-
Notifications
You must be signed in to change notification settings - Fork 1
/
diffloopVoomToLonginteract_threshold.py
189 lines (157 loc) · 8.16 KB
/
diffloopVoomToLonginteract_threshold.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
#!/Users/jra/miniconda3/bin/python3
import sys
import pandas as pd
import numpy as np
import subprocess
pd.set_option('mode.chained_assignment', None)
userInputFile = sys.argv[1]
inputFile = pd.read_csv(userInputFile, sep=',')
# print(inputFile.head(1))
read_coverage_per_rep = inputFile.iloc[:, 6:-10] # get 7th row and all coverage columns (should take all replicates)
#print(read_coverage_per_rep.head(5))
read_coverage = read_coverage_per_rep.sum(axis=1)
df = inputFile[['chr_1','start_1','end_1','chr_2','start_2','end_2','logFC','adj.P.Val']]
df["score"] = read_coverage.clip(upper=1000, lower=0) # has to be in this range to appease UCSC
df.drop(df[df['score']<5].index, inplace=True) #remove rows with less than 5 total read alignments
cutoff = 0.05 # min adj.P.Val cutoff
#df["color"] = np.where( df['logFC'] < 0, "246,133,50", "38,103,153" ) # condition, true, false
df["color"] = np.where( df['logFC'] < 0, "1,160,115", "158,53,158" ) # for WT ESC vs EpiLC
df["color"] = np.where( df['adj.P.Val'] < cutoff, df["color"], "222,222,222" ) # set non-sigificant loops to grey
df["NA"] = "." # fill in unnecessary columns
df["colour_calc"] = df["score"] # was previously used for gradient colours
df = df.rename(columns={'chr_1': '#chr', 'start_1': 'start', 'end_1': 'end', 'start_1': 'start'})
df_to_print = df[['#chr', 'start', 'end_2', 'NA', 'score', 'colour_calc', 'NA', 'color', '#chr', 'start', 'end', 'NA', 'NA', 'chr_2', 'start_2', 'end_2', 'NA', 'NA']]
#print(df.head(5))
#print(df_to_print.head(2))
outTable = "%s_diffloops_x5_FDR_colors_thresh.bed" % (userInputFile)
df_to_print.to_csv(outTable, sep='\t', header=False, index=False)
'''
> library(diffloopdata)
> library(ggplot2)
> library(GenomicRanges)
> library(ggrepel)
> library(DESeq2)
> beddir <- "/scratch/hicpro_all_samples_full_depth/test6"
> samples <- c("D4_DnmtWT_rep1.filt.intra","D4_DnmtWT_rep2.filt.intra","D4_DnmtTKO_rep1.filt.intra","D4_DnmtTKO_rep2.filt.intra")
> FullR1 <- loopsMake(beddir,samples=samples)
dim(FullR1)
anchors interactions samples colData rowData
1 52310 636656 4 2 1
> FullR1_mango <- mangoCorrection(FullR1, FDR = 0.05)
dim(FullR1_mango)
^C
>
>
>
> FullR1_mango <- mangoCorrection(FullR1, FDR = 0.05)
>
> dim(FullR1_mango)
anchors interactions samples colData rowData
1 50232 340992 4 2 3
> p1 <- loopDistancePlot(FullR1_mango)
> p1
>
>
>
> view(p1)
Error in view(p1) : could not find function "view"
> p1.show()
Error in p1.show() : could not find function "p1.show"
> show(p1)
>
>
> pdf("test_plot.pdf")
> plot(p1)
> dev.off()
pdf
2
>
>
> loopMetrics(FullR1_mango)
D4_DnmtWT_rep1.filt.intra D4_DnmtWT_rep2.filt.intra
unique 268650 276181
D4_DnmtTKO_rep1.filt.intra D4_DnmtTKO_rep2.filt.intra
unique 251928 257501
> pcp1dat <- FullR1_mango
> pcp1dat@colData$sizeFactor <- 1
> pcp1 <- pcaPlot(pcp1dat) + geom_text_repel(aes(label=samples)) +
scale_x_continuous(limits = c(-140, 230)) + ggtitle("PC Plot with no Size Factor Correction") +
theme(legend.position="none")
>
> pdf("test_plot2.pdf")
> plot(pcp1)
Warning messages:
1: Removed 1 rows containing missing values (geom_point).
2: Removed 1 rows containing missing values (geom_text_repel).
> dev.off()
pdf
2
> samples
[1] "D4_DnmtWT_rep1.filt.intra" "D4_DnmtWT_rep2.filt.intra"
[3] "D4_DnmtTKO_rep1.filt.intra" "D4_DnmtTKO_rep2.filt.intra"
> pcp1 <- pcaPlot(pcp1dat) + geom_text_repel(aes(label=samples)) +
scale_x_continuous(ggtitle("PC Plot with no Size Factor Correction") +
theme(legend.position="none")
+
+
+
+
> pcp1 <- pcaPlot(pcp1dat) + geom_text_repel(aes(label=samples)) +
+
theme(legend.position="none")
Error in `+.gg`:
! Cannot use `+.gg()` with a single argument. Did you accidentally put + on a new line?
Run `rlang::last_error()` to see where the error occurred.
>
> pcp1 <- pcaPlot(pcp1dat) + geom_text_repel(aes(label=samples)) +
theme(legend.position="none")
> pdf("test_plot2.pdf")
> plot(pcp1)
> dev.off()
pdf
2
> pcp2 <-pcaPlot(qc_filt) + geom_text_repel(aes(label=samples)) +
theme(legend.position="none")
Error in h(simpleError(msg, call)) :
error in evaluating the argument 'dlo' in selecting a method for function 'pcaPlot': object 'qc_filt' not found
> pcp2 <-pcaPlot(qc_filt) + geom_text_repel(aes(label=samples))
Error in h(simpleError(msg, call)) :
error in evaluating the argument 'dlo' in selecting a method for function 'pcaPlot': object 'qc_filt' not found
> pcp2 <-pcaPlot(qc_filt) + geom_text_repel(aes(label=samples)))
Error: unexpected ')' in "pcp2 <-pcaPlot(qc_filt) + geom_text_repel(aes(label=samples)))"
> pcp2 <-pcaPlot(qc_filt) + geom_text_repel(aes(label=samples))))
Error: unexpected ')' in "pcp2 <-pcaPlot(qc_filt) + geom_text_repel(aes(label=samples)))"
> pcp2 <-pcaPlot(qc_filt) + geom_text_repel(aes(label=samples))
Error in h(simpleError(msg, call)) :
error in evaluating the argument 'dlo' in selecting a method for function 'pcaPlot': object 'qc_filt' not found
> loopMetrics(FullR1_mango)
D4_DnmtWT_rep1.filt.intra D4_DnmtWT_rep2.filt.intra
unique 268650 276181
D4_DnmtTKO_rep1.filt.intra D4_DnmtTKO_rep2.filt.intra
unique 251928 257501
>
> client_loop: send disconnect: Broken pipe
(base) jra@greenberg14:~/Desktop$ ijm
ssh: connect to host epipax.ijm.univ-paris-diderot.priv port 22: Operation timed out
'''
#Information on file formats:
#https://genome.ucsc.edu/goldenPath/help/interact.html
#"interaction between two regions"
# string chrom; "Chromosome (or contig, scaffold, etc.). For interchromosomal, use 2 records"
# uint chromStart; "Start position of lower region. For interchromosomal, set to chromStart of this region"
# uint chromEnd; "End position of upper region. For interchromosomal, set to chromEnd of this region"
# string name; "Name of item, for display. Usually 'sourceName/targetName/exp' or empty"
# uint score; "Score (0-1000)"
# double value; "Strength of interaction or other data value. Typically basis for score"
# string exp; "Experiment name (metadata for filtering). Use . if not applicable"
# string color; "Item color. Specified as r,g,b or hexadecimal #RRGGBB or html color name, as in //www.w3.org/TR/css3-color/#html4. Use 0 and spectrum setting to shade by score"
# string sourceChrom; "Chromosome of source region (directional) or lower region. For non-directional interchromosomal, chrom of this region."
# uint sourceStart; "Start position in chromosome of source/lower/this region"
# uint sourceEnd; "End position in chromosome of source/lower/this region"
# string sourceName; "Identifier of source/lower/this region"
# string sourceStrand; "Orientation of source/lower/this region: + or -. Use . if not applicable"
# string targetChrom; "Chromosome of target region (directional) or upper region. For non-directional interchromosomal, chrom of other region"
# uint targetStart; "Start position in chromosome of target/upper/this region"
# uint targetEnd; "End position in chromosome of target/upper/this region"
# string targetName; "Identifier of target/upper/this region"
# string targetStrand; "Orientation of target/upper/this region: + or -. Use . if not applicable"