rifiComparative
2024-05-01
0. Installation
Required dependencies are cited on README, please make sure they are properly installed (README). All functions should be located on the same folder and add them to your path directory.
I. Introduction
rifiComparative is a successor framework of rifi
(https://github.com/CyanolabFreiburg/rifi). Generated outputs from the same
organism with different treatments could be compared. Trying to combine segments
of the same gene from different conditions is not straight forward and makes the
data analysis nearly impossible. Therefore we developed a new workflow,
rifiComparative, with an easy strategy to make 2 conditions comparable.
The principle of rifiComparative consists on segmenting the half-life
(difference between half-life (condition1) and half-life (condition2)
at probe/bin level) and segmenting intensity using the log2FC(mRNA at time 0).
The workflow does not apply any hierarchy. Half-life (in some cases, HL) and
intensity segmentation are independent.
The fragments result of clustering from half-life and intensity are compared using
log2FC(log2FC(half-life)/log2FC(intensity)). These values are a pre-analysis for
transcription and post-transcription regulation. Events for each treatment are
depicted with the position on the genome (For more detail, refer to section visualization).
P-values from statistical tests are estimated. rifiComparative generates data frame summary, genome plot and several figures (refer to section Plots for more details).
II. Workflow
1. Joining data
The first step is combining the data from two conditions. The data are combined by row on one hand and combined by column on the other hand. Both objects are saved and used as input for the next analysis.
The functions used are:
loading_fun: you need to load either rifi_fit or rifi_stats outputs from
each condition and place all in one directory. rifi_fit is sufficient to run the
workflow unless if you want to select more column from rifi_stats for more
analysis or plot. The “cdt” is added referring to the sample condition.
Very important: you will need to run the
differential expression at probe/bin level. This is the log2FC(intensity) or
log2FC(mRNA at time 0). Pick-up the logFC, the p_value adjusted, probe position
and strand columns. Save the first two as logFC_int and P.Value. You can use either left_join or right_join from the dplyr
package to join both data by strand and position.
data(stats_se_cdt1)
data(stats_se_cdt2)
data(differential_expression)
inp_s <-
loading_fun(stats_se_cdt1, stats_se_cdt2, differential_expression)[[1]]
head(inp_s, 5)## strand position ID FLT intensity probe_TI flag position_segment delay
## 1 + 67 1 0 1367.080 -1 _ S_1 1.4190839
## 2 + 153 2 0 3316.336 -1 _ S_1 1.9343216
## 3 + 199 3 0 1112.101 -1 _ S_1 0.6442441
## 4 + 259 4 0 2012.294 1 _ S_1 0.0010000
## 5 + 320 5 0 1627.467 -1 _ S_1 1.9506707
## half_life TI_termination_factor delay_fragment velocity_fragment intercept
## 1 0.63658399 NA D_1 5381.643 1.707418
## 2 0.07033786 NA D_1 5381.643 1.707418
## 3 1.23339859 NA D_1_O 5381.643 1.707418
## 4 0.05594761 NA D_1_O 5381.643 1.707418
## 5 0.07012892 NA D_1 5381.643 1.707418
## slope HL_fragment HL_mean_fragment intensity_fragment
## 1 0.0001858169 Dc_1 0.4851184 I_1
## 2 0.0001858169 Dc_1 0.4851184 I_1
## 3 0.0001858169 Dc_1 0.4851184 I_1
## 4 0.0001858169 Dc_1 0.4851184 I_1
## 5 0.0001858169 Dc_1 0.4851184 I_1
## intensity_mean_fragment TU TI_termination_fragment
## 1 1467.208 TU_1 <NA>
## 2 1467.208 TU_1 <NA>
## 3 1467.208 TU_1 <NA>
## 4 1467.208 TU_1 <NA>
## 5 1467.208 TU_1 <NA>
## TI_mean_termination_factor seg_ID pausing_site iTSS_I
## 1 NA S_1|TU_1|D_1|Dc_1|I_1 - -
## 2 NA S_1|TU_1|D_1|Dc_1|I_1 - -
## 3 NA S_1|TU_1|D_1|Dc_1|I_1 - -
## 4 NA S_1|TU_1|D_1|Dc_1|I_1 - -
## 5 NA S_1|TU_1|D_1|Dc_1|I_1 - -
## ps_ts_fragment event_duration event_ps_itss_p_value_Ttest p_value_slope
## 1 <NA> NA NA NA
## 2 <NA> NA NA NA
## 3 <NA> NA NA NA
## 4 <NA> NA NA NA
## 5 <NA> NA NA NA
## delay_frg_slope velocity_ratio event_position FC_fragment_HL FC_HL p_value_HL
## 1 <NA> NA NA <NA> NA NA
## 2 <NA> NA NA <NA> NA NA
## 3 <NA> NA NA <NA> NA NA
## 4 <NA> NA NA <NA> NA NA
## 5 <NA> NA NA <NA> NA NA
## FC_fragment_intensity FC_intensity p_value_intensity FC_HL_intensity
## 1 <NA> NA NA NA
## 2 <NA> NA NA NA
## 3 <NA> NA NA NA
## 4 <NA> NA NA NA
## 5 <NA> NA NA NA
## FC_HL_intensity_fragment FC_HL_adapted synthesis_ratio synthesis_ratio_event
## 1 <NA> NA NA <NA>
## 2 <NA> NA NA <NA>
## 3 <NA> NA NA <NA>
## 4 <NA> NA NA <NA>
## 5 <NA> NA NA <NA>
## p_value_Manova p_value_TI TI_fragments_p_value cdt logFC_int P.Value
## 1 NA NA <NA> cdt1 -0.20137817 0.033395012
## 2 NA NA <NA> cdt1 0.07306854 0.674892028
## 3 NA NA <NA> cdt1 -0.04264460 0.512469947
## 4 NA NA <NA> cdt1 -0.37075316 0.002843646
## 5 NA NA <NA> cdt1 -0.14561154 0.280129057inp_f <-
loading_fun(stats_se_cdt1, stats_se_cdt2, differential_expression)[[2]]
head(inp_f, 5)## strand position ID FLT intensity probe_TI flag position_segment delay
## 1 + 67 1 0 1885.621 -1 _ S_1 1.8146852
## 2 + 153 2 0 4311.070 1 _ S_1 7.0634447
## 3 + 199 3 0 1285.397 -1 _ S_1 0.7950453
## 4 + 259 4 0 3393.836 1 _ABG_ S_1 126.7149209
## 5 + 320 5 0 2245.636 -1 _ S_1 1.4386446
## half_life TI_termination_factor delay_fragment velocity_fragment intercept
## 1 0.08018944 NA D_1 Inf 1.794761
## 2 0.29227109 NA D_1 Inf 1.794761
## 3 0.77429388 NA D_1 Inf 1.794761
## 4 23.34891958 NA D_1_O Inf 1.794761
## 5 0.41681710 NA D_1 Inf 1.794761
## slope HL_fragment HL_mean_fragment intensity_fragment intensity_mean_fragment
## 1 0 Dc_1 0.3598576 I_1 2371.559
## 2 0 Dc_1 0.3598576 I_1 2371.559
## 3 0 Dc_1 0.3598576 I_1 2371.559
## 4 0 Dc_1_O 0.3598576 I_1 2371.559
## 5 0 Dc_1 0.3598576 I_1 2371.559
## TU TI_termination_fragment TI_mean_termination_factor seg_ID
## 1 TU_1 <NA> NA S_1|TU_1|D_1|Dc_1|I_1
## 2 TU_1 <NA> NA S_1|TU_1|D_1|Dc_1|I_1
## 3 TU_1 <NA> NA S_1|TU_1|D_1|Dc_1|I_1
## 4 TU_1 <NA> NA S_1|TU_1|D_1|Dc_1|I_1
## 5 TU_1 <NA> NA S_1|TU_1|D_1|Dc_1|I_1
## pausing_site iTSS_I ps_ts_fragment event_duration event_ps_itss_p_value_Ttest
## 1 - - <NA> NA NA
## 2 - - <NA> NA NA
## 3 - - <NA> NA NA
## 4 - - <NA> NA NA
## 5 - - <NA> NA NA
## p_value_slope delay_frg_slope velocity_ratio event_position FC_fragment_HL
## 1 NA <NA> NA NA <NA>
## 2 NA <NA> NA NA <NA>
## 3 NA <NA> NA NA <NA>
## 4 NA <NA> NA NA <NA>
## 5 NA <NA> NA NA <NA>
## FC_HL p_value_HL FC_fragment_intensity FC_intensity p_value_intensity
## 1 NA NA I_1:I_2 -0.7701639 0.04602176
## 2 NA NA I_1:I_2 -0.7701639 0.04602176
## 3 NA NA I_1:I_2 -0.7701639 0.04602176
## 4 NA NA I_1:I_2 -0.7701639 0.04602176
## 5 NA NA I_1:I_2 -0.7701639 0.04602176
## FC_HL_intensity FC_HL_intensity_fragment FC_HL_adapted synthesis_ratio
## 1 NA Dc_1:Dc_1;I_1:I_2 -0.7410098 -0.034867
## 2 NA Dc_1:Dc_1;I_1:I_2 -0.7410098 -0.034867
## 3 NA Dc_1:Dc_1;I_1:I_2 -0.7410098 -0.034867
## 4 NA Dc_1:Dc_1;I_1:I_2 -0.7410098 -0.034867
## 5 NA Dc_1:Dc_1;I_1:I_2 -0.7410098 -0.034867
## synthesis_ratio_event p_value_Manova p_value_TI TI_fragments_p_value cdt
## 1 Termination 0.1569589 NA <NA> cdt2
## 2 Termination 0.1569589 NA <NA> cdt2
## 3 Termination 0.1569589 NA <NA> cdt2
## 4 Termination 0.1569589 NA <NA> cdt2
## 5 Termination 0.1569589 NA <NA> cdt2
## logFC_int P.Value
## 1 -0.20137817 0.033395012
## 2 0.07306854 0.674892028
## 3 -0.04264460 0.512469947
## 4 -0.37075316 0.002843646
## 5 -0.14561154 0.280129057joining_data_row: contains joining_data_row function. It gathers data frame
from both conditions in one by rows. The object is called data_combined_se.rda
data(inp_s)
data(inp_f)
data_combined_minimal <-
joining_data_row(input1 = inp_s, input2 = inp_f)
head(data_combined_minimal, 5)## strand position ID FLT intensity probe_TI flag position_segment delay
## 1 + 67 1 0 1367.080 -1 _ S_1 1.4190839
## 2 + 153 2 0 3316.336 -1 _ S_1 1.9343216
## 3 + 199 3 0 1112.101 -1 _ S_1 0.6442441
## 4 + 259 4 0 2012.294 1 _ S_1 0.0010000
## 5 + 320 5 0 1627.467 -1 _ S_1 1.9506707
## half_life TI_termination_factor delay_fragment velocity_fragment intercept
## 1 0.63658399 NA D_1 5381.643 1.707418
## 2 0.07033786 NA D_1 5381.643 1.707418
## 3 1.23339859 NA D_1_O 5381.643 1.707418
## 4 0.05594761 NA D_1_O 5381.643 1.707418
## 5 0.07012892 NA D_1 5381.643 1.707418
## slope HL_fragment HL_mean_fragment intensity_fragment
## 1 0.0001858169 Dc_1 0.4851184 I_1
## 2 0.0001858169 Dc_1 0.4851184 I_1
## 3 0.0001858169 Dc_1 0.4851184 I_1
## 4 0.0001858169 Dc_1 0.4851184 I_1
## 5 0.0001858169 Dc_1 0.4851184 I_1
## intensity_mean_fragment TU TI_termination_fragment
## 1 1467.208 TU_1 <NA>
## 2 1467.208 TU_1 <NA>
## 3 1467.208 TU_1 <NA>
## 4 1467.208 TU_1 <NA>
## 5 1467.208 TU_1 <NA>
## TI_mean_termination_factor seg_ID pausing_site iTSS_I
## 1 NA S_1|TU_1|D_1|Dc_1|I_1 - -
## 2 NA S_1|TU_1|D_1|Dc_1|I_1 - -
## 3 NA S_1|TU_1|D_1|Dc_1|I_1 - -
## 4 NA S_1|TU_1|D_1|Dc_1|I_1 - -
## 5 NA S_1|TU_1|D_1|Dc_1|I_1 - -
## ps_ts_fragment event_duration event_ps_itss_p_value_Ttest p_value_slope
## 1 <NA> NA NA NA
## 2 <NA> NA NA NA
## 3 <NA> NA NA NA
## 4 <NA> NA NA NA
## 5 <NA> NA NA NA
## delay_frg_slope velocity_ratio event_position FC_fragment_HL FC_HL p_value_HL
## 1 <NA> NA NA <NA> NA NA
## 2 <NA> NA NA <NA> NA NA
## 3 <NA> NA NA <NA> NA NA
## 4 <NA> NA NA <NA> NA NA
## 5 <NA> NA NA <NA> NA NA
## FC_fragment_intensity FC_intensity p_value_intensity FC_HL_intensity
## 1 <NA> NA NA NA
## 2 <NA> NA NA NA
## 3 <NA> NA NA NA
## 4 <NA> NA NA NA
## 5 <NA> NA NA NA
## FC_HL_intensity_fragment FC_HL_adapted synthesis_ratio synthesis_ratio_event
## 1 <NA> NA NA <NA>
## 2 <NA> NA NA <NA>
## 3 <NA> NA NA <NA>
## 4 <NA> NA NA <NA>
## 5 <NA> NA NA <NA>
## p_value_Manova p_value_TI TI_fragments_p_value cdt logFC_int P.Value
## 1 NA NA <NA> cdt1 -0.20137817 0.033395012
## 2 NA NA <NA> cdt1 0.07306854 0.674892028
## 3 NA NA <NA> cdt1 -0.04264460 0.512469947
## 4 NA NA <NA> cdt1 -0.37075316 0.002843646
## 5 NA NA <NA> cdt1 -0.14561154 0.280129057joining_data_column: contains joining_data_column function. It gathers
data frame from both conditions in one by columns. The object is called
df_comb_se.rda
data(data_combined_minimal)
df_comb_minimal <- joining_data_column(data = data_combined_minimal)
head(df_comb_minimal, 5)## strand position ID intensity.cdt1 position_segment half_life.cdt1
## 1 + 67 1 1367.080 S_1 0.63658399
## 2 + 153 2 3316.336 S_1 0.07033786
## 3 + 199 3 1112.101 S_1 1.23339859
## 4 + 259 4 2012.294 S_1 0.05594761
## 5 + 320 5 1627.467 S_1 0.07012892
## TI_termination_factor.cdt1 HL_fragment.cdt1 intensity_fragment.cdt1
## 1 NA Dc_1 I_1
## 2 NA Dc_1 I_1
## 3 NA Dc_1 I_1
## 4 NA Dc_1 I_1
## 5 NA Dc_1 I_1
## TI_termination_fragment.cdt1 logFC_int P.Value intensity.cdt2
## 1 <NA> -0.20137817 0.033395012 1885.621
## 2 <NA> 0.07306854 0.674892028 4311.070
## 3 <NA> -0.04264460 0.512469947 1285.397
## 4 <NA> -0.37075316 0.002843646 3393.836
## 5 <NA> -0.14561154 0.280129057 2245.636
## half_life.cdt2 TI_termination_factor.cdt2 HL_fragment.cdt2
## 1 0.08018944 NA Dc_1
## 2 0.29227109 NA Dc_1
## 3 0.77429388 NA Dc_1
## 4 23.34891958 NA Dc_1_O
## 5 0.41681710 NA Dc_1
## intensity_fragment.cdt2 TI_termination_fragment.cdt2
## 1 I_1 <NA>
## 2 I_1 <NA>
## 3 I_1 <NA>
## 4 I_1 <NA>
## 5 I_1 <NA>2. Penalties
Same as rifi workflow, to get the best segmentation we
need the optimal penalties.
To calculate half-life penalty, the difference between half-life from both conditions is calculated and added as distance_HL variable to df_comb_minimal
data frame.
On other hand the logFC_int is used to assign penalties for intensity values
and added as distance_int variable. df_comb_minimal with the additional variables
is named penalties_df.
The functions needed for penalty are:
make_pen calls one of two available penalty functions to automatically assign
penalties for the dynamic programming. Four functions are called:
make_penfragment_HL_penfragment_inty_penscore_fun_ave
1. make_pen
make_pen calls one of two available penalty functions to automatically
assign penalties for the dynamic programming. the function 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. Also, outlier penalties cannot be smaller
than 40% of the respective penalty. For more detail check vignette from rifi package.
2. fragment_HL_pen
fragment_HL_pen is called by make_pen function to automatically assign
penalties for the dynamic programming of half-life fragments. The function used
for fragment_HL_pen is score_fun_ave. score_fun_ave scores the values of
y on how close they are to the mean. for more details, see below.
df_comb_minimal[,"distance_HL"] <-
df_comb_minimal[, "half_life.cdt1"] - df_comb_minimal[, "half_life.cdt2"]
pen_HL <- make_pen(
probe = df_comb_minimal,
FUN = rifiComparative:::fragment_HL_pen,
cores = 2,
logs = as.numeric(rep(NA, 8))
)3. fragment_inty_pen
fragment_inty_pen is called by make_pen function to automatically assign
penalties for the dynamic programming of intensity fragments.
The function used is score_fun_ave.
df_comb_minimal[,"distance_int"] <- df_comb_minimal[,"logFC_int"]
pen_int <- make_pen(
probe = df_comb_minimal,
FUN = rifiComparative:::fragment_inty_pen,
cores = 2,
logs = as.numeric(rep(NA, 8))
)data(df_comb_minimal)
penalties_df <- penalties(df_comb_minimal)[[1]]
pen_HL <- penalties(df_comb_minimal)[[2]]
pen_int <- penalties(df_comb_minimal)[[3]]
head(penalties_df, 5)## strand position ID intensity.cdt1 position_segment half_life.cdt1
## 1 + 67 1 1367.080 S_1 0.63658399
## 2 + 153 2 3316.336 S_1 0.07033786
## 3 + 199 3 1112.101 S_1 1.23339859
## 4 + 259 4 2012.294 S_1 0.05594761
## 5 + 320 5 1627.467 S_1 0.07012892
## TI_termination_factor.cdt1 HL_fragment.cdt1 intensity_fragment.cdt1
## 1 NA Dc_1 I_1
## 2 NA Dc_1 I_1
## 3 NA Dc_1 I_1
## 4 NA Dc_1 I_1
## 5 NA Dc_1 I_1
## TI_termination_fragment.cdt1 logFC_int P.Value intensity.cdt2
## 1 <NA> -0.20137817 0.033395012 1885.621
## 2 <NA> 0.07306854 0.674892028 4311.070
## 3 <NA> -0.04264460 0.512469947 1285.397
## 4 <NA> -0.37075316 0.002843646 3393.836
## 5 <NA> -0.14561154 0.280129057 2245.636
## half_life.cdt2 TI_termination_factor.cdt2 HL_fragment.cdt2
## 1 0.08018944 NA Dc_1
## 2 0.29227109 NA Dc_1
## 3 0.77429388 NA Dc_1
## 4 23.34891958 NA Dc_1_O
## 5 0.41681710 NA Dc_1
## intensity_fragment.cdt2 TI_termination_fragment.cdt2 distance_HL distance_int
## 1 I_1 <NA> 0.5563945 -0.20137817
## 2 I_1 <NA> -0.2219332 0.07306854
## 3 I_1 <NA> 0.4591047 -0.04264460
## 4 I_1 <NA> -23.2929720 -0.37075316
## 5 I_1 <NA> -0.3466882 -0.145611544. score_fun_ave
score_fun_ave scores the values of y on how close they are to the mean.
for more details, see below.
3. Fragmentation
After finding the optimal set of penalties, fragmentation process could be applied. The functions used are:
fragment_HL
fragment_inty
score_fun_ave
1. fragment_HL
fragment_HL performs the half_life fragmentation and assigns all gathered
information to the probe based data frame. The columns HL_comb_fragment and
HL_mean_comb_fragment are added to data frame. fragment_HL makes
half-life_fragments and assigns the mean of each fragment.
penalties_df <-
fragment_HL(
probe = penalties_df,
cores = 2,
pen = pen_HL[[1]][[9]],
pen_out = pen_HL[[1]][[10]]
)2. fragment_inty
fragment_inty performs the intensity fragmentation and assigns all gathered
information to the probe based data frame. The columns intensity_comb_fragment
and intensity_mean_comb_fragment are added to the data frame. fragment_inty
makes intensity_fragments and assigns the mean of each fragment.
The hierarchy is not followed, fragments from different size could be generated
independently of half-life fragments.
fragment_int <-
fragment_inty(
probe = penalties_df,
cores = 2,
pen = pen_int[[1]][[9]],
pen_out = pen_int[[1]][[10]]
)
head(fragment_int, 5)## strand position ID intensity.cdt1 position_segment half_life.cdt1
## 1 + 67 1 1367.080 S_1 0.63658399
## 2 + 153 2 3316.336 S_1 0.07033786
## 3 + 199 3 1112.101 S_1 1.23339859
## 4 + 259 4 2012.294 S_1 0.05594761
## 5 + 320 5 1627.467 S_1 0.07012892
## TI_termination_factor.cdt1 HL_fragment.cdt1 intensity_fragment.cdt1
## 1 NA Dc_1 I_1
## 2 NA Dc_1 I_1
## 3 NA Dc_1 I_1
## 4 NA Dc_1 I_1
## 5 NA Dc_1 I_1
## TI_termination_fragment.cdt1 logFC_int P.Value intensity.cdt2
## 1 <NA> -0.20137817 0.033395012 1885.621
## 2 <NA> 0.07306854 0.674892028 4311.070
## 3 <NA> -0.04264460 0.512469947 1285.397
## 4 <NA> -0.37075316 0.002843646 3393.836
## 5 <NA> -0.14561154 0.280129057 2245.636
## half_life.cdt2 TI_termination_factor.cdt2 HL_fragment.cdt2
## 1 0.08018944 NA Dc_1
## 2 0.29227109 NA Dc_1
## 3 0.77429388 NA Dc_1
## 4 23.34891958 NA Dc_1_O
## 5 0.41681710 NA Dc_1
## intensity_fragment.cdt2 TI_termination_fragment.cdt2 distance_HL distance_int
## 1 I_1 <NA> 0.5563945 -0.20137817
## 2 I_1 <NA> -0.2219332 0.07306854
## 3 I_1 <NA> 0.4591047 -0.04264460
## 4 I_1 <NA> -23.2929720 -0.37075316
## 5 I_1 <NA> -0.3466882 -0.14561154
## HL_comb_fragment HL_mean_comb_fragment intensity_comb_fragment
## 1 Dc_1 0.2202841 I_1
## 2 Dc_1 0.2202841 I_1
## 3 Dc_1 0.2202841 I_1
## 4 Dc_1_O 0.2202841 I_1
## 5 Dc_1 0.2202841 I_1
## intensity_mean_comb_fragment
## 1 -0.08128129
## 2 -0.08128129
## 3 -0.08128129
## 4 -0.08128129
## 5 -0.081281293. score_fun_ave
score_fun_ave is the score function used by dynamic programming for intensity
fragmentation, for more details, see below.
4. Statistics
To check segment significance, t-test with two.sided was used. Each fragment was tested for the number of probes involved in each condition.
data(fragment_int)
stats_df_comb_minimal <- statistics(data= fragment_int)[[1]]
df_comb_uniq_minimal <- statistics(data= fragment_int)[[2]]5. Visualization
The visualization depicts half-life and intensity slots of the fragments. Since hierarchy is not applied, the fragments from half-life and intensity are independent.
data(data_combined_minimal)
data(stats_df_comb_minimal)
data(annot_g)
rifi_visualization_comparison(
data = data_combined_minimal,
data_c = stats_df_comb_minimal,
genomeLength = annot_g[[2]],
annot = annot_g[[1]]
)Three objects are required:
data_combined_minimal : data frame from joined data by row.
df_comb_minimal : data frame from joined data by column
annot : ggf3 preprocessed (for more information, see below)
The plot is located on vignette “genome_fragments_comparison.pdf” and shows 3 sections: annotation, half-life difference and log2FC (mRNA=time0 or intensity).
Either half_life difference or log2FC(intensity), the line 0 indicates no changes between both conditions. Conditions 1 and 2 are indicated by blue and lilac color respectively. Fragments result of dynamic programming are indicated by different colors.
The annotation englobes genome annotation preprocessed by gff3_preprocessing function included on the package and a superposed TU annotation of both conditions from
rifi output.
Figure 1: genome fragments visualization of both conditions
III. Outputs
1. adjusting_HLToInt
adjusting_HLToInt function combines half-life and intensity fragments generated
without hierarchy on one hand and the genome annotation on other hand. The first
step is adjusting the fragments from half-life to intensity and vise-versa and join
them to the genome annotation. To make half-life and intensity segments comparable,
log2FC(HL) is used instead of distance_HL. At least one fragment should have
a significant p_value from t-test, either half-life or intensity.
To generate the data frame, two objects are required:
df_comb_minimal : data frame from joined data by column.
annot : ggf3 preprocessed (for more information, see below).
The functions used are:
p_value_function extracts and return the p_values of half-life and intensity segments respectively.
eliminate_outlier_hl eliminates outliers from half-life fragments.
eliminate_outlier_int eliminates outliers from intensity fragments.
mean_length_int extracts the mean of the log2FC(intensity) fragments adapted
to HL_fragments and their lengths.
mean_length_hl extracts the mean of log2FC(HL) fragments adapted to the
intensity fragments and their lengths.
calculating_rate calculates decay rate and log2FC(intensity). Both are used to
calculate synthesis rate.
The output data frame contains the corresponding columns:
position: position of the first fragment region: region annotation covering the fragments gene: gene annotation covering the fragments locus_tag: locus_tag annotation covering the fragments strand: The bin/probe specific strand (+/-) fragment_HL: Half-life fragments fragment_int: intensity fragments position_frg_int: position of the first fragment and the last position of the last fragment. mean_HL_fragment: mean of the HL of the fragments involved. mean_int_fragment: mean of the intensity of the fragments involved. log2FC(decay_rate): log2FC(decay(condition1)/decay(condition2)). log2FC(synthesis_rate): sum of log2FC(decay_rate) and log2FC(intensity). Log2FC(HL)-Log2FC(int): sum of log2FC(decay_rate) and log2FC(intensity). intensity_FC: log2FC(mean(intensity(condition1))/mean(intensity(condition2))). Log2FC(HL)-Log2FC(int): sum of log2FC(decay_rate) and log2FC(intensity). p_value: indicated by "*" means at least one fragment either half-life fragment or intensity fragment has a significant p_value.
data(stats_df_comb_minimal)
data(annot_g)
df_adjusting_HLToInt <- adjusting_HLToInt(data = stats_df_comb_minimal,
annotation = annot_g[[1]])
head(df_adjusting_HLToInt, 5)## Position Region Gene Locus_tag STrand
## 1 67 CDS|5'UTR slr0612|slr0613|sds slr0612|slr0613|slr0611-2 +
## 2 7228 CDS|5'UTR psbA2|slr1311 slr1311 +
## 3 8574 CDS speA slr1312 +
## 4 12826 CDS fecC|fecD|slr1315 slr1316|slr1317|slr1315 +
## 5 12826 CDS fecC|fecD|slr1315 slr1316|slr1317|slr1315 +
## Fragment_HL Fragment_int position_frg_int Mean_HL_fragment Mean_int_fragment
## 1 <NA> I_1 67:2604 0.8798527 -0.09571587
## 2 <NA> I_2 7228:8311 0.5484517 -0.41072266
## 3 <NA> I_3 8574:10736 0.6870409 -0.02241187
## 4 Dc_4 I_4 12826:13363 0.3837561 -0.02872339
## 5 Dc_4 I_5 13473:13970 -0.0304837 -0.96316418
## Decay_rate Synthesis_rate intensity_FC p_value
## 1 -0.38918785 -0.8310518 -0.4418639 *
## 2 -0.52376161 -0.9201845 -0.3964229 *
## 3 -0.25668854 -0.5292553 -0.2725668 *
## 4 -0.03047461 -0.4145714 -0.3840968 <NA>
## 5 -0.08398353 -1.3048974 -1.2209139 *IV. Plots
A serie of plots could be generated using the figures_fun. The functions
included are:
plot_decay_synt
plot_heatscatter
plot_density
plot_histogram
plot_scatter
plot_volcano
data(data_combined_minimal)
data(df_comb_minimal)
data(differential_expression)
data(df_mean_minimal)
figures_fun(data.1 = df_mean_minimal, data.2 = data_combined_minimal,
input.1 = df_comb_minimal, input.2 = differential_expression, cdt1 = "sc",
cdt2 = "fe") 1. plot_decay_synt
The generated data frame df_mean_minimal could be used to plot changes in RNA
decay rates (log fold, x-axis) versus the changes in RNA synthesis rates (log fold, y-axis) in the condition 1 versus condition 2. The black lines horizontal, vertical and diagonal are the median of synthesis_rate, decay_rate and mRNA at time 0 respectively. Dashed gray lines indicate 0.5-fold changes from 0 (gray lines) referring to unchanged fold.
Coloration could be adjusted upon the parameter selected. In this case decay rate
above 0.5 and synthesis rate below -0.5 are highlighted in yellow.
Segments could be labeled using geom_text_repel function, they are commented on
the function.
Figure 2: Decay rate vs. Synthesis rate
2. plot_heatscatter
Heatscatter plot could be generated using “df_mean_minimal” data frame. It plots the changes in RNA decay rates (log fold, x-axis) versus the changes in RNA synthesis rates (log fold, y-axis) in the condition 1 versus condition 2. The coloring indicates the local point density.
Figure 3: Heatscatter Decay rate vs. Synthesis rate
3. plot_density
The function uses the data_combined_minimal to plot the probe/bin half-life
density in both conditions. Condition 1 and 2 could be indicated.
Figure 4: Half-life Density
4. plot_histogram
The function uses df_comb_minimal to plot a histogram of probe/bin half-life
categories from 2 to 20 minutes in both conditions. Condition 1 and 2 could be
indicated.
Figure 5: Half-life Classification
5. plot_scatter
A scatter plot of the bin/probe half-life in condition 1 vs. condition 2.
Figure 6: Half-life Scatter Plot
6. plot_volcano
A volcano plot of statistical significance (P value) versus magnitude of change (fold change).
Figure 7: Volcano Plot
III. Additional functions
1. score_fun_ave
score_fun_ave scores the difference of the values from their mean.
score_fun_ave calculates the mean of a minimum 2 values y and substrates
the difference from their mean. The IDs z and the sum of differences from
the mean are stored. A new value y is added, the mean is calculated and the new
IDs and sum of differences are stored. After several rounds, the minimum score
and the corresponding IDs is selected and stored as the best fragment.
score_fun_ave selects simultaneously for outliers, the maximum number is fixed
previously. Outliers are those values with high difference from the mean, they
are stored but excluded from the next calculation. The output of the function is
a vector of IDs separated by “,”, a vector of mean separated by "_" and a
vector of outliers separated by “,”.
2. gff3_preprocess
gff3_preprocess processes gff3 file from database, extracting gene names and
locus_tag from all coding regions (CDS). Other features like UTRs, ncRNA, asRNA
ect.. if available and the genome length are extracted. The output is a list of
2 elements.
The output data frame from gff3_preprocess function contains the following
columns:
- region: CDS or any other available like UTRs, ncRNA, asRNA
- start: start position of the gene
- end: end position of the gene
- strand: +/-
- gene: gene annotation if available otherwise locus_tag annotation replaces it
- locus_tag: locus_tag annotation
gff3_preprocess(path = gzfile(
system.file("extdata", "gff_synechocystis_6803.gff.gz", package = "rifiComparative")
))## [[1]]
## DataFrame with 5853 rows and 6 columns
## region start end strand gene locus_tag
## <factor> <integer> <integer> <character> <character> <character>
## 1 CDS 1 772 + sds slr0611-2
## 2 asRNA 689 909 - slr0612-as slr0612-as
## 3 CDS 802 1494 + slr0612 slr0612
## 4 5'UTR 1532 1576 + slr0613 slr0613
## 5 CDS 1577 2098 + slr0613 slr0613
## ... ... ... ... ... ... ...
## 5849 CDS 3571612 3572403 + slr0610 slr0610
## 5850 ncRNA 3572945 3573067 - SyR52 ncl1790
## 5851 ncRNA 3573080 3573200 - ncl1800 ncl1800
## 5852 5'UTR 3573218 3573270 + slr0611 slr0611
## 5853 CDS 3573271 3573470 + sds slr0611
##
## [[2]]
## [1] 3573470sessionInfo()## R version 4.4.0 beta (2024-04-15 r86425)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] SummarizedExperiment_1.34.0 Biobase_2.64.0
## [3] GenomicRanges_1.56.0 GenomeInfoDb_1.40.0
## [5] IRanges_2.38.0 S4Vectors_0.42.0
## [7] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
## [9] matrixStats_1.3.0 rifiComparative_1.4.0
## [11] BiocStyle_2.32.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 gridExtra_2.3 writexl_1.5.0
## [4] remotes_2.5.0 rlang_1.1.3 magrittr_2.0.3
## [7] compiler_4.4.0 reshape2_1.4.4 vctrs_0.6.5
## [10] stringr_1.5.1 profvis_0.3.8 pkgconfig_2.0.3
## [13] crayon_1.5.2 fastmap_1.1.1 XVector_0.44.0
## [16] ellipsis_0.3.2 utf8_1.2.4 Rsamtools_2.20.0
## [19] promises_1.3.0 rmarkdown_2.26 sessioninfo_1.2.2
## [22] UCSC.utils_1.0.0 purrr_1.0.2 xfun_0.43
## [25] zlibbioc_1.50.0 cachem_1.0.8 jsonlite_1.8.8
## [28] highr_0.10 later_1.3.2 DelayedArray_0.30.0
## [31] BiocParallel_1.38.0 parallel_4.4.0 R6_2.5.1
## [34] bslib_0.7.0 stringi_1.8.3 rtracklayer_1.64.0
## [37] pkgload_1.3.4 jquerylib_0.1.4 Rcpp_1.0.12
## [40] bookdown_0.39 iterators_1.0.14 knitr_1.46
## [43] usethis_2.2.3 Matrix_1.7-0 httpuv_1.6.15
## [46] nnet_7.3-19 tidyselect_1.2.1 abind_1.4-5
## [49] rstudioapi_0.16.0 yaml_2.3.8 codetools_0.2-20
## [52] miniUI_0.1.1.1 curl_5.2.1 pkgbuild_1.4.4
## [55] plyr_1.8.9 lattice_0.22-6 tibble_3.2.1
## [58] shiny_1.8.1.1 withr_3.0.0 evaluate_0.23
## [61] desc_1.4.3 urlchecker_1.0.1 DTA_2.50.0
## [64] Biostrings_2.72.0 pillar_1.9.0 BiocManager_1.30.22
## [67] foreach_1.5.2 generics_0.1.3 rprojroot_2.0.4
## [70] RCurl_1.98-1.14 ggplot2_3.5.1 munsell_0.5.1
## [73] scales_1.3.0 xtable_1.8-4 glue_1.7.0
## [76] scatterplot3d_0.3-44 tools_4.4.0 BiocIO_1.14.0
## [79] egg_0.4.5 GenomicAlignments_1.40.0 fs_1.6.4
## [82] XML_3.99-0.16.1 cowplot_1.1.3 grid_4.4.0
## [85] devtools_2.4.5 colorspace_2.1-0 doMC_1.3.8
## [88] GenomeInfoDbData_1.2.12 restfulr_0.0.15 cli_3.6.2
## [91] LSD_4.1-0 fansi_1.0.6 S4Arrays_1.4.0
## [94] dplyr_1.1.4 gtable_0.3.5 sass_0.4.9
## [97] digest_0.6.35 SparseArray_1.4.0 ggrepel_0.9.5
## [100] rjson_0.2.21 htmlwidgets_1.6.4 memoise_2.0.1
## [103] htmltools_0.5.8.1 lifecycle_1.0.4 httr_1.4.7
## [106] mime_0.12