rifiComparative
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 0.280129057## 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.
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.
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.
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.
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.
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.
Half-life Classification
5. plot_scatter
A scatter plot of the bin/probe half-life in condition 1 vs. condition 2.
Half-life Scatter Plot
6. plot_volcano
A volcano plot of statistical significance (P value) versus magnitude of change (fold change).
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] 3573470## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [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: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] SummarizedExperiment_1.37.0 Biobase_2.67.0
## [3] GenomicRanges_1.59.1 GenomeInfoDb_1.43.2
## [5] IRanges_2.41.2 S4Vectors_0.45.2
## [7] BiocGenerics_0.53.3 generics_0.1.3
## [9] MatrixGenerics_1.19.0 matrixStats_1.4.1
## [11] rifiComparative_1.7.0 BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-9 gridExtra_2.3 writexl_1.5.1
## [4] remotes_2.5.0 rlang_1.1.4 magrittr_2.0.3
## [7] compiler_4.4.2 reshape2_1.4.4 vctrs_0.6.5
## [10] stringr_1.5.1 profvis_0.4.0 pkgconfig_2.0.3
## [13] crayon_1.5.3 fastmap_1.2.0 XVector_0.47.0
## [16] ellipsis_0.3.2 utf8_1.2.4 Rsamtools_2.23.1
## [19] promises_1.3.2 rmarkdown_2.29 sessioninfo_1.2.2
## [22] UCSC.utils_1.3.0 purrr_1.0.2 xfun_0.49
## [25] zlibbioc_1.52.0 cachem_1.1.0 jsonlite_1.8.9
## [28] later_1.4.1 DelayedArray_0.33.3 BiocParallel_1.41.0
## [31] parallel_4.4.2 R6_2.5.1 bslib_0.8.0
## [34] stringi_1.8.4 rtracklayer_1.67.0 pkgload_1.4.0
## [37] jquerylib_0.1.4 Rcpp_1.0.13-1 iterators_1.0.14
## [40] knitr_1.49 usethis_3.1.0 Matrix_1.7-1
## [43] httpuv_1.6.15 nnet_7.3-19 tidyselect_1.2.1
## [46] abind_1.4-8 rstudioapi_0.17.1 yaml_2.3.10
## [49] codetools_0.2-20 miniUI_0.1.1.1 curl_6.0.1
## [52] pkgbuild_1.4.5 plyr_1.8.9 lattice_0.22-6
## [55] tibble_3.2.1 shiny_1.10.0 withr_3.0.2
## [58] evaluate_1.0.1 desc_1.4.3 urlchecker_1.0.1
## [61] DTA_2.53.0 Biostrings_2.75.2 pillar_1.9.0
## [64] BiocManager_1.30.25 foreach_1.5.2 rprojroot_2.0.4
## [67] RCurl_1.98-1.16 ggplot2_3.5.1 munsell_0.5.1
## [70] scales_1.3.0 xtable_1.8-4 glue_1.8.0
## [73] scatterplot3d_0.3-44 maketools_1.3.1 tools_4.4.2
## [76] BiocIO_1.17.1 sys_3.4.3 egg_0.4.5
## [79] GenomicAlignments_1.43.0 buildtools_1.0.0 fs_1.6.5
## [82] XML_3.99-0.17 cowplot_1.1.3 grid_4.4.2
## [85] devtools_2.4.5 colorspace_2.1-1 doMC_1.3.8
## [88] GenomeInfoDbData_1.2.13 restfulr_0.0.15 cli_3.6.3
## [91] LSD_4.1-0 fansi_1.0.6 S4Arrays_1.7.1
## [94] dplyr_1.1.4 gtable_0.3.6 sass_0.4.9
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## [100] rjson_0.2.23 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