ggplot2：如何定制点的颜色和形状？ [英] ggplot2: how to customize color and shape of points?
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问题描述
我正在尝试使用 ggplot2 创建一个带状图。以下是 tbl 的子集，它包含我正在使用的相关列，以及 dput 。
> tbl [，c（'Study_ID'，'Probe_ID'，'Group1'，'Group2'，'LogFC'，'adj_P_Value'，'P_Value'，'CI_L'，'CI_R'，'Disease'）]
Study_ID Probe_ID Group1 Group2 LogFC adj_P_Value P_Value CI_L CI_R
1 GSE2461 220307_at男女-0.09017596 1.000000e + 00 5.662047e-01 -0.43955752 0.25920561
2 GSE2461 220307_at溃疡性结肠炎过敏性肠综合征0.08704844 1.000000e + 00 5.784053e -01 -0.26134341 0.43544028
3 GSE27887 220307_at nonlesional skin lesional skin -0.03501474 1.000000e + 00 4.409881e-01 -0.12677636 0.05674688
4 GSE27887 220307_at pretreatment posttreatment 0.01096914 1.000000e + 00 8.080366e-01 -0.08064105 0.10257932
5 GSE42296 7921677 Infliximab治疗前-0.03707265 1.000000e + 00 3.979403e-01 -0.12407201 0.04992672
6 GSE42296 7921677 Re sponder无反应者0.07644834 1.000000e + 00 1.505444e-01 -0.02849309 0.18138977
7 GSE42296 7921677类风湿性关节炎克罗恩病0.42318863 3.960125e-06 1.989713e-10 0.31076269 0.53561457
8 GSE58558 220307_at MF -0.11881801 1.000000e + 00 1.130180e-01 -0.26629675 0.02866072
9 GSE58558 220307_at非皮损皮损-0.18914128 1.000000e + 00 3.696739e-03 -0.31525660 -0.06302596
10 GSE58558 220307_at无响应者-0.14470319 1.000000e + 00 2.328062e -01 -0.38396386 0.09455748
11 GSE58558 220307_at本周12天1 -0.39619004 4.311942e-01 2.215798e-05 -0.57226227 -0.22011781
12 GSE58558 220307_本周2天1 -0.28765455 1.000000e + 00 8.753977e- 04 -0.45375957 -0.12154953
13 GSE59294 220307_at C Dupilumab 300 mg B Dupilumab 150 mg 0.16853309 1.000000 e + 00 1.140155e-01 -0.04273877 0.37980494
14 GSE59294 220307_at D安慰剂B Dupilumab 150 mg -0.18995566 1.000000e + 00 2.264691e-01 -0.50367856 0.12376724
15 GSE59294 220307_at NL皮肤LS皮肤0.01376129 1.000000e +00 9.041383e-01 -0.21711706 0.24463964
16 GSE59294 220307_at Pre Post 0.02234607 1.000000e + 00 8.069367e-01 -0.16235054 0.20704268
疾病
1肠易激综合征;溃疡性结肠炎
2肠易激综合征;溃疡性结肠炎
3特应性皮炎
4特应性皮炎
5克罗恩氏病;类风湿性关节炎
6克罗恩病;类风湿性关节炎
7克罗恩病;类风湿性关节炎
8特应性皮炎
9特应性皮炎
10特应性皮炎
11特应性皮炎
12特应性皮炎
13特应性皮炎
14特应性皮炎
15特应性皮炎
16特应性皮炎
以下是 dput ：
>输入（小滴（tbl [，c（'Study_ID'，'Probe_ID'，'Group1'，'Group2'，'LogFC'，'adj_P_Value'，'P_Value'，'CI_L'，'CI_R'，'疾病'）] ））
结构（列表（Study_ID = c（GSE2461，GSE2461，GSE27887，
GSE27887，GSE42296，GSE42296，GSE42296，GSE58558 GSE58558，
GSE58558，GSE58558，GSE58558，GSE59294，GSE59294，GSE59294，
GSE59294），Probe_ID = c（220307_at， 220307_at，220307_at，
220307_at，7921677，7921677，7921677，220307_at，220307_at，
220307_at，220307_at220307_at ，220307_at，220307_at，
220307_at，220307_at），Group1 = c（男性，溃疡性结肠炎，
非病变性皮肤，预处理 ，响应者，
类风湿性关节炎，M，非皮损皮肤，响应者，
第12周，第2周，C Dupilumab 300mg， D安慰剂，NL皮肤，
Pre），组2 = c（女性，肠易激综合征，损伤皮肤，
后处理治疗前，无应答者，克罗恩病，
F，损伤皮肤，无反应者，第1天，第1天，B Dupilumab 150 mg Logford = c（-0.0901759558643281，
0.0870484364429408，-0.0350147376937934,0.0109691380052655，
-0.0370726462749328，0.0764483363743359,0.423188628619509，-0.118818013184408，-0.118818013184408，
-0.189141277685995，-0.144703191279992，-0.396190039768736，-0.28765454670704，
0.168533085440721，-0.189955660434197,0.0137612879743023,0.0223460675171673
），adj_P_Value = c（1,1,1,1,1,1,1， 1，1，1，1，1），P_Value = c（0.566204678925109，
0.578405275354266,0.440988072013756,0.808036622723435,0.397940346528484，
0.150544373610059，1.98971262936634e-10,0.11301796668591,0.00369673863311212，
0.232806229179741,2.21579776371792e-05,0.000875397680320129，
0.114015475901252,0.226469133014055,0.904138332714553,0.806936684043 586
），CI_L = c（-0.439557521861354，-0.261343410788222，-0.12677635951562，
-0.0806410486876688，-0.124072011981945，-0.0284930943795223，
0.310762687356251，-0.26629674914578，-0.315256597358499，-0.383963864121397，
-0.57226227039893，-0.453759565458485，-0.0427387734415052，-0.503678563834605，
-0.217117064412363，-0.162350541147386），CI_R = C（0.259205610132698，
0.435440283674103，0.0567468841280329，0.1025793246982，0.0499267194320791，
0.181389767128194，0.535614569882768 ，0.0286607227769647，-0.0630259580134921，
0.0945574815614131，-0.220117809138542，-0.121549527955595,0.379804944322947，
0.12376724296621,0.244639640360967,0.207042676181721），疾病= c（过敏性肠综合征;溃疡性结肠炎，
过敏性肠综合征;溃疡性结肠炎，特应性皮炎，
特应性皮炎，克罗恩病;类风湿性关节炎，
克罗恩氏病;类风湿性关节炎，克罗恩氏病;特应性皮炎，特应性皮炎，
特应性皮炎，特应性皮炎，特应性皮炎，
特应性皮炎 ，特应性皮炎，特应性皮炎
）），.Names = c（Study_ID，Probe_ID，Group1，Group2，LogFC，
adj_P_Value， P_Value，CI_L，CI_R，疾病），row.names = c（NA，
-16L），class =data.frame）
$ b
$ b
code> #test使用ggplot2
maxFC = max（as.numeric（as.character（tbl $ LogFC）））
minFC = min（as.numeric（as.character（tbl $ LogFC ）））
datasetList = tbl $ Study_ID
hLines =（which（duplicated（datasetList）== FALSE） - 0.5）
tbl $ ylab < - paste（tbl $ Group2，\U2192，\\\
，tbl $ Group1，sep =）
p < - ggplot（data = tbl，aes（x = LogFC，y = Probe_ID，group = Study_ID））+
geom_vline（xintercept = log（0.5,2），size = 0.2）+
geom_vline（xintercept = log（2 / 3,2），size = 0.2）+
geom_vline（xintercept = log（1.5,2），size = 0.2）+
geom_vline（xintercept = log（2,2），size = 0.2）+
labs（title = tbl $ gene，y =Contrasts，x = bquote（〜Log [2]〜'（Fold Change）'））+
geom_errorbarh（aes（x = LogFC，xmin = CI_L，xmax = CI_R），height = .1）+
geom_point（aes（color = cut（adj_P_Value，c（-Inf，0.01，0.05，Inf ）））+
scale_color_manual（name =P Value，
values = c（（ - Inf，0.01）=red，
（0.01,0.05）） =橙，
（0.05，Inf）=黑），
标签= c（<= 0.01，0.01 < P值≤0.05，> 0.05））+
scale_shape_manual（values = c（4,15,19））+
coord_cartesian（xlim = c（min（-2，minFC），max（2，maxFC）））+
theme（axis.text.y = element_blank（），strip.text.y = element_text（angle = 180），
＃panel.grid.major = element_blank（），
＃panel.grid.minor = element_blank（），
axis.line.y = element_blank（），
axis.line.x = element_blank（），
＃panel.background = element_rect（fill ='white'，color ='white'），
＃panel.grid = element_blank（），
panel.spacing.y =单位（0.5，'lines'），
axis.ticks.y = element_blank（））+
facet_grid（Study_ID + ylab〜。，scales ='free'，space ='free'，switch ='both'）
p
基本上，点的实际位置是通过它们的 LogFC 值，但具有 adj_P_Value <= 0.01 的点应显示为红色圆圈，介于0.01和0.0之间5作为橙色方块，并且> = 0.05 作为黑色十字（即，我提供的数据不应该显示任何方块）。我的尝试是在 geom_point 中使用 cut ，但这似乎不起作用。颜色显示正确，但形状不正确。这一直在困扰我一段时间。如果有任何我打破的约定或标准（我可能是），请让我知道并提出一些可以实现我已经完成的事情。谢谢！
更新：
#test使用ggplot2
maxFC = max（as.numeric（as.character（tbl $ LogFC）））
minFC = min（as.numeric（as.character（tbl $ LogFC）））
datasetList = tbl $ Study_ID
hLines =（which（duplicated（datasetList）== FALSE） - 0.5）
tbl $ ylab < - paste（tbl $ Group2，\U2192，\\\
，tbl $ Group1，sep =）
tbl < - tbl％>％
mutate（colourgroup = case_when （
。$adj_P_Value≤0.01〜1，
。$ adj_P_Value> 0.01&。$ adj_P_Value <0.05〜2，
。$ adj_P_Value> = 0.05〜3
$ bp < - ggplot（data = tbl，aes（x = LogFC，y = Probe_ID，color = colourgroup，shape = colourgroup））+
#geom_point +
geom_vline（xintercept = log（0.5,2），size = 0.2）+
geom_vline（xintercept = log（2 / 3,2），size = 0.2）+
geom_vline = log（1.5,2），size = 0.2）+
geom_vline（xintercept = log（2,2），size = 0.2）+
labs （标题= tbl $基因，y =对比度，x = bquote（〜Log [2]〜'（倍数变化）'））+
geom_errorbarh（aes（x = LogFC，xmin = CI_L，xmax = CI_R），height = .1）+
geom_point（）+
scale_color_manual（c（'red'，'orange'，'black'））+
scale_shape_manual（c ，4））+
coord_cartesian（xlim = c（min（-2，minFC），max（2，maxFC）））+
theme（axis.text.y = element_blank（），strip。 text.y = element_text（angle = 180），
＃panel.grid.major = element_blank（），
＃panel.grid.minor = element_blank（），
axis.line.y = element_blank（），
axis.line.x = element_blank（），
＃panel.background = element_rect（fill ='white'，color ='white'），
#panel。 grid = element_blank（），
panel.spacing.y = unit（0.5，'lines'），
axis.ticks.y = element_blank（））+
facet_grid（Study_ID + ylab〜 。，scale ='free'，space ='free'，switch ='both'）
p
解决方案
添加你想要的因子列
library （dplyr）
tbl< - tbl％>％
mutate（colourgroup = case_when（
adj_P_Value< = 0.01〜1，
adj_P_Value> 0.01& adj_P_Value< 0.05〜2，
adj_P_Value> = 0.05〜3））
p>
aes（x = LogFC，y = Probe_ID，group = Study_ID）
$ b $ p
$ b $ $ $ $ $ $ $ $ $ aes（x = LogFC，y = Probe_ID， color = factor（colourgroup），shape = factor（colourgroup））
和
scale_color_manual（values = c（red，orange，black））+
scale_shape_manual（values = c（1， 2,3））
最小示例
这个最简单的 ggplot 命令适用于我。注意我有意地切换了 x 和 y 值， orange 可能难以区分
ggplot（df2， aes（x = Probe_ID，y = LogFC，color = factor（colourgroup），shape = factor（colourgroup）））+
geom_point（）+
scale_color_manual（values = c（red，orange ，black））+
scale_shape_manual（values = c（1,2,3））
I am trying to create a stripchart using ggplot2. Below is a subset of tbl which has the relevant columns I am using, along with a dput.
> tbl[,c('Study_ID', 'Probe_ID', 'Group1','Group2','LogFC', 'adj_P_Value', 'P_Value', 'CI_L','CI_R','Disease')]
Study_ID Probe_ID Group1 Group2 LogFC adj_P_Value P_Value CI_L CI_R
1 GSE2461 220307_at Male Female -0.09017596 1.000000e+00 5.662047e-01 -0.43955752 0.25920561
2 GSE2461 220307_at ulcerative colitis irritable bowel syndrome 0.08704844 1.000000e+00 5.784053e-01 -0.26134341 0.43544028
3 GSE27887 220307_at nonlesional skin lesional skin -0.03501474 1.000000e+00 4.409881e-01 -0.12677636 0.05674688
4 GSE27887 220307_at pretreatment posttreatment 0.01096914 1.000000e+00 8.080366e-01 -0.08064105 0.10257932
5 GSE42296 7921677 Infliximab Before treatment -0.03707265 1.000000e+00 3.979403e-01 -0.12407201 0.04992672
6 GSE42296 7921677 Responder Nonresponder 0.07644834 1.000000e+00 1.505444e-01 -0.02849309 0.18138977
7 GSE42296 7921677 Rheumatoid Arthritis Crohn's Disease 0.42318863 3.960125e-06 1.989713e-10 0.31076269 0.53561457
8 GSE58558 220307_at M F -0.11881801 1.000000e+00 1.130180e-01 -0.26629675 0.02866072
9 GSE58558 220307_at non lesional skin lesional skin -0.18914128 1.000000e+00 3.696739e-03 -0.31525660 -0.06302596
10 GSE58558 220307_at responder nonresponder -0.14470319 1.000000e+00 2.328062e-01 -0.38396386 0.09455748
11 GSE58558 220307_at week 12 day 1 -0.39619004 4.311942e-01 2.215798e-05 -0.57226227 -0.22011781
12 GSE58558 220307_at week 2 day 1 -0.28765455 1.000000e+00 8.753977e-04 -0.45375957 -0.12154953
13 GSE59294 220307_at C Dupilumab 300 mg B Dupilumab 150 mg 0.16853309 1.000000e+00 1.140155e-01 -0.04273877 0.37980494
14 GSE59294 220307_at D Placebo B Dupilumab 150 mg -0.18995566 1.000000e+00 2.264691e-01 -0.50367856 0.12376724
15 GSE59294 220307_at NL skin LS skin 0.01376129 1.000000e+00 9.041383e-01 -0.21711706 0.24463964
16 GSE59294 220307_at Pre Post 0.02234607 1.000000e+00 8.069367e-01 -0.16235054 0.20704268
Disease
1 irritable bowel syndrome; ulcerative colitis
2 irritable bowel syndrome; ulcerative colitis
3 atopic Dermatitis
4 atopic Dermatitis
5 Crohn's Disease; Rheumatoid Arthritis
6 Crohn's Disease; Rheumatoid Arthritis
7 Crohn's Disease; Rheumatoid Arthritis
8 Atopic Dermatitis
9 Atopic Dermatitis
10 Atopic Dermatitis
11 Atopic Dermatitis
12 Atopic Dermatitis
13 atopic Dermatitis
14 atopic Dermatitis
15 atopic Dermatitis
16 atopic Dermatitis
Here is the dput :
> dput(droplevels(tbl[,c('Study_ID', 'Probe_ID', 'Group1','Group2','LogFC', 'adj_P_Value', 'P_Value', 'CI_L','CI_R','Disease')]))
structure(list(Study_ID = c("GSE2461", "GSE2461", "GSE27887",
"GSE27887", "GSE42296", "GSE42296", "GSE42296", "GSE58558", "GSE58558",
"GSE58558", "GSE58558", "GSE58558", "GSE59294", "GSE59294", "GSE59294",
"GSE59294"), Probe_ID = c("220307_at", "220307_at", "220307_at",
"220307_at", "7921677", "7921677", "7921677", "220307_at", "220307_at",
"220307_at", "220307_at", "220307_at", "220307_at", "220307_at",
"220307_at", "220307_at"), Group1 = c("Male", "ulcerative colitis",
"nonlesional skin", "pretreatment", "Infliximab", "Responder",
"Rheumatoid Arthritis", "M", "non lesional skin", "responder",
"week 12", "week 2", "C Dupilumab 300 mg", "D Placebo", "NL skin",
"Pre"), Group2 = c("Female", "irritable bowel syndrome", "lesional skin",
"posttreatment", "Before treatment", "Nonresponder", "Crohn's Disease",
"F", "lesional skin", "nonresponder", "day 1", "day 1", "B Dupilumab 150 mg",
"B Dupilumab 150 mg", "LS skin", "Post"), LogFC = c(-0.0901759558643281,
0.0870484364429408, -0.0350147376937934, 0.0109691380052655,
-0.0370726462749328, 0.0764483363743359, 0.423188628619509, -0.118818013184408,
-0.189141277685995, -0.144703191279992, -0.396190039768736, -0.28765454670704,
0.168533085440721, -0.189955660434197, 0.0137612879743023, 0.0223460675171673
), adj_P_Value = c(1, 1, 1, 1, 1, 1, 3.96012504622782e-06, 1,
1, 1, 0.431194244819507, 1, 1, 1, 1, 1), P_Value = c(0.566204678925109,
0.578405275354266, 0.440988072013756, 0.808036622723435, 0.397940346528484,
0.150544373610059, 1.98971262936634e-10, 0.11301796668591, 0.00369673863311212,
0.232806229179741, 2.21579776371792e-05, 0.000875397680320129,
0.114015475901252, 0.226469133014055, 0.904138332714553, 0.806936684043586
), CI_L = c(-0.439557521861354, -0.261343410788222, -0.12677635951562,
-0.0806410486876688, -0.124072011981945, -0.0284930943795223,
0.310762687356251, -0.26629674914578, -0.315256597358499, -0.383963864121397,
-0.57226227039893, -0.453759565458485, -0.0427387734415052, -0.503678563834605,
-0.217117064412363, -0.162350541147386), CI_R = c(0.259205610132698,
0.435440283674103, 0.0567468841280329, 0.1025793246982, 0.0499267194320791,
0.181389767128194, 0.535614569882768, 0.0286607227769647, -0.0630259580134921,
0.0945574815614131, -0.220117809138542, -0.121549527955595, 0.379804944322947,
0.12376724296621, 0.244639640360967, 0.207042676181721), Disease = c("irritable bowel syndrome; ulcerative colitis",
"irritable bowel syndrome; ulcerative colitis", "atopic Dermatitis",
"atopic Dermatitis", "Crohn's Disease; Rheumatoid Arthritis",
"Crohn's Disease; Rheumatoid Arthritis", "Crohn's Disease; Rheumatoid Arthritis",
"Atopic Dermatitis", "Atopic Dermatitis", "Atopic Dermatitis",
"Atopic Dermatitis", "Atopic Dermatitis", "atopic Dermatitis",
"atopic Dermatitis", "atopic Dermatitis", "atopic Dermatitis"
)), .Names = c("Study_ID", "Probe_ID", "Group1", "Group2", "LogFC",
"adj_P_Value", "P_Value", "CI_L", "CI_R", "Disease"), row.names = c(NA,
-16L), class = "data.frame")
Finally, here the code that I have so far.
#test using ggplot2
maxFC = max(as.numeric(as.character(tbl$LogFC)))
minFC = min(as.numeric(as.character(tbl$LogFC)))
datasetList = tbl$Study_ID
hLines =(which(duplicated(datasetList) == FALSE) - 0.5)
tbl$ylab <- paste(tbl$Group2," \U2192 ","\n", tbl$Group1, sep = "")
p <- ggplot(data = tbl, aes(x = LogFC, y = Probe_ID, group = Study_ID)) +
geom_vline(xintercept = log(0.5,2), size = 0.2) +
geom_vline(xintercept = log(2/3,2), size = 0.2) +
geom_vline(xintercept = log(1.5,2), size = 0.2) +
geom_vline(xintercept = log(2,2), size = 0.2) +
labs(title = tbl$gene, y = "Contrasts", x = bquote(~Log[2]~'(Fold Change)')) +
geom_errorbarh(aes(x = LogFC, xmin = CI_L, xmax = CI_R), height = .1) +
geom_point(aes(colour = cut(adj_P_Value, c(-Inf, 0.01, 0.05, Inf)))) +
scale_color_manual(name = "P Value",
values = c("(-Inf,0.01]" = "red",
"(0.01,0.05)" = "orange",
"(0.05, Inf]" = "black"),
labels = c("<= 0.01", "0.01 < P Value <= 0.05", "> 0.05")) +
scale_shape_manual(values = c( 4,15,19)) +
coord_cartesian(xlim = c(min(-2,minFC),max(2,maxFC))) +
theme(axis.text.y = element_blank(), strip.text.y = element_text(angle = 180),
#panel.grid.major = element_blank(),
#panel.grid.minor = element_blank(),
axis.line.y = element_blank(),
axis.line.x = element_blank(),
#panel.background = element_rect(fill = 'white', colour = 'white'),
#panel.grid = element_blank(),
panel.spacing.y = unit(0.5,'lines'),
axis.ticks.y = element_blank()) +
facet_grid(Study_ID+ylab~ ., scales = 'free', space = 'free', switch = 'both')
p
Basically, the points actual position is determined via their LogFC value, but the points that have an adj_P_Value <= 0.01 should be displayed as red circles, in between 0.01 and 0.05 as orange squares, and >= 0.05 as black crosses (i.e. the data that I've provided shouldn't show any squares). My attempt as this was using cut in the geom_pointbut this doesn't seem to work. The colors are showing up correct, but the shapes are not. This has been bugging me for a while. If there's any conventions or standards that I'm breaking (which I probably am), please let me know and suggest something that would achieve exactly what I have already. Thanks!
Update:
#test using ggplot2
maxFC = max(as.numeric(as.character(tbl$LogFC)))
minFC = min(as.numeric(as.character(tbl$LogFC)))
datasetList = tbl$Study_ID
hLines =(which(duplicated(datasetList) == FALSE) - 0.5)
tbl$ylab <- paste(tbl$Group2," \U2192 ","\n", tbl$Group1, sep = "")
tbl <- tbl %>%
mutate(colourgroup = case_when(
.$adj_P_Value <= 0.01 ~ 1,
.$adj_P_Value > 0.01 & .$adj_P_Value < 0.05 ~ 2,
.$adj_P_Value >= 0.05 ~ 3
) )
p <- ggplot(data = tbl, aes(x = LogFC, y = Probe_ID, colour = colourgroup, shape = colourgroup)) +
#geom_point() +
geom_vline(xintercept = log(0.5,2), size = 0.2) +
geom_vline(xintercept = log(2/3,2), size = 0.2) +
geom_vline(xintercept = log(1.5,2), size = 0.2) +
geom_vline(xintercept = log(2,2), size = 0.2) +
labs(title = tbl$gene, y = "Contrasts", x = bquote(~Log[2]~'(Fold Change)')) +
geom_errorbarh(aes(x = LogFC, xmin = CI_L, xmax = CI_R), height = .1) +
geom_point() +
scale_color_manual(c('red','orange','black')) +
scale_shape_manual(c(19,15,4))+
coord_cartesian(xlim = c(min(-2,minFC),max(2,maxFC))) +
theme(axis.text.y = element_blank(), strip.text.y = element_text(angle = 180),
#panel.grid.major = element_blank(),
#panel.grid.minor = element_blank(),
axis.line.y = element_blank(),
axis.line.x = element_blank(),
#panel.background = element_rect(fill = 'white', colour = 'white'),
#panel.grid = element_blank(),
panel.spacing.y = unit(0.5,'lines'),
axis.ticks.y = element_blank()) +
facet_grid(Study_ID+ylab~ ., scales = 'free', space = 'free', switch = 'both')
p
解决方案
Add a column of the factor you want
library(dplyr)
tbl <- tbl %>%
mutate(colourgroup = case_when(
adj_P_Value <= 0.01 ~ 1,
adj_P_Value > 0.01 & adj_P_Value < 0.05 ~ 2,
adj_P_Value >= 0.05 ~ 3 ))
Then change
aes(x = LogFC, y = Probe_ID, group = Study_ID)
To
aes(x = LogFC, y = Probe_ID, colour = factor(colourgroup), shape = factor(colourgroup))
And
scale_color_manual(values=c("red","orange","black")) +
scale_shape_manual(values=c(1,2,3))
MINIMAL EXAMPLE
This minimal ggplot command worked for me. Note I switched the x and y values on purpose, and red and orange can be difficult to distinguish
ggplot(df2, aes(x = Probe_ID, y=LogFC, colour=factor(colourgroup), shape=factor(colourgroup))) +
geom_point() +
scale_color_manual(values=c("red","orange","black")) +
scale_shape_manual(values=c(1,2,3))
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