'rifi' anyalyses data from rifampicin time series ceated by microarray or RNAseq


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Documentation for package ‘rifi’ version 1.0.0

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apply_ancova apply_ancova: is a statistical test to check variances between 2 segments showing pausing site (ps) or internal starting site (ITSS) independently. apply_ancova: is a statistical test to check if fragments showing ps and ITSS events have significant slope using Ancova test. The function uses ancova test. Ancova is applied when the data contains independent variables, dependent variables and covariant variables. In this case, segments are independent variables, position is the dependent variable and the delay is the covariant. The dataframe is prepared as depicted below. The lm fit is applied and p_value is extracted delay position segment S1 S1 S1 S1 S2 S2 S2 S2
apply_event_position apply_event_position: is a short version of apply_Ttest_delay function to extract event time duration as pausing site or iTSS happens.
apply_manova apply_manova: this function checks if the ratio of hl ratio and intensity ratio is statistically significant. apply_manova compares the variance between two fold-changes,HL and intensity within the same TU (half-life frgA/half-life frgB/ intensity frgA/intensity frgB). HL fragment could cover two intensity fragments therefore this function sets first fragments borders and uses manova_function. Manova checks the variance between 2 segments (independent variables) and two dependents variables (HL and intensity).
apply_Ttest_delay apply_Ttest_delay: is a statistical test to check the significance of the point between 2 segments showing pausing site (ps) and internal starting site (ITSS) independently. apply_Ttest_delay uses t-test. The last point from the first segment and the first point from the second segment are selected and added to the residuals of each model. The sum is subjected to t-test.
apply_t_test apply_t_test: it uses the statistical t_test to check if the fold-change of half-life (HL) fragments and the fold-change intensity fragments respectively are significant.
apply_t_test_ti apply_t_test_ti: compares the mean of two neighboring TI fragments within the same TU. apply_t_test_ti: this function uses the statistical t_test to check if two neighboring TI fragments are significant.
check_input check_input: reviews the input given by the user. 'check_input' stops the operation if the input data frame has severe faults. Less severe faults lead to the removal of wrong IDs and a warnings describing the problem. The Summarized Experiment colData must have the columns "timepoint" with the timepoints convertible to numeric and containing the timepoint 0. If replicates are used the column in colData must be called "replicate". The replicate must be convertible to numeric. In the RowRanges, optionally, IDs can be given as character (except ",","|","_"),but need to refer to a unique position/strand combination. Strand information need to be given. The relative intensity in the assay must be numeric. The relative intensity for the first time point cannot be 0 or NA.
dataframe_summary dataframe_summary: creates two tables relating gene annotation to fragments. dataframe_summary creates two tables summary of segments and their half-lives. The first output is bin/probe features and the second one is intensity fragment based. The dataframe_summary creates one table with feature_type, gene, locus_tag, position, strand, TU, delay_fragment, HL_fragment, half_life, intensity_fragment, intensity and velocity. The second table is similar to the first one but in compact form. It contains the same columns, the only difference is on position where a start and end position are indicated separately. Strand is indicated in case of stranded data to select the corresponding positions.
dataframe_summary_events dataframe_summary_events creates one table with all events between the segments. The dataframe_summary_events creates one table with the following columns: event, features, p_value, event_position, event_duration, position, region, gene, locus_tag, strand, TU, segment_1, segment_2, length, velocity_ratio, FC_HL, FC_intensity, FC_HL/FC_intensity. The columns are: 1. event: event type, pausing site, iTSS_I, iTSS_II, Termination, HL_event, Int_event, HL_Int_event and velocity_change. 2. FC_HL: fold change between 2 half-life fragments. 3. FC_intensity: fold change between 2 intensity fragments. 4. FC_HL/FC_intensity: ratio of fold change between 2 half-life fragments and fold change between 2 intensity fragments. 5. velocity_ratio: ratio between any two fragment where the event happen. 6. p_value: depending on the event, t-test, manova test p_value is assigned. 7. feature_type: indicated on the output data frame as region, are the feature type covering the event. 8. gene: gene covering the event. 9. locus_tag: locus_tag covering the event. 10. strand: +/- indicated in case of stranded data. 11. TU: TU covering the event. 12. segment_1: the first segment of the event, includes the segment, TU, delay fragment in case of ps or iTSS_I. The rest of the events include HL fragment and intensity fragment. 13. segment_2: same description as segment_1 but is the second fragment of the event. 14. event_position: the position of event, calculated dividing the last position of the first fragment and the first position of the next fragment on 2. 15. event_duration: the difference (min) between 2 delay fragment when ps or iTSS_I happen. 16. gap_fragments: length in position (nt), calculated by the difference between the last position of the first fragment and the first position of the second fragment. 17. features: number of segment involved on the event.
dataframe_summary_events_HL_int dataframe_summary_events_HL_int creates one table with all events between the segments. The dataframe_summary_events_HL_int creates one table with the following columns: event, features, p_value, event_position, event_duration, position, region, gene, locus_tag, strand, TU, segment_1, segment_2, length, FC_HL, FC_intensity, FC_HL/FC_intensity. The columns are: 1. event: event type, pausing site, iTSS_I, iTSS_II, Termination, HL_event, Int_event, HL_Int_event and velocity_change. 2. FC_HL: fold change between 2 half-life fragments 3. FC_intensity: fold change between 2 intensity fragments 4. FC_HL/FC_intensity: ratio of fold change between 2 half-life fragments and fold change between 2 intensity fragments. 5. p_value: depending on the event, t-test, manova test p_value is assigned. 6. feature_type: indicated on the output data frame as region, are the feature type covering the event. 7. gene: gene covering the event. 8. locus_tag: locus_tag covering the event. 9. strand: +/- indicated in case of stranded data. 10. TU: TU covering the event. 11. segment_1: the first segment of the event, includes the segment, TU, delay fragment in case of ps or iTSS_I. The rest of the events include HL fragment and could be extended intensity fragment. 12. segment_2: same description as segment_1 but is the second fragment of the event. 13. event_position: the position of event, calculated dividing the last position of the first fragment and the first position of the next fragment on 2. 14. event_duration: the difference (min) between 2 delay fragment when ps or iTSS_I happen. 15. gap_fragments: length in position (nt), calculated by the difference between the last position of the first fragment and the first position of the second fragment. 16. features: number of segment involved on the event.
dataframe_summary_events_ps_itss dataframe_summary_events_ps_itss creates one table with all events between the segments. The dataframe_summary_events_ps_itss creates one table with the following columns: event, features, p_value, event_position, event_duration, position, region, gene, locus_tag, strand, TU, segment_1, segment_2, length, velocity_ratio. The columns are: 1. event: event type, pausing site, iTSS_I, iTSS_II, Termination, HL_event, Int_event, HL_Int_event and velocity_change. 2. velocity_ratio: ratio between any two fragment where the event happen. 3. p_value: depending on the event, t-test, manova test p_value is assigned. 4. feature_type: indicated on the output data frame as region, are the feature type covering the event. 5. gene: gene covering the event. 6. locus_tag: locus_tag covering the event. 7. strand: +/- indicated in case of stranded data. 8. TU: TU covering the event. 9. segment_1: the first segment of the event, includes the segment, TU, delay fragment in case of ps or iTSS_I. 10. segment_2: same description as segment_1 but is the second fragment of the event. 11. event_position: the position of event, calculated dividing the last position of the first fragment and the first position of the next fragment on 2. 12. event_duration: the difference (min) between 2 delay fragment when ps or iTSS_I happen. 13. gap_fragments: length in position (nt), calculated by the difference between the last position of the first fragment and the first position of the second fragment. 14. features: number of segment involved on the event.
dataframe_summary_events_velocity dataframe_summary_events_velocity creates one table with all events between the segments. The dataframe_summary_events_velocity creates one table with the following columns: event, features, p_value, event_position, event_duration, position, region, gene, locus_tag, strand, TU, segment_1, segment_2, length, velocity_ratio. The columns are: 1. event: event type, pausing site, iTSS_I, iTSS_II, Termination, HL_event, Int_event, HL_Int_event and velocity_change. 2. velocity_ratio: ratio between any two fragment where the event happen. 3. p_value: depending on the event, t-test, manova test p_value is assigned. 4. feature_type: indicated on the output data frame as region, are the feature type covering the event. 5. gene: gene covering the event. 6. locus_tag: locus_tag covering the event. 7. strand: +/- indicated in case of stranded data. 8. TU: TU covering the event. 9. segment_1: the first segment of the event, includes the segment, TU, delay fragment in case of ps or iTSS_I. The rest of the events include HL fragment and could be extended intensity fragment. 10. segment_2: same description as segment_1 but is the second fragment of the event. 11. event_position: the position of event, calculated dividing the last position of the first fragment and the first position of the next fragment on 2. 12. event_duration: the difference (min) between 2 delay fragment when ps or iTSS_I happen. 13. gap_fragments: length in position (nt), calculated by the difference between the last position of the first fragment and the first position of the second fragment. 14. features: number of segment involved on the event.
dataframe_summary_TI dataframe_summary_TI creates one table with all TI fragments, p_value and the coordinates. The dataframe_summary creates one table with the following columns: event, TI_fragment, TI_factor, TI_fragments_TU, p_value, feature_type, gene, locus_tag, strand, TU, features, event_position, position_1 and position_2. The columns are: 1. event: event type, transcription interference. 2. TI_fragment: Transcription interference fragment. 3. TI_factor: Transcription interference factor. 4. TI_fragments_TU: Transcription interference fragments included on the TU. 5. p_value: TI p_value between two successive fragments is assigned. 6. feature_type: indicated on the output data frame as region, are the feature type covering the TI. 7. gene: the genes covering the TI. 8. locus_tag: the locus_tags covering the TI. 9. strand: +/- indicated in case of stranded data. 10. TU: TU covering the TI. 11. features: number of segment TI involved on a TU. 12. event_position : position between two TI fragments. 13. position_1 : the first position of TI fragment, if 2 fragments, first position is from the first fragment. 14. position_2 : the last position of TI fragment, if 2 fragments, last position is from the second fragment.
event_dataframe event_dataframe: creates a dataframe only with events for segments and genes. The function used are: position_function: adds the specific position of ps or iTSS event annotation_function_event: adds the events to the annotated genes. gff3 file has to be supplied. Strand is indicated in case of stranded data The event_dataframe selects columns with statistical features. ID, position, strand and TU columns are required. Two major dataframe are generated, df gathers t-test and Manova test and df1 gathers ps and ITSS with the corresponding features. df selects only unique intensity fragments since they are the lowest on the hierarchy. One new column is added to df, "synthesis_ratio_event", it corresponds to the FC-HL/FC-intensity and assignment of an event to the synthesis ratio respectively. df adds a new column to indicate the position of the ps or ITSS event.
example_input_e_coli An example SummarizedExperiment from E. coli An example SummarizedExperiment from RNA-seq containing information about the intensities at all time points (assay). Seqnames, IRanges and strand columns (rowRanges)and colData with time point series and replicates.
example_input_minimal An artificial example SummarizedExperiment An example SummarizedExperiment containing information about the intensities at all time points (assay). Seqnames, IRanges and strand columns (rowRanges) and colData with time point series and replicates.
example_input_synechocystis_6803 An example input data frame from Synechocystis PCC 6803 A SummarizedExperiment from microarrays data containing information about the intensities at all time points (assay), Seqnames, IRanges and strand columns (rowRanges) and colData with time point series and averaged replicates.
finding_PDD finding_PDD: flags potential candidates for post transcription decay. 'finding_PDD' uses 'score_fun_linear_PDD' to make groups by the difference to the slope. Then the slope is checked for steepness to decide for PDD. '_PDD_' is added to the 'flag' column. Post transcription decay is characterized by a strong decrease of intensity by position. The rowRanges need to contain at least 'ID', 'intensity', 'position' and 'position_segment'!
finding_TI finding_TI: flags potential candidates for transcription interference. 'finding_TI' uses 'score_fun_ave' to make groups by the mean of "probe_TI". "TI" is added to the "flag" column. TI is characterized by relative intensities at time points later than "0". The rowRanges need to contain at least "ID", "probe_TI" and "position_segment"!
fit_e_coli The result of rifi_fit for E.coli example data A SummarizedExperiment containing the output from rifi_fit as an extension of rowRanges and metadata.
fit_minimal The artificial result of rifi_fit for artificial example data A SummarizedExperiment containing the output from rifi_fit.
fit_synechocystis_6803 The result of rifi_fit for Synechocystis 6803 example data A SummarizedExperiment containing the output from rifi_fit as an extension of rowRanges and metadata.
fold_change fold_change: it sets a fold-change ratio between the neighboring fragments of Half-life (HL) and intensity. fold_change sets fold change on intensity and fold change HL fragments of two successive fragments. Two intensity fragments could belong to one HL fragment. This function sets first the borders using the position and applies the fold change ratio between the neighboring fragments of HL and those from intensity (intensity frgA/intensity frgB/half-life frgA/half-life frgB). All grepped fragments are from the same TU excluding outliers.
fragmentation_e_coli The result of rifi_fragmentation for E.coli example data A SummarizedExperiment containing the output from rifi_fragmentation as an extension of rowRanges
fragmentation_minimal The result of rifi_fragmentation for artificial example data A SummarizedExperiment containing the output from rifi_fragmentation as an extension of rowRanges and metadata.
fragmentation_synechocystis_6803 The result of rifi_fragmentation for Synechocystis 6803 example data A SummarizedExperiment containing the output from rifi_fragmentation as an extension fo rowRanges
fragment_delay fragment_delay: performs the delay fragmentation. fragment_delay makes delay_fragments based on position_segments and assigns all gathered information to the SummarizedExperiment object. The columns "delay_fragment", "velocity_fragment", "intercept" and "slope" are added. fragment_delay makes delay_fragments, assigns slopes, which are 1/velocity at the same time, and intercepts for the TU calculation. The function used is: score_fun_linear the input is the SummarizedExperiment object. pen is the penalty for new fragments in the dynamic programming, pen_out is the outlier penalty.
fragment_HL fragment_HL: performs the half_life fragmentation fragment_HL makes HL_fragments based on delay_fragments and assigns all gathered information to the SummarizedExperiment object. The columns "HL_fragment" and "HL_mean_fragment" are added. fragment_HL makes half-life_fragments and assigns the mean of each fragment. The function used is: .score_fun_ave. The input the SummarizedExperiment object. pen is the penalty for new fragments in the dynamic programming, pen_out is the outlier penalty.
fragment_inty fragment_inty: performs the intensity fragmentation fragment_inty makes intensity_fragments based on HL_fragments and assigns all gathered information to the SummarizedExperiment object. The columns "intensity_fragment" and "intensity_mean_fragment" are added. fragment_inty makes intensity_fragments and assigns the mean of each fragment. The function used is: .score_fun_ave. The input is the the SummarizedExperiment object. pen is the penalty for new fragments in the dynamic programming, pen_out is the outlier penalty.
fragment_TI fragment_TI: performs the TI fragmentation. fragment_TI makes TI_fragments based on TUs and assigns all gathered information to the SummarizedExperiment object. The columns "TI_termination_fragment" and the TI_mean_termination_factor are added. The function used is: .score_fun_ave. The input is the SummarizedExperiment object. pen is the penalty for new fragments in the dynamic programming, pen_out is the outlier penalty.
gff3_preprocess gff3_preprocess: process gff3 file from database for multiple usage. gff3_preprocess processes the gff3 file extracting gene names and locus_tag from all coding regions (CDS), UTRs/ncRNA/asRNA are also extracted if available. The resulting dataframe contains region, positions, strand, gene and locus_tag.
make_df make_df: adds important columns to the SummarizedExperiment object. 'make_df' adds to the SummarizedExperiment object with the columns: "intensity", "probe_TI" and "flag". The replicates are collapsed into their respective means. "intensity" is the mean intensity from time point 0. "probe_TI" is a value needed for the distribution for the different fitting models. "flag" contains information or the distribution for the different fitting models. Here, probes that don't reach the background level expression are flagged as "_ABG_" ("above background"). This is only needed for microarray data and is controlled by the bg parameter. The default for bg = 0, resulting in all probes to be above background (0 is advised for RNAseq data). Probes where all replicates were filtered in the optional filtration step can be fully removed by rm_FLT = TRUE! If you wish to keep all information in the assay set to FALSE!
make_pen make_pen: automatically assigns a penalties. 'make_pen' calls one of four available penalty functions to automatically assign penalties for the dynamic programming. The four functions to be called are: 1. fragment_delay_pen 2. fragment_HL_pen 3. fragment_inty_pen 4. fragment_TI_pen. These functions return the amount of statistically correct and statistically wrong splits at a specific pair of penalties. 'make_pen' iterates over many penalty pairs and picks the most suitable pair based on the difference between wrong and correct splits. The sample size, penalty range and resolution as well as the number of cycles can be customized. The primary start parameters create a matrix with n = rez_pen rows and n = rez_pen_out columns with values between sta_pen/sta_pen_out and end_pen/end_pen_out. The best penalty pair is picked. If dept is bigger than 1 the same process is repeated with a new matrix of the same size based on the result of the previous cycle. Only position segments with length within the sample size range are considered for the penalties to increase run time. Returns a penalty object (list of 4 objects) the first being the logbook.
nls2_fit nls2_fit: estimates decay for each probe or bin. nls2_fit uses nls2 function to fit a probe or bin using intensities of the time series data from different time point. nls2 uses different starting values through expand grid and selects the best fit. Different filters could be applied prior fitting to the model. To apply nls2_fit function, prior filtration could applied. 1. generic_filter_BG: filter probes with intensities below background using threshold. Those probes are filtered. 2. filtration_below_backg: additional functions exclusive to microarrays could be applied. Its very strict to the background (not recommended in usual case). 3. filtration_above_backg: selects probes with a very high intensity and above the background (recommended for special transcripts). Probes are flagged with "_ABG_". Those transcripts are usually related to a specific function in bacteria. This filter selects all probes with the same ID, the mean is applied, the last time point is selected and compared to the threshold.
penalties_e_coli The result of rifi_penalties for E.coli example data. A SummarizedExperiment containing the output from rifi_penalties including the logbook and the four penalty objects as metadata.
penalties_minimal The result of rifi_penalties for artificial example data A SummarizedExperiment containing the output from rifi_penalties including the logbook and the four penalty objects as metadata.
penalties_synechocystis_6803 The result of rifi_penalties for Synechocystis 6803 example data. A SummarizedExperiment containing the output from rifi_penalties including the logbook and the four penalty objects as metadata.
predict_ps_itss predict_ps_itss: predicts pausing sites (ps) and internal starting sites (ITSS) between delay fragments. predict_ps_itss predicts ps and ITSS within the same TU. Neighboring delay segments are compared to each other by positioning the intercept of the second segment into the first segment using slope and intercept coefficients.#' predict_ps_itss uses 3 steps to identify ps and ITSS: 1. select unique TU. 2. select from the input dataframe the columns: ID, position, strand, delay, delay fragment, TU and slope coordinates, velocity_fragment and intercept. 3. select delay segments in the TU. 4. loop into all delay segments and estimate the coordinates of the last point of the first segment using the coefficients of the second segment and vice versa. We get two predicted positions, the difference between them is compared to the threshold. In case the strand is "-", additional steps are added: The positions of both segments are ordered from the last position to the first one. all positions are merged in one column and subtracted from the maximum position. the column is split in 2. The first and second correspond to the positions of the first and second segments respectively. Both segments are subjected to lm fit and the positions predicted are used on the same way as the opposite strand. if the difference between the positions predicted is lower than negative threshold, ps is assigned otherwise, and if the difference is higher than the positive threshold, ITSS is assigned.
preprocess_e_coli The result of rifi_preprocess for E.coli example data A SummarizedExperiment containing the output from rifi_penalties including the logbook and the four penalty objects as metadata. A list containing the output from rifi_preprocess, including the inp and the modified input_df.
preprocess_minimal The result of rifi_preprocess for artificial example data A SummarizedExperiment containing the output from rifi_preprocess
preprocess_synechocystis_6803 The result of rifi_preprocess for Synechocystis 6803 example data is a A SummarizedExperiment containing the output of rifi_preprocess as an extention to rowRanges
res_minimal The result of event_dataframe for E.coli artificial example. A data frame combining the processed genome annotation and a SummarizedExperiment data from rifi_stats. The dataframe is
rifi_fit rifi_fit: conveniently wraps all fitting steps
rifi_fragmentation rifi_fragmentation: conveniently wraps all fragmentation steps
rifi_penalties rifi_penalties: conveniently wraps all penalty steps
rifi_preprocess rifi_preprocess: conveniently wraps all pre-processing steps. Wraps the functions: 1. check_input 2. make_df 3. function_seg 4. finding_PDD 5. finding_TI Allows for the optional integration of filter functions. Filter functions mark replicates with TRUE. Those are then not considered in the fit! FUN_filter is a general filter usually to exclude probes with low expression or "bad" patterns.
rifi_stats rifi_stats: conveniently wraps all statistical prediction steps. Wraps the functions: predict_ps_itss, apply_Ttest_delay, apply_ancova, apply_event_position, apply_t_test, fold_change, apply_manova, apply_t_test_ti and gff3_preprocess.
rifi_summary rifi_summary: conveniently wraps and summarize all rifi outputs. Wraps the functions: event_dataframe, dataframe_summary, dataframe_summary_events, dataframe_summary_events_HL_int, dataframe_summary_events_ps_itss, dataframe_summary_events_velocity and dataframe_summary_TI.
rifi_visualization rifi_visualization: plots all the data with fragments and events from both strands. rifi_visualization: plots the whole genome with genes, transcription units (TUs), delay, half-life (HL), intensity fragments features, events, velocity, annotation, coverage if available. rifi_visualization uses several functions to plot the genes including as-RNA and ncRNA and TUs as segments. The function plots delay, HL and intensity fragments with statistical t-test between the neighboring fragment, significant t-test is assigned with '_'. t-test and Manova statistical test are also depicted as '_'. The functions used are: annotation_plot: plots the corresponding annotation positive_strand_function: plots delay, HL, intensity and events of positive strand negative_strand_function: plots delay, HL, intensity and events of negative strand empty_data_positive: plots empty boxes in case no data is available for positive strand empty_data_negative: plots empty boxes in case no data is available for negative strand strand_selection: check if data is stranded and arrange by position. splitGenome_function: splits the genome into fragments indice_function: assign a new column to the data to distinguish between fragments, outliers from delay or HL or intensity. TU_annotation: designs the segments border for the genes and TUs annotation gene_annot_function: it requires gff3 file, returns a dataframe adjusting each fragment according to its annotation. It allows as well the plot of genes and TUs shared into two pages label_log2_function: used to add log scale to intensity values. label_square_function: used to add square scale to coverage values. coverage_function: this function is used only in case of coverage is available. secondaryAxis: adjusts the half-life or delay to 20 in case of the dataframe row numbers is equal to 1 and the half-life or delay exceed the limit, they are plotted with different shape and color. add_genomeBorders: when the annotated genes are on the borders, they can not be plotted, therefore the region was split in 2 adding the row corresponding to the split part to the next annotation (i + 1) except for the first page. my_arrow: creates an arrow for the annotation. arrange_byGroup: selects the last row for each segment and add 40 nucleotides in case of negative strand for a nice plot. regr: plots the predicted delay from linear regression if the data is on negative strand meanPosition: assign a mean position for the plot. delay_mean: adds a column in case of velocity is NA or equal to 60. The mean of the delay is calculated outliers. my_segment_T: plots terminals and pausing sites labels. my_segment_NS: plots internal starting sites 'iTSS'. min_value: returns minimum value for event plots in intensity plot. velocity_fun: function for velocity plot limit_function: for values above 10 or 20 in delay and hl. Limit of the axis is set differently. y-axis limit is applied only if we have more than 3 values above 10 and lower or equal to 20. An exception is added in case a dataframe has less than 3 rows and 1 or more values are above 10, the rest of the values above 20 are adjusted to 20 on "secondaryAxis" function. empty_boxes: used only in case the dataframe from the positive strand is not empty, the TU are annotated. function_TU_arrow: used to avoid plotting arrows when a TU is split into two pages. terminal_plot_lm: draws a linear regression line when terminal outliers have an intensity above a certain threshold and are consecutive. Usually are smallRNA (ncRNA, asRNA). slope_function: replaces slope lower than 0.0009 to 0. velo_function: replaces infinite velocity with NA. plot the coverage of RNA_seq in exponential phase growth
rifi_wrapper rifi_wrapper: conveniently wraps all functions included on rifi workflow. Wraps the functions: rifi_preprocess, rifi_fit, rifi_penalties, rifi_fragmentation, rifi_stats, rifi_summary and rifi_visualization.
segment_pos segment_pos: divides all IDs by position into position_segments. segment_pos adds the column "position_segment" to the rowRanges. To reduce run time, the data is divided by regions of no expression larger than "dist" nucleotides.
stats_e_coli The result of rifi_stats for E.coli example data A SummarizedExperiment containing the output from rifi_stats
stats_minimal The result of rifi_stats for artificial example data A SummarizedExperiment containing the output of rifi_stats as an extention to rowRanges and metadata (gff file processed, see gff file documentation)
stats_synechocystis_6803 The result of rifi_stats for Synechocystis 6803 example data A SummarizedExperiment containing the output of rifi_stats as an extention to rowRanges
summary_e_coli The result of rifi_summary for E.coli example data A SummarizedExperiment containing the output of rifi_stats as an extention to rowRanges
summary_minimal The result of rifi_summary for artificial example data A SummarizedExperiment with the output from rifi_summary as metadata
summary_synechocystis_6803 The result of rifi_summary for Synechocystis 6803 example data A list containing the output from rifi_summary, including the fragment based data frame, bin based data frame, event data frame and the TI dataframe.
TI_fit TI_fit: estimates transcription interference and termination factor using nls function for probe or bin flagged as "TI". TI_fit uses nls2 function to fit the flagged probes or bins with "TI" found using finding_TI.r. It estimates the transcription interference level (referred later to TI) as well as the transcription factor fitting the probes/bins with nls function looping into several starting values. To determine TI and termination factor, TI_fit function is applied to the flagged probes and to the probes localized 1000 nucleotides upstream. Before applying TI_fit function, some probes/bins are filtered out if they are below the background using generic_filter_BG. The model loops into a dataframe containing sequences of starting values and the coefficients are extracted from the fit with the lowest residuals. When many residuals are equal to 0, the lowest residual can not be determined and the coefficients extracted could be wrong. Therefore, a second filter was developed. First we loop into all starting values, we collect nls objects and the corresponding residuals. They are sorted and residuals non equal to 0 are collected in a vector. If the first residuals are not equal to 0, 20 % of the best residuals are collected in tmp_r_min vector and the minimum termination factor is selected. In case the first residuals are equal to 0 then values between 0 to 20% of the values collected in tmp_r_min vector are gathered. The minimum termination factor coefficient is determined and saved. The coefficients are gathered in res vector and saved as an object.
TUgether TUgether: combines delay fragments into TUs.
viz_pen_obj viz_pen_obj: visualizes penalty objects. An optional visualization of any penalty object created by make_pen. Can be customized to show only the n = top_i top results.
wrapper_e_coli The result of rifi_wrapper for E.coli example data A list of SummarizedExperiment containing the output of rifi_wrapper. The list contains 6 elements of SummarizedExperiment output of rifi_preprocess, rifi_fit, rifi_penalties, rifi_fragmentation, rifi_stats and rifi_summary. The plot is generated from rifi_visualization. for more detail, please refer to each function separately.
wrapper_minimal The result of rifi_wrapper for E.coli artificial example. A list of SummarizedExperiment containing the output of rifi_wrapper. The list contains 6 elements of SummarizedExperiment output of rifi_preprocess, rifi_fit, rifi_penalties, rifi_fragmentation, rifi_stats and rifi_summary. The plot is generated from rifi_visualization. for more detail, please refer to each function separately.
wrapper_summary_synechocystis_6803 The result of rifi_wrapper for summary_synechocystis_6803 example data A list of SummarizedExperiment containing the output of rifi_wrapper. The list contains 6 elements of SummarizedExperiment output of rifi_preprocess, rifi_fit, rifi_penalties, rifi_fragmentation, rifi_stats and rifi_summary. The plot is generated from rifi_visualization. for more detail, please refer to each function separately.