df_mirtarbase

Format

A data frame with the columns "miRNA_tarbase", "Target", and "PMID".

Details

miRTarBase was published in

Hsi-Yuan Huang, Yang-Chi-Dung Lin, Jing Li, et al., miRTarBase 2020: updates to the experimentally validated microRNA–target interaction database, Nucleic Acids Research, Volume 48, Issue D1, 08 January 2020, Pages D148–D154, https://doi.org/10.1093/nar/gkz896

Source

https://miRTarBase.cuhk.edu.cn:443/

Dataset of PubMed data of miRNAs in Pancreatic Cancer

Description

A dataset PubMed abstracts of miRNAs in Pancreatic Cancer.

Usage

df_panc

Format

A data frame.

Source

https://pubmed.ncbi.nlm.nih.gov/

Test dataset of PubMed abstracts

Description

Test dataset of 20 PubMed abstracts.

Usage

df_test

Format

A data frame.

Source

https://pubmed.ncbi.nlm.nih.gov/

Extract miRNA names from abstracts in data frame

Description

Extract miRNA names from abstracts in a data frame.

Usage

extract_mir_df(
  df,
  threshold = 1,
  col.abstract = Abstract,
  extract_letters = FALSE
)

Arguments

Details

Extract miRNA names from abstracts in a data frame. miRNA names can either be extracted with their stem only, e.g. miR-23, or with their trailing letter, e.g. miR-23a. miRNA names are adapted to the most recent miRBase version (e.g. miR-97, miR-102, miR-180(a/b) become miR-30a, miR-29a, and miR-172(a/b), respectively). Additionally, how often a miRNA must be mentioned in an abstract to be extracted can be regulated via the threshold argument. Ultimately, abstracts not containing any miRNA names are silently dropped. As many abstracts do not adhere to the miRNA nomenclature, it is recommended to extract only the miRNA stem with extract_letters = FALSE.

Value

Data frame with miRNA names extracted from abstracts.

See Also

extract_mir_string()

Other extract functions: extract_mir_string(), extract_snp()

Extract miRNA names from string

Description

Extract miRNA names from a string.

Usage

extract_mir_string(string, threshold = 1, extract_letters = FALSE)

Arguments

Details

Extract miRNA names from a string. miRNA names can either be extracted with their stem only, e.g. miR-23, or with their trailing letter, e.g. miR-23a. Furthermore, miRNA names are adapted to the most recent miRBase version (e.g. miR-97, miR-102, miR-180(a/b) become miR-30a, miR-29a, and miR-172(a/b), respectively).

Value

Character vector containing miRNA names, if miRNA names are present in the string. If no miRNA names are present in the string, a message is returned saying "No miRNA found.".

See Also

extract_mir_df()

Other extract functions: extract_mir_df(), extract_snp()

Extract SNPs from abstracts in data frame

Description

Extract SNPs from abstracts in a data frame.

Usage

extract_snp(
  df,
  pattern = snp_pattern,
  col.abstract = Abstract,
  indicate = FALSE,
  discard = FALSE
)

Arguments

Details

Extract SNPs from abstracts in a data frame. SNPs are added to the data frame in a separate column. Furthermore, an optional column can indicate if SNPs are generally present in an abstract.

Value

Data frame. If discard = FALSE, return the data frame with an additional column for SNPs. If discard = TRUE, return only abstracts containing SNPs.

See Also

count_snp(), get_snp(), subset_snp()

Other extract functions: extract_mir_df(), extract_mir_string()

Fit LDA-model

Description

Fit LDA-model with k topics.

Usage

fit_lda(
  df,
  k,
  stopwords = stopwords_miretrieve,
  method = "gibbs",
  control = NULL,
  seed = 42,
  col.abstract = Abstract,
  col.pmid = PMID
)

Arguments

Details

Fit LDA-model with k topics from a data frame. fit_lda() is based on LDA() from the package topicmodels.

Value

LDA-model.

See Also

plot_perplexity()

Other LDA functions: assign_topic_lda(), plot_lda_term(), plot_perplexity()

Generate data frame containing stop words

Description

Generate a data frame containing stop words.

Usage

generate_stopwords(stopwords, combine_with = NULL)

Arguments

Details

Generate data frame containing stop words from a character vector. This data frame consists of two columns, namely word, containing the stop words, and lexicon, containing the string "self-defined". Additionally, the created data frame can be combined with other stop words containing data frames, e.g. tidytext::stop_words or stopwords_miretrieve.

Value

Data frame containing stop words.

References

Silge, Julia, and David Robinson. 2016. “tidytext: Text Mining and Analysis Using Tidy Data Principles in R.” JOSS 1 (3). The Open Journal. https://doi.org/10.21105/joss.00037.

See Also

combine_stopwords(), stopwords_miretrieve, tidytext::stop_words

Other stopword functions: combine_stopwords()

Identify top miRNA names distinct for one topic compared to another topic

Description

Identify top miRNA names distinct for one topic compared to another topic in a data frame.

Usage

get_distinct_mir_df(
  df,
  distinct,
  top = 5,
  topic = NULL,
  col.topic = Topic,
  col.mir = miRNA,
  col.pmid = PMID
)

Arguments

Details

Get top distinct miRNA names of one topic compared to another topic in a data frame. get_distinct_mir_df() compares the top miRNA names of two topics and returns the miRNA names that are exclusive for distinct.

Value

Character vector containing miRNA names distinct for distinct compared to the second topic provided in topic.

See Also

Other get functions: get_distinct_mir_vec(), get_mir(), get_pmid(), get_shared_mir_df(), get_shared_mir_vec(), get_snp()

Identify miRNA names distinct for one vector compared to another vector

Description

Identify miRNA names distinct for one vector compared to another vector.

Usage

get_distinct_mir_vec(mirna.vec.1, mirna.vec.2)

Arguments

Details

Get distinct miRNA names of one vector compared to another vector. get_distinct_mir() compares two vectors containing miRNA names and returns the miRNA names that are exclusive for mirna.vec.1.

Value

Character vector containing miRNA names distinct for mirna.vec.1 compared to mirna.vec.2.

See Also

Other get functions: get_distinct_mir_df(), get_mir(), get_pmid(), get_shared_mir_df(), get_shared_mir_vec(), get_snp()

Get miRNA names from a data frame

Description

Get miRNA names from a data frame. These miRNA names can either be the most frequent ones, or the ones exceeding a threshold.

Usage

get_mir(
  df,
  top = NULL,
  threshold = NULL,
  topic = NULL,
  col.mir = miRNA,
  col.pmid = PMID,
  col.topic = Topic
)

Arguments

Details

Get miRNA names from a data frame. These miRNA names can either be the most frequent ones, or the ones exceeding a threshold. Furthermore, if the data frame contains abstracts of different topics, only the miRNA names of specific topics can be obtained by setting the topic argument.

• To get the most frequent miRNA names, set the top argument. top determines how many most frequent miRNA names are returned, according to their rank. Ties among the most frequently mentioned miRNAs are treated as the same rank, e.g. if miR-126, miR-34, and miR-29 were all mentioned the most often with the same frequency, they would all be returned by specifying top = 1, top = 2, and top = 3.

• To get the miRNA names exceeding a threshold, set the threshold argument. threshold can either be an absolute value, e.g. 3, or a float between 0 and 1, e.g. 0.2. If threshold is an absolute value, get_mir() returns only the miRNA names mentioned in at least threshold abstracts. If threshold is a float between 0 and 1, get_mir() returns only miRNA names mentioned in at least threshold abstracts of all abstracts. threshold requires the data frame to have a column with PubMed IDs.

If neither top nor threshold is set, top is automatically set to 5.

Value

Character vector containing miRNA names.

See Also

Other get functions: get_distinct_mir_df(), get_distinct_mir_vec(), get_pmid(), get_shared_mir_df(), get_shared_mir_vec(), get_snp()

Get PubMed-IDs of a data frame

Description

Get PubMed-IDs of a data frame.

Usage

get_pmid(df, col.pmid = PMID, copy = TRUE)

Arguments

Details

Get PubMed-IDs of a data frame. get_pmid returns either a character vector, containing PubMed-IDs, or copies PubMed-IDs to clipboard. If PubMed-IDs are copied to the clipboard, they can be used e.g. to search for abstracts on PubMed.

Value

Copy to clipboard or character vector. If copy = TRUE, get_pmid() copies PubMed-IDs to clipboard. If copy = FALSE, get_pmid() returns a character vector, containing PubMed-IDs.

See Also

Other get functions: get_distinct_mir_df(), get_distinct_mir_vec(), get_mir(), get_shared_mir_df(), get_shared_mir_vec(), get_snp()

Get top miRNA names in common between two topics of a data frame

Description

Get top miRNA names in common between two topics of a data frame.

Usage

get_shared_mir_df(
  df,
  top = 5,
  topic = NULL,
  col.topic = Topic,
  col.mir = miRNA,
  col.pmid = PMID
)

Arguments

Details

Get top miRNA names in common between two topics of a data frame. get_shared_mir_df() compares the top miRNA names of two topics in a data frame and returns the miRNA names in common.

Value

Character vector containing miRNA names in common between two topics.

See Also

Other get functions: get_distinct_mir_df(), get_distinct_mir_vec(), get_mir(), get_pmid(), get_shared_mir_vec(), get_snp()

Get miRNA names in common between two vectors

Description

Get miRNA names in common between two vectors.

Usage

get_shared_mir_vec(mirna.vec.1, mirna.vec.2)

Arguments

Details

Get miRNA names in common between two vectors. get_shared_mir_vec() compares two vectors containing miRNA names and returns the miRNA names that are in both vectors.

Value

Character vector containing miRNA names in common between two vectors.

See Also

Other get functions: get_distinct_mir_df(), get_distinct_mir_vec(), get_mir(), get_pmid(), get_shared_mir_df(), get_snp()

Get SNPs from a data frame

Description

Get SNPs from a data frame.

Usage

get_snp(df, row = NULL, top = NULL, col.snp = SNPs, col.pmid = PMID)

Arguments

Details

Get SNPs from a data frame.

• If a data frame containing SNP counts as from count_snp() is provided, these SNPs are specified by the row they are listed in. To get the SNPs by row, set the row argument.

• If a data frame with PubMed IDs is provided, these SNPs are specified by their top occurrence. To get the SNPs by frequency, set the top argument.

If neither row nor top is provided, row is automatically set to 1.

Value

String or character vector containing SNPs.

See Also

extract_snp(), count_snp(), subset_snp()

Other get functions: get_distinct_mir_df(), get_distinct_mir_vec(), get_mir(), get_pmid(), get_shared_mir_df(), get_shared_mir_vec()

Indicate if a miRNA name is contained in an abstract

Description

Indicate if a miRNA name is contained in an abstract with "Yes"/"No".

Usage

indicate_mir(df, indicate.mir, col.mir = miRNA)

Arguments

Details

Indicate if a miRNA name is contained in an abstract with "Yes"/"No". This requires miRNA names already to be extracted, e.g. with extract_mir_df(), and to be stored in a separate column, specified by col.mir. indicate_mir() adds another column to a data frame which bears the name of the miRNA(s) of interest. Within this column, a "Yes" or "No" specifies if this miRNA name is contained in the corresponding abstract.

Value

Data frame with as many columns added as miRNA names given in indicate.mir. Per column, a "Yes" or "No" indicates if the miRNA name of interest is present in the corresponding abstract.

See Also

extract_mir_df(), indicate_term()

Other indicate functions: indicate_term()

Indicate if a term is contained in abstracts

Description

Indicate if a term is contained in abstracts.

Usage

indicate_term(
  df,
  term,
  threshold = 1,
  case = FALSE,
  discard = FALSE,
  col.abstract = Abstract
)

Arguments

Details

Indicate if a term is contained in an abstract. Terms provided can either be case sensitive or insensitive. Per term, a new column is added to the data frame indicating if the term is present in an abstract. Furthermore, if a term is considered "present" in an abstract can be regulated via the threshold argument. threshold determines how often a term must be in an abstract to be considered "present".

Value

Data frame. If discard = FALSE, the original data frame with additional columns per term is returned. If discard = TRUE, only abstracts containing the terms in term are returned.

See Also

indicate_mir()

Other indicate functions: indicate_mir()

Add miRNA targets from miRTarBase version 8.0

Description

Add miRNA targets from miRTarBase version 8.0 to a data frame.

Usage

join_mirtarbase(
  df,
  col.pmid.df = PMID,
  col.topic.df = NULL,
  filter_na = TRUE,
  reduce = FALSE
)

Arguments

Details

Add miRNA targets from miRTarBase version 8.0 to a data frame. join_mirtarbase() can return two different data frames, regulated by reduce:

1. If reduce = FALSE, join_mirtarbase() adds targets from miRTarBase 8.0 to the data frame in a new column. These targets then correspond to the targets determined in the research paper, but do not necessarily correspond to the miRNA names mentioned in the abstract.

2. If reduce = TRUE, join_mirtarbase() adds targets from miRTarBase 8.0 to the data frame in a new column. However, an altered data frame is returned, containing the PubMed-IDs, targets, and miRNAs from miRTarBase 8.0.

miRTarBase was published in

Hsi-Yuan Huang, Yang-Chi-Dung Lin, Jing Li, et al., miRTarBase 2020: updates to the experimentally validated microRNA–target interaction database, Nucleic Acids Research, Volume 48, Issue D1, 08 January 2020, Pages D148–D154, https://doi.org/10.1093/nar/gkz896

Value

Data frame containing miRNA targets.

See Also

Other target functions: count_target(), join_targets(), plot_target_count(), plot_target_mir_scatter()

Add miRNA targets from an xlsx-file to a data frame

Description

Add miRNA targets from an external xlsx-file to a data frame.

Usage

join_targets(
  df,
  excel_file,
  col.pmid.excel,
  col.target.excel,
  col.mir.excel = NULL,
  col.pmid.df = PMID,
  col.topic.df = NULL,
  filter_na = TRUE,
  stem_mir_excel = TRUE,
  reduce = FALSE
)

Arguments

Details

Add miRNA targets from an external xlsx-file to a data frame. To add the targets to the data frame, the xlsx-file and the data frame need to have one column in common, such as PubMed-IDs. join_targets() can return two different data frames, regulated by reduce:

1. If reduce = FALSE, join_targets() adds targets from an excel-file to the data frame in a new column. These targets then correspond to the targets determined in the research paper, but do not necessarily correspond to the miRNA names mentioned in the abstract.

2. If reduce = TRUE, join_targets() adds targets from an xlsx-file to the data frame in a new column. However, an altered data frame is returned, containing the PubMed-IDs, targets, and miRNAs from the excel-file. For reduce = TRUE to work, the xlsx-file provided must contain a column with miRNA names.

Value

Data frame containing miRNA targets.

See Also

Other target functions: count_target(), join_mirtarbase(), plot_target_count(), plot_target_mir_scatter()

Stop words for n-grams

Description

Vector containing stop words for n-grams, based on tidytext::stop_words.

Usage

ngram_stopwords

Format

Character vector.

Source

tidytext::stop_words

Keywords - patients.

Description

Keywords to identify abstracts investigating miRNAs in patients.

Usage

patients_keywords

Format

An object of class character of length 10.

Plot terms associated with LDA-fitted topics

Description

Plot terms associated with LDA-fitted topics.

Usage

plot_lda_term(lda_model, top.terms = 10, title = NULL)

Arguments

Details

Plot terms associated with LDA-fitted topics. For each topic in the LDA-model, the top terms are plotted. Plotting top.terms for each topic can help identifying its subject.

Value

Bar plot with top terms per topic.

See Also

Other LDA functions: assign_topic_lda(), fit_lda(), plot_perplexity()

Plot count of most frequently mentioned miRNA names

Description

Plot count of most frequently mentioned miRNA names in a data frame.

Usage

plot_mir_count(
  df,
  top = 10,
  colour = "steelblue3",
  col.mir = miRNA,
  title = NULL
)

Arguments

Details

Plot count of most frequently mentioned miRNA names in a data frame. How many most frequently mentioned miRNAs are plotted is determined via the top argument. Ties among the most frequently mentioned miRNAs are treated as the same rank, e.g. if miR-126, miR-34, and miR-29 were all mentioned the most often, they would all be plotted by specifying top = 1, top = 2, or top = 3.

Value

Bar plot with the most frequently mentioned miRNAs names in df.

See Also

count_mir(), count_mir_threshold(), plot_mir_count_threshold()

Other count functions: count_mir_threshold(), count_mir(), count_snp(), plot_mir_count_threshold()

Plot occurrence count of miRNA names over different thresholds

Description

Plot occurrence count of distinct miRNA names over different thresholds.

Usage

plot_mir_count_threshold(
  df,
  start = 1,
  end = 5,
  bins = NULL,
  colour = "steelblue3",
  col.mir = miRNA,
  col.pmid = PMID,
  title = NULL
)

Arguments

Details

Plot occurrence of distinct miRNA names over different thresholds. These thresholds can either be absolute values or floating values between 0 and 1. If the thresholds are absolute values, number of distinct miRNA names mentioned in at least n abstracts are plotted, where n is the range of thresholds defined by start and end. If the thresholds are floating values, bins must be specified as well. Then the umber of distinct miRNA names mentioned in at least n abstracts over bins are plotted, where n is the range of thresholds between start and end. Overall, plotting can help in identifying if the abstracts at hand mention different miRNAs in a balanced way, or if there are few miRNAs dominating the field.

Value

Bar plot counting the occurrence of miRNA names above different thresholds.

See Also

count_mir_threshold(), count_mir(), plot_mir_count()

Other count functions: count_mir_threshold(), count_mir(), count_snp(), plot_mir_count()

Plot development of miRNA name mentioning over time

Description

Plot development of miRNA name mentioning over time.

Usage

plot_mir_development(
  df,
  mir,
  start = NULL,
  end = NULL,
  linetype = "miRNA",
  alpha = 0.8,
  width = 0.3,
  col.mir = miRNA,
  col.year = Year,
  title = NULL
)

Arguments

Details

Plot how often a miRNA name was mentioned per year.

Value

Line plot displaying how often a miRNA name was mentioned per year..

See Also

Other miR development functions: plot_mir_new()

Plot number of newly mentioned miRNA names/year

Description

Plot number of newly mentioned miRNA names/year.

Usage

plot_mir_new(
  df,
  threshold = 1,
  start = NULL,
  end = NULL,
  colour = "steelblue3",
  col.mir = miRNA,
  col.year = Year,
  title = NULL
)

Arguments

Details

Plot how many miRNAs are mentioned for the first time in different year. If a miRNA is considered to be "mentioned" in a year can be regulated via the threshold argument. If, for example, threshold is set to 3, but a miRNA is mentioned only twice in a year, it is not considered to be "mentioned" for this year.

Value

Bar plot displaying the number of newly mentioned miRNA names/year.

See Also

Other miR development functions: plot_mir_development()

Plot count of top terms associated with a miRNA name

Description

Plot count of top terms associated with a miRNA name.

Usage

plot_mir_terms(
  df,
  mir,
  top = 20,
  tf.idf = FALSE,
  token = "words",
  ...,
  stopwords = stopwords_miretrieve,
  stopwords_ngram = TRUE,
  normalize = TRUE,
  colour = "steelblue3",
  col.mir = miRNA,
  col.abstract = Abstract,
  col.pmid = PMID,
  title = NULL
)

Arguments

Details

Plot count of top terms associated with a miRNA name. Top terms associated with mir have to be in df as abstracts. Number of top terms to plot is regulated via the top argument. Terms can either be evaluated as their count or in a tf-idf fashion. If terms are evaluated as their count, they can either be evaluated as their raw count, e.g. in how many abstracts they are mentioned in conjunction with the miRNA name, or as their relative count, e.g. in how many abstracts containing the miRNA they are mentioned compared to all abstracts containing the miRNA. If terms are evaluated in a tf-idf fashion, miRNA names are considered as separate documents and terms often associated with one miRNA, but not with other miRNAs get more weight. plot_mir_terms() is based on the tools available in the tidytext package.

Value

Bar plot displaying the count of the top terms associated with a miRNA name.

See Also

plot_wordcloud(), tidytext::unnest_tokens()

Other miR term functions: plot_wordcloud()

Plot perplexity score of various LDA models

Description

Plot perplexity score of various LDA models.

Usage

plot_perplexity(
  df,
  start = 2,
  end = 5,
  stopwords = stopwords_miretrieve,
  method = "gibbs",
  control = NULL,
  col.abstract = Abstract,
  col.pmid = PMID,
  title = NULL
)

Arguments

Details

Plot perplexity score of various LDA models. plot_perplexity() fits different LDA models for k topics in the range between start and end. For each LDA model, the perplexity score is plotted against the corresponding value of k. Plotting the perplexity score of various LDA models can help in identifying the optimal number of topics to fit an LDA model for. plot_perplexity() is based on LDA() from the package topicmodels.

Value

Elbow plot displaying perplexity scores of different LDA models.

See Also

fit_lda()

Other LDA functions: assign_topic_lda(), fit_lda(), plot_lda_term()

Plot frequency of animal model scores in abstracts

Description

Plot frequency of animal model scores in abstracts.

Usage

plot_score_animals(
  df,
  keywords = animal_keywords,
  case = FALSE,
  bins = NULL,
  colour = "steelblue3",
  col.abstract = Abstract,
  col.pmid = PMID,
  title = NULL
)

Arguments

Details

Plots a frequency distribution of animal model scores in abstracts of a data frame. The animal model score is influenced by the choice of terms in keywords. Plotting the distribution can help deciding if the terms are well-chosen, or in choosing the right threshold to decide which abstracts are considered to contain animal models.

Value

Histogram displaying the distribution of animal scores in abstracts.

See Also

calculate_score_animals()

Other score functions: assign_topic(), calculate_score_animals(), calculate_score_biomarker(), calculate_score_patients(), calculate_score_topic(), plot_score_biomarker(), plot_score_patients(), plot_score_topic()

Plot frequency of biomarker scores in abstracts

Description

Plot frequency of biomarker scores in abstracts.

Usage

plot_score_biomarker(
  df,
  keywords = biomarker_keywords,
  case = FALSE,
  bins = NULL,
  colour = "steelblue3",
  col.abstract = Abstract,
  col.pmid = PMID,
  title = NULL
)

Arguments

Details

Plots a frequency distribution of biomarker scores in abstracts of a data frame. The biomarker score is influenced by the choice of terms in keywords. Plotting the distribution can help deciding if the terms are well-chosen, or in choosing the right threshold to decide which abstracts are considered to contain use of miRNAs as biomarker.

Value

Histogram displaying the distribution of biomarker scores in abstracts.

See Also

calculate_score_biomarker()

Other score functions: assign_topic(), calculate_score_animals(), calculate_score_biomarker(), calculate_score_patients(), calculate_score_topic(), plot_score_animals(), plot_score_patients(), plot_score_topic()

Plot frequency of patient scores in abstracts

Description

Plot frequency of patient scores in abstracts.

Usage

plot_score_patients(
  df,
  keywords = patients_keywords,
  case = FALSE,
  bins = NULL,
  colour = "steelblue3",
  col.abstract = Abstract,
  col.pmid = PMID,
  title = NULL
)

Arguments

Details

Plots a frequency distribution of patient scores in abstracts of a data frame. The patient score is influenced by the choice of terms in keywords. Plotting the distribution can help deciding if the terms are well-chosen, or in choosing the right threshold to decide which abstracts are considered to contain patient material

Value

Histogram displaying the distribution of patient scores in abstracts.

See Also

calculate_score_patients()

Other score functions: assign_topic(), calculate_score_animals(), calculate_score_biomarker(), calculate_score_patients(), calculate_score_topic(), plot_score_animals(), plot_score_biomarker(), plot_score_topic()

Plot frequency of self-chosen topic scores in abstracts

Description

Plot frequency of self-chosen topic scores in abstracts.

Usage

plot_score_topic(
  df,
  keywords,
  case = FALSE,
  name.topic = "TOPIC",
  bins = NULL,
  colour = "steelblue3",
  col.abstract = Abstract,
  col.pmid = PMID,
  title = NULL
)

Arguments

Details

Plots a frequency distribution of self-chosen topic scores in abstracts of a data frame. The topic score is influenced by the choice of terms in keywords. Plotting the distribution can help in choosing the right threshold to decide which abstracts correspond to the self-chosen topic.

Value

Histogram displaying the distribution of self-chosen topic scores in abstracts.

See Also

calculate_score_topic(), assign_topic()

Other score functions: assign_topic(), calculate_score_animals(), calculate_score_biomarker(), calculate_score_patients(), calculate_score_topic(), plot_score_animals(), plot_score_biomarker(), plot_score_patients()

Plot count of miRNA targets

Description

Plot count of miRNA targets.

Usage

plot_target_count(
  df,
  top = NULL,
  threshold = NULL,
  colour = "steelblue3",
  col.target = Target,
  title = NULL
)

Arguments

Details

Plot count of miRNA targets as a bar plot. How many targets are plotted is determined either by the top or by the threshold argument. If top is given, targets with the highest count are plotted. Ties among targets with the highest count are treated as the same rank, e.g. if PTEN, AKT, and VEGFA all had the highest count, they would all be plotted by specifying top = 1, top = 2, and top = 3. If threshold is given, only targets with a count of at least threshold are plotted. If neither top nor threshold is given, top is automatically set to 5.

Value

Bar plot with target counts.

See Also

count_target(), join_targets()

Other target functions: count_target(), join_mirtarbase(), join_targets(), plot_target_mir_scatter()

Plot targets and corresponding miRNAs as a scatter plot

Description

Plot targets and corresponding miRNAs as a scatter plot.

Usage

plot_target_mir_scatter(
  df,
  mir = NULL,
  target = NULL,
  top = NULL,
  threshold = NULL,
  filter_for = "target",
  col.target = Target,
  col.mir = miRNA,
  col.topic = Topic,
  col.pmid = PMID,
  title = NULL,
  height = 0.05,
  width = 0.05,
  alpha = 0.6
)

Arguments

Details

Plot targets and corresponding miRNAs as a scatter plot. With filter_for, it can be determined if the focus shall be on the top targets to plot their corresponding miRNAs, or if the focus shall be on the top miRNA names to plot their corresponding targets. What "top targets" or "top miRNA names" mean can be determined via the top and threshold arguments.

• If top is given, df is filtered for the most frequent targets/miRNA names.

• If threshold is given, data frame is filtered for all targets/miRNA names mentioned at least threshold times.

• If neither top nor threshold is given, top is automatically set to 5.

By plotting miRNAs against their targets, it is visualized if one miRNA regulates many targets, or if one target is regulated by many miRNAs. Furthermore, the miRNA-target interactions are labelled according to their topic in col.topic, thereby facilitating comparison of miRNA-target interactions across different topics.

Value

Scatter plot with targets and corresponding miRNAs.

See Also

join_targets()

Other target functions: count_target(), join_mirtarbase(), join_targets(), plot_target_count()

Create wordcloud of terms associated with a miRNA name

Description

Create wordcloud of terms associated with a miRNA name.

Usage

plot_wordcloud(
  df,
  mir,
  min.freq = 1,
  max.terms = 20,
  tf.idf = FALSE,
  token = "words",
  ...,
  stopwords = stopwords_miretrieve,
  stopwords_ngram = TRUE,
  colours = "black",
  random.colour = TRUE,
  ordered.colour = FALSE,
  col.mir = miRNA,
  col.abstract = Abstract,
  col.pmid = PMID
)

Arguments

Details

Create wordcloud of terms associated with a miRNA name. miRNA names must be in a data frame df, while terms are taken from abstracts contained in df. Number of terms to plot is regulated by max.terms, while min.freq regulates the least number of times a term must be mentioned to be plotted. Terms can either be evaluated as their raw count, e.g. how often they are mentioned in conjunction with the miRNA of interest, or weighed in a tf-idf fashion. If tf.idf = TRUE, miRNA names are considered as separate documents, and terms often associated with one miRNA, but not with other miRNAs get more weight. plot_wordcloud() is based on the tools available in the wordcloud package.

Value

Wordcloud of terms associated with a miRNA name.

See Also

plot_mir_terms(), wordcloud::wordcloud(), tidytext::unnest_tokens()

Other miR term functions: plot_mir_terms()

Convert PubMed-file from PubMed into a data frame

Description

Convert PubMed-file from PubMed into a data frame.

Usage

read_pubmed(pubmed_file, topic = NULL)

Arguments

Details

Convert an PubMed-file from PubMed into a data frame. The PubMed-file should contain PubMed-IDs, abstracts from research articles, abstract title, publication year, abstract language, and article type. The data frame created holds at least six columns, namely

• PMID, containing the PubMed-ID,

• Year, containing the publication year,

• Title, containing the title of the abstracts,

• Abstract, containing the actual abstract,

• Language, containing the language(s) of the paper,

• Type, containing the article type.

If topic is provided, a "Topic" column is added, assigning all abstracts in df to topic.

read_pubmed() is faster than read_pubmed_jats() and thus recommended.

Value

Data frame containing PubMed-IDs, abstracts, abstract titles, publication years, languages, and article types.

See Also

read_pubmed_jats()

Other external data functions: read_pubmed_jats(), save_excel(), save_plot()

Convert JATS-file from PubMed into a data frame

Description

Convert JATS-file from PubMed into a data frame.

Usage

read_pubmed_jats(jats_file, topic = NULL)

Arguments

Details

Converts an JATS-file from PubMed into a data frame. The JATS-file should contain PubMed-IDs, abstracts from research articles, abstract title, publication year, abstract language, and article type. The data frame created holds at least six columns, namely

• PMID, containing the PubMed-ID,

• Year, containing the publication year,

• Title, containing the title of the abstracts,

• Abstract, containing the actual abstract,

• Language, containing the language(s) of the paper,

• Type, containing the article type.

If topic is provided, a "Topic" column is added, assigning all abstracts in df to topic.

read_pubmed() is faster than read_pubmed_jats() and thus recommended.

Value

Data frame containing PubMed-IDs, abstracts, abstract titles, publication years, languages, and article types.

See Also

read_pubmed()

Other external data functions: read_pubmed(), save_excel(), save_plot()

Save data frame(s) as xlsx-file

Description

Save data frame(s) locally as an xlsx-file.

Usage

save_excel(..., excel_file = "miRetrieve_data.xlsx")

Arguments

Details

Saves data frame locally as an xlsx-file. If more than one data frame is provided, data frames are saved in an xlsx-file with one sheet per data frame.

Wrapper function of write.xlsx() from openxlsx.

Value

xlsx-file, locally saved.

See Also

openxlsx::write.xlsx()

Other external data functions: read_pubmed_jats(), read_pubmed(), save_plot()

Save the last generated figure

Description

Save the last generated figure locally.

Usage

save_plot(
  plot_file,
  width = NULL,
  height = NULL,
  units = "in",
  dpi = 300,
  device = NULL
)

Arguments

Details

Saves the last generated figure locally. Wrapper function of ggsave() from ggplot2. For further details, please see ?ggplot2::ggsave.

Value

Plot, locally saved.

See Also

ggplot2::ggsave()

Other external data functions: read_pubmed_jats(), read_pubmed(), save_excel()

Stop words for text mining with common PubMed 2-grams

Description

Data frame containing PubMed 2-gram stop words, manually curated from PubMed abstracts

Usage

stopwords_2gram

Format

Tibble.

• word: Column containing stop words. Pulled from various PubMed abstracts.

• lexicon: Column specifying lexicon.

Source

Manually created from various PubMed abstracts.

Stop words for text mining with miRetrieve

Description

Data frame containing English stop words, PubMed stop words, and common 2-gram stopwords. English stop words are based on tidytext::stop_words, while PubMed stop words are manually curated from PubMed abstracts

Usage

stopwords_miretrieve

Format

Tibble.

• word: Column containing stop words. Pulled from various PubMed abstracts.

• lexicon: Column specifying lexicon.

Source

tidytext::stop_words; manually created from various PubMed abstracts.

Stop words for text mining from PubMed abstracts

Description

Data frame containing PubMed stop words, manually curated from PubMed abstracts

Usage

stopwords_pubmed

Format

Tibble.

• word: Column containing stop words. Pulled from various PubMed abstracts.

• lexicon: Column specifying lexicon.

Source

Manually created from various PubMed abstracts.

Subset data frame for a term

Description

Subset data frame for a term in a specified column.

Usage

subset_df(df, col.filter, filter_for = "Yes")

Arguments

Details

Subset data frame for a term in a specified column. subset_df() filters a data frame for a certain term in a specified column. All rows containing the term in the specified column are kept, while the other rows are silently dropped. Here, col.filter is a string rather than a symbol to facilitate filtering in columns that carry special characters such as '-' in their name.

Value

Data frame, subset for rows where filter_for was present in col.filter.

See Also

indicate_term(), indicate_mir(), extract_snp()

Other subset functions: subset_mir_threshold(), subset_mir(), subset_research(), subset_review(), subset_snp(), subset_year()

Subset data frame for specific miRNA names

Description

Subset data frame for specific miRNA names only.

Usage

subset_mir(df, mir.retain, col.mir = miRNA)

Arguments

Details

Subset data frame for specific miRNA names only.

Value

Data frame containing only specified miRNA names. If no miRNA name in mir.retain matches a miRNA name in col.mir, subset_mir() stops with a warning saying "No miRNA name in 'mir.retain' matches a miRNA name in 'col.mir'. Could not filter for miRNA name.".

See Also

get_mir(), subset_mir_threshold()

Other subset functions: subset_df(), subset_mir_threshold(), subset_research(), subset_review(), subset_snp(), subset_year()

Subset data frame for miRNA names exceeding a threshold

Description

Subset data frame for miRNA names whose frequency exceeds a threshold.

Usage

subset_mir_threshold(df, threshold = 1, col.mir = miRNA, col.pmid = PMID)

Arguments

Details

Subset data frame for miRNA names whose frequency exceeds a threshold. This threshold can either be an absolute value, e.g. 3, or a float between 0 and 1, e.g. 0.2. If threshold is an absolute value, subset_mir_threshold() retains miRNA names mentioned in at least threshold abstracts. If threshold is a float between 0 and 1, subset_mir_threshold() retains miRNA names mentioned in at least threshold abstracts of all abstracts in df.

Value

Data frame, subset for miRNA names whose frequency exceeds a threshold.

See Also

get_mir(), subset_mir()

Other subset functions: subset_df(), subset_mir(), subset_research(), subset_review(), subset_snp(), subset_year()

Subset data frame for abstracts of research articles

Description

Subset data frame for abstracts of research articles only.

Usage

subset_research(df, col.type = Type)

Arguments

Details

Subset data frame for abstracts of research articles only. At the same time, abstracts from other article types such as Review, Letter, etc. are dropped.

Value

Data frame containing abstracts of research articles only.

See Also

subset_review(), subset_year()

Other subset functions: subset_df(), subset_mir_threshold(), subset_mir(), subset_review(), subset_snp(), subset_year()

Subset data frame for abstracts of review articles

Description

Subset data frame for abstracts of review articles only.

Usage

subset_review(df, col.type = Type)

Arguments

Details

Subset data frame for abstracts of review articles only. At the same time, abstracts from other article types such as Journal Article, Letter, etc. are dropped.

Value

Data frame containing abstracts of review articles only.

See Also

subset_research(), subset_year()

Other subset functions: subset_df(), subset_mir_threshold(), subset_mir(), subset_research(), subset_snp(), subset_year()

Subset data frame for specific SNPs

Description

Subset data frame for specific SNPs only.

Usage

subset_snp(df, snp.retain, col.snp = SNPs)

Arguments

Details

Subset data frame for specific SNPs only.

Value

Data frame containing only specified SNPs. If no SNP in snp.retain matches a SNP in col.snp, subset_snp() stops with a warning saying "No SNP in 'snp.retain' matches a SNP in 'col.snp'. Could not filter for SNP.".

See Also

extract_snp(), count_snp(), get_snp()

Other subset functions: subset_df(), subset_mir_threshold(), subset_mir(), subset_research(), subset_review(), subset_year()

Subset data frame for abstracts published in a specific period

Description

Subset data frame for abstracts published in a specific period only.

Usage

subset_year(df, col.year = Year, start = NULL, end = NULL)

Arguments

Details

Subset data frame for abstracts published in a specific period only. All other abstracts published not within this period are silently dropped.

Value

Data frame containing abstracts published in a specific period only.

See Also

subset_research(), subset_review()

Other subset functions: subset_df(), subset_mir_threshold(), subset_mir(), subset_research(), subset_review(), subset_snp()